Table of Contents
This chapter explains how to optimize MySQL performance and provides examples. Optimization involves configuring, tuning, and measuring performance, at several levels. Depending on your job role (developer, DBA, or a combination of both), you might optimize at the level of individual SQL statements, entire applications, a single database server, or multiple networked database servers. Sometimes you can be proactive and plan in advance for performance, while other times you might troubleshoot a configuration or code issue after a problem occurs. Optimizing CPU and memory usage can also improve scalability, allowing the database to handle more load without slowing down.
Database performance depends on several factors at the database level, such as tables, queries, and configuration settings. These software constructs result in CPU and I/O operations at the hardware level, which you must minimize and make as efficient as possible. As you work on database performance, you start by learning the high-level rules and guidelines for the software side, and measuring performance using wall-clock time. As you become an expert, you learn more about what happens internally, and start measuring things such as CPU cycles and I/O operations.
Typical users aim to get the best database performance out of their existing software and hardware configurations. Advanced users look for opportunities to improve the MySQL software itself, or develop their own storage engines and hardware appliances to expand the MySQL ecosystem.
The most important factor in making a database application fast is its basic design:
Are the tables structured properly? In particular, do the columns have the right data types, and does each table have the appropriate columns for the type of work? For example, applications that perform frequent updates often have many tables with few columns, while applications that analyze large amounts of data often have few tables with many columns.
Are the right indexes in place to make queries efficient?
Are you using the appropriate storage engine for each table,
and taking advantage of the strengths and features of each
storage engine you use? In particular, the choice of a
transactional storage engine such as
InnoDB
or a nontransactional one such as
MyISAM
can be very important for performance and scalability.
InnoDB
is the default storage engine
for new tables. In practice, the advanced
InnoDB
performance features mean that
InnoDB
tables often outperform the
simpler MyISAM
tables, especially for a
busy database.
Does each table use an appropriate row format? This choice
also depends on the storage engine used for the table. In
particular, compressed tables use less disk space and so
require less disk I/O to read and write the data.
Compression is available for all kinds of workloads with
InnoDB
tables, and for read-only
MyISAM
tables.
Does the application use an appropriate
locking strategy? For
example, by allowing shared access when possible so that
database operations can run concurrently, and requesting
exclusive access when appropriate so that critical
operations get top priority. Again, the choice of storage
engine is significant. The InnoDB
storage
engine handles most locking issues without involvement from
you, allowing for better concurrency in the database and
reducing the amount of experimentation and tuning for your
code.
Are all memory areas used
for caching sized correctly? That is, large enough to
hold frequently accessed data, but not so large that they
overload physical memory and cause paging. The main memory
areas to configure are the InnoDB
buffer
pool, the MyISAM
key cache, and the MySQL
query cache.
Any database application eventually hits hardware limits as the database becomes more and more busy. A DBA must evaluate whether it is possible to tune the application or reconfigure the server to avoid these bottlenecks, or whether more hardware resources are required. System bottlenecks typically arise from these sources:
Disk seeks. It takes time for the disk to find a piece of data. With modern disks, the mean time for this is usually lower than 10ms, so we can in theory do about 100 seeks a second. This time improves slowly with new disks and is very hard to optimize for a single table. The way to optimize seek time is to distribute the data onto more than one disk.
Disk reading and writing. When the disk is at the correct position, we need to read or write the data. With modern disks, one disk delivers at least 10–20MB/s throughput. This is easier to optimize than seeks because you can read in parallel from multiple disks.
CPU cycles. When the data is in main memory, we must process it to get our result. Having large tables compared to the amount of memory is the most common limiting factor. But with small tables, speed is usually not the problem.
Memory bandwidth. When the CPU needs more data than can fit in the CPU cache, main memory bandwidth becomes a bottleneck. This is an uncommon bottleneck for most systems, but one to be aware of.
To use performance-oriented SQL extensions in a portable MySQL
program, you can wrap MySQL-specific keywords in a statement
within /*! */
comment delimiters. Other SQL
servers ignore the commented keywords. For information about
writing comments, see Section 9.6, “Comment Syntax”.
The core logic of a database application is performed through SQL statements, whether issued directly through an interpreter or submitted behind the scenes through an API. The tuning guidelines in this section help to speed up all kinds of MySQL applications. The guidelines cover SQL operations that read and write data, the behind-the-scenes overhead for SQL operations in general, and operations used in specific scenarios such as database monitoring.
Queries, in the form of SELECT
statements, perform all the lookup operations in the database.
Tuning these statements is a top priority, whether to achieve
sub-second response times for dynamic web pages, or to chop
hours off the time to generate huge overnight reports.
Besides SELECT
statements, the
tuning techniques for queries also apply to constructs such as
CREATE
TABLE...AS SELECT
,
INSERT
INTO...SELECT
, and WHERE
clauses in
DELETE
statements. Those
statements have additional performance considerations because
they combine write operations with the read-oriented query
operations.
NDB Cluster supports a join pushdown optimization whereby a qualifying join is sent in its entirety to NDB Cluster data nodes, where it can be distributed among them and executed in parallel. For more information about this optimization, see Conditions for NDB pushdown joins.
The main considerations for optimizing queries are:
To make a slow SELECT ... WHERE
query
faster, the first thing to check is whether you can add an
index. Set up indexes on
columns used in the WHERE
clause, to
speed up evaluation, filtering, and the final retrieval of
results. To avoid wasted disk space, construct a small set
of indexes that speed up many related queries used in your
application.
Indexes are especially important for queries that reference
different tables, using features such as
joins and
foreign keys. You
can use the EXPLAIN
statement
to determine which indexes are used for a
SELECT
. See
Section 8.3.1, “How MySQL Uses Indexes” and
Section 8.8.1, “Optimizing Queries with EXPLAIN”.
Isolate and tune any part of the query, such as a function call, that takes excessive time. Depending on how the query is structured, a function could be called once for every row in the result set, or even once for every row in the table, greatly magnifying any inefficiency.
Minimize the number of full table scans in your queries, particularly for big tables.
Keep table statistics up to date by using the
ANALYZE TABLE
statement
periodically, so the optimizer has the information needed to
construct an efficient execution plan.
Learn the tuning techniques, indexing techniques, and
configuration parameters that are specific to the storage
engine for each table. Both InnoDB
and
MyISAM
have sets of guidelines for
enabling and sustaining high performance in queries. For
details, see Section 8.5.6, “Optimizing InnoDB Queries” and
Section 8.6.1, “Optimizing MyISAM Queries”.
You can optimize single-query transactions for
InnoDB
tables, using the technique in
Section 8.5.3, “Optimizing InnoDB Read-Only Transactions”.
Avoid transforming the query in ways that make it hard to understand, especially if the optimizer does some of the same transformations automatically.
If a performance issue is not easily solved by one of the
basic guidelines, investigate the internal details of the
specific query by reading the
EXPLAIN
plan and adjusting
your indexes, WHERE
clauses, join
clauses, and so on. (When you reach a certain level of
expertise, reading the
EXPLAIN
plan might be your
first step for every query.)
Adjust the size and properties of the memory areas that
MySQL uses for caching. With efficient use of the
InnoDB
buffer pool,
MyISAM
key cache, and the MySQL query
cache, repeated queries run faster because the results are
retrieved from memory the second and subsequent times.
Even for a query that runs fast using the cache memory areas, you might still optimize further so that they require less cache memory, making your application more scalable. Scalability means that your application can handle more simultaneous users, larger requests, and so on without experiencing a big drop in performance.
Deal with locking issues, where the speed of your query might be affected by other sessions accessing the tables at the same time.
This section discusses optimizations that can be made for
processing WHERE
clauses. The examples use
SELECT
statements, but the same
optimizations apply for WHERE
clauses in
DELETE
and
UPDATE
statements.
Because work on the MySQL optimizer is ongoing, not all of the optimizations that MySQL performs are documented here.
You might be tempted to rewrite your queries to make arithmetic operations faster, while sacrificing readability. Because MySQL does similar optimizations automatically, you can often avoid this work, and leave the query in a more understandable and maintainable form. Some of the optimizations performed by MySQL follow:
Removal of unnecessary parentheses:
((a AND b) AND c OR (((a AND b) AND (c AND d)))) -> (a AND b AND c) OR (a AND b AND c AND d)
Constant folding:
(a<b AND b=c) AND a=5 -> b>5 AND b=c AND a=5
Constant condition removal:
(b>=5 AND b=5) OR (b=6 AND 5=5) OR (b=7 AND 5=6) -> b=5 OR b=6
Constant expressions used by indexes are evaluated only once.
COUNT(*)
on a single table
without a WHERE
is retrieved directly
from the table information for MyISAM
and MEMORY
tables. This is also done
for any NOT NULL
expression when used
with only one table.
Early detection of invalid constant expressions. MySQL
quickly detects that some
SELECT
statements are
impossible and returns no rows.
HAVING
is merged with
WHERE
if you do not use GROUP
BY
or aggregate functions
(COUNT()
,
MIN()
, and so on).
For each table in a join, a simpler
WHERE
is constructed to get a fast
WHERE
evaluation for the table and also
to skip rows as soon as possible.
All constant tables are read first before any other tables in the query. A constant table is any of the following:
An empty table or a table with one row.
A table that is used with a WHERE
clause on a PRIMARY KEY
or a
UNIQUE
index, where all index parts
are compared to constant expressions and are defined
as NOT NULL
.
All of the following tables are used as constant tables:
SELECT * FROM t WHEREprimary_key
=1; SELECT * FROM t1,t2 WHERE t1.primary_key
=1 AND t2.primary_key
=t1.id;
The best join combination for joining the tables is found
by trying all possibilities. If all columns in
ORDER BY
and GROUP
BY
clauses come from the same table, that table
is preferred first when joining.
If there is an ORDER BY
clause and a
different GROUP BY
clause, or if the
ORDER BY
or GROUP BY
contains columns from tables other than the first table in
the join queue, a temporary table is created.
If you use the SQL_SMALL_RESULT
modifier, MySQL uses an in-memory temporary table.
Each table index is queried, and the best index is used unless the optimizer believes that it is more efficient to use a table scan. At one time, a scan was used based on whether the best index spanned more than 30% of the table, but a fixed percentage no longer determines the choice between using an index or a scan. The optimizer now is more complex and bases its estimate on additional factors such as table size, number of rows, and I/O block size.
In some cases, MySQL can read rows from the index without even consulting the data file. If all columns used from the index are numeric, only the index tree is used to resolve the query.
Before each row is output, those that do not match the
HAVING
clause are skipped.
Some examples of queries that are very fast:
SELECT COUNT(*) FROMtbl_name
; SELECT MIN(key_part1
),MAX(key_part1
) FROMtbl_name
; SELECT MAX(key_part2
) FROMtbl_name
WHEREkey_part1
=constant
; SELECT ... FROMtbl_name
ORDER BYkey_part1
,key_part2
,... LIMIT 10; SELECT ... FROMtbl_name
ORDER BYkey_part1
DESC,key_part2
DESC, ... LIMIT 10;
MySQL resolves the following queries using only the index tree, assuming that the indexed columns are numeric:
SELECTkey_part1
,key_part2
FROMtbl_name
WHEREkey_part1
=val
; SELECT COUNT(*) FROMtbl_name
WHEREkey_part1
=val1
ANDkey_part2
=val2
; SELECTkey_part2
FROMtbl_name
GROUP BYkey_part1
;
The following queries use indexing to retrieve the rows in sorted order without a separate sorting pass:
SELECT ... FROMtbl_name
ORDER BYkey_part1
,key_part2
,... ; SELECT ... FROMtbl_name
ORDER BYkey_part1
DESC,key_part2
DESC, ... ;
The range
access method
uses a single index to retrieve a subset of table rows that
are contained within one or several index value intervals. It
can be used for a single-part or multiple-part index. The
following sections describe conditions under which the
optimizer uses range access.
For a single-part index, index value intervals can be
conveniently represented by corresponding conditions in the
WHERE
clause, denoted as
range conditions
rather than “intervals.”
The definition of a range condition for a single-part index is as follows:
For both BTREE
and
HASH
indexes, comparison of a key
part with a constant value is a range condition when
using the
=
,
<=>
,
IN()
, IS
NULL
, or IS NOT
NULL
operators.
Additionally, for BTREE
indexes,
comparison of a key part with a constant value is a
range condition when using the
>
,
<
,
>=
,
<=
,
BETWEEN
,
!=
,
or
<>
operators, or LIKE
comparisons if the argument to
LIKE
is a constant string
that does not start with a wildcard character.
For all index types, multiple range conditions combined
with OR
or
AND
form a range condition.
“Constant value” in the preceding descriptions means one of the following:
Here are some examples of queries with range conditions in
the WHERE
clause:
SELECT * FROM t1 WHEREkey_col
> 1 ANDkey_col
< 10; SELECT * FROM t1 WHEREkey_col
= 1 ORkey_col
IN (15,18,20); SELECT * FROM t1 WHEREkey_col
LIKE 'ab%' ORkey_col
BETWEEN 'bar' AND 'foo';
Some nonconstant values may be converted to constants during the optimizer constant propagation phase.
MySQL tries to extract range conditions from the
WHERE
clause for each of the possible
indexes. During the extraction process, conditions that
cannot be used for constructing the range condition are
dropped, conditions that produce overlapping ranges are
combined, and conditions that produce empty ranges are
removed.
Consider the following statement, where
key1
is an indexed column and
nonkey
is not indexed:
SELECT * FROM t1 WHERE (key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR (key1 < 'bar' AND nonkey = 4) OR (key1 < 'uux' AND key1 > 'z');
The extraction process for key key1
is as
follows:
Start with original WHERE
clause:
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR (key1 < 'bar' AND nonkey = 4) OR (key1 < 'uux' AND key1 > 'z')
Remove nonkey = 4
and key1
LIKE '%b'
because they cannot be used for a
range scan. The correct way to remove them is to replace
them with TRUE
, so that we do not
miss any matching rows when doing the range scan.
Replacing them with TRUE
yields:
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR TRUE)) OR (key1 < 'bar' AND TRUE) OR (key1 < 'uux' AND key1 > 'z')
Collapse conditions that are always true or false:
(key1 LIKE 'abcde%' OR TRUE)
is
always true
(key1 < 'uux' AND key1 >
'z')
is always false
Replacing these conditions with constants yields:
(key1 < 'abc' AND TRUE) OR (key1 < 'bar' AND TRUE) OR (FALSE)
Removing unnecessary TRUE
and
FALSE
constants yields:
(key1 < 'abc') OR (key1 < 'bar')
Combining overlapping intervals into one yields the final condition to be used for the range scan:
(key1 < 'bar')
In general (and as demonstrated by the preceding example),
the condition used for a range scan is less restrictive than
the WHERE
clause. MySQL performs an
additional check to filter out rows that satisfy the range
condition but not the full WHERE
clause.
The range condition extraction algorithm can handle nested
AND
/OR
constructs of arbitrary depth, and its output does not
depend on the order in which conditions appear in
WHERE
clause.
MySQL does not support merging multiple ranges for the
range
access method for
spatial indexes. To work around this limitation, you can use
a UNION
with identical
SELECT
statements, except
that you put each spatial predicate in a different
SELECT
.
Range conditions on a multiple-part index are an extension of range conditions for a single-part index. A range condition on a multiple-part index restricts index rows to lie within one or several key tuple intervals. Key tuple intervals are defined over a set of key tuples, using ordering from the index.
For example, consider a multiple-part index defined as
key1(
, and the
following set of key tuples listed in key order:
key_part1
,
key_part2
,
key_part3
)
key_part1
key_part2
key_part3
NULL 1 'abc' NULL 1 'xyz' NULL 2 'foo' 1 1 'abc' 1 1 'xyz' 1 2 'abc' 2 1 'aaa'
The condition
defines this interval:
key_part1
= 1
(1,-inf,-inf) <= (key_part1
,key_part2
,key_part3
) < (1,+inf,+inf)
The interval covers the 4th, 5th, and 6th tuples in the preceding data set and can be used by the range access method.
By contrast, the condition
does not define a single interval and cannot
be used by the range access method.
key_part3
=
'abc'
The following descriptions indicate how range conditions work for multiple-part indexes in greater detail.
For HASH
indexes, each interval
containing identical values can be used. This means that
the interval can be produced only for conditions in the
following form:
key_part1
cmp
const1
ANDkey_part2
cmp
const2
AND ... ANDkey_partN
cmp
constN
;
Here, const1
,
const2
, … are
constants, cmp
is one of the
=
,
<=>
,
or IS NULL
comparison
operators, and the conditions cover all index parts.
(That is, there are N
conditions, one for each part of an
N
-part index.) For example,
the following is a range condition for a three-part
HASH
index:
key_part1
= 1 ANDkey_part2
IS NULL ANDkey_part3
= 'foo'
For the definition of what is considered to be a constant, see Range Access Method for Single-Part Indexes.
For a BTREE
index, an interval might
be usable for conditions combined with
AND
, where each condition
compares a key part with a constant value using
=
,
<=>
,
IS NULL
,
>
,
<
,
>=
,
<=
,
!=
,
<>
,
BETWEEN
, or
LIKE
'
(where
pattern
''
does not start with a wildcard). An interval can be used
as long as it is possible to determine a single key
tuple containing all rows that match the condition (or
two intervals if
pattern
'<>
or !=
is used).
The optimizer attempts to use additional key parts to
determine the interval as long as the comparison
operator is
=
,
<=>
,
or IS NULL
. If the operator
is
>
,
<
,
>=
,
<=
,
!=
,
<>
,
BETWEEN
, or
LIKE
, the
optimizer uses it but considers no more key parts. For
the following expression, the optimizer uses
=
from
the first comparison. It also uses
>=
from the second comparison but considers no further key
parts and does not use the third comparison for interval
construction:
key_part1
= 'foo' ANDkey_part2
>= 10 ANDkey_part3
> 10
The single interval is:
('foo',10,-inf) < (key_part1
,key_part2
,key_part3
) < ('foo',+inf,+inf)
It is possible that the created interval contains more
rows than the initial condition. For example, the
preceding interval includes the value ('foo',
11, 0)
, which does not satisfy the original
condition.
If conditions that cover sets of rows contained within
intervals are combined with
OR
, they form a condition
that covers a set of rows contained within the union of
their intervals. If the conditions are combined with
AND
, they form a condition
that covers a set of rows contained within the
intersection of their intervals. For example, for this
condition on a two-part index:
(key_part1
= 1 ANDkey_part2
< 2) OR (key_part1
> 5)
The intervals are:
(1,-inf) < (key_part1
,key_part2
) < (1,2) (5,-inf) < (key_part1
,key_part2
)
In this example, the interval on the first line uses one
key part for the left bound and two key parts for the
right bound. The interval on the second line uses only
one key part. The key_len
column in
the EXPLAIN
output
indicates the maximum length of the key prefix used.
In some cases, key_len
may indicate
that a key part was used, but that might be not what you
would expect. Suppose that
key_part1
and
key_part2
can be
NULL
. Then the
key_len
column displays two key part
lengths for the following condition:
key_part1
>= 1 ANDkey_part2
< 2
But, in fact, the condition is converted to this:
key_part1
>= 1 ANDkey_part2
IS NOT NULL
For a description of how optimizations are performed to combine or eliminate intervals for range conditions on a single-part index, see Range Access Method for Single-Part Indexes. Analogous steps are performed for range conditions on multiple-part indexes.
Consider these expressions, where
col_name
is an indexed column:
col_name
IN(val1
, ...,valN
)col_name
=val1
OR ... ORcol_name
=valN
Each expression is true if
col_name
is equal to any of
several values. These comparisons are equality range
comparisons (where the “range” is a single
value). The optimizer estimates the cost of reading
qualifying rows for equality range comparisons as follows:
If there is a unique index on
col_name
, the row estimate
for each range is 1 because at most one row can have the
given value.
Otherwise, any index on
col_name
is nonunique and the
optimizer can estimate the row count for each range
using dives into the index or index statistics.
With index dives, the optimizer makes a dive at each end of
a range and uses the number of rows in the range as the
estimate. For example, the expression
has three equality ranges and the optimizer
makes two dives per range to generate a row estimate. Each
pair of dives yields an estimate of the number of rows that
have the given value.
col_name
IN (10, 20,
30)
Index dives provide accurate row estimates, but as the number of comparison values in the expression increases, the optimizer takes longer to generate a row estimate. Use of index statistics is less accurate than index dives but permits faster row estimation for large value lists.
The
eq_range_index_dive_limit
system variable enables you to configure the number of
values at which the optimizer switches from one row
estimation strategy to the other. To permit use of index
dives for comparisons of up to N
equality ranges, set
eq_range_index_dive_limit
to N
+ 1. To disable use of
statistics and always use index dives regardless of
N
, set
eq_range_index_dive_limit
to 0.
To update table index statistics for best estimates, use
ANALYZE TABLE
.
Even under conditions when index dives would otherwise be used, they are skipped for queries that satisfy all these conditions:
A single-index FORCE INDEX
index hint
is present. The idea is that if index use is forced,
there is nothing to be gained from the additional
overhead of performing dives into the index.
The index is nonunique and not a
FULLTEXT
index.
No subquery is present.
No DISTINCT
, GROUP
BY
, or ORDER BY
clause is
present.
Those dive-skipping conditions apply only for single-table queries. Index dives are not skipped for multiple-table queries (joins).
The optimizer is able to apply the range scan access method to queries of this form:
SELECT ... FROM t1 WHERE ( col_1, col_2 ) IN (( 'a', 'b' ), ( 'c', 'd' ));
Previously, for range scans to be used, it was necessary to write the query as:
SELECT ... FROM t1 WHERE ( col_1 = 'a' AND col_2 = 'b' ) OR ( col_1 = 'c' AND col_2 = 'd' );
For the optimizer to use a range scan, queries must satisfy these conditions:
On the left side of the
IN()
predicate, the row
constructor contains only column references.
On the right side of the
IN()
predicate, row
constructors contain only runtime constants, which are
either literals or local column references that are
bound to constants during execution.
On the right side of the
IN()
predicate, there is
more than one row constructor.
For more information about the optimizer and row constructors, see Section 8.2.1.19, “Row Constructor Expression Optimization”
To control the memory available to the range optimizer, use
the
range_optimizer_max_mem_size
system variable:
A value of 0 means “no limit.”
With a value greater than 0, the optimizer tracks the
memory consumed when considering the range access
method. If the specified limit is about to be exceeded,
the range access method is abandoned and other methods,
including a full table scan, are considered instead.
This could be less optimal. If this happens, the
following warning occurs (where
N
is the current
range_optimizer_max_mem_size
value):
Warning 3170 Memory capacity of N
bytes for
'range_optimizer_max_mem_size' exceeded. Range
optimization was not done for this query.
For UPDATE
and
DELETE
statements, if the
optimizer falls back to a full table scan and the
sql_safe_updates
system
variable is enabled, an error occurs rather than a
warning because, in effect, no key is used to determine
which rows to modify. For more information, see
Using Safe-Updates Mode (--safe-updates).
For individual queries that exceed the available range
optimization memory and for which the optimizer falls back
to less optimal plans, increasing the
range_optimizer_max_mem_size
value may improve performance.
To estimate the amount of memory needed to process a range expression, use these guidelines:
For a simple query such as the following, where there is
one candidate key for the range access method, each
predicate combined with OR
uses approximately 230 bytes:
SELECT COUNT(*) FROM t
WHERE a=1 OR a=2 OR a=3 OR .. . a=N
;
Similarly for a query such as the following, each
predicate combined with AND
uses approximately 125 bytes:
SELECT COUNT(*) FROM t
WHERE a=1 AND b=1 AND c=1 ... N
;
For a query with IN()
predicates:
SELECT COUNT(*) FROM t WHERE a IN (1,2, ...,M
) AND b IN (1,2, ...,N
);
Each literal value in an
IN()
list counts as a
predicate combined with OR
.
If there are two IN()
lists, the number of predicates combined with
OR
is the product of the
number of literal values in each list. Thus, the number
of predicates combined with
OR
in the preceding case is
M
×
N
.
Before 5.7.11, the number of bytes per predicate combined
with OR
was higher,
approximately 700 bytes.
The Index Merge access
method retrieves rows with multiple
range
scans and merges
their results into one. This access method merges index scans
from a single table only, not scans across multiple tables.
The merge can produce unions, intersections, or
unions-of-intersections of its underlying scans.
Example queries for which Index Merge may be used:
SELECT * FROMtbl_name
WHEREkey1
= 10 ORkey2
= 20; SELECT * FROMtbl_name
WHERE (key1
= 10 ORkey2
= 20) ANDnon_key
= 30; SELECT * FROM t1, t2 WHERE (t1.key1
IN (1,2) OR t1.key2
LIKE 'value
%') AND t2.key1
= t1.some_col
; SELECT * FROM t1, t2 WHERE t1.key1
= 1 AND (t2.key1
= t1.some_col
OR t2.key2
= t1.some_col2
);
The Index Merge optimization algorithm has the following known limitations:
If your query has a complex WHERE
clause with deep
AND
/OR
nesting and MySQL does not choose the optimal plan, try
distributing terms using the following identity
transformations:
(x
ANDy
) ORz
=> (x
ORz
) AND (y
ORz
) (x
ORy
) ANDz
=> (x
ANDz
) OR (y
ANDz
)
Index Merge is not applicable to full-text indexes.
In EXPLAIN
output, the Index
Merge method appears as
index_merge
in the
type
column. In this case, the
key
column contains a list of indexes used,
and key_len
contains a list of the longest
key parts for those indexes.
The Index Merge access method has several algorithms, which
are displayed in the Extra
field of
EXPLAIN
output:
Using intersect(...)
Using union(...)
Using sort_union(...)
The following sections describe these algorithms in greater detail. The optimizer chooses between different possible Index Merge algorithms and other access methods based on cost estimates of the various available options.
Use of Index Merge is subject to the value of the
index_merge
,
index_merge_intersection
,
index_merge_union
, and
index_merge_sort_union
flags of the
optimizer_switch
system
variable. See Section 8.9.2, “Switchable Optimizations”. By
default, all those flags are on
. To enable
only certain algorithms, set index_merge
to
off
, and enable only such of the others as
should be permitted.
This access algorithm is applicable when a
WHERE
clause is converted to several
range conditions on different keys combined with
AND
, and each condition is one
of the following:
An N
-part expression of this
form, where the index has exactly
N
parts (that is, all index
parts are covered):
key_part1
=const1
ANDkey_part2
=const2
... ANDkey_partN
=constN
Any range condition over the primary key of an
InnoDB
table.
Examples:
SELECT * FROMinnodb_table
WHEREprimary_key
< 10 ANDkey_col1
= 20; SELECT * FROMtbl_name
WHEREkey1_part1
= 1 ANDkey1_part2
= 2 ANDkey2
= 2;
The Index Merge intersection algorithm performs simultaneous scans on all used indexes and produces the intersection of row sequences that it receives from the merged index scans.
If all columns used in the query are covered by the used
indexes, full table rows are not retrieved
(EXPLAIN
output contains
Using index
in Extra
field in this case). Here is an example of such a query:
SELECT COUNT(*) FROM t1 WHERE key1 = 1 AND key2 = 1;
If the used indexes do not cover all columns used in the query, full rows are retrieved only when the range conditions for all used keys are satisfied.
If one of the merged conditions is a condition over the
primary key of an InnoDB
table, it is not
used for row retrieval, but is used to filter out rows
retrieved using other conditions.
The criteria for this algorithm are similar to those for the
Index Merge intersection algorithm. The algorithm is
applicable when the table's WHERE
clause is converted to several range conditions on different
keys combined with OR
, and each
condition is one of the following:
An N
-part expression of this
form, where the index has exactly
N
parts (that is, all index
parts are covered):
key_part1
=const1
ANDkey_part2
=const2
... ANDkey_partN
=constN
Any range condition over a primary key of an
InnoDB
table.
A condition for which the Index Merge intersection algorithm is applicable.
Examples:
SELECT * FROM t1 WHEREkey1
= 1 ORkey2
= 2 ORkey3
= 3; SELECT * FROMinnodb_table
WHERE (key1
= 1 ANDkey2
= 2) OR (key3
= 'foo' ANDkey4
= 'bar') ANDkey5
= 5;
This access algorithm is applicable when the
WHERE
clause is converted to several
range conditions combined by
OR
, but the Index Merge union
algorithm is not applicable.
Examples:
SELECT * FROMtbl_name
WHEREkey_col1
< 10 ORkey_col2
< 20; SELECT * FROMtbl_name
WHERE (key_col1
> 10 ORkey_col2
= 20) ANDnonkey_col
= 30;
The difference between the sort-union algorithm and the union algorithm is that the sort-union algorithm must first fetch row IDs for all rows and sort them before returning any rows.
This optimization improves the efficiency of direct
comparisons between a nonindexed column and a constant. In
such cases, the condition is “pushed down” to the
storage engine for evaluation. This optimization can be used
only by the NDB
storage engine.
For NDB Cluster, this optimization can eliminate the need to send nonmatching rows over the network between the cluster's data nodes and the MySQL server that issued the query, and can speed up queries where it is used by a factor of 5 to 10 times over cases where condition pushdown could be but is not used.
Suppose that an NDB Cluster table is defined as follows:
CREATE TABLE t1 ( a INT, b INT, KEY(a) ) ENGINE=NDB;
Condition pushdown can be used with queries such as the one shown here, which includes a comparison between a nonindexed column and a constant:
SELECT a, b FROM t1 WHERE b = 10;
The use of condition pushdown can be seen in the output of
EXPLAIN
:
mysql> EXPLAIN SELECT a,b FROM t1 WHERE b = 10\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 10
Extra: Using where with pushed condition
However, condition pushdown cannot be used with either of these two queries:
SELECT a,b FROM t1 WHERE a = 10; SELECT a,b FROM t1 WHERE b + 1 = 10;
Condition pushdown is not applicable to the first query
because an index exists on column a
. (An
index access method would be more efficient and so would be
chosen in preference to condition pushdown.) Condition
pushdown cannot be employed for the second query because the
comparison involving the nonindexed column
b
is indirect. (However, condition pushdown
could be applied if you were to reduce b + 1 =
10
to b = 9
in the
WHERE
clause.)
Condition pushdown may also be employed when an indexed column
is compared with a constant using a >
or
<
operator:
mysql> EXPLAIN SELECT a, b FROM t1 WHERE a < 2\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
type: range
possible_keys: a
key: a
key_len: 5
ref: NULL
rows: 2
Extra: Using where with pushed condition
Other supported comparisons for condition pushdown include the following:
column
[NOT] LIKE
pattern
pattern
must be a string
literal containing the pattern to be matched; for syntax,
see Section 12.7.1, “String Comparison Functions and Operators”.
column
IS [NOT]
NULL
column
IN
(value_list
)
Each item in the value_list
must be a constant, literal value.
column
BETWEEN
constant1
AND
constant2
constant1
and
constant2
must each be a
constant, literal value.
In all of the cases in the preceding list, it is possible for the condition to be converted into the form of one or more direct comparisons between a column and a constant.
Engine condition pushdown is enabled by default. To disable it
at server startup, set the
optimizer_switch
system
variable. For example, in a my.cnf
file,
use these lines:
[mysqld] optimizer_switch=engine_condition_pushdown=off
At runtime, disable condition pushdown like this:
SET optimizer_switch='engine_condition_pushdown=off';
Limitations. Engine condition pushdown is subject to the following limitations:
Condition pushdown is supported only by the
NDB
storage engine.
Columns may be compared with constants only; however, this includes expressions which evaluate to constant values.
Columns used in comparisons cannot be of any of the
BLOB
or
TEXT
types. This exclusion
extends to JSON
,
BIT
, and
ENUM
columns as well.
A string value to be compared with a column must use the same collation as the column.
Joins are not directly supported; conditions involving
multiple tables are pushed separately where possible. Use
extended EXPLAIN
output to
determine which conditions are actually pushed down. See
Section 8.8.3, “Extended EXPLAIN Output Format”.
Index Condition Pushdown (ICP) is an optimization for the case
where MySQL retrieves rows from a table using an index.
Without ICP, the storage engine traverses the index to locate
rows in the base table and returns them to the MySQL server
which evaluates the WHERE
condition for the
rows. With ICP enabled, and if parts of the
WHERE
condition can be evaluated by using
only columns from the index, the MySQL server pushes this part
of the WHERE
condition down to the storage
engine. The storage engine then evaluates the pushed index
condition by using the index entry and only if this is
satisfied is the row read from the table. ICP can reduce the
number of times the storage engine must access the base table
and the number of times the MySQL server must access the
storage engine.
Applicability of the Index Condition Pushdown optimization is subject to these conditions:
ICP is used for the
range
,
ref
,
eq_ref
, and
ref_or_null
access
methods when there is a need to access full table rows.
ICP can be used for InnoDB
and MyISAM
tables, including
partitioned InnoDB
and
MyISAM
tables.
For InnoDB
tables, ICP is used only for
secondary indexes. The goal of ICP is to reduce the number
of full-row reads and thereby reduce I/O operations. For
InnoDB
clustered indexes, the complete
record is already read into the InnoDB
buffer. Using ICP in this case does not reduce I/O.
ICP is not supported with secondary indexes created on
virtual generated columns. InnoDB
supports secondary indexes on virtual generated columns.
Conditions that refer to subqueries cannot be pushed down.
Conditions that refer to stored functions cannot be pushed down. Storage engines cannot invoke stored functions.
Triggered conditions cannot be pushed down. (For information about triggered conditions, see Section 8.2.2.3, “Optimizing Subqueries with the EXISTS Strategy”.)
To understand how this optimization works, first consider how an index scan proceeds when Index Condition Pushdown is not used:
Get the next row, first by reading the index tuple, and then by using the index tuple to locate and read the full table row.
Test the part of the WHERE
condition
that applies to this table. Accept or reject the row based
on the test result.
Using Index Condition Pushdown, the scan proceeds like this instead:
Get the next row's index tuple (but not the full table row).
Test the part of the WHERE
condition
that applies to this table and can be checked using only
index columns. If the condition is not satisfied, proceed
to the index tuple for the next row.
If the condition is satisfied, use the index tuple to locate and read the full table row.
Test the remaining part of the WHERE
condition that applies to this table. Accept or reject the
row based on the test result.
EXPLAIN
output shows
Using index condition
in the
Extra
column when Index Condition Pushdown
is used. It does not show Using index
because that does not apply when full table rows must be read.
Suppose that a table contains information about people and
their addresses and that the table has an index defined as
INDEX (zipcode, lastname, firstname)
. If we
know a person's zipcode
value but are
not sure about the last name, we can search like this:
SELECT * FROM people WHERE zipcode='95054' AND lastname LIKE '%etrunia%' AND address LIKE '%Main Street%';
MySQL can use the index to scan through people with
zipcode='95054'
. The second part
(lastname LIKE '%etrunia%'
) cannot be used
to limit the number of rows that must be scanned, so without
Index Condition Pushdown, this query must retrieve full table
rows for all people who have
zipcode='95054'
.
With Index Condition Pushdown, MySQL checks the
lastname LIKE '%etrunia%'
part before
reading the full table row. This avoids reading full rows
corresponding to index tuples that match the
zipcode
condition but not the
lastname
condition.
Index Condition Pushdown is enabled by default. It can be
controlled with the
optimizer_switch
system
variable by setting the
index_condition_pushdown
flag:
SET optimizer_switch = 'index_condition_pushdown=off'; SET optimizer_switch = 'index_condition_pushdown=on';
MySQL executes joins between tables using a nested-loop algorithm or variations on it.
A simple nested-loop join (NLJ) algorithm reads rows from the first table in a loop one at a time, passing each row to a nested loop that processes the next table in the join. This process is repeated as many times as there remain tables to be joined.
Assume that a join between three tables
t1
, t2
, and
t3
is to be executed using the following
join types:
Table Join Type t1 range t2 ref t3 ALL
If a simple NLJ algorithm is used, the join is processed like this:
for each row in t1 matching range { for each row in t2 matching reference key { for each row in t3 { if row satisfies join conditions, send to client } } }
Because the NLJ algorithm passes rows one at a time from outer loops to inner loops, it typically reads tables processed in the inner loops many times.
A Block Nested-Loop (BNL) join algorithm uses buffering of rows read in outer loops to reduce the number of times that tables in inner loops must be read. For example, if 10 rows are read into a buffer and the buffer is passed to the next inner loop, each row read in the inner loop can be compared against all 10 rows in the buffer. This reduces by an order of magnitude the number of times the inner table must be read.
MySQL join buffering has these characteristics:
Join buffering can be used when the join is of type
ALL
or
index
(in other
words, when no possible keys can be used, and a full
scan is done, of either the data or index rows,
respectively), or
range
. Use of
buffering is also applicable to outer joins, as
described in Section 8.2.1.11, “Block Nested-Loop and Batched Key Access Joins”.
A join buffer is never allocated for the first
nonconstant table, even if it would be of type
ALL
or
index
.
Only columns of interest to a join are stored in its join buffer, not whole rows.
The join_buffer_size
system variable determines the size of each join buffer
used to process a query.
One buffer is allocated for each join that can be buffered, so a given query might be processed using multiple join buffers.
A join buffer is allocated prior to executing the join and freed after the query is done.
For the example join described previously for the NLJ algorithm (without buffering), the join is done as follows using join buffering:
for each row in t1 matching range { for each row in t2 matching reference key { store used columns from t1, t2 in join buffer if buffer is full { for each row in t3 { for each t1, t2 combination in join buffer { if row satisfies join conditions, send to client } } empty join buffer } } } if buffer is not empty { for each row in t3 { for each t1, t2 combination in join buffer { if row satisfies join conditions, send to client } } }
If S
is the size of each stored
t1
, t2
combination in
the join buffer and C
is the
number of combinations in the buffer, the number of times
table t3
is scanned is:
(S
*C
)/join_buffer_size + 1
The number of t3
scans decreases as the
value of join_buffer_size
increases, up to the point when
join_buffer_size
is large
enough to hold all previous row combinations. At that point,
no speed is gained by making it larger.
The syntax for expressing joins permits nested joins. The following discussion refers to the join syntax described in Section 13.2.9.2, “JOIN Clause”.
The syntax of table_factor
is
extended in comparison with the SQL Standard. The latter
accepts only table_reference
, not a
list of them inside a pair of parentheses. This is a
conservative extension if we consider each comma in a list of
table_reference
items as equivalent
to an inner join. For example:
SELECT * FROM t1 LEFT JOIN (t2, t3, t4) ON (t2.a=t1.a AND t3.b=t1.b AND t4.c=t1.c)
Is equivalent to:
SELECT * FROM t1 LEFT JOIN (t2 CROSS JOIN t3 CROSS JOIN t4) ON (t2.a=t1.a AND t3.b=t1.b AND t4.c=t1.c)
In MySQL, CROSS JOIN
is syntactically
equivalent to INNER JOIN
; they can replace
each other. In standard SQL, they are not equivalent.
INNER JOIN
is used with an
ON
clause; CROSS JOIN
is
used otherwise.
In general, parentheses can be ignored in join expressions containing only inner join operations. Consider this join expression:
t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL) ON t1.a=t2.a
After removing parentheses and grouping operations to the left, that join expression transforms into this expression:
(t1 LEFT JOIN t2 ON t1.a=t2.a) LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL
Yet, the two expressions are not equivalent. To see this,
suppose that the tables t1
,
t2
, and t3
have the
following state:
Table t1
contains rows
(1)
, (2)
Table t2
contains row
(1,101)
Table t3
contains row
(101)
In this case, the first expression returns a result set
including the rows (1,1,101,101)
,
(2,NULL,NULL,NULL)
, whereas the second
expression returns the rows (1,1,101,101)
,
(2,NULL,NULL,101)
:
mysql>SELECT *
FROM t1
LEFT JOIN
(t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
ON t1.a=t2.a;
+------+------+------+------+ | a | a | b | b | +------+------+------+------+ | 1 | 1 | 101 | 101 | | 2 | NULL | NULL | NULL | +------+------+------+------+ mysql>SELECT *
FROM (t1 LEFT JOIN t2 ON t1.a=t2.a)
LEFT JOIN t3
ON t2.b=t3.b OR t2.b IS NULL;
+------+------+------+------+ | a | a | b | b | +------+------+------+------+ | 1 | 1 | 101 | 101 | | 2 | NULL | NULL | 101 | +------+------+------+------+
In the following example, an outer join operation is used together with an inner join operation:
t1 LEFT JOIN (t2, t3) ON t1.a=t2.a
That expression cannot be transformed into the following expression:
t1 LEFT JOIN t2 ON t1.a=t2.a, t3
For the given table states, the two expressions return different sets of rows:
mysql>SELECT *
FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a;
+------+------+------+------+ | a | a | b | b | +------+------+------+------+ | 1 | 1 | 101 | 101 | | 2 | NULL | NULL | NULL | +------+------+------+------+ mysql>SELECT *
FROM t1 LEFT JOIN t2 ON t1.a=t2.a, t3;
+------+------+------+------+ | a | a | b | b | +------+------+------+------+ | 1 | 1 | 101 | 101 | | 2 | NULL | NULL | 101 | +------+------+------+------+
Therefore, if we omit parentheses in a join expression with outer join operators, we might change the result set for the original expression.
More exactly, we cannot ignore parentheses in the right operand of the left outer join operation and in the left operand of a right join operation. In other words, we cannot ignore parentheses for the inner table expressions of outer join operations. Parentheses for the other operand (operand for the outer table) can be ignored.
The following expression:
(t1,t2) LEFT JOIN t3 ON P(t2.b,t3.b)
Is equivalent to this expression for any tables
t1,t2,t3
and any condition
P
over attributes t2.b
and t3.b
:
t1, t2 LEFT JOIN t3 ON P(t2.b,t3.b)
Whenever the order of execution of join operations in a join
expression (joined_table
) is not
from left to right, we talk about nested joins. Consider the
following queries:
SELECT * FROM t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b) ON t1.a=t2.a WHERE t1.a > 1 SELECT * FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a WHERE (t2.b=t3.b OR t2.b IS NULL) AND t1.a > 1
Those queries are considered to contain these nested joins:
t2 LEFT JOIN t3 ON t2.b=t3.b t2, t3
In the first query, the nested join is formed with a left join operation. In the second query, it is formed with an inner join operation.
In the first query, the parentheses can be omitted: The
grammatical structure of the join expression will dictate the
same order of execution for join operations. For the second
query, the parentheses cannot be omitted, although the join
expression here can be interpreted unambiguously without them.
In our extended syntax, the parentheses in (t2,
t3)
of the second query are required, although
theoretically the query could be parsed without them: We still
would have unambiguous syntactical structure for the query
because LEFT JOIN
and ON
play the role of the left and right delimiters for the
expression (t2,t3)
.
The preceding examples demonstrate these points:
For join expressions involving only inner joins (and not outer joins), parentheses can be removed and joins evaluated left to right. In fact, tables can be evaluated in any order.
The same is not true, in general, for outer joins or for outer joins mixed with inner joins. Removal of parentheses may change the result.
Queries with nested outer joins are executed in the same
pipeline manner as queries with inner joins. More exactly, a
variation of the nested-loop join algorithm is exploited.
Recall the algorithm by which the nested-loop join executes a
query (see Section 8.2.1.6, “Nested-Loop Join Algorithms”). Suppose that
a join query over 3 tables T1,T2,T3
has
this form:
SELECT * FROM T1 INNER JOIN T2 ON P1(T1,T2) INNER JOIN T3 ON P2(T2,T3) WHERE P(T1,T2,T3)
Here, P1(T1,T2)
and
P2(T3,T3)
are some join conditions (on
expressions), whereas P(T1,T2,T3)
is a
condition over columns of tables T1,T2,T3
.
The nested-loop join algorithm would execute this query in the following manner:
FOR each row t1 in T1 { FOR each row t2 in T2 such that P1(t1,t2) { FOR each row t3 in T3 such that P2(t2,t3) { IF P(t1,t2,t3) { t:=t1||t2||t3; OUTPUT t; } } } }
The notation t1||t2||t3
indicates a row
constructed by concatenating the columns of rows
t1
, t2
, and
t3
. In some of the following examples,
NULL
where a table name appears means a row
in which NULL
is used for each column of
that table. For example, t1||t2||NULL
indicates a row constructed by concatenating the columns of
rows t1
and t2
, and
NULL
for each column of
t3
. Such a row is said to be
NULL
-complemented.
Now consider a query with nested outer joins:
SELECT * FROM T1 LEFT JOIN (T2 LEFT JOIN T3 ON P2(T2,T3)) ON P1(T1,T2) WHERE P(T1,T2,T3)
For this query, modify the nested-loop pattern to obtain:
FOR each row t1 in T1 { BOOL f1:=FALSE; FOR each row t2 in T2 such that P1(t1,t2) { BOOL f2:=FALSE; FOR each row t3 in T3 such that P2(t2,t3) { IF P(t1,t2,t3) { t:=t1||t2||t3; OUTPUT t; } f2=TRUE; f1=TRUE; } IF (!f2) { IF P(t1,t2,NULL) { t:=t1||t2||NULL; OUTPUT t; } f1=TRUE; } } IF (!f1) { IF P(t1,NULL,NULL) { t:=t1||NULL||NULL; OUTPUT t; } } }
In general, for any nested loop for the first inner table in
an outer join operation, a flag is introduced that is turned
off before the loop and is checked after the loop. The flag is
turned on when for the current row from the outer table a
match from the table representing the inner operand is found.
If at the end of the loop cycle the flag is still off, no
match has been found for the current row of the outer table.
In this case, the row is complemented by
NULL
values for the columns of the inner
tables. The result row is passed to the final check for the
output or into the next nested loop, but only if the row
satisfies the join condition of all embedded outer joins.
In the example, the outer join table expressed by the following expression is embedded:
(T2 LEFT JOIN T3 ON P2(T2,T3))
For the query with inner joins, the optimizer could choose a different order of nested loops, such as this one:
FOR each row t3 in T3 { FOR each row t2 in T2 such that P2(t2,t3) { FOR each row t1 in T1 such that P1(t1,t2) { IF P(t1,t2,t3) { t:=t1||t2||t3; OUTPUT t; } } } }
For queries with outer joins, the optimizer can choose only
such an order where loops for outer tables precede loops for
inner tables. Thus, for our query with outer joins, only one
nesting order is possible. For the following query, the
optimizer evaluates two different nestings. In both nestings,
T1
must be processed in the outer loop
because it is used in an outer join. T2
and
T3
are used in an inner join, so that join
must be processed in the inner loop. However, because the join
is an inner join, T2
and
T3
can be processed in either order.
SELECT * T1 LEFT JOIN (T2,T3) ON P1(T1,T2) AND P2(T1,T3) WHERE P(T1,T2,T3)
One nesting evaluates T2
, then
T3
:
FOR each row t1 in T1 { BOOL f1:=FALSE; FOR each row t2 in T2 such that P1(t1,t2) { FOR each row t3 in T3 such that P2(t1,t3) { IF P(t1,t2,t3) { t:=t1||t2||t3; OUTPUT t; } f1:=TRUE } } IF (!f1) { IF P(t1,NULL,NULL) { t:=t1||NULL||NULL; OUTPUT t; } } }
The other nesting evaluates T3
, then
T2
:
FOR each row t1 in T1 { BOOL f1:=FALSE; FOR each row t3 in T3 such that P2(t1,t3) { FOR each row t2 in T2 such that P1(t1,t2) { IF P(t1,t2,t3) { t:=t1||t2||t3; OUTPUT t; } f1:=TRUE } } IF (!f1) { IF P(t1,NULL,NULL) { t:=t1||NULL||NULL; OUTPUT t; } } }
When discussing the nested-loop algorithm for inner joins, we
omitted some details whose impact on the performance of query
execution may be huge. We did not mention so-called
“pushed-down” conditions. Suppose that our
WHERE
condition
P(T1,T2,T3)
can be represented by a
conjunctive formula:
P(T1,T2,T2) = C1(T1) AND C2(T2) AND C3(T3).
In this case, MySQL actually uses the following nested-loop algorithm for the execution of the query with inner joins:
FOR each row t1 in T1 such that C1(t1) { FOR each row t2 in T2 such that P1(t1,t2) AND C2(t2) { FOR each row t3 in T3 such that P2(t2,t3) AND C3(t3) { IF P(t1,t2,t3) { t:=t1||t2||t3; OUTPUT t; } } } }
You see that each of the conjuncts C1(T1)
,
C2(T2)
, C3(T3)
are
pushed out of the most inner loop to the most outer loop where
it can be evaluated. If C1(T1)
is a very
restrictive condition, this condition pushdown may greatly
reduce the number of rows from table T1
passed to the inner loops. As a result, the execution time for
the query may improve immensely.
For a query with outer joins, the WHERE
condition is to be checked only after it has been found that
the current row from the outer table has a match in the inner
tables. Thus, the optimization of pushing conditions out of
the inner nested loops cannot be applied directly to queries
with outer joins. Here we must introduce conditional
pushed-down predicates guarded by the flags that are turned on
when a match has been encountered.
Recall this example with outer joins:
P(T1,T2,T3)=C1(T1) AND C(T2) AND C3(T3)
For that example, the nested-loop algorithm using guarded pushed-down conditions looks like this:
FOR each row t1 in T1 such that C1(t1) { BOOL f1:=FALSE; FOR each row t2 in T2 such that P1(t1,t2) AND (f1?C2(t2):TRUE) { BOOL f2:=FALSE; FOR each row t3 in T3 such that P2(t2,t3) AND (f1&&f2?C3(t3):TRUE) { IF (f1&&f2?TRUE:(C2(t2) AND C3(t3))) { t:=t1||t2||t3; OUTPUT t; } f2=TRUE; f1=TRUE; } IF (!f2) { IF (f1?TRUE:C2(t2) && P(t1,t2,NULL)) { t:=t1||t2||NULL; OUTPUT t; } f1=TRUE; } } IF (!f1 && P(t1,NULL,NULL)) { t:=t1||NULL||NULL; OUTPUT t; } }
In general, pushed-down predicates can be extracted from join
conditions such as P1(T1,T2)
and
P(T2,T3)
. In this case, a pushed-down
predicate is guarded also by a flag that prevents checking the
predicate for the NULL
-complemented row
generated by the corresponding outer join operation.
Access by key from one inner table to another in the same
nested join is prohibited if it is induced by a predicate from
the WHERE
condition.
Outer joins include LEFT JOIN
and
RIGHT JOIN
.
MySQL implements an
as
follows:
A
LEFT
JOIN B
join_specification
Table B
is set to depend on
table A
and all tables on which
A
depends.
Table A
is set to depend on all
tables (except B
) that are used
in the LEFT JOIN
condition.
The LEFT JOIN
condition is used to
decide how to retrieve rows from table
B
. (In other words, any
condition in the WHERE
clause is not
used.)
All standard join optimizations are performed, with the exception that a table is always read after all tables on which it depends. If there is a circular dependency, an error occurs.
All standard WHERE
optimizations are
performed.
If there is a row in A
that
matches the WHERE
clause, but there is
no row in B
that matches the
ON
condition, an extra
B
row is generated with all
columns set to NULL
.
If you use LEFT JOIN
to find rows that
do not exist in some table and you have the following
test:
in the col_name
IS
NULLWHERE
part, where
col_name
is a column that is
declared as NOT NULL
, MySQL stops
searching for more rows (for a particular key combination)
after it has found one row that matches the LEFT
JOIN
condition.
The RIGHT JOIN
implementation is analogous
to that of LEFT JOIN
with the table roles
reversed. Right joins are converted to equivalent left joins,
as described in Section 8.2.1.9, “Outer Join Simplification”.
For a LEFT JOIN
, if the
WHERE
condition is always false for the
generated NULL
row, the LEFT
JOIN
is changed to an inner join. For example, the
WHERE
clause would be false in the
following query if t2.column1
were
NULL
:
SELECT * FROM t1 LEFT JOIN t2 ON (column1) WHERE t2.column2=5;
Therefore, it is safe to convert the query to an inner join:
SELECT * FROM t1, t2 WHERE t2.column2=5 AND t1.column1=t2.column1;
Now the optimizer can use table t2
before
table t1
if doing so would result in a
better query plan. To provide a hint about the table join
order, use STRAIGHT_JOIN
; see
Section 13.2.9, “SELECT Statement”. However,
STRAIGHT_JOIN
may prevent indexes from
being used because it disables semijoin transformations; see
Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin
Transformations”.
Table expressions in the FROM
clause of a
query are simplified in many cases.
At the parser stage, queries with right outer join operations are converted to equivalent queries containing only left join operations. In the general case, the conversion is performed such that this right join:
(T1, ...) RIGHT JOIN (T2, ...) ON P(T1, ..., T2, ...)
Becomes this equivalent left join:
(T2, ...) LEFT JOIN (T1, ...) ON P(T1, ..., T2, ...)
All inner join expressions of the form T1 INNER JOIN
T2 ON P(T1,T2)
are replaced by the list
T1,T2
, P(T1,T2)
being
joined as a conjunct to the WHERE
condition
(or to the join condition of the embedding join, if there is
any).
When the optimizer evaluates plans for outer join operations, it takes into consideration only plans where, for each such operation, the outer tables are accessed before the inner tables. The optimizer choices are limited because only such plans enable outer joins to be executed using the nested-loop algorithm.
Consider a query of this form, where R(T2)
greatly narrows the number of matching rows from table
T2
:
SELECT * T1 LEFT JOIN T2 ON P1(T1,T2) WHERE P(T1,T2) AND R(T2)
If the query is executed as written, the optimizer has no
choice but to access the less-restricted table
T1
before the more-restricted table
T2
, which may produce a very inefficient
execution plan.
Instead, MySQL converts the query to a query with no outer
join operation if the WHERE
condition is
null-rejected. (That is, it converts the outer join to an
inner join.) A condition is said to be null-rejected for an
outer join operation if it evaluates to
FALSE
or UNKNOWN
for any
NULL
-complemented row generated for the
operation.
Thus, for this outer join:
T1 LEFT JOIN T2 ON T1.A=T2.A
Conditions such as these are null-rejected because they cannot
be true for any NULL
-complemented row (with
T2
columns set to NULL
):
T2.B IS NOT NULL T2.B > 3 T2.C <= T1.C T2.B < 2 OR T2.C > 1
Conditions such as these are not null-rejected because they
might be true for a NULL
-complemented row:
T2.B IS NULL T1.B < 3 OR T2.B IS NOT NULL T1.B < 3 OR T2.B > 3
The general rules for checking whether a condition is null-rejected for an outer join operation are simple:
It is of the form A IS NOT NULL
, where
A
is an attribute of any of the inner
tables
It is a predicate containing a reference to an inner table
that evaluates to UNKNOWN
when one of
its arguments is NULL
It is a conjunction containing a null-rejected condition as a conjunct
It is a disjunction of null-rejected conditions
A condition can be null-rejected for one outer join operation
in a query and not null-rejected for another. In this query,
the WHERE
condition is null-rejected for
the second outer join operation but is not null-rejected for
the first one:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A LEFT JOIN T3 ON T3.B=T1.B WHERE T3.C > 0
If the WHERE
condition is null-rejected for
an outer join operation in a query, the outer join operation
is replaced by an inner join operation.
For example, in the preceding query, the second outer join is null-rejected and can be replaced by an inner join:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A INNER JOIN T3 ON T3.B=T1.B WHERE T3.C > 0
For the original query, the optimizer evaluates only plans
compatible with the single table-access order
T1,T2,T3
. For the rewritten query, it
additionally considers the access order
T3,T1,T2
.
A conversion of one outer join operation may trigger a conversion of another. Thus, the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A LEFT JOIN T3 ON T3.B=T2.B WHERE T3.C > 0
Is first converted to the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A INNER JOIN T3 ON T3.B=T2.B WHERE T3.C > 0
Which is equivalent to the query:
SELECT * FROM (T1 LEFT JOIN T2 ON T2.A=T1.A), T3 WHERE T3.C > 0 AND T3.B=T2.B
The remaining outer join operation can also be replaced by an
inner join because the condition T3.B=T2.B
is null-rejected. This results in a query with no outer joins
at all:
SELECT * FROM (T1 INNER JOIN T2 ON T2.A=T1.A), T3 WHERE T3.C > 0 AND T3.B=T2.B
Sometimes the optimizer succeeds in replacing an embedded outer join operation, but cannot convert the embedding outer join. The following query:
SELECT * FROM T1 LEFT JOIN (T2 LEFT JOIN T3 ON T3.B=T2.B) ON T2.A=T1.A WHERE T3.C > 0
Is converted to:
SELECT * FROM T1 LEFT JOIN (T2 INNER JOIN T3 ON T3.B=T2.B) ON T2.A=T1.A WHERE T3.C > 0
That can be rewritten only to the form still containing the embedding outer join operation:
SELECT * FROM T1 LEFT JOIN (T2,T3) ON (T2.A=T1.A AND T3.B=T2.B) WHERE T3.C > 0
Any attempt to convert an embedded outer join operation in a
query must take into account the join condition for the
embedding outer join together with the
WHERE
condition. In this query, the
WHERE
condition is not null-rejected for
the embedded outer join, but the join condition of the
embedding outer join T2.A=T1.A AND
T3.C=T1.C
is null-rejected:
SELECT * FROM T1 LEFT JOIN (T2 LEFT JOIN T3 ON T3.B=T2.B) ON T2.A=T1.A AND T3.C=T1.C WHERE T3.D > 0 OR T1.D > 0
Consequently, the query can be converted to:
SELECT * FROM T1 LEFT JOIN (T2, T3) ON T2.A=T1.A AND T3.C=T1.C AND T3.B=T2.B WHERE T3.D > 0 OR T1.D > 0
Reading rows using a range scan on a secondary index can result in many random disk accesses to the base table when the table is large and not stored in the storage engine's cache. With the Disk-Sweep Multi-Range Read (MRR) optimization, MySQL tries to reduce the number of random disk access for range scans by first scanning the index only and collecting the keys for the relevant rows. Then the keys are sorted and finally the rows are retrieved from the base table using the order of the primary key. The motivation for Disk-sweep MRR is to reduce the number of random disk accesses and instead achieve a more sequential scan of the base table data.
The Multi-Range Read optimization provides these benefits:
MRR enables data rows to be accessed sequentially rather than in random order, based on index tuples. The server obtains a set of index tuples that satisfy the query conditions, sorts them according to data row ID order, and uses the sorted tuples to retrieve data rows in order. This makes data access more efficient and less expensive.
MRR enables batch processing of requests for key access for operations that require access to data rows through index tuples, such as range index scans and equi-joins that use an index for the join attribute. MRR iterates over a sequence of index ranges to obtain qualifying index tuples. As these results accumulate, they are used to access the corresponding data rows. It is not necessary to acquire all index tuples before starting to read data rows.
The MRR optimization is not supported with secondary indexes
created on virtual generated columns.
InnoDB
supports secondary indexes on
virtual generated columns.
The following scenarios illustrate when MRR optimization can be advantageous:
Scenario A: MRR can be used for InnoDB
and
MyISAM
tables for index range scans and
equi-join operations.
A portion of the index tuples are accumulated in a buffer.
The tuples in the buffer are sorted by their data row ID.
Data rows are accessed according to the sorted index tuple sequence.
Scenario B: MRR can be used for
NDB
tables for multiple-range
index scans or when performing an equi-join by an attribute.
A portion of ranges, possibly single-key ranges, is accumulated in a buffer on the central node where the query is submitted.
The ranges are sent to the execution nodes that access data rows.
The accessed rows are packed into packages and sent back to the central node.
The received packages with data rows are placed in a buffer.
Data rows are read from the buffer.
When MRR is used, the Extra
column in
EXPLAIN
output shows
Using MRR
.
InnoDB
and MyISAM
do not
use MRR if full table rows need not be accessed to produce the
query result. This is the case if results can be produced
entirely on the basis on information in the index tuples
(through a covering
index); MRR provides no benefit.
Two optimizer_switch
system
variable flags provide an interface to the use of MRR
optimization. The mrr
flag controls whether
MRR is enabled. If mrr
is enabled
(on
), the mrr_cost_based
flag controls whether the optimizer attempts to make a
cost-based choice between using and not using MRR
(on
) or uses MRR whenever possible
(off
). By default, mrr
is on
and mrr_cost_based
is on
. See
Section 8.9.2, “Switchable Optimizations”.
For MRR, a storage engine uses the value of the
read_rnd_buffer_size
system
variable as a guideline for how much memory it can allocate
for its buffer. The engine uses up to
read_rnd_buffer_size
bytes
and determines the number of ranges to process in a single
pass.
In MySQL, a Batched Key Access (BKA) Join algorithm is available that uses both index access to the joined table and a join buffer. The BKA algorithm supports inner join, outer join, and semijoin operations, including nested outer joins. Benefits of BKA include improved join performance due to more efficient table scanning. Also, the Block Nested-Loop (BNL) Join algorithm previously used only for inner joins is extended and can be employed for outer join and semijoin operations, including nested outer joins.
The following sections discuss the join buffer management that underlies the extension of the original BNL algorithm, the extended BNL algorithm, and the BKA algorithm. For information about semijoin strategies, see Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin Transformations”
MySQL can employ join buffers to execute not only inner joins without index access to the inner table, but also outer joins and semijoins that appear after subquery flattening. Moreover, a join buffer can be effectively used when there is an index access to the inner table.
The join buffer management code slightly more efficiently
utilizes join buffer space when storing the values of the
interesting row columns: No additional bytes are allocated
in buffers for a row column if its value is
NULL
, and the minimum number of bytes is
allocated for any value of the
VARCHAR
type.
The code supports two types of buffers, regular and
incremental. Suppose that join buffer B1
is employed to join tables t1
and
t2
and the result of this operation is
joined with table t3
using join buffer
B2
:
A regular join buffer contains columns from each join
operand. If B2
is a regular join
buffer, each row r
put into
B2
is composed of the columns of a
row r1
from
B1
and the interesting columns of a
matching row r2
from table
t3
.
An incremental join buffer contains only columns from
rows of the table produced by the second join operand.
That is, it is incremental to a row from the first
operand buffer. If B2
is an
incremental join buffer, it contains the interesting
columns of the row r2
together with a link to the row
r1
from
B1
.
Incremental join buffers are always incremental relative to
a join buffer from an earlier join operation, so the buffer
from the first join operation is always a regular buffer. In
the example just given, the buffer B1
used to join tables t1
and
t2
must be a regular buffer.
Each row of the incremental buffer used for a join operation
contains only the interesting columns of a row from the
table to be joined. These columns are augmented with a
reference to the interesting columns of the matched row from
the table produced by the first join operand. Several rows
in the incremental buffer can refer to the same row
r
whose columns are stored in the
previous join buffers insofar as all these rows match row
r
.
Incremental buffers enable less frequent copying of columns from buffers used for previous join operations. This provides a savings in buffer space because in the general case a row produced by the first join operand can be matched by several rows produced by the second join operand. It is unnecessary to make several copies of a row from the first operand. Incremental buffers also provide a savings in processing time due to the reduction in copying time.
The block_nested_loop
and
batched_key_access
flags of the
optimizer_switch
system
variable control how the optimizer uses the Block
Nested-Loop and Batched Key Access join algorithms. By
default, block_nested_loop
is
on
and
batched_key_access
is
off
. See
Section 8.9.2, “Switchable Optimizations”. Optimizer hints
may also be applied; see
Optimizer Hints for Block Nested-Loop and Batched Key Access Algorithms.
For information about semijoin strategies, see Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin Transformations”
The original implementation of the MySQL BNL algorithm is extended to support outer join and semijoin operations.
When these operations are executed with a join buffer, each row put into the buffer is supplied with a match flag.
If an outer join operation is executed using a join buffer,
each row of the table produced by the second operand is
checked for a match against each row in the join buffer.
When a match is found, a new extended row is formed (the
original row plus columns from the second operand) and sent
for further extensions by the remaining join operations. In
addition, the match flag of the matched row in the buffer is
enabled. After all rows of the table to be joined have been
examined, the join buffer is scanned. Each row from the
buffer that does not have its match flag enabled is extended
by NULL
complements
(NULL
values for each column in the
second operand) and sent for further extensions by the
remaining join operations.
The block_nested_loop
flag of the
optimizer_switch
system
variable controls how the optimizer uses the Block
Nested-Loop algorithm. By default,
block_nested_loop
is
on
. See
Section 8.9.2, “Switchable Optimizations”. Optimizer hints
may also be applied; see
Optimizer Hints for Block Nested-Loop and Batched Key Access Algorithms.
In EXPLAIN
output, use of BNL
for a table is signified when the Extra
value contains Using join buffer (Block Nested
Loop)
and the type
value is
ALL
,
index
, or
range
.
For information about semijoin strategies, see Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin Transformations”
MySQL implements a method of joining tables called the Batched Key Access (BKA) join algorithm. BKA can be applied when there is an index access to the table produced by the second join operand. Like the BNL join algorithm, the BKA join algorithm employs a join buffer to accumulate the interesting columns of the rows produced by the first operand of the join operation. Then the BKA algorithm builds keys to access the table to be joined for all rows in the buffer and submits these keys in a batch to the database engine for index lookups. The keys are submitted to the engine through the Multi-Range Read (MRR) interface (see Section 8.2.1.10, “Multi-Range Read Optimization”). After submission of the keys, the MRR engine functions perform lookups in the index in an optimal way, fetching the rows of the joined table found by these keys, and starts feeding the BKA join algorithm with matching rows. Each matching row is coupled with a reference to a row in the join buffer.
When BKA is used, the value of
join_buffer_size
defines
how large the batch of keys is in each request to the
storage engine. The larger the buffer, the more sequential
access will be to the right hand table of a join operation,
which can significantly improve performance.
For BKA to be used, the
batched_key_access
flag of the
optimizer_switch
system
variable must be set to on
. BKA uses MRR,
so the mrr
flag must also be
on
. Currently, the cost estimation for
MRR is too pessimistic. Hence, it is also necessary for
mrr_cost_based
to be
off
for BKA to be used. The following
setting enables BKA:
mysql> SET optimizer_switch='mrr=on,mrr_cost_based=off,batched_key_access=on';
There are two scenarios by which MRR functions execute:
The first scenario is used for conventional disk-based
storage engines such as
InnoDB
and
MyISAM
. For these engines,
usually the keys for all rows from the join buffer are
submitted to the MRR interface at once. Engine-specific
MRR functions perform index lookups for the submitted
keys, get row IDs (or primary keys) from them, and then
fetch rows for all these selected row IDs one by one by
request from BKA algorithm. Every row is returned with
an association reference that enables access to the
matched row in the join buffer. The rows are fetched by
the MRR functions in an optimal way: They are fetched in
the row ID (primary key) order. This improves
performance because reads are in disk order rather than
random order.
The second scenario is used for remote storage engines
such as NDB
. A package of
keys for a portion of rows from the join buffer,
together with their associations, is sent by a MySQL
Server (SQL node) to NDB Cluster data nodes. In return,
the SQL node receives a package (or several packages) of
matching rows coupled with corresponding associations.
The BKA join algorithm takes these rows and builds new
joined rows. Then a new set of keys is sent to the data
nodes and the rows from the returned packages are used
to build new joined rows. The process continues until
the last keys from the join buffer are sent to the data
nodes, and the SQL node has received and joined all rows
matching these keys. This improves performance because
fewer key-bearing packages sent by the SQL node to the
data nodes means fewer round trips between it and the
data nodes to perform the join operation.
With the first scenario, a portion of the join buffer is reserved to store row IDs (primary keys) selected by index lookups and passed as a parameter to the MRR functions.
There is no special buffer to store keys built for rows from the join buffer. Instead, a function that builds the key for the next row in the buffer is passed as a parameter to the MRR functions.
In EXPLAIN
output, use of BKA
for a table is signified when the Extra
value contains Using join buffer (Batched Key
Access)
and the type
value is
ref
or
eq_ref
.
In addition to using the
optimizer_switch
system
variable to control optimizer use of the BNL and BKA
algorithms session-wide, MySQL supports optimizer hints to
influence the optimizer on a per-statement basis. See
Section 8.9.3, “Optimizer Hints”.
To use a BNL or BKA hint to enable join buffering for any inner table of an outer join, join buffering must be enabled for all inner tables of the outer join.
In join processing, prefix rows are those rows passed from one table in a join to the next. In general, the optimizer attempts to put tables with low prefix counts early in the join order to keep the number of row combinations from increasing rapidly. To the extent that the optimizer can use information about conditions on rows selected from one table and passed to the next, the more accurately it can compute row estimates and choose the best execution plan.
Without condition filtering, the prefix row count for a table
is based on the estimated number of rows selected by the
WHERE
clause according to whichever access
method the optimizer chooses. Condition filtering enables the
optimizer to use other relevant conditions in the
WHERE
clause not taken into account by the
access method, and thus improve its prefix row count
estimates. For example, even though there might be an
index-based access method that can be used to select rows from
the current table in a join, there might also be additional
conditions for the table in the WHERE
clause that can filter (further restrict) the estimate for
qualifying rows passed to the next table.
A condition contributes to the filtering estimate only if:
It refers to the current table.
It depends on a constant value or values from earlier tables in the join sequence.
It was not already taken into account by the access method.
In EXPLAIN
output, the
rows
column indicates the row estimate for
the chosen access method, and the filtered
column reflects the effect of condition filtering.
filtered
values are expressed as
percentages. The maximum value is 100, which means no
filtering of rows occurred. Values decreasing from 100
indicate increasing amounts of filtering.
The prefix row count (the number of rows estimated to be
passed from the current table in a join to the next) is the
product of the rows
and
filtered
values. That is, the prefix row
count is the estimated row count, reduced by the estimated
filtering effect. For example, if rows
is
1000 and filtered
is 20%, condition
filtering reduces the estimated row count of 1000 to a prefix
row count of 1000 × 20% = 1000 × .2 = 200.
Consider the following query:
SELECT * FROM employee JOIN department ON employee.dept_no = department.dept_no WHERE employee.first_name = 'John' AND employee.hire_date BETWEEN '2018-01-01' AND '2018-06-01';
Suppose that the data set has these characteristics:
The employee
table has 1024 rows.
The department
table has 12 rows.
Both tables have an index on dept_no
.
The employee
table has an index on
first_name
.
8 rows satisfy this condition on
employee.first_name
:
employee.first_name = 'John'
150 rows satisfy this condition on
employee.hire_date
:
employee.hire_date BETWEEN '2018-01-01' AND '2018-06-01'
1 row satisfies both conditions:
employee.first_name = 'John' AND employee.hire_date BETWEEN '2018-01-01' AND '2018-06-01'
Without condition filtering,
EXPLAIN
produces output like
this:
+----+------------+--------+------------------+---------+---------+------+----------+ | id | table | type | possible_keys | key | ref | rows | filtered | +----+------------+--------+------------------+---------+---------+------+----------+ | 1 | employee | ref | name,h_date,dept | name | const | 8 | 100.00 | | 1 | department | eq_ref | PRIMARY | PRIMARY | dept_no | 1 | 100.00 | +----+------------+--------+------------------+---------+---------+------+----------+
For employee
, the access method on the
name
index picks up the 8 rows that match a
name of 'John'
. No filtering is done
(filtered
is 100%), so all rows are prefix
rows for the next table: The prefix row count is
rows
× filtered
=
8 × 100% = 8.
With condition filtering, the optimizer additionally takes
into account conditions from the WHERE
clause not taken into account by the access method. In this
case, the optimizer uses heuristics to estimate a filtering
effect of 16.31% for the BETWEEN
condition on employee.hire_date
. As a
result, EXPLAIN
produces output
like this:
+----+------------+--------+------------------+---------+---------+------+----------+ | id | table | type | possible_keys | key | ref | rows | filtered | +----+------------+--------+------------------+---------+---------+------+----------+ | 1 | employee | ref | name,h_date,dept | name | const | 8 | 16.31 | | 1 | department | eq_ref | PRIMARY | PRIMARY | dept_no | 1 | 100.00 | +----+------------+--------+------------------+---------+---------+------+----------+
Now the prefix row count is rows
×
filtered
= 8 × 16.31% = 1.3, which
more closely reflects actual data set.
Normally, the optimizer does not calculate the condition
filtering effect (prefix row count reduction) for the last
joined table because there is no next table to pass rows to.
An exception occurs for
EXPLAIN
: To provide more
information, the filtering effect is calculated for all joined
tables, including the last one.
To control whether the optimizer considers additional
filtering conditions, use the
condition_fanout_filter
flag of the
optimizer_switch
system
variable (see Section 8.9.2, “Switchable Optimizations”).
This flag is enabled by default but can be disabled to
suppress condition filtering (for example, if a particular
query is found to yield better performance without it).
If the optimizer overestimates the effect of condition filtering, performance may be worse than if condition filtering is not used. In such cases, these techniques may help:
If a column is not indexed, index it so that the optimizer has some information about the distribution of column values and can improve its row estimates.
Change the join order. Ways to accomplish this include
join-order optimizer hints (see
Section 8.9.3, “Optimizer Hints”),
STRAIGHT_JOIN
immediately following the
SELECT
, and the
STRAIGHT_JOIN
join operator.
Disable condition filtering for the session:
SET optimizer_switch = 'condition_fanout_filter=off';
MySQL can perform the same optimization on
col_name
IS
NULL
that it can use for
col_name
=
constant_value
. For example, MySQL
can use indexes and ranges to search for
NULL
with IS
NULL
.
Examples:
SELECT * FROMtbl_name
WHEREkey_col
IS NULL; SELECT * FROMtbl_name
WHEREkey_col
<=> NULL; SELECT * FROMtbl_name
WHEREkey_col
=const1
ORkey_col
=const2
ORkey_col
IS NULL;
If a WHERE
clause includes a
col_name
IS
NULL
condition for a column that is declared as
NOT NULL
, that expression is optimized
away. This optimization does not occur in cases when the
column might produce NULL
anyway (for
example, if it comes from a table on the right side of a
LEFT JOIN
).
MySQL can also optimize the combination
, a form
that is common in resolved subqueries.
col_name
=
expr
OR
col_name
IS NULLEXPLAIN
shows
ref_or_null
when this
optimization is used.
This optimization can handle one IS
NULL
for any key part.
Some examples of queries that are optimized, assuming that
there is an index on columns a
and
b
of table t2
:
SELECT * FROM t1 WHERE t1.a=expr
OR t1.a IS NULL;
SELECT * FROM t1, t2 WHERE t1.a=t2.a OR t2.a IS NULL;
SELECT * FROM t1, t2
WHERE (t1.a=t2.a OR t2.a IS NULL) AND t2.b=t1.b;
SELECT * FROM t1, t2
WHERE t1.a=t2.a AND (t2.b=t1.b OR t2.b IS NULL);
SELECT * FROM t1, t2
WHERE (t1.a=t2.a AND t2.a IS NULL AND ...)
OR (t1.a=t2.a AND t2.a IS NULL AND ...);
ref_or_null
works by first
doing a read on the reference key, and then a separate search
for rows with a NULL
key value.
The optimization can handle only one IS
NULL
level. In the following query, MySQL uses key
lookups only on the expression (t1.a=t2.a AND t2.a IS
NULL)
and is not able to use the key part on
b
:
SELECT * FROM t1, t2 WHERE (t1.a=t2.a AND t2.a IS NULL) OR (t1.b=t2.b AND t2.b IS NULL);
This section describes when MySQL can use an index to satisfy
an ORDER BY
clause, the
filesort
operation used when an index
cannot be used, and execution plan information available from
the optimizer about ORDER BY
.
An ORDER BY
with and without
LIMIT
may return rows in different orders,
as discussed in Section 8.2.1.17, “LIMIT Query Optimization”.
In some cases, MySQL may use an index to satisfy an
ORDER BY
clause and avoid the extra
sorting involved in performing a filesort
operation.
The index may also be used even if the ORDER
BY
does not match the index exactly, as long as
all unused portions of the index and all extra
ORDER BY
columns are constants in the
WHERE
clause. If the index does not
contain all columns accessed by the query, the index is used
only if index access is cheaper than other access methods.
Assuming that there is an index on
(
, the
following queries may use the index to resolve the
key_part1
,
key_part2
)ORDER BY
part. Whether the optimizer
actually does so depends on whether reading the index is
more efficient than a table scan if columns not in the index
must also be read.
In this query, the index on
(
enables
the optimizer to avoid sorting:
key_part1
,
key_part2
)
SELECT * FROM t1 ORDER BYkey_part1
,key_part2
;
However, the query uses SELECT *
,
which may select more columns than
key_part1
and
key_part2
. In that case,
scanning an entire index and looking up table rows to
find columns not in the index may be more expensive than
scanning the table and sorting the results. If so, the
optimizer probably will not use the index. If
SELECT *
selects only the index
columns, the index will be used and sorting avoided.
If t1
is an InnoDB
table, the table primary key is implicitly part of the
index, and the index can be used to resolve the
ORDER BY
for this query:
SELECTpk
,key_part1
,key_part2
FROM t1 ORDER BYkey_part1
,key_part2
;
In this query, key_part1
is
constant, so all rows accessed through the index are in
key_part2
order, and an index
on (
avoids
sorting if the key_part1
,
key_part2
)WHERE
clause is
selective enough to make an index range scan cheaper
than a table scan:
SELECT * FROM t1 WHEREkey_part1
=constant
ORDER BYkey_part2
;
In the next two queries, whether the index is used is
similar to the same queries without
DESC
shown previously:
SELECT * FROM t1 ORDER BYkey_part1
DESC,key_part2
DESC; SELECT * FROM t1 WHEREkey_part1
=constant
ORDER BYkey_part2
DESC;
In the next two queries,
key_part1
is compared to a
constant. The index will be used if the
WHERE
clause is selective enough to
make an index range scan cheaper than a table scan:
SELECT * FROM t1 WHEREkey_part1
>constant
ORDER BYkey_part1
ASC; SELECT * FROM t1 WHEREkey_part1
<constant
ORDER BYkey_part1
DESC;
In the next query, the ORDER BY
does
not name key_part1
, but all
rows selected have a constant
key_part1
value, so the index
can still be used:
SELECT * FROM t1 WHEREkey_part1
=constant1
ANDkey_part2
>constant2
ORDER BYkey_part2
;
In some cases, MySQL cannot use indexes
to resolve the ORDER BY
, although it may
still use indexes to find the rows that match the
WHERE
clause. Examples:
The query uses ORDER BY
on different
indexes:
SELECT * FROM t1 ORDER BYkey1
,key2
;
The query uses ORDER BY
on
nonconsecutive parts of an index:
SELECT * FROM t1 WHEREkey2
=constant
ORDER BYkey1_part1
,key1_part3
;
The query mixes ASC
and
DESC
:
SELECT * FROM t1 ORDER BYkey_part1
DESC,key_part2
ASC;
The index used to fetch the rows differs from the one
used in the ORDER BY
:
SELECT * FROM t1 WHEREkey2
=constant
ORDER BYkey1
;
The query uses ORDER BY
with an
expression that includes terms other than the index
column name:
SELECT * FROM t1 ORDER BY ABS(key
); SELECT * FROM t1 ORDER BY -key
;
The query joins many tables, and the columns in the
ORDER BY
are not all from the first
nonconstant table that is used to retrieve rows. (This
is the first table in the
EXPLAIN
output that does
not have a const
join
type.)
The query has different ORDER BY
and
GROUP BY
expressions.
There is an index on only a prefix of a column named in
the ORDER BY
clause. In this case,
the index cannot be used to fully resolve the sort
order. For example, if only the first 10 bytes of a
CHAR(20)
column are
indexed, the index cannot distinguish values past the
10th byte and a filesort
is needed.
The index does not store rows in order. For example,
this is true for a HASH
index in a
MEMORY
table.
Availability of an index for sorting may be affected by the
use of column aliases. Suppose that the column
t1.a
is indexed. In this statement, the
name of the column in the select list is
a
. It refers to t1.a
,
as does the reference to a
in the
ORDER BY
, so the index on
t1.a
can be used:
SELECT a FROM t1 ORDER BY a;
In this statement, the name of the column in the select list
is also a
, but it is the alias name. It
refers to ABS(a)
, as does the reference
to a
in the ORDER BY
,
so the index on t1.a
cannot be used:
SELECT ABS(a) AS a FROM t1 ORDER BY a;
In the following statement, the ORDER BY
refers to a name that is not the name of a column in the
select list. But there is a column in t1
named a
, so the ORDER
BY
refers to t1.a
and the index
on t1.a
can be used. (The resulting sort
order may be completely different from the order for
ABS(a)
, of course.)
SELECT ABS(a) AS b FROM t1 ORDER BY a;
By default, MySQL sorts GROUP BY
queries as if
you also included col1
,
col2
, ...ORDER BY
in the query.
If you include an explicit col1
,
col2
, ...ORDER BY
clause that contains the same column list, MySQL optimizes
it away without any speed penalty, although the sorting
still occurs.
If a query includes GROUP BY
but you want
to avoid the overhead of sorting the result, you can
suppress sorting by specifying ORDER BY
NULL
. For example:
INSERT INTO foo SELECT a, COUNT(*) FROM bar GROUP BY a ORDER BY NULL;
The optimizer may still choose to use sorting to implement
grouping operations. ORDER BY NULL
suppresses sorting of the result, not prior sorting done by
grouping operations to determine the result.
GROUP BY
implicitly sorts by default
(that is, in the absence of ASC
or
DESC
designators for GROUP
BY
columns). However, relying on implicit
GROUP BY
sorting (that is, sorting in
the absence of ASC
or
DESC
designators) or explicit sorting
for GROUP BY
(that is, by using
explicit ASC
or DESC
designators for GROUP BY
columns) is
deprecated. To produce a given sort order, provide an
ORDER BY
clause.
If an index cannot be used to satisfy an ORDER
BY
clause, MySQL performs a
filesort
operation that reads table rows
and sorts them. A filesort
constitutes an
extra sorting phase in query execution.
To obtain memory for filesort
operations,
the optimizer allocates a fixed amount of
sort_buffer_size
bytes up
front. Individual sessions can change the session value of
this variable as desired to avoid excessive memory use, or
to allocate more memory as necessary.
A filesort
operation uses temporary disk
files as necessary if the result set is too large to fit in
memory. Some types of queries are particularly suited to
completely in-memory filesort
operations.
For example, the optimizer can use
filesort
to efficiently handle in memory,
without temporary files, the ORDER BY
operation for queries (and subqueries) of the following
form:
SELECT ... FROMsingle_table
... ORDER BYnon_index_column
[DESC] LIMIT [M
,]N
;
Such queries are common in web applications that display only a few rows from a larger result set. Examples:
SELECT col1, ... FROM t1 ... ORDER BY name LIMIT 10; SELECT col1, ... FROM t1 ... ORDER BY RAND() LIMIT 15;
For slow ORDER BY
queries for which
filesort
is not used, try lowering the
max_length_for_sort_data
system variable to a value that is appropriate to trigger a
filesort
. (A symptom of setting the value
of this variable too high is a combination of high disk
activity and low CPU activity.)
To increase ORDER BY
speed, check whether
you can get MySQL to use indexes rather than an extra
sorting phase. If this is not possible, try the following
strategies:
Increase the
sort_buffer_size
variable value. Ideally, the value should be large
enough for the entire result set to fit in the sort
buffer (to avoid writes to disk and merge passes), but
at minimum the value must be large enough to accommodate
15 tuples. (Up to 15 temporary disk files are merged and
there must be room in memory for at least one tuple per
file.)
Take into account that the size of column values stored
in the sort buffer is affected by the
max_sort_length
system
variable value. For example, if tuples store values of
long string columns and you increase the value of
max_sort_length
, the
size of sort buffer tuples increases as well and may
require you to increase
sort_buffer_size
. For
column values calculated as a result of string
expressions (such as those that invoke a string-valued
function), the filesort
algorithm
cannot tell the maximum length of expression values, so
it must allocate
max_sort_length
bytes
for each tuple.
To monitor the number of merge passes (to merge
temporary files), check the
Sort_merge_passes
status variable.
Increase the
read_rnd_buffer_size
variable value so that more rows are read at a time.
Change the tmpdir
system variable to point to a dedicated file system with
large amounts of free space. The variable value can list
several paths that are used in round-robin fashion; you
can use this feature to spread the load across several
directories. Separate the paths by colon characters
(:
) on Unix and semicolon characters
(;
) on Windows. The paths should name
directories in file systems located on different
physical disks, not different
partitions on the same disk.
With
EXPLAIN
(see Section 8.8.1, “Optimizing Queries with EXPLAIN”), you can check whether
MySQL can use indexes to resolve an ORDER
BY
clause:
In addition, if a filesort
is performed,
optimizer trace output includes a
filesort_summary
block. For example:
"filesort_summary": { "rows": 100, "examined_rows": 100, "number_of_tmp_files": 0, "sort_buffer_size": 25192, "sort_mode": "<sort_key, packed_additional_fields>" }
The sort_mode
value provides information
about the contents of tuples in the sort buffer:
<sort_key, rowid>
: This
indicates that sort buffer tuples are pairs that contain
the sort key value and row ID of the original table row.
Tuples are sorted by sort key value and the row ID is
used to read the row from the table.
<sort_key, additional_fields>
:
This indicates that sort buffer tuples contain the sort
key value and columns referenced by the query. Tuples
are sorted by sort key value and column values are read
directly from the tuple.
<sort_key,
packed_additional_fields>
: Like the
previous variant, but the additional columns are packed
tightly together instead of using a fixed-length
encoding.
EXPLAIN
does not distinguish
whether the optimizer does or does not perform a
filesort
in memory. Use of an in-memory
filesort
can be seen in optimizer trace
output. Look for
filesort_priority_queue_optimization
. For
information about the optimizer trace, see
MySQL
Internals: Tracing the Optimizer.
The most general way to satisfy a GROUP BY
clause is to scan the whole table and create a new temporary
table where all rows from each group are consecutive, and then
use this temporary table to discover groups and apply
aggregate functions (if any). In some cases, MySQL is able to
do much better than that and avoid creation of temporary
tables by using index access.
The most important preconditions for using indexes for
GROUP BY
are that all GROUP
BY
columns reference attributes from the same index,
and that the index stores its keys in order (as is true, for
example, for a BTREE
index, but not for a
HASH
index). Whether use of temporary
tables can be replaced by index access also depends on which
parts of an index are used in a query, the conditions
specified for these parts, and the selected aggregate
functions.
There are two ways to execute a GROUP BY
query through index access, as detailed in the following
sections. The first method applies the grouping operation
together with all range predicates (if any). The second method
first performs a range scan, and then groups the resulting
tuples.
In MySQL, GROUP BY
is used for sorting, so
the server may also apply ORDER BY
optimizations to grouping. However, relying on implicit or
explicit GROUP BY
sorting is deprecated.
See Section 8.2.1.14, “ORDER BY Optimization”.
The most efficient way to process GROUP
BY
is when an index is used to directly retrieve
the grouping columns. With this access method, MySQL uses
the property of some index types that the keys are ordered
(for example, BTREE
). This property
enables use of lookup groups in an index without having to
consider all keys in the index that satisfy all
WHERE
conditions. This access method
considers only a fraction of the keys in an index, so it is
called a Loose Index
Scan. When there is no WHERE
clause, a Loose Index Scan reads as many keys as the number
of groups, which may be a much smaller number than that of
all keys. If the WHERE
clause contains
range predicates (see the discussion of the
range
join type in
Section 8.8.1, “Optimizing Queries with EXPLAIN”), a Loose Index Scan looks
up the first key of each group that satisfies the range
conditions, and again reads the smallest possible number of
keys. This is possible under the following conditions:
The query is over a single table.
The GROUP BY
names only columns that
form a leftmost prefix of the index and no other
columns. (If, instead of GROUP BY
,
the query has a DISTINCT
clause, all
distinct attributes refer to columns that form a
leftmost prefix of the index.) For example, if a table
t1
has an index on
(c1,c2,c3)
, Loose Index Scan is
applicable if the query has GROUP BY c1,
c2
. It is not applicable if the query has
GROUP BY c2, c3
(the columns are not
a leftmost prefix) or GROUP BY c1, c2,
c4
(c4
is not in the
index).
The only aggregate functions used in the select list (if
any) are MIN()
and
MAX()
, and all of them
refer to the same column. The column must be in the
index and must immediately follow the columns in the
GROUP BY
.
Any other parts of the index than those from the
GROUP BY
referenced in the query must
be constants (that is, they must be referenced in
equalities with constants), except for the argument of
MIN()
or
MAX()
functions.
For columns in the index, full column values must be
indexed, not just a prefix. For example, with
c1 VARCHAR(20), INDEX (c1(10))
, the
index uses only a prefix of c1
values
and cannot be used for Loose Index Scan.
If Loose Index Scan is applicable to a query, the
EXPLAIN
output shows
Using index for group-by
in the
Extra
column.
Assume that there is an index
idx(c1,c2,c3)
on table
t1(c1,c2,c3,c4)
. The Loose Index Scan
access method can be used for the following queries:
SELECT c1, c2 FROM t1 GROUP BY c1, c2; SELECT DISTINCT c1, c2 FROM t1; SELECT c1, MIN(c2) FROM t1 GROUP BY c1; SELECT c1, c2 FROM t1 WHERE c1 <const
GROUP BY c1, c2; SELECT MAX(c3), MIN(c3), c1, c2 FROM t1 WHERE c2 >const
GROUP BY c1, c2; SELECT c2 FROM t1 WHERE c1 <const
GROUP BY c1, c2; SELECT c1, c2 FROM t1 WHERE c3 =const
GROUP BY c1, c2;
The following queries cannot be executed with this quick select method, for the reasons given:
There are aggregate functions other than
MIN()
or
MAX()
:
SELECT c1, SUM(c2) FROM t1 GROUP BY c1;
The columns in the GROUP BY
clause do
not form a leftmost prefix of the index:
SELECT c1, c2 FROM t1 GROUP BY c2, c3;
The query refers to a part of a key that comes after the
GROUP BY
part, and for which there is
no equality with a constant:
SELECT c1, c3 FROM t1 GROUP BY c1, c2;
Were the query to include WHERE c3 =
, Loose Index
Scan could be used.
const
The Loose Index Scan access method can be applied to other
forms of aggregate function references in the select list,
in addition to the MIN()
and
MAX()
references already
supported:
AVG(DISTINCT)
,
SUM(DISTINCT)
, and
COUNT(DISTINCT)
are
supported. AVG(DISTINCT)
and SUM(DISTINCT)
take a
single argument.
COUNT(DISTINCT)
can have
more than one column argument.
There must be no GROUP BY
or
DISTINCT
clause in the query.
The Loose Index Scan limitations described previously still apply.
Assume that there is an index
idx(c1,c2,c3)
on table
t1(c1,c2,c3,c4)
. The Loose Index Scan
access method can be used for the following queries:
SELECT COUNT(DISTINCT c1), SUM(DISTINCT c1) FROM t1; SELECT COUNT(DISTINCT c1, c2), COUNT(DISTINCT c2, c1) FROM t1;
A Tight Index Scan may be either a full index scan or a range index scan, depending on the query conditions.
When the conditions for a Loose Index Scan are not met, it
still may be possible to avoid creation of temporary tables
for GROUP BY
queries. If there are range
conditions in the WHERE
clause, this
method reads only the keys that satisfy these conditions.
Otherwise, it performs an index scan. Because this method
reads all keys in each range defined by the
WHERE
clause, or scans the whole index if
there are no range conditions, it is called a
Tight Index Scan. With
a Tight Index Scan, the grouping operation is performed only
after all keys that satisfy the range conditions have been
found.
For this method to work, it is sufficient that there be a
constant equality condition for all columns in a query
referring to parts of the key coming before or in between
parts of the GROUP BY
key. The constants
from the equality conditions fill in any “gaps”
in the search keys so that it is possible to form complete
prefixes of the index. These index prefixes then can be used
for index lookups. If the GROUP BY
result
requires sorting, and it is possible to form search keys
that are prefixes of the index, MySQL also avoids extra
sorting operations because searching with prefixes in an
ordered index already retrieves all the keys in order.
Assume that there is an index
idx(c1,c2,c3)
on table
t1(c1,c2,c3,c4)
. The following queries do
not work with the Loose Index Scan access method described
previously, but still work with the Tight Index Scan access
method.
There is a gap in the GROUP BY
, but
it is covered by the condition c2 =
'a'
:
SELECT c1, c2, c3 FROM t1 WHERE c2 = 'a' GROUP BY c1, c3;
The GROUP BY
does not begin with the
first part of the key, but there is a condition that
provides a constant for that part:
SELECT c1, c2, c3 FROM t1 WHERE c1 = 'a' GROUP BY c2, c3;
DISTINCT
combined with ORDER
BY
needs a temporary table in many cases.
Because DISTINCT
may use GROUP
BY
, learn how MySQL works with columns in
ORDER BY
or HAVING
clauses that are not part of the selected columns. See
Section 12.20.3, “MySQL Handling of GROUP BY”.
In most cases, a DISTINCT
clause can be
considered as a special case of GROUP BY
.
For example, the following two queries are equivalent:
SELECT DISTINCT c1, c2, c3 FROM t1 WHERE c1 >const
; SELECT c1, c2, c3 FROM t1 WHERE c1 >const
GROUP BY c1, c2, c3;
Due to this equivalence, the optimizations applicable to
GROUP BY
queries can be also applied to
queries with a DISTINCT
clause. Thus, for
more details on the optimization possibilities for
DISTINCT
queries, see
Section 8.2.1.15, “GROUP BY Optimization”.
When combining LIMIT
with
row_count
DISTINCT
, MySQL stops as soon as it finds
row_count
unique rows.
If you do not use columns from all tables named in a query,
MySQL stops scanning any unused tables as soon as it finds the
first match. In the following case, assuming that
t1
is used before t2
(which you can check with
EXPLAIN
), MySQL stops reading
from t2
(for any particular row in
t1
) when it finds the first row in
t2
:
SELECT DISTINCT t1.a FROM t1, t2 where t1.a=t2.a;
If you need only a specified number of rows from a result set,
use a LIMIT
clause in the query, rather
than fetching the whole result set and throwing away the extra
data.
MySQL sometimes optimizes a query that has a LIMIT
clause and no
row_count
HAVING
clause:
If you select only a few rows with
LIMIT
, MySQL uses indexes in some cases
when normally it would prefer to do a full table scan.
If you combine LIMIT
with
row_count
ORDER BY
, MySQL stops sorting as soon
as it has found the first
row_count
rows of the sorted
result, rather than sorting the entire result. If ordering
is done by using an index, this is very fast. If a
filesort must be done, all rows that match the query
without the LIMIT
clause are selected,
and most or all of them are sorted, before the first
row_count
are found. After the
initial rows have been found, MySQL does not sort any
remainder of the result set.
One manifestation of this behavior is that an
ORDER BY
query with and without
LIMIT
may return rows in different
order, as described later in this section.
If you combine LIMIT
with
row_count
DISTINCT
, MySQL stops as soon as it
finds row_count
unique rows.
In some cases, a GROUP BY
can be
resolved by reading the index in order (or doing a sort on
the index), then calculating summaries until the index
value changes. In this case, LIMIT
does not
calculate any unnecessary row_count
GROUP BY
values.
As soon as MySQL has sent the required number of rows to
the client, it aborts the query unless you are using
SQL_CALC_FOUND_ROWS
. In that case, the
number of rows can be retrieved with SELECT
FOUND_ROWS()
. See
Section 12.15, “Information Functions”.
LIMIT 0
quickly returns an empty set.
This can be useful for checking the validity of a query.
It can also be employed to obtain the types of the result
columns within applications that use a MySQL API that
makes result set metadata available. With the
mysql client program, you can use the
--column-type-info
option to
display result column types.
If the server uses temporary tables to resolve a query, it
uses the LIMIT
clause to
calculate how much space is required.
row_count
If an index is not used for ORDER BY
but a LIMIT
clause is also present, the
optimizer may be able to avoid using a merge file and sort
the rows in memory using an in-memory
filesort
operation.
If multiple rows have identical values in the ORDER
BY
columns, the server is free to return those rows
in any order, and may do so differently depending on the
overall execution plan. In other words, the sort order of
those rows is nondeterministic with respect to the nonordered
columns.
One factor that affects the execution plan is
LIMIT
, so an ORDER BY
query with and without LIMIT
may return
rows in different orders. Consider this query, which is sorted
by the category
column but nondeterministic
with respect to the id
and
rating
columns:
mysql> SELECT * FROM ratings ORDER BY category;
+----+----------+--------+
| id | category | rating |
+----+----------+--------+
| 1 | 1 | 4.5 |
| 5 | 1 | 3.2 |
| 3 | 2 | 3.7 |
| 4 | 2 | 3.5 |
| 6 | 2 | 3.5 |
| 2 | 3 | 5.0 |
| 7 | 3 | 2.7 |
+----+----------+--------+
Including LIMIT
may affect order of rows
within each category
value. For example,
this is a valid query result:
mysql> SELECT * FROM ratings ORDER BY category LIMIT 5;
+----+----------+--------+
| id | category | rating |
+----+----------+--------+
| 1 | 1 | 4.5 |
| 5 | 1 | 3.2 |
| 4 | 2 | 3.5 |
| 3 | 2 | 3.7 |
| 6 | 2 | 3.5 |
+----+----------+--------+
In each case, the rows are sorted by the ORDER
BY
column, which is all that is required by the SQL
standard.
If it is important to ensure the same row order with and
without LIMIT
, include additional columns
in the ORDER BY
clause to make the order
deterministic. For example, if id
values
are unique, you can make rows for a given
category
value appear in
id
order by sorting like this:
mysql>SELECT * FROM ratings ORDER BY category, id;
+----+----------+--------+ | id | category | rating | +----+----------+--------+ | 1 | 1 | 4.5 | | 5 | 1 | 3.2 | | 3 | 2 | 3.7 | | 4 | 2 | 3.5 | | 6 | 2 | 3.5 | | 2 | 3 | 5.0 | | 7 | 3 | 2.7 | +----+----------+--------+ mysql>SELECT * FROM ratings ORDER BY category, id LIMIT 5;
+----+----------+--------+ | id | category | rating | +----+----------+--------+ | 1 | 1 | 4.5 | | 5 | 1 | 3.2 | | 3 | 2 | 3.7 | | 4 | 2 | 3.5 | | 6 | 2 | 3.5 | +----+----------+--------+
MySQL functions are tagged internally as deterministic or
nondeterministic. A function is nondeterministic if, given
fixed values for its arguments, it can return different
results for different invocations. Examples of
nondeterministic functions:
RAND()
,
UUID()
.
If a function is tagged nondeterministic, a reference to it in
a WHERE
clause is evaluated for every row
(when selecting from one table) or combination of rows (when
selecting from a multiple-table join).
MySQL also determines when to evaluate functions based on types of arguments, whether the arguments are table columns or constant values. A deterministic function that takes a table column as argument must be evaluated whenever that column changes value.
Nondeterministic functions may affect query performance. For
example, some optimizations may not be available, or more
locking might be required. The following discussion uses
RAND()
but applies to other
nondeterministic functions as well.
Suppose that a table t
has this definition:
CREATE TABLE t (id INT NOT NULL PRIMARY KEY, col_a VARCHAR(100));
Consider these two queries:
SELECT * FROM t WHERE id = POW(1,2); SELECT * FROM t WHERE id = FLOOR(1 + RAND() * 49);
Both queries appear to use a primary key lookup because of the equality comparison against the primary key, but that is true only for the first of them:
The first query always produces a maximum of one row
because POW()
with constant
arguments is a constant value and is used for index
lookup.
The second query contains an expression that uses the
nondeterministic function
RAND()
, which is not
constant in the query but in fact has a new value for
every row of table t
. Consequently, the
query reads every row of the table, evaluates the
predicate for each row, and outputs all rows for which the
primary key matches the random value. This might be zero,
one, or multiple rows, depending on the
id
column values and the values in the
RAND()
sequence.
The effects of nondeterminism are not limited to
SELECT
statements. This
UPDATE
statement uses a
nondeterministic function to select rows to be modified:
UPDATE t SET col_a = some_expr
WHERE id = FLOOR(1 + RAND() * 49);
Presumably the intent is to update at most a single row for
which the primary key matches the expression. However, it
might update zero, one, or multiple rows, depending on the
id
column values and the values in the
RAND()
sequence.
The behavior just described has implications for performance and replication:
Because a nondeterministic function does not produce a constant value, the optimizer cannot use strategies that might otherwise be applicable, such as index lookups. The result may be a table scan.
InnoDB
might escalate to a range-key
lock rather than taking a single row lock for one matching
row.
Updates that do not execute deterministically are unsafe for replication.
The difficulties stem from the fact that the
RAND()
function is evaluated
once for every row of the table. To avoid multiple function
evaluations, use one of these techniques:
Move the expression containing the nondeterministic function to a separate statement, saving the value in a variable. In the original statement, replace the expression with a reference to the variable, which the optimizer can treat as a constant value:
SET @keyval = FLOOR(1 + RAND() * 49);
UPDATE t SET col_a = some_expr
WHERE id = @keyval;
Assign the random value to a variable in a derived table.
This technique causes the variable to be assigned a value,
once, prior to its use in the comparison in the
WHERE
clause:
SET optimizer_switch = 'derived_merge=off';
UPDATE t, (SELECT @keyval := FLOOR(1 + RAND() * 49)) AS dt
SET col_a = some_expr
WHERE id = @keyval;
As mentioned previously, a nondeterministic expression in the
WHERE
clause might prevent optimizations
and result in a table scan. However, it may be possible to
partially optimize the WHERE
clause if
other expressions are deterministic. For example:
SELECT * FROM t WHERE partial_key=5 AND some_column=RAND();
If the optimizer can use partial_key
to
reduce the set of rows selected,
RAND()
is executed fewer times,
which diminishes the effect of nondeterminism on optimization.
Row constructors permit simultaneous comparisons of multiple values. For example, these two statements are semantically equivalent:
SELECT * FROM t1 WHERE (column1,column2) = (1,1); SELECT * FROM t1 WHERE column1 = 1 AND column2 = 1;
In addition, the optimizer handles both expressions the same way.
The optimizer is less likely to use available indexes if the
row constructor columns do not cover the prefix of an index.
Consider the following table, which has a primary key on
(c1, c2, c3)
:
CREATE TABLE t1 ( c1 INT, c2 INT, c3 INT, c4 CHAR(100), PRIMARY KEY(c1,c2,c3) );
In this query, the WHERE
clause uses all
columns in the index. However, the row constructor itself does
not cover an index prefix, with the result that the optimizer
uses only c1
(key_len=4
,
the size of c1
):
mysql>EXPLAIN SELECT * FROM t1
WHERE c1=1 AND (c2,c3) > (1,1)\G
*************************** 1. row *************************** id: 1 select_type: SIMPLE table: t1 partitions: NULL type: ref possible_keys: PRIMARY key: PRIMARY key_len: 4 ref: const rows: 3 filtered: 100.00 Extra: Using where
In such cases, rewriting the row constructor expression using an equivalent nonconstructor expression may result in more complete index use. For the given query, the row constructor and equivalent nonconstructor expressions are:
(c2,c3) > (1,1) c2 > 1 OR ((c2 = 1) AND (c3 > 1))
Rewriting the query to use the nonconstructor expression
results in the optimizer using all three columns in the index
(key_len=12
):
mysql>EXPLAIN SELECT * FROM t1
WHERE c1 = 1 AND (c2 > 1 OR ((c2 = 1) AND (c3 > 1)))\G
*************************** 1. row *************************** id: 1 select_type: SIMPLE table: t1 partitions: NULL type: range possible_keys: PRIMARY key: PRIMARY key_len: 12 ref: NULL rows: 3 filtered: 100.00 Extra: Using where
Thus, for better results, avoid mixing row constructors with
AND
/OR
expressions. Use one or the other.
Under certain conditions, the optimizer can apply the range
access method to IN()
expressions
that have row constructor arguments. See
Range Optimization of Row Constructor Expressions.
The output from EXPLAIN
shows
ALL
in the
type
column when MySQL uses a
full table scan to
resolve a query. This usually happens under the following
conditions:
The table is so small that it is faster to perform a table scan than to bother with a key lookup. This is common for tables with fewer than 10 rows and a short row length.
There are no usable restrictions in the
ON
or WHERE
clause
for indexed columns.
You are comparing indexed columns with constant values and MySQL has calculated (based on the index tree) that the constants cover too large a part of the table and that a table scan would be faster. See Section 8.2.1.1, “WHERE Clause Optimization”.
You are using a key with low cardinality (many rows match the key value) through another column. In this case, MySQL assumes that by using the key it probably will do many key lookups and that a table scan would be faster.
For small tables, a table scan often is appropriate and the performance impact is negligible. For large tables, try the following techniques to avoid having the optimizer incorrectly choose a table scan:
Use ANALYZE TABLE
to update
the key distributions for the scanned table. See
Section 13.7.2.1, “ANALYZE TABLE Statement”.
tbl_name
Use FORCE INDEX
for the scanned table
to tell MySQL that table scans are very expensive compared
to using the given index:
SELECT * FROM t1, t2 FORCE INDEX (index_for_column
) WHERE t1.col_name
=t2.col_name
;
Start mysqld with the
--max-seeks-for-key=1000
option or use SET
max_seeks_for_key=1000
to tell the optimizer to
assume that no key scan causes more than 1,000 key seeks.
See Section 5.1.7, “Server System Variables”.
The MySQL query optimizer has different strategies available to evaluate subqueries:
For IN
(or =ANY
)
subqueries, the optimizer has these choices:
Semijoin
Materialization
EXISTS
strategy
For NOT IN
(or
<>ALL
) subqueries, the optimizer
has these choices:
Materialization
EXISTS
strategy
For derived tables, the optimizer has these choices (which also apply to view references):
Merge the derived table into the outer query block
Materialize the derived table to an internal temporary table
The following discussion provides more information about the preceding optimization strategies.
A limitation on UPDATE
and
DELETE
statements that use a
subquery to modify a single table is that the optimizer does
not use semijoin or materialization subquery optimizations. As
a workaround, try rewriting them as multiple-table
UPDATE
and
DELETE
statements that use a
join rather than a subquery.
A semijoin is a preparation-time transformation that enables multiple execution strategies such as table pullout, duplicate weedout, first match, loose scan, and materialization. The optimizer uses semijoin strategies to improve subquery execution, as described in this section.
For an inner join between two tables, the join returns a row
from one table as many times as there are matches in the other
table. But for some questions, the only information that
matters is whether there is a match, not the number of
matches. Suppose that there are tables named
class
and roster
that
list classes in a course curriculum and class rosters
(students enrolled in each class), respectively. To list the
classes that actually have students enrolled, you could use
this join:
SELECT class.class_num, class.class_name FROM class INNER JOIN roster WHERE class.class_num = roster.class_num;
However, the result lists each class once for each enrolled student. For the question being asked, this is unnecessary duplication of information.
Assuming that class_num
is a primary key in
the class
table, duplicate suppression is
possible by using
SELECT
DISTINCT
, but it is inefficient to generate all
matching rows first only to eliminate duplicates later.
The same duplicate-free result can be obtained by using a subquery:
SELECT class_num, class_name FROM class WHERE class_num IN (SELECT class_num FROM roster);
Here, the optimizer can recognize that the
IN
clause requires the subquery to return
only one instance of each class number from the
roster
table. In this case, the query can
use a semijoin; that is,
an operation that returns only one instance of each row in
class
that is matched by rows in
roster
.
Outer join and inner join syntax is permitted in the outer query specification, and table references may be base tables, derived tables, or view references.
In MySQL, a subquery must satisfy these criteria to be handled as a semijoin:
It must be an IN
(or
=ANY
) subquery that appears at the top
level of the WHERE
or
ON
clause, possibly as a term in an
AND
expression. For example:
SELECT ... FROM ot1, ... WHERE (oe1, ...) IN (SELECT ie1, ... FROM it1, ... WHERE ...);
Here, ot_
and i
it_
represent tables in the outer and inner parts of the
query, and
i
oe_
and
i
ie_
represent expressions that refer to columns in the outer
and inner tables.
i
It must not contain a GROUP BY
or
HAVING
clause.
It must not be implicitly grouped (it must contain no aggregate functions).
It must not have ORDER BY
with
LIMIT
.
The statement must not use the
STRAIGHT_JOIN
join type in the outer
query.
The STRAIGHT_JOIN
modifier must not be
present.
The number of outer and inner tables together must be less than the maximum number of tables permitted in a join.
The subquery may be correlated or uncorrelated.
DISTINCT
is permitted, as is
LIMIT
unless ORDER BY
is
also used.
If a subquery meets the preceding criteria, MySQL converts it to a semijoin and makes a cost-based choice from these strategies:
Convert the subquery to a join, or use table pullout and run the query as an inner join between subquery tables and outer tables. Table pullout pulls a table out from the subquery to the outer query.
Duplicate Weedout: Run the semijoin as if it was a join and remove duplicate records using a temporary table.
FirstMatch: When scanning the inner tables for row combinations and there are multiple instances of a given value group, choose one rather than returning them all. This "shortcuts" scanning and eliminates production of unnecessary rows.
LooseScan: Scan a subquery table using an index that enables a single value to be chosen from each subquery's value group.
Materialize the subquery into an indexed temporary table that is used to perform a join, where the index is used to remove duplicates. The index might also be used later for lookups when joining the temporary table with the outer tables; if not, the table is scanned. For more information about materialization, see Section 8.2.2.2, “Optimizing Subqueries with Materialization”.
Each of these strategies can be enabled or disabled using the
following optimizer_switch
system variable flags:
The semijoin
flag controls whether
semijoins are used.
If semijoin
is enabled, the
firstmatch
,
loosescan
,
duplicateweedout
, and
materialization
flags enable finer
control over the permitted semijoin strategies.
If the duplicateweedout
semijoin
strategy is disabled, it is not used unless all other
applicable strategies are also disabled.
If duplicateweedout
is disabled, on
occasion the optimizer may generate a query plan that is
far from optimal. This occurs due to heuristic pruning
during greedy search, which can be avoided by setting
optimizer_prune_level=0
.
These flags are enabled by default. See Section 8.9.2, “Switchable Optimizations”.
The optimizer minimizes differences in handling of views and
derived tables. This affects queries that use the
STRAIGHT_JOIN
modifier and a view with an
IN
subquery that can be converted to a
semijoin. The following query illustrates this because the
change in processing causes a change in transformation, and
thus a different execution strategy:
CREATE VIEW v AS SELECT * FROM t1 WHERE a IN (SELECT b FROM t2); SELECT STRAIGHT_JOIN * FROM t3 JOIN v ON t3.x = v.a;
The optimizer first looks at the view and converts the
IN
subquery to a semijoin, then checks
whether it is possible to merge the view into the outer query.
Because the STRAIGHT_JOIN
modifier in the
outer query prevents semijoin, the optimizer refuses the
merge, causing derived table evaluation using a materialized
table.
EXPLAIN
output indicates the
use of semijoin strategies as follows:
Semijoined tables show up in the outer select. For
extended EXPLAIN
output,
the text displayed by a following
SHOW WARNINGS
shows the
rewritten query, which displays the semijoin structure.
(See Section 8.8.3, “Extended EXPLAIN Output Format”.) From this you
can get an idea about which tables were pulled out of the
semijoin. If a subquery was converted to a semijoin, you
will see that the subquery predicate is gone and its
tables and WHERE
clause were merged
into the outer query join list and
WHERE
clause.
Temporary table use for Duplicate Weedout is indicated by
Start temporary
and End
temporary
in the Extra
column. Tables that were not pulled out and are in the
range of EXPLAIN
output
rows covered by Start temporary
and
End temporary
have their
rowid
in the temporary table.
FirstMatch(
in the tbl_name
)Extra
column indicates join
shortcutting.
LooseScan(
in the m
..n
)Extra
column indicates use of
the LooseScan strategy. m
and
n
are key part numbers.
Temporary table use for materialization is indicated by
rows with a select_type
value of
MATERIALIZED
and rows with a
table
value of
<subquery
.
N
>
The optimizer uses materialization to enable more efficient subquery processing. Materialization speeds up query execution by generating a subquery result as a temporary table, normally in memory. The first time MySQL needs the subquery result, it materializes that result into a temporary table. Any subsequent time the result is needed, MySQL refers again to the temporary table. The optimizer may index the table with a hash index to make lookups fast and inexpensive. The index contains unique values to eliminate duplicates and make the table smaller.
Subquery materialization uses an in-memory temporary table when possible, falling back to on-disk storage if the table becomes too large. See Section 8.4.4, “Internal Temporary Table Use in MySQL”.
If materialization is not used, the optimizer sometimes
rewrites a noncorrelated subquery as a correlated subquery.
For example, the following IN
subquery is
noncorrelated (where_condition
involves only columns from t2
and not
t1
):
SELECT * FROM t1
WHERE t1.a IN (SELECT t2.b FROM t2 WHERE where_condition
);
The optimizer might rewrite this as an
EXISTS
correlated subquery:
SELECT * FROM t1
WHERE EXISTS (SELECT t2.b FROM t2 WHERE where_condition
AND t1.a=t2.b);
Subquery materialization using a temporary table avoids such rewrites and makes it possible to execute the subquery only once rather than once per row of the outer query.
For subquery materialization to be used in MySQL, the
optimizer_switch
system
variable materialization
flag must be
enabled. (See Section 8.9.2, “Switchable Optimizations”.)
With the materialization
flag enabled,
materialization applies to subquery predicates that appear
anywhere (in the select list, WHERE
,
ON
, GROUP BY
,
HAVING
, or ORDER BY
),
for predicates that fall into any of these use cases:
The predicate has this form, when no outer expression
oe_i
or inner expression
ie_i
is nullable.
N
is 1 or larger.
(oe_1
,oe_2
, ...,oe_N
) [NOT] IN (SELECTie_1
,i_2
, ...,ie_N
...)
The predicate has this form, when there is a single outer
expression oe
and inner
expression ie
. The expressions
can be nullable.
oe
[NOT] IN (SELECTie
...)
The predicate is IN
or NOT
IN
and a result of UNKNOWN
(NULL
) has the same meaning as a result
of FALSE
.
The following examples illustrate how the requirement for
equivalence of UNKNOWN
and
FALSE
predicate evaluation affects whether
subquery materialization can be used. Assume that
where_condition
involves columns
only from t2
and not t1
so that the subquery is noncorrelated.
This query is subject to materialization:
SELECT * FROM t1
WHERE t1.a IN (SELECT t2.b FROM t2 WHERE where_condition
);
Here, it does not matter whether the IN
predicate returns UNKNOWN
or
FALSE
. Either way, the row from
t1
is not included in the query result.
An example where subquery materialization is not used is the
following query, where t2.b
is a nullable
column:
SELECT * FROM t1
WHERE (t1.a,t1.b) NOT IN (SELECT t2.a,t2.b FROM t2
WHERE where_condition
);
The following restrictions apply to the use of subquery materialization:
The types of the inner and outer expressions must match. For example, the optimizer might be able to use materialization if both expressions are integer or both are decimal, but cannot if one expression is integer and the other is decimal.
The inner expression cannot be a
BLOB
.
Use of EXPLAIN
with a query
provides some indication of whether the optimizer uses
subquery materialization:
Compared to query execution that does not use
materialization, select_type
may change
from DEPENDENT SUBQUERY
to
SUBQUERY
. This indicates that, for a
subquery that would be executed once per outer row,
materialization enables the subquery to be executed just
once.
For extended EXPLAIN
output, the text displayed by a following
SHOW WARNINGS
includes
materialize
and
materialized-subquery
.
Certain optimizations are applicable to comparisons that use
the IN
(or =ANY
)
operator to test subquery results. This section discusses
these optimizations, particularly with regard to the
challenges that NULL
values present. The
last part of the discussion suggests how you can help the
optimizer.
Consider the following subquery comparison:
outer_expr
IN (SELECTinner_expr
FROM ... WHEREsubquery_where
)
MySQL evaluates queries “from outside to inside.”
That is, it first obtains the value of the outer expression
outer_expr
, and then runs the
subquery and captures the rows that it produces.
A very useful optimization is to “inform” the
subquery that the only rows of interest are those where the
inner expression inner_expr
is
equal to outer_expr
. This is done
by pushing down an appropriate equality into the
subquery's WHERE
clause to make it
more restrictive. The converted comparison looks like this:
EXISTS (SELECT 1 FROM ... WHEREsubquery_where
ANDouter_expr
=inner_expr
)
After the conversion, MySQL can use the pushed-down equality to limit the number of rows it must examine to evaluate the subquery.
More generally, a comparison of N
values to a subquery that returns
N
-value rows is subject to the same
conversion. If oe_i
and
ie_i
represent corresponding outer
and inner expression values, this subquery comparison:
(oe_1
, ...,oe_N
) IN (SELECTie_1
, ...,ie_N
FROM ... WHEREsubquery_where
)
Becomes:
EXISTS (SELECT 1 FROM ... WHEREsubquery_where
ANDoe_1
=ie_1
AND ... ANDoe_N
=ie_N
)
For simplicity, the following discussion assumes a single pair of outer and inner expression values.
The conversion just described has its limitations. It is valid
only if we ignore possible NULL
values.
That is, the “pushdown” strategy works as long as
both of these conditions are true:
outer_expr
and
inner_expr
cannot be
NULL
.
You need not distinguish NULL
from
FALSE
subquery results. If the subquery
is a part of an OR
or
AND
expression in the
WHERE
clause, MySQL assumes that you do
not care. Another instance where the optimizer notices
that NULL
and FALSE
subquery results need not be distinguished is this
construct:
... WHEREouter_expr
IN (subquery
)
In this case, the WHERE
clause rejects
the row whether IN
(
returns
subquery
)NULL
or FALSE
.
When either or both of those conditions do not hold, optimization is more complex.
Suppose that outer_expr
is known to
be a non-NULL
value but the subquery does
not produce a row such that
outer_expr
=
inner_expr
. Then
evaluates as follows:
outer_expr
IN (SELECT
...)
In this situation, the approach of looking for rows with
is no longer
valid. It is necessary to look for such rows, but if none are
found, also look for rows where
outer_expr
=
inner_expr
inner_expr
is
NULL
. Roughly speaking, the subquery can be
converted to something like this:
EXISTS (SELECT 1 FROM ... WHEREsubquery_where
AND (outer_expr
=inner_expr
ORinner_expr
IS NULL))
The need to evaluate the extra IS
NULL
condition is why MySQL has the
ref_or_null
access method:
mysql>EXPLAIN
SELECT
outer_expr
IN (SELECT t2.maybe_null_keyFROM t2, t3 WHERE ...)
FROM t1;
*************************** 1. row *************************** id: 1 select_type: PRIMARY table: t1 ... *************************** 2. row *************************** id: 2 select_type: DEPENDENT SUBQUERY table: t2 type: ref_or_null possible_keys: maybe_null_key key: maybe_null_key key_len: 5 ref: func rows: 2 Extra: Using where; Using index ...
The unique_subquery
and
index_subquery
subquery-specific access methods also have “or
NULL
” variants.
The additional OR ... IS NULL
condition
makes query execution slightly more complicated (and some
optimizations within the subquery become inapplicable), but
generally this is tolerable.
The situation is much worse when
outer_expr
can be
NULL
. According to the SQL interpretation
of NULL
as “unknown value,”
NULL IN (SELECT
should evaluate to:
inner_expr
...)
For proper evaluation, it is necessary to be able to check
whether the SELECT
has produced
any rows at all, so
cannot be
pushed down into the subquery. This is a problem because many
real world subqueries become very slow unless the equality can
be pushed down.
outer_expr
=
inner_expr
Essentially, there must be different ways to execute the
subquery depending on the value of
outer_expr
.
The optimizer chooses SQL compliance over speed, so it
accounts for the possibility that
outer_expr
might be
NULL
:
If outer_expr
is
NULL
, to evaluate the following
expression, it is necessary to execute the
SELECT
to determine whether
it produces any rows:
NULL IN (SELECTinner_expr
FROM ... WHEREsubquery_where
)
It is necessary to execute the original
SELECT
here, without any
pushed-down equalities of the kind mentioned previously.
On the other hand, when
outer_expr
is not
NULL
, it is absolutely essential that
this comparison:
outer_expr
IN (SELECTinner_expr
FROM ... WHEREsubquery_where
)
Be converted to this expression that uses a pushed-down condition:
EXISTS (SELECT 1 FROM ... WHEREsubquery_where
ANDouter_expr
=inner_expr
)
Without this conversion, subqueries will be slow.
To solve the dilemma of whether or not to push down conditions into the subquery, the conditions are wrapped within “trigger” functions. Thus, an expression of the following form:
outer_expr
IN (SELECTinner_expr
FROM ... WHEREsubquery_where
)
Is converted into:
EXISTS (SELECT 1 FROM ... WHEREsubquery_where
AND trigcond(outer_expr
=inner_expr
))
More generally, if the subquery comparison is based on several pairs of outer and inner expressions, the conversion takes this comparison:
(oe_1
, ...,oe_N
) IN (SELECTie_1
, ...,ie_N
FROM ... WHEREsubquery_where
)
And converts it to this expression:
EXISTS (SELECT 1 FROM ... WHEREsubquery_where
AND trigcond(oe_1
=ie_1
) AND ... AND trigcond(oe_N
=ie_N
) )
Each trigcond(
is a special function that evaluates to the following values:
X
)
X
when the
“linked” outer expression
oe_i
is not
NULL
TRUE
when the “linked”
outer expression oe_i
is
NULL
Trigger functions are not triggers of
the kind that you create with CREATE
TRIGGER
.
Equalities that are wrapped within
trigcond()
functions are not first class
predicates for the query optimizer. Most optimizations cannot
deal with predicates that may be turned on and off at query
execution time, so they assume any
trigcond(
to
be an unknown function and ignore it. Triggered equalities can
be used by those optimizations:
X
)
Reference optimizations:
trigcond(
can
be used to construct
X
=Y
[OR Y
IS NULL])ref
,
eq_ref
, or
ref_or_null
table
accesses.
Index lookup-based subquery execution engines:
trigcond(
can be used to construct
X
=Y
)unique_subquery
or
index_subquery
accesses.
Table-condition generator: If the subquery is a join of several tables, the triggered condition is checked as soon as possible.
When the optimizer uses a triggered condition to create some
kind of index lookup-based access (as for the first two items
of the preceding list), it must have a fallback strategy for
the case when the condition is turned off. This fallback
strategy is always the same: Do a full table scan. In
EXPLAIN
output, the fallback
shows up as Full scan on NULL key
in the
Extra
column:
mysql>EXPLAIN SELECT t1.col1,
t1.col1 IN (SELECT t2.key1 FROM t2 WHERE t2.col2=t1.col2) FROM t1\G
*************************** 1. row *************************** id: 1 select_type: PRIMARY table: t1 ... *************************** 2. row *************************** id: 2 select_type: DEPENDENT SUBQUERY table: t2 type: index_subquery possible_keys: key1 key: key1 key_len: 5 ref: func rows: 2 Extra: Using where; Full scan on NULL key
If you run EXPLAIN
followed by
SHOW WARNINGS
, you can see the
triggered condition:
*************************** 1. row *************************** Level: Note Code: 1003 Message: select `test`.`t1`.`col1` AS `col1`, <in_optimizer>(`test`.`t1`.`col1`, <exists>(<index_lookup>(<cache>(`test`.`t1`.`col1`) in t2 on key1 checking NULL where (`test`.`t2`.`col2` = `test`.`t1`.`col2`) having trigcond(<is_not_null_test>(`test`.`t2`.`key1`))))) AS `t1.col1 IN (select t2.key1 from t2 where t2.col2=t1.col2)` from `test`.`t1`
The use of triggered conditions has some performance
implications. A NULL IN (SELECT ...)
expression now may cause a full table scan (which is slow)
when it previously did not. This is the price paid for correct
results (the goal of the trigger-condition strategy is to
improve compliance, not speed).
For multiple-table subqueries, execution of NULL IN
(SELECT ...)
is particularly slow because the join
optimizer does not optimize for the case where the outer
expression is NULL
. It assumes that
subquery evaluations with NULL
on the left
side are very rare, even if there are statistics that indicate
otherwise. On the other hand, if the outer expression might be
NULL
but never actually is, there is no
performance penalty.
To help the query optimizer better execute your queries, use these suggestions:
Declare a column as NOT NULL
if it
really is. This also helps other aspects of the optimizer
by simplifying condition testing for the column.
If you need not distinguish a NULL
from
FALSE
subquery result, you can easily
avoid the slow execution path. Replace a comparison that
looks like this:
outer_expr
IN (SELECTinner_expr
FROM ...)
with this expression:
(outer_expr
IS NOT NULL) AND (outer_expr
IN (SELECTinner_expr
FROM ...))
Then NULL IN (SELECT ...)
is never
evaluated because MySQL stops evaluating
AND
parts as soon as the
expression result is clear.
Another possible rewrite:
EXISTS (SELECTinner_expr
FROM ... WHEREinner_expr
=outer_expr
)
This would apply when you need not distinguish
NULL
from FALSE
subquery results, in which case you may actually want
EXISTS
.
The subquery_materialization_cost_based
flag of the optimizer_switch
system variable enables control over the choice between
subquery materialization and
IN
-to-EXISTS
subquery
transformation. See
Section 8.9.2, “Switchable Optimizations”.
The optimizer can handle derived table references using two strategies (which also apply to view references):
Merge the derived table into the outer query block
Materialize the derived table to an internal temporary table
Example 1:
SELECT * FROM (SELECT * FROM t1) AS derived_t1;
With merging of the derived table
derived_t1
, that query is executed similar
to:
SELECT * FROM t1;
Example 2:
SELECT * FROM t1 JOIN (SELECT t2.f1 FROM t2) AS derived_t2 ON t1.f2=derived_t2.f1 WHERE t1.f1 > 0;
With merging of the derived table
derived_t2
, that query is executed similar
to:
SELECT t1.*, t2.f1 FROM t1 JOIN t2 ON t1.f2=t2.f1 WHERE t1.f1 > 0;
With materialization, derived_t1
and
derived_t2
are each treated as a separate
table within their respective queries.
The optimizer handles derived tables and view references the same way: It avoids unnecessary materialization whenever possible, which enables pushing down conditions from the outer query to derived tables and produces more efficient execution plans. (For an example, see Section 8.2.2.2, “Optimizing Subqueries with Materialization”.)
If merging would result in an outer query block that references more than 61 base tables, the optimizer chooses materialization instead.
The optimizer propagates an ORDER BY
clause
in a derived table or view reference to the outer query block
if these conditions are all true:
The outer query is not grouped or aggregated.
The outer query does not specify
DISTINCT
, HAVING
, or
ORDER BY
.
The outer query has this derived table or view reference
as the only source in the FROM
clause.
Otherwise, the optimizer ignores the ORDER
BY
clause.
The following means are available to influence whether the optimizer attempts to merge derived tables and view references into the outer query block:
The derived_merge
flag of the
optimizer_switch
system
variable can be used, assuming that no other rule prevents
merging. See Section 8.9.2, “Switchable Optimizations”.
By default, the flag is enabled to permit merging.
Disabling the flag prevents merging and avoids
ER_UPDATE_TABLE_USED
errors.
The derived_merge
flag also applies to
views that contain no ALGORITHM
clause.
Thus, if an
ER_UPDATE_TABLE_USED
error
occurs for a view reference that uses an expression
equivalent to the subquery, adding
ALGORITHM=TEMPTABLE
to the view
definition prevents merging and takes precedence over the
derived_merge
value.
It is possible to disable merging by using in the subquery any constructs that prevent merging, although these are not as explicit in their effect on materialization. Constructs that prevent merging are the same for derived tables and view references:
The derived_merge
flag also applies to
views that contain no ALGORITHM
clause.
Thus, if an
ER_UPDATE_TABLE_USED
error
occurs for a view reference that uses an expression equivalent
to the subquery, adding ALGORITHM=TEMPTABLE
to the view definition prevents merging and takes precedence
over the current derived_merge
value.
If the optimizer chooses the materialization strategy rather than merging for a derived table, it handles the query as follows:
The optimizer postpones derived table materialization until its contents are needed during query execution. This improves performance because delaying materialization may result in not having to do it at all. Consider a query that joins the result of a derived table to another table: If the optimizer processes that other table first and finds that it returns no rows, the join need not be carried out further and the optimizer can completely skip materializing the derived table.
During query execution, the optimizer may add an index to a derived table to speed up row retrieval from it.
Consider the following EXPLAIN
statement, for a SELECT
query
that contains a derived table:
EXPLAIN SELECT * FROM (SELECT * FROM t1) AS derived_t1;
The optimizer avoids materializing the derived table by
delaying it until the result is needed during
SELECT
execution. In this case,
the query is not executed (because it occurs in an
EXPLAIN
statement), so the
result is never needed.
Even for queries that are executed, delay of derived table materialization may enable the optimizer to avoid materialization entirely. When this happens, query execution is quicker by the time needed to perform materialization. Consider the following query, which joins the result of a derived table to another table:
SELECT * FROM t1 JOIN (SELECT t2.f1 FROM t2) AS derived_t2 ON t1.f2=derived_t2.f1 WHERE t1.f1 > 0;
If the optimization processes t1
first and
the WHERE
clause produces an empty result,
the join must necessarily be empty and the derived table need
not be materialized.
For cases when a derived table requires materialization, the
optimizer may add an index to the materialized table to speed
up access to it. If such an index enables
ref
access to the table, it
can greatly reduce amount of data read during query execution.
Consider the following query:
SELECT * FROM t1 JOIN (SELECT DISTINCT f1 FROM t2) AS derived_t2 ON t1.f1=derived_t2.f1;
The optimizer constructs an index over column
f1
from derived_t2
if
doing so would enable use of
ref
access for the lowest
cost execution plan. After adding the index, the optimizer can
treat the materialized derived table the same as a regular
table with an index, and it benefits similarly from the
generated index. The overhead of index creation is negligible
compared to the cost of query execution without the index. If
ref
access would result in
higher cost than some other access method, the optimizer
creates no index and loses nothing.
For optimizer trace output, a merged derived table or view reference is not shown as a node. Only its underlying tables appear in the top query's plan.
Applications that monitor databases may make frequent use of
INFORMATION_SCHEMA
tables. Certain types of
queries for INFORMATION_SCHEMA
tables can be
optimized to execute more quickly. The goal is to minimize file
operations (for example, scanning a directory or opening a table
file) to collect the information that makes up these dynamic
tables.
Comparison behavior for database and table names in
INFORMATION_SCHEMA
queries might differ
from what you expect. For details, see
Section 10.8.7, “Using Collation in INFORMATION_SCHEMA Searches”.
1) Try to use constant lookup values for
database and table names in the WHERE
clause
You can take advantage of this principle as follows:
To look up databases or tables, use expressions that evaluate to a constant, such as literal values, functions that return a constant, or scalar subqueries.
Avoid queries that use a nonconstant database name lookup value (or no lookup value) because they require a scan of the data directory to find matching database directory names.
Within a database, avoid queries that use a nonconstant table name lookup value (or no lookup value) because they require a scan of the database directory to find matching table files.
This principle applies to the
INFORMATION_SCHEMA
tables shown in the
following table, which shows the columns for which a constant
lookup value enables the server to avoid a directory scan. For
example, if you are selecting from
TABLES
, using a constant lookup
value for TABLE_SCHEMA
in the
WHERE
clause enables a data directory scan to
be avoided.
Table | Column to specify to avoid data directory scan | Column to specify to avoid database directory scan |
---|---|---|
COLUMNS |
TABLE_SCHEMA |
TABLE_NAME |
KEY_COLUMN_USAGE |
TABLE_SCHEMA |
TABLE_NAME |
PARTITIONS |
TABLE_SCHEMA |
TABLE_NAME |
REFERENTIAL_CONSTRAINTS |
CONSTRAINT_SCHEMA |
TABLE_NAME |
STATISTICS |
TABLE_SCHEMA |
TABLE_NAME |
TABLES |
TABLE_SCHEMA |
TABLE_NAME |
TABLE_CONSTRAINTS |
TABLE_SCHEMA |
TABLE_NAME |
TRIGGERS |
EVENT_OBJECT_SCHEMA |
EVENT_OBJECT_TABLE |
VIEWS |
TABLE_SCHEMA |
TABLE_NAME |
The benefit of a query that is limited to a specific constant database name is that checks need be made only for the named database directory. Example:
SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'test';
Use of the literal database name test
enables
the server to check only the test
database
directory, regardless of how many databases there might be. By
contrast, the following query is less efficient because it
requires a scan of the data directory to determine which
database names match the pattern 'test%'
:
SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA LIKE 'test%';
For a query that is limited to a specific constant table name, checks need be made only for the named table within the corresponding database directory. Example:
SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'test' AND TABLE_NAME = 't1';
Use of the literal table name t1
enables the
server to check only the files for the t1
table, regardless of how many tables there might be in the
test
database. By contrast, the following
query requires a scan of the test
database
directory to determine which table names match the pattern
't%'
:
SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'test' AND TABLE_NAME LIKE 't%';
The following query requires a scan of the database directory to
determine matching database names for the pattern
'test%'
, and for each matching database, it
requires a scan of the database directory to determine matching
table names for the pattern 't%'
:
SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'test%' AND TABLE_NAME LIKE 't%';
2) Write queries that minimize the number of table files that must be opened
For queries that refer to certain
INFORMATION_SCHEMA
table columns, several
optimizations are available that minimize the number of table
files that must be opened. Example:
SELECT TABLE_NAME, ENGINE FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'test';
In this case, after the server has scanned the database
directory to determine the names of the tables in the database,
those names become available with no further file system
lookups. Thus, TABLE_NAME
requires no files
to be opened. The ENGINE
(storage engine)
value can be determined by opening the table's
.frm
file, without touching other table
files such as the .MYD
or
.MYI
file.
Some values, such as INDEX_LENGTH
for
MyISAM
tables, require opening the
.MYD
or .MYI
file as
well.
The file-opening optimization types are denoted thus:
SKIP_OPEN_TABLE
: Table files do not need
to be opened. The information has already become available
within the query by scanning the database directory.
OPEN_FRM_ONLY
: Only the table's
.frm
file need be opened.
OPEN_TRIGGER_ONLY
: Only the table's
.TRG
file need be opened.
OPEN_FULL_TABLE
: The unoptimized
information lookup. The .frm
,
.MYD
, and .MYI
files must be opened.
The following list indicates how the preceding optimization
types apply to INFORMATION_SCHEMA
table
columns. For tables and columns not named, none of the
optimizations apply.
COLUMNS
:
OPEN_FRM_ONLY
applies to all columns
KEY_COLUMN_USAGE
:
OPEN_FULL_TABLE
applies to all columns
PARTITIONS
:
OPEN_FULL_TABLE
applies to all columns
REFERENTIAL_CONSTRAINTS
:
OPEN_FULL_TABLE
applies to all columns
Column | Optimization type |
---|---|
TABLE_CATALOG |
OPEN_FRM_ONLY |
TABLE_SCHEMA |
OPEN_FRM_ONLY |
TABLE_NAME |
OPEN_FRM_ONLY |
NON_UNIQUE |
OPEN_FRM_ONLY |
INDEX_SCHEMA |
OPEN_FRM_ONLY |
INDEX_NAME |
OPEN_FRM_ONLY |
SEQ_IN_INDEX |
OPEN_FRM_ONLY |
COLUMN_NAME |
OPEN_FRM_ONLY |
COLLATION |
OPEN_FRM_ONLY |
CARDINALITY |
OPEN_FULL_TABLE |
SUB_PART |
OPEN_FRM_ONLY |
PACKED |
OPEN_FRM_ONLY |
NULLABLE |
OPEN_FRM_ONLY |
INDEX_TYPE |
OPEN_FULL_TABLE |
COMMENT |
OPEN_FRM_ONLY |
Column | Optimization type |
---|---|
TABLE_CATALOG |
SKIP_OPEN_TABLE |
TABLE_SCHEMA |
SKIP_OPEN_TABLE |
TABLE_NAME |
SKIP_OPEN_TABLE |
TABLE_TYPE |
OPEN_FRM_ONLY |
ENGINE |
OPEN_FRM_ONLY |
VERSION |
OPEN_FRM_ONLY |
ROW_FORMAT |
OPEN_FULL_TABLE |
TABLE_ROWS |
OPEN_FULL_TABLE |
AVG_ROW_LENGTH |
OPEN_FULL_TABLE |
DATA_LENGTH |
OPEN_FULL_TABLE |
MAX_DATA_LENGTH |
OPEN_FULL_TABLE |
INDEX_LENGTH |
OPEN_FULL_TABLE |
DATA_FREE |
OPEN_FULL_TABLE |
AUTO_INCREMENT |
OPEN_FULL_TABLE |
CREATE_TIME |
OPEN_FULL_TABLE |
UPDATE_TIME |
OPEN_FULL_TABLE |
CHECK_TIME |
OPEN_FULL_TABLE |
TABLE_COLLATION |
OPEN_FRM_ONLY |
CHECKSUM |
OPEN_FULL_TABLE |
CREATE_OPTIONS |
OPEN_FRM_ONLY |
TABLE_COMMENT |
OPEN_FRM_ONLY |
TABLE_CONSTRAINTS
:
OPEN_FULL_TABLE
applies to all columns
TRIGGERS
:
OPEN_TRIGGER_ONLY
applies to all columns
Column | Optimization type |
---|---|
TABLE_CATALOG |
OPEN_FRM_ONLY |
TABLE_SCHEMA |
OPEN_FRM_ONLY |
TABLE_NAME |
OPEN_FRM_ONLY |
VIEW_DEFINITION |
OPEN_FRM_ONLY |
CHECK_OPTION |
OPEN_FRM_ONLY |
IS_UPDATABLE |
OPEN_FULL_TABLE |
DEFINER |
OPEN_FRM_ONLY |
SECURITY_TYPE |
OPEN_FRM_ONLY |
CHARACTER_SET_CLIENT |
OPEN_FRM_ONLY |
COLLATION_CONNECTION |
OPEN_FRM_ONLY |
3) Use
EXPLAIN
to determine whether the
server can use INFORMATION_SCHEMA
optimizations for a query
This applies particularly for
INFORMATION_SCHEMA
queries that search for
information from more than one database, which might take a long
time and impact performance. The Extra
value
in EXPLAIN
output indicates
which, if any, of the optimizations described earlier the server
can use to evaluate INFORMATION_SCHEMA
queries. The following examples demonstrate the kinds of
information you can expect to see in the
Extra
value.
mysql>EXPLAIN SELECT TABLE_NAME FROM INFORMATION_SCHEMA.VIEWS WHERE
TABLE_SCHEMA = 'test' AND TABLE_NAME = 'v1'\G
*************************** 1. row *************************** id: 1 select_type: SIMPLE table: VIEWS type: ALL possible_keys: NULL key: TABLE_SCHEMA,TABLE_NAME key_len: NULL ref: NULL rows: NULL Extra: Using where; Open_frm_only; Scanned 0 databases
Use of constant database and table lookup values enables the
server to avoid directory scans. For references to
VIEWS.TABLE_NAME
, only the
.frm
file need be opened.
mysql> EXPLAIN SELECT TABLE_NAME, ROW_FORMAT FROM INFORMATION_SCHEMA.TABLES\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: TABLES
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: Open_full_table; Scanned all databases
No lookup values are provided (there is no
WHERE
clause), so the server must scan the
data directory and each database directory. For each table thus
identified, the table name and row format are selected.
TABLE_NAME
requires no further table files to
be opened (the SKIP_OPEN_TABLE
optimization
applies). ROW_FORMAT
requires all table files
to be opened (OPEN_FULL_TABLE
applies).
EXPLAIN
reports
OPEN_FULL_TABLE
because it is more expensive
than SKIP_OPEN_TABLE
.
mysql>EXPLAIN SELECT TABLE_NAME, TABLE_TYPE FROM INFORMATION_SCHEMA.TABLES
WHERE TABLE_SCHEMA = 'test'\G
*************************** 1. row *************************** id: 1 select_type: SIMPLE table: TABLES type: ALL possible_keys: NULL key: TABLE_SCHEMA key_len: NULL ref: NULL rows: NULL Extra: Using where; Open_frm_only; Scanned 1 database
No table name lookup value is provided, so the server must scan
the test
database directory. For the
TABLE_NAME
and TABLE_TYPE
columns, the SKIP_OPEN_TABLE
and
OPEN_FRM_ONLY
optimizations apply,
respectively. EXPLAIN
reports
OPEN_FRM_ONLY
because it is more expensive.
mysql>EXPLAIN SELECT B.TABLE_NAME
FROM INFORMATION_SCHEMA.TABLES AS A, INFORMATION_SCHEMA.COLUMNS AS B
WHERE A.TABLE_SCHEMA = 'test'
AND A.TABLE_NAME = 't1'
AND B.TABLE_NAME = A.TABLE_NAME\G
*************************** 1. row *************************** id: 1 select_type: SIMPLE table: A type: ALL possible_keys: NULL key: TABLE_SCHEMA,TABLE_NAME key_len: NULL ref: NULL rows: NULL Extra: Using where; Skip_open_table; Scanned 0 databases *************************** 2. row *************************** id: 1 select_type: SIMPLE table: B type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows: NULL Extra: Using where; Open_frm_only; Scanned all databases; Using join buffer
For the first EXPLAIN
output row:
Constant database and table lookup values enable the server to
avoid directory scans for TABLES
values.
References to TABLES.TABLE_NAME
require no
further table files.
For the second EXPLAIN
output
row: All COLUMNS
table values are
OPEN_FRM_ONLY
lookups, so
COLUMNS.TABLE_NAME
requires the
.frm
file to be opened.
mysql> EXPLAIN SELECT * FROM INFORMATION_SCHEMA.COLLATIONS\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: COLLATIONS
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra:
In this case, no optimizations apply because
COLLATIONS
is not one of the
INFORMATION_SCHEMA
tables for which
optimizations are available.
This section explains how to speed up data change statements:
INSERT
,
UPDATE
, and
DELETE
. Traditional OLTP
applications and modern web applications typically do many small
data change operations, where concurrency is vital. Data
analysis and reporting applications typically run data change
operations that affect many rows at once, where the main
considerations is the I/O to write large amounts of data and
keep indexes up-to-date. For inserting and updating large
volumes of data (known in the industry as ETL, for
“extract-transform-load”), sometimes you use other
SQL statements or external commands, that mimic the effects of
INSERT
,
UPDATE
, and
DELETE
statements.
To optimize insert speed, combine many small operations into a single large operation. Ideally, you make a single connection, send the data for many new rows at once, and delay all index updates and consistency checking until the very end.
The time required for inserting a row is determined by the following factors, where the numbers indicate approximate proportions:
Connecting: (3)
Sending query to server: (2)
Parsing query: (2)
Inserting row: (1 × size of row)
Inserting indexes: (1 × number of indexes)
Closing: (1)
This does not take into consideration the initial overhead to open tables, which is done once for each concurrently running query.
The size of the table slows down the insertion of indexes by
log N
, assuming B-tree indexes.
You can use the following methods to speed up inserts:
If you are inserting many rows from the same client at the
same time, use INSERT
statements with multiple VALUES
lists
to insert several rows at a time. This is considerably
faster (many times faster in some cases) than using
separate single-row INSERT
statements. If you are adding data to a nonempty table,
you can tune the
bulk_insert_buffer_size
variable to make data insertion even faster. See
Section 5.1.7, “Server System Variables”.
When loading a table from a text file, use
LOAD DATA
. This is usually
20 times faster than using
INSERT
statements. See
Section 13.2.6, “LOAD DATA Statement”.
Take advantage of the fact that columns have default values. Insert values explicitly only when the value to be inserted differs from the default. This reduces the parsing that MySQL must do and improves the insert speed.
See Section 8.5.5, “Bulk Data Loading for InnoDB Tables”
for tips specific to InnoDB
tables.
See Section 8.6.2, “Bulk Data Loading for MyISAM Tables”
for tips specific to MyISAM
tables.
An update statement is optimized like a
SELECT
query with the
additional overhead of a write. The speed of the write depends
on the amount of data being updated and the number of indexes
that are updated. Indexes that are not changed do not get
updated.
Another way to get fast updates is to delay updates and then do many updates in a row later. Performing multiple updates together is much quicker than doing one at a time if you lock the table.
For a MyISAM
table that uses dynamic row
format, updating a row to a longer total length may split the
row. If you do this often, it is very important to use
OPTIMIZE TABLE
occasionally.
See Section 13.7.2.4, “OPTIMIZE TABLE Statement”.
The time required to delete individual rows in a
MyISAM
table is exactly proportional to the
number of indexes. To delete rows more quickly, you can
increase the size of the key cache by increasing the
key_buffer_size
system
variable. See Section 5.1.1, “Configuring the Server”.
To delete all rows from a MyISAM
table,
TRUNCATE TABLE
is faster than
tbl_name
DELETE FROM
. Truncate
operations are not transaction-safe; an error occurs when
attempting one in the course of an active transaction or
active table lock. See Section 13.1.34, “TRUNCATE TABLE Statement”.
tbl_name
The more complex your privilege setup, the more overhead applies
to all SQL statements. Simplifying the privileges established by
GRANT
statements enables MySQL to
reduce permission-checking overhead when clients execute
statements. For example, if you do not grant any table-level or
column-level privileges, the server need not ever check the
contents of the tables_priv
and
columns_priv
tables. Similarly, if you place
no resource limits on any accounts, the server does not have to
perform resource counting. If you have a very high
statement-processing load, consider using a simplified grant
structure to reduce permission-checking overhead.
This section lists a number of miscellaneous tips for improving query processing speed:
If your application makes several database requests to perform related updates, combining the statements into a stored routine can help performance. Similarly, if your application computes a single result based on several column values or large volumes of data, combining the computation into a UDF (user-defined function) can help performance. The resulting fast database operations are then available to be reused by other queries, applications, and even code written in different programming languages. See Section 23.2, “Using Stored Routines” and Section 28.4, “Adding Functions to MySQL” for more information.
To fix any compression issues that occur with
ARCHIVE
tables, use
OPTIMIZE TABLE
. See
Section 15.5, “The ARCHIVE Storage Engine”.
If possible, classify reports as “live” or as “statistical”, where data needed for statistical reports is created only from summary tables that are generated periodically from the live data.
If you have data that does not conform well to a
rows-and-columns table structure, you can pack and store
data into a BLOB
column. In
this case, you must provide code in your application to pack
and unpack information, but this might save I/O operations
to read and write the sets of related values.
With Web servers, store images and other binary assets as files, with the path name stored in the database rather than the file itself. Most Web servers are better at caching files than database contents, so using files is generally faster. (Although you must handle backups and storage issues yourself in this case.)
If you need really high speed, look at the low-level MySQL
interfaces. For example, by accessing the MySQL
InnoDB
or MyISAM
storage engine directly, you could get a substantial speed
increase compared to using the SQL interface.
Replication can provide a performance benefit for some operations. You can distribute client retrievals among replication servers to split up the load. To avoid slowing down the master while making backups, you can make backups using a slave server. See Chapter 16, Replication.
The best way to improve the performance of
SELECT
operations is to create
indexes on one or more of the columns that are tested in the
query. The index entries act like pointers to the table rows,
allowing the query to quickly determine which rows match a
condition in the WHERE
clause, and retrieve the
other column values for those rows. All MySQL data types can be
indexed.
Although it can be tempting to create an indexes for every possible column used in a query, unnecessary indexes waste space and waste time for MySQL to determine which indexes to use. Indexes also add to the cost of inserts, updates, and deletes because each index must be updated. You must find the right balance to achieve fast queries using the optimal set of indexes.
Indexes are used to find rows with specific column values quickly. Without an index, MySQL must begin with the first row and then read through the entire table to find the relevant rows. The larger the table, the more this costs. If the table has an index for the columns in question, MySQL can quickly determine the position to seek to in the middle of the data file without having to look at all the data. This is much faster than reading every row sequentially.
Most MySQL indexes (PRIMARY KEY
,
UNIQUE
, INDEX
, and
FULLTEXT
) are stored in
B-trees. Exceptions: Indexes
on spatial data types use R-trees; MEMORY
tables also support hash
indexes; InnoDB
uses inverted lists
for FULLTEXT
indexes.
In general, indexes are used as described in the following
discussion. Characteristics specific to hash indexes (as used in
MEMORY
tables) are described in
Section 8.3.8, “Comparison of B-Tree and Hash Indexes”.
MySQL uses indexes for these operations:
To find the rows matching a WHERE
clause
quickly.
To eliminate rows from consideration. If there is a choice between multiple indexes, MySQL normally uses the index that finds the smallest number of rows (the most selective index).
If the table has a multiple-column index, any leftmost
prefix of the index can be used by the optimizer to look up
rows. For example, if you have a three-column index on
(col1, col2, col3)
, you have indexed
search capabilities on (col1)
,
(col1, col2)
, and (col1, col2,
col3)
. For more information, see
Section 8.3.5, “Multiple-Column Indexes”.
To retrieve rows from other tables when performing joins.
MySQL can use indexes on columns more efficiently if they
are declared as the same type and size. In this context,
VARCHAR
and
CHAR
are considered the same
if they are declared as the same size. For example,
VARCHAR(10)
and
CHAR(10)
are the same size, but
VARCHAR(10)
and
CHAR(15)
are not.
For comparisons between nonbinary string columns, both
columns should use the same character set. For example,
comparing a utf8
column with a
latin1
column precludes use of an index.
Comparison of dissimilar columns (comparing a string column
to a temporal or numeric column, for example) may prevent
use of indexes if values cannot be compared directly without
conversion. For a given value such as 1
in the numeric column, it might compare equal to any number
of values in the string column such as
'1'
, ' 1'
,
'00001'
, or '01.e1'
.
This rules out use of any indexes for the string column.
To find the MIN()
or
MAX()
value for a specific
indexed column key_col
. This is
optimized by a preprocessor that checks whether you are
using WHERE
on all key
parts that occur before key_part_N
=
constant
key_col
in the index. In this case, MySQL does a single key lookup
for each MIN()
or
MAX()
expression and replaces
it with a constant. If all expressions are replaced with
constants, the query returns at once. For example:
SELECT MIN(key_part2
),MAX(key_part2
) FROMtbl_name
WHEREkey_part1
=10;
To sort or group a table if the sorting or grouping is done
on a leftmost prefix of a usable index (for example,
ORDER BY
). If all key
parts are followed by key_part1
,
key_part2
DESC
, the key is
read in reverse order. See
Section 8.2.1.14, “ORDER BY Optimization”, and
Section 8.2.1.15, “GROUP BY Optimization”.
In some cases, a query can be optimized to retrieve values without consulting the data rows. (An index that provides all the necessary results for a query is called a covering index.) If a query uses from a table only columns that are included in some index, the selected values can be retrieved from the index tree for greater speed:
SELECTkey_part3
FROMtbl_name
WHEREkey_part1
=1
Indexes are less important for queries on small tables, or big tables where report queries process most or all of the rows. When a query needs to access most of the rows, reading sequentially is faster than working through an index. Sequential reads minimize disk seeks, even if not all the rows are needed for the query. See Section 8.2.1.20, “Avoiding Full Table Scans” for details.
The primary key for a table represents the column or set of
columns that you use in your most vital queries. It has an
associated index, for fast query performance. Query performance
benefits from the NOT NULL
optimization,
because it cannot include any NULL
values.
With the InnoDB
storage engine, the table
data is physically organized to do ultra-fast lookups and sorts
based on the primary key column or columns.
If your table is big and important, but does not have an obvious column or set of columns to use as a primary key, you might create a separate column with auto-increment values to use as the primary key. These unique IDs can serve as pointers to corresponding rows in other tables when you join tables using foreign keys.
If a table has many columns, and you query many different combinations of columns, it might be efficient to split the less-frequently used data into separate tables with a few columns each, and relate them back to the main table by duplicating the numeric ID column from the main table. That way, each small table can have a primary key for fast lookups of its data, and you can query just the set of columns that you need using a join operation. Depending on how the data is distributed, the queries might perform less I/O and take up less cache memory because the relevant columns are packed together on disk. (To maximize performance, queries try to read as few data blocks as possible from disk; tables with only a few columns can fit more rows in each data block.)
The most common type of index involves a single column, storing
copies of the values from that column in a data structure,
allowing fast lookups for the rows with the corresponding column
values. The B-tree data structure lets the index quickly find a
specific value, a set of values, or a range of values,
corresponding to operators such as =
,
>
, ≤
,
BETWEEN
, IN
, and so on, in
a WHERE
clause.
The maximum number of indexes per table and the maximum index length is defined per storage engine. See Chapter 14, The InnoDB Storage Engine, and Chapter 15, Alternative Storage Engines. All storage engines support at least 16 indexes per table and a total index length of at least 256 bytes. Most storage engines have higher limits.
For additional information about column indexes, see Section 13.1.14, “CREATE INDEX Statement”.
With
syntax in an index specification for a string column, you can
create an index that uses only the first
col_name
(N
)N
characters of the column.
Indexing only a prefix of column values in this way can make
the index file much smaller. When you index a
BLOB
or
TEXT
column, you
must specify a prefix length for the
index. For example:
CREATE TABLE test (blob_col BLOB, INDEX(blob_col(10)));
Prefixes can be up to 1000 bytes long (767 bytes for
InnoDB
tables, unless you have
innodb_large_prefix
set).
Prefix limits are measured in bytes, whereas the prefix
length in CREATE TABLE
,
ALTER TABLE
, and
CREATE INDEX
statements is
interpreted as number of characters for nonbinary string
types (CHAR
,
VARCHAR
,
TEXT
) and number of bytes for
binary string types (BINARY
,
VARBINARY
,
BLOB
). Take this into account
when specifying a prefix length for a nonbinary string
column that uses a multibyte character set.
If a search term exceeds the index prefix length, the index is used to exclude non-matching rows, and the remaining rows are examined for possible matches.
For additional information about index prefixes, see Section 13.1.14, “CREATE INDEX Statement”.
FULLTEXT
indexes are used for full-text
searches. Only the InnoDB
and
MyISAM
storage engines support
FULLTEXT
indexes and only for
CHAR
,
VARCHAR
, and
TEXT
columns. Indexing always
takes place over the entire column and column prefix indexing
is not supported. For details, see
Section 12.9, “Full-Text Search Functions”.
Optimizations are applied to certain kinds of
FULLTEXT
queries against single
InnoDB
tables. Queries with these
characteristics are particularly efficient:
FULLTEXT
queries that only return the
document ID, or the document ID and the search rank.
FULLTEXT
queries that sort the matching
rows in descending order of score and apply a
LIMIT
clause to take the top N matching
rows. For this optimization to apply, there must be no
WHERE
clauses and only a single
ORDER BY
clause in descending order.
FULLTEXT
queries that retrieve only the
COUNT(*)
value of rows matching a
search term, with no additional WHERE
clauses. Code the WHERE
clause as
WHERE MATCH(
,
without any text
)
AGAINST
('other_text
')> 0
comparison operator.
For queries that contain full-text expressions, MySQL evaluates those expressions during the optimization phase of query execution. The optimizer does not just look at full-text expressions and make estimates, it actually evaluates them in the process of developing an execution plan.
An implication of this behavior is that
EXPLAIN
for full-text queries
is typically slower than for non-full-text queries for which
no expression evaluation occurs during the optimization phase.
EXPLAIN
for full-text queries
may show Select tables optimized away
in
the Extra
column due to matching occurring
during optimization; in this case, no table access need occur
during later execution.
MySQL can create composite indexes (that is, indexes on multiple columns). An index may consist of up to 16 columns. For certain data types, you can index a prefix of the column (see Section 8.3.4, “Column Indexes”).
MySQL can use multiple-column indexes for queries that test all the columns in the index, or queries that test just the first column, the first two columns, the first three columns, and so on. If you specify the columns in the right order in the index definition, a single composite index can speed up several kinds of queries on the same table.
A multiple-column index can be considered a sorted array, the rows of which contain values that are created by concatenating the values of the indexed columns.
As an alternative to a composite index, you can introduce a column that is “hashed” based on information from other columns. If this column is short, reasonably unique, and indexed, it might be faster than a “wide” index on many columns. In MySQL, it is very easy to use this extra column:
SELECT * FROMtbl_name
WHEREhash_col
=MD5(CONCAT(val1
,val2
)) ANDcol1
=val1
ANDcol2
=val2
;
Suppose that a table has the following specification:
CREATE TABLE test ( id INT NOT NULL, last_name CHAR(30) NOT NULL, first_name CHAR(30) NOT NULL, PRIMARY KEY (id), INDEX name (last_name,first_name) );
The name
index is an index over the
last_name
and first_name
columns. The index can be used for lookups in queries that
specify values in a known range for combinations of
last_name
and first_name
values. It can also be used for queries that specify just a
last_name
value because that column is a
leftmost prefix of the index (as described later in this
section). Therefore, the name
index is used
for lookups in the following queries:
SELECT * FROM test WHERE last_name='Jones'; SELECT * FROM test WHERE last_name='Jones' AND first_name='John'; SELECT * FROM test WHERE last_name='Jones' AND (first_name='John' OR first_name='Jon'); SELECT * FROM test WHERE last_name='Jones' AND first_name >='M' AND first_name < 'N';
However, the name
index is
not used for lookups in the following
queries:
SELECT * FROM test WHERE first_name='John'; SELECT * FROM test WHERE last_name='Jones' OR first_name='John';
Suppose that you issue the following
SELECT
statement:
SELECT * FROMtbl_name
WHERE col1=val1
AND col2=val2
;
If a multiple-column index exists on col1
and
col2
, the appropriate rows can be fetched
directly. If separate single-column indexes exist on
col1
and col2
, the
optimizer attempts to use the Index Merge optimization (see
Section 8.2.1.3, “Index Merge Optimization”), or attempts to find
the most restrictive index by deciding which index excludes more
rows and using that index to fetch the rows.
If the table has a multiple-column index, any leftmost prefix of
the index can be used by the optimizer to look up rows. For
example, if you have a three-column index on (col1,
col2, col3)
, you have indexed search capabilities on
(col1)
, (col1, col2)
, and
(col1, col2, col3)
.
MySQL cannot use the index to perform lookups if the columns do
not form a leftmost prefix of the index. Suppose that you have
the SELECT
statements shown here:
SELECT * FROMtbl_name
WHERE col1=val1
; SELECT * FROMtbl_name
WHERE col1=val1
AND col2=val2
; SELECT * FROMtbl_name
WHERE col2=val2
; SELECT * FROMtbl_name
WHERE col2=val2
AND col3=val3
;
If an index exists on (col1, col2, col3)
,
only the first two queries use the index. The third and fourth
queries do involve indexed columns, but do not use an index to
perform lookups because (col2)
and
(col2, col3)
are not leftmost prefixes of
(col1, col2, col3)
.
Always check whether all your queries really use the indexes
that you have created in the tables. Use the
EXPLAIN
statement, as described
in Section 8.8.1, “Optimizing Queries with EXPLAIN”.
Storage engines collect statistics about tables for use by the optimizer. Table statistics are based on value groups, where a value group is a set of rows with the same key prefix value. For optimizer purposes, an important statistic is the average value group size.
MySQL uses the average value group size in the following ways:
To estimate how many rows must be read for each
ref
access
To estimate how many rows a partial join will produce; that is, the number of rows that an operation of this form will produce:
(...) JOINtbl_name
ONtbl_name
.key
=expr
As the average value group size for an index increases, the index is less useful for those two purposes because the average number of rows per lookup increases: For the index to be good for optimization purposes, it is best that each index value target a small number of rows in the table. When a given index value yields a large number of rows, the index is less useful and MySQL is less likely to use it.
The average value group size is related to table cardinality,
which is the number of value groups. The
SHOW INDEX
statement displays a
cardinality value based on N/S
, where
N
is the number of rows in the table
and S
is the average value group
size. That ratio yields an approximate number of value groups in
the table.
For a join based on the <=>
comparison
operator, NULL
is not treated differently
from any other value: NULL <=> NULL
,
just as
for any other
N
<=>
N
N
.
However, for a join based on the =
operator,
NULL
is different from
non-NULL
values:
is not true when
expr1
=
expr2
expr1
or
expr2
(or both) are
NULL
. This affects
ref
accesses for comparisons
of the form
: MySQL will not access
the table if the current value of
tbl_name.key
=
expr
expr
is NULL
,
because the comparison cannot be true.
For =
comparisons, it does not matter how
many NULL
values are in the table. For
optimization purposes, the relevant value is the average size of
the non-NULL
value groups. However, MySQL
does not currently enable that average size to be collected or
used.
For InnoDB
and MyISAM
tables, you have some control over collection of table
statistics by means of the
innodb_stats_method
and
myisam_stats_method
system
variables, respectively. These variables have three possible
values, which differ as follows:
When the variable is set to nulls_equal
,
all NULL
values are treated as identical
(that is, they all form a single value group).
If the NULL
value group size is much
higher than the average non-NULL
value
group size, this method skews the average value group size
upward. This makes index appear to the optimizer to be less
useful than it really is for joins that look for
non-NULL
values. Consequently, the
nulls_equal
method may cause the
optimizer not to use the index for
ref
accesses when it
should.
When the variable is set to
nulls_unequal
, NULL
values are not considered the same. Instead, each
NULL
value forms a separate value group
of size 1.
If you have many NULL
values, this method
skews the average value group size downward. If the average
non-NULL
value group size is large,
counting NULL
values each as a group of
size 1 causes the optimizer to overestimate the value of the
index for joins that look for non-NULL
values. Consequently, the nulls_unequal
method may cause the optimizer to use this index for
ref
lookups when other
methods may be better.
When the variable is set to
nulls_ignored
, NULL
values are ignored.
If you tend to use many joins that use
<=>
rather than =
,
NULL
values are not special in comparisons
and one NULL
is equal to another. In this
case, nulls_equal
is the appropriate
statistics method.
The innodb_stats_method
system
variable has a global value; the
myisam_stats_method
system
variable has both global and session values. Setting the global
value affects statistics collection for tables from the
corresponding storage engine. Setting the session value affects
statistics collection only for the current client connection.
This means that you can force a table's statistics to be
regenerated with a given method without affecting other clients
by setting the session value of
myisam_stats_method
.
To regenerate MyISAM
table statistics, you
can use any of the following methods:
Change the table to cause its statistics to go out of date
(for example, insert a row and then delete it), and then set
myisam_stats_method
and
issue an ANALYZE TABLE
statement
Some caveats regarding the use of
innodb_stats_method
and
myisam_stats_method
:
You can force table statistics to be collected explicitly,
as just described. However, MySQL may also collect
statistics automatically. For example, if during the course
of executing statements for a table, some of those
statements modify the table, MySQL may collect statistics.
(This may occur for bulk inserts or deletes, or some
ALTER TABLE
statements, for
example.) If this happens, the statistics are collected
using whatever value
innodb_stats_method
or
myisam_stats_method
has at
the time. Thus, if you collect statistics using one method,
but the system variable is set to the other method when a
table's statistics are collected automatically later,
the other method will be used.
There is no way to tell which method was used to generate statistics for a given table.
These variables apply only to InnoDB
and
MyISAM
tables. Other storage engines have
only one method for collecting table statistics. Usually it
is closer to the nulls_equal
method.
Understanding the B-tree and hash data structures can help
predict how different queries perform on different storage
engines that use these data structures in their indexes,
particularly for the MEMORY
storage engine
that lets you choose B-tree or hash indexes.
A B-tree index can be used for column comparisons in
expressions that use the
=
,
>
,
>=
,
<
,
<=
,
or BETWEEN
operators. The index
also can be used for LIKE
comparisons if the argument to
LIKE
is a constant string that
does not start with a wildcard character. For example, the
following SELECT
statements use
indexes:
SELECT * FROMtbl_name
WHEREkey_col
LIKE 'Patrick%'; SELECT * FROMtbl_name
WHEREkey_col
LIKE 'Pat%_ck%';
In the first statement, only rows with 'Patrick'
<=
are considered. In the second statement,
only rows with key_col
<
'Patricl''Pat' <=
are
considered.
key_col
< 'Pau'
The following SELECT
statements
do not use indexes:
SELECT * FROMtbl_name
WHEREkey_col
LIKE '%Patrick%'; SELECT * FROMtbl_name
WHEREkey_col
LIKEother_col
;
In the first statement, the LIKE
value begins with a wildcard character. In the second
statement, the LIKE
value is not
a constant.
If you use ... LIKE
'%
and
string
%'string
is longer than three
characters, MySQL uses the Turbo
Boyer-Moore algorithm to initialize the pattern for
the string and then uses this pattern to perform the search
more quickly.
A search using
employs indexes if
col_name
IS
NULLcol_name
is indexed.
Any index that does not span all
AND
levels in the
WHERE
clause is not used to optimize the
query. In other words, to be able to use an index, a prefix of
the index must be used in every
AND
group.
The following WHERE
clauses use indexes:
... WHEREindex_part1
=1 ANDindex_part2
=2 ANDother_column
=3 /*index
= 1 ORindex
= 2 */ ... WHEREindex
=1 OR A=10 ANDindex
=2 /* optimized like "index_part1
='hello'" */ ... WHEREindex_part1
='hello' ANDindex_part3
=5 /* Can use index onindex1
but not onindex2
orindex3
*/ ... WHEREindex1
=1 ANDindex2
=2 ORindex1
=3 ANDindex3
=3;
These WHERE
clauses do
not use indexes:
/*index_part1
is not used */ ... WHEREindex_part2
=1 ANDindex_part3
=2 /* Index is not used in both parts of the WHERE clause */ ... WHEREindex
=1 OR A=10 /* No index spans all rows */ ... WHEREindex_part1
=1 ORindex_part2
=10
Sometimes MySQL does not use an index, even if one is
available. One circumstance under which this occurs is when
the optimizer estimates that using the index would require
MySQL to access a very large percentage of the rows in the
table. (In this case, a table scan is likely to be much faster
because it requires fewer seeks.) However, if such a query
uses LIMIT
to retrieve only some of the
rows, MySQL uses an index anyway, because it can much more
quickly find the few rows to return in the result.
Hash indexes have somewhat different characteristics from those just discussed:
They are used only for equality comparisons that use the
=
or <=>
operators (but are very fast). They
are not used for comparison operators such as
<
that find a range of values.
Systems that rely on this type of single-value lookup are
known as “key-value stores”; to use MySQL for
such applications, use hash indexes wherever possible.
The optimizer cannot use a hash index to speed up
ORDER BY
operations. (This type of
index cannot be used to search for the next entry in
order.)
MySQL cannot determine approximately how many rows there
are between two values (this is used by the range
optimizer to decide which index to use). This may affect
some queries if you change a MyISAM
or
InnoDB
table to a hash-indexed
MEMORY
table.
Only whole keys can be used to search for a row. (With a B-tree index, any leftmost prefix of the key can be used to find rows.)
InnoDB
automatically extends each
secondary index by appending the primary key columns to it.
Consider this table definition:
CREATE TABLE t1 ( i1 INT NOT NULL DEFAULT 0, i2 INT NOT NULL DEFAULT 0, d DATE DEFAULT NULL, PRIMARY KEY (i1, i2), INDEX k_d (d) ) ENGINE = InnoDB;
This table defines the primary key on columns (i1,
i2)
. It also defines a secondary index
k_d
on column (d)
, but
internally InnoDB
extends this index and
treats it as columns (d, i1, i2)
.
The optimizer takes into account the primary key columns of the extended secondary index when determining how and whether to use that index. This can result in more efficient query execution plans and better performance.
The optimizer can use extended secondary indexes for
ref
, range
, and
index_merge
index access, for Loose Index
Scan access, for join and sorting optimization, and for
MIN()
/MAX()
optimization.
The following example shows how execution plans are affected by
whether the optimizer uses extended secondary indexes. Suppose
that t1
is populated with these rows:
INSERT INTO t1 VALUES (1, 1, '1998-01-01'), (1, 2, '1999-01-01'), (1, 3, '2000-01-01'), (1, 4, '2001-01-01'), (1, 5, '2002-01-01'), (2, 1, '1998-01-01'), (2, 2, '1999-01-01'), (2, 3, '2000-01-01'), (2, 4, '2001-01-01'), (2, 5, '2002-01-01'), (3, 1, '1998-01-01'), (3, 2, '1999-01-01'), (3, 3, '2000-01-01'), (3, 4, '2001-01-01'), (3, 5, '2002-01-01'), (4, 1, '1998-01-01'), (4, 2, '1999-01-01'), (4, 3, '2000-01-01'), (4, 4, '2001-01-01'), (4, 5, '2002-01-01'), (5, 1, '1998-01-01'), (5, 2, '1999-01-01'), (5, 3, '2000-01-01'), (5, 4, '2001-01-01'), (5, 5, '2002-01-01');
Now consider this query:
EXPLAIN SELECT COUNT(*) FROM t1 WHERE i1 = 3 AND d = '2000-01-01'
The execution plan depends on whether the extended index is used.
When the optimizer does not consider index extensions, it treats
the index k_d
as only (d)
.
EXPLAIN
for the query produces
this result:
mysql> EXPLAIN SELECT COUNT(*) FROM t1 WHERE i1 = 3 AND d = '2000-01-01'\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
type: ref
possible_keys: PRIMARY,k_d
key: k_d
key_len: 4
ref: const
rows: 5
Extra: Using where; Using index
When the optimizer takes index extensions into account, it
treats k_d
as (d, i1, i2)
.
In this case, it can use the leftmost index prefix (d,
i1)
to produce a better execution plan:
mysql> EXPLAIN SELECT COUNT(*) FROM t1 WHERE i1 = 3 AND d = '2000-01-01'\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
type: ref
possible_keys: PRIMARY,k_d
key: k_d
key_len: 8
ref: const,const
rows: 1
Extra: Using index
In both cases, key
indicates that the
optimizer will use secondary index k_d
but
the EXPLAIN
output shows these
improvements from using the extended index:
key_len
goes from 4 bytes to 8 bytes,
indicating that key lookups use columns d
and i1
, not just d
.
The ref
value changes from
const
to const,const
because the key lookup uses two key parts, not one.
The rows
count decreases from 5 to 1,
indicating that InnoDB
should need to
examine fewer rows to produce the result.
The Extra
value changes from
Using where; Using index
to
Using index
. This means that rows can be
read using only the index, without consulting columns in the
data row.
Differences in optimizer behavior for use of extended indexes
can also be seen with SHOW
STATUS
:
FLUSH TABLE t1; FLUSH STATUS; SELECT COUNT(*) FROM t1 WHERE i1 = 3 AND d = '2000-01-01'; SHOW STATUS LIKE 'handler_read%'
The preceding statements include FLUSH
TABLES
and FLUSH STATUS
to flush the table cache and clear the status counters.
Without index extensions, SHOW
STATUS
produces this result:
+-----------------------+-------+ | Variable_name | Value | +-----------------------+-------+ | Handler_read_first | 0 | | Handler_read_key | 1 | | Handler_read_last | 0 | | Handler_read_next | 5 | | Handler_read_prev | 0 | | Handler_read_rnd | 0 | | Handler_read_rnd_next | 0 | +-----------------------+-------+
With index extensions, SHOW
STATUS
produces this result. The
Handler_read_next
value
decreases from 5 to 1, indicating more efficient use of the
index:
+-----------------------+-------+ | Variable_name | Value | +-----------------------+-------+ | Handler_read_first | 0 | | Handler_read_key | 1 | | Handler_read_last | 0 | | Handler_read_next | 1 | | Handler_read_prev | 0 | | Handler_read_rnd | 0 | | Handler_read_rnd_next | 0 | +-----------------------+-------+
The use_index_extensions
flag of the
optimizer_switch
system
variable permits control over whether the optimizer takes the
primary key columns into account when determining how to use an
InnoDB
table's secondary indexes. By
default, use_index_extensions
is enabled. To
check whether disabling use of index extensions will improve
performance, use this statement:
SET optimizer_switch = 'use_index_extensions=off';
Use of index extensions by the optimizer is subject to the usual limits on the number of key parts in an index (16) and the maximum key length (3072 bytes).
MySQL supports indexes on generated columns. For example:
CREATE TABLE t1 (f1 INT, gc INT AS (f1 + 1) STORED, INDEX (gc));
The generated column, gc
, is defined as the
expression f1 + 1
. The column is also indexed
and the optimizer can take that index into account during
execution plan construction. In the following query, the
WHERE
clause refers to gc
and the optimizer considers whether the index on that column
yields a more efficient plan:
SELECT * FROM t1 WHERE gc > 9;
The optimizer can use indexes on generated columns to generate
execution plans, even in the absence of direct references in
queries to those columns by name. This occurs if the
WHERE
, ORDER BY
, or
GROUP BY
clause refers to an expression that
matches the definition of some indexed generated column. The
following query does not refer directly to gc
but does use an expression that matches the definition of
gc
:
SELECT * FROM t1 WHERE f1 + 1 > 9;
The optimizer recognizes that the expression f1 +
1
matches the definition of gc
and
that gc
is indexed, so it considers that
index during execution plan construction. You can see this using
EXPLAIN
:
mysql> EXPLAIN SELECT * FROM t1 WHERE f1 + 1 > 9\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
partitions: NULL
type: range
possible_keys: gc
key: gc
key_len: 5
ref: NULL
rows: 1
filtered: 100.00
Extra: Using index condition
In effect, the optimizer has replaced the expression f1
+ 1
with the name of the generated column that matches
the expression. That is also apparent in the rewritten query
available in the extended EXPLAIN
information displayed by SHOW
WARNINGS
:
mysql> SHOW WARNINGS\G
*************************** 1. row ***************************
Level: Note
Code: 1003
Message: /* select#1 */ select `test`.`t1`.`f1` AS `f1`,`test`.`t1`.`gc`
AS `gc` from `test`.`t1` where (`test`.`t1`.`gc` > 9)
The following restrictions and conditions apply to the optimizer's use of generated column indexes:
For a query expression to match a generated column
definition, the expression must be identical and it must
have the same result type. For example, if the generated
column expression is f1 + 1
, the
optimizer will not recognize a match if the query uses
1 + f1
, or if f1 + 1
(an integer expression) is compared with a string.
The optimization applies to these operators:
=
,
<
,
<=
,
>
,
>=
,
BETWEEN
, and
IN()
.
For operators other than
BETWEEN
and
IN()
, either operand can be
replaced by a matching generated column. For
BETWEEN
and
IN()
, only the first argument
can be replaced by a matching generated column, and the
other arguments must have the same result type.
BETWEEN
and
IN()
are not yet supported for
comparisons involving JSON values.
The generated column must be defined as an expression that
contains at least a function call or one of the operators
mentioned in the preceding item. The expression cannot
consist of a simple reference to another column. For
example, gc INT AS (f1) STORED
consists
only of a column reference, so indexes on
gc
are not considered.
For comparisons of strings to indexed generated columns that
compute a value from a JSON function that returns a quoted
string, JSON_UNQUOTE()
is
needed in the column definition to remove the extra quotes
from the function value. (For direct comparison of a string
to the function result, the JSON comparator handles quote
removal, but this does not occur for index lookups.) For
example, instead of writing a column definition like this:
doc_name TEXT AS (JSON_EXTRACT(jdoc, '$.name')) STORED
Write it like this:
doc_name TEXT AS (JSON_UNQUOTE(JSON_EXTRACT(jdoc, '$.name'))) STORED
With the latter definition, the optimizer can detect a match for both of these comparisons:
... WHERE JSON_EXTRACT(jdoc, '$.name') = 'some_string
' ... ... WHERE JSON_UNQUOTE(JSON_EXTRACT(jdoc, '$.name')) = 'some_string
' ...
Without JSON_UNQUOTE()
in the
column definition, the optimizer detects a match only for
the first of those comparisons.
If the optimizer fails to choose the desired index, an index hint can be used to force the optimizer to make a different choice.
Temporal values are stored in
TIMESTAMP
columns as UTC values,
and values inserted into and retrieved from
TIMESTAMP
columns are converted
between the session time zone and UTC. (This is the same type of
conversion performed by the
CONVERT_TZ()
function. If the
session time zone is UTC, there is effectively no time zone
conversion.)
Due to conventions for local time zone changes such as Daylight Saving Time (DST), conversions between UTC and non-UTC time zones are not one-to-one in both directions. UTC values that are distinct may not be distinct in another time zone. The following example shows distinct UTC values that become identical in a non-UTC time zone:
mysql>CREATE TABLE tstable (ts TIMESTAMP);
mysql>SET time_zone = 'UTC'; -- insert UTC values
mysql>INSERT INTO tstable VALUES
('2018-10-28 00:30:00'),
('2018-10-28 01:30:00');
mysql>SELECT ts FROM tstable;
+---------------------+ | ts | +---------------------+ | 2018-10-28 00:30:00 | | 2018-10-28 01:30:00 | +---------------------+ mysql>SET time_zone = 'MET'; -- retrieve non-UTC values
mysql>SELECT ts FROM tstable;
+---------------------+ | ts | +---------------------+ | 2018-10-28 02:30:00 | | 2018-10-28 02:30:00 | +---------------------+
To use named time zones such as 'MET'
or
'Europe/Amsterdam'
, the time zone tables
must be properly set up. For instructions, see
Section 5.1.12, “MySQL Server Time Zone Support”.
You can see that the two distinct UTC values are the same when
converted to the 'MET'
time zone. This
phenomenon can lead to different results for a given
TIMESTAMP
column query, depending
on whether the optimizer uses an index to execute the query.
Suppose that a query selects values from the table shown earlier
using a WHERE
clause to search the
ts
column for a single specific value such as
a user-provided timestamp literal:
SELECT ts FROM tstable
WHERE ts = 'literal
';
Suppose further that the query executes under these conditions:
The session time zone is not UTC and has a DST shift. For example:
SET time_zone = 'MET';
Unique UTC values stored in the
TIMESTAMP
column are not
unique in the session time zone due to DST shifts. (The
example shown earlier illustrates how this can occur.)
The query specifies a search value that is within the hour of entry into DST in the session time zone.
Under those conditions, the comparison in the
WHERE
clause occurs in different ways for
nonindexed and indexed lookups and leads to different results:
If there is no index or the optimizer cannot use it,
comparisons occur in the session time zone. The optimizer
performs a table scan in which it retrieves each
ts
column value, converts it from UTC to
the session time zone, and compares it to the search value
(also interpreted in the session time zone):
mysql>SELECT ts FROM tstable
WHERE ts = '2018-10-28 02:30:00';
+---------------------+ | ts | +---------------------+ | 2018-10-28 02:30:00 | | 2018-10-28 02:30:00 | +---------------------+
Because the stored ts
values are
converted to the session time zone, it is possible for the
query to return two timestamp values that are distinct as
UTC values but equal in the session time zone: One value
that occurs before the DST shift when clocks are changed,
and one value that was occurs after the DST shift.
If there is a usable index, comparisons occur in UTC. The optimizer performs an index scan, first converting the search value from the session time zone to UTC, then comparing the result to the UTC index entries:
mysql>ALTER TABLE tstable ADD INDEX (ts);
mysql>SELECT ts FROM tstable
WHERE ts = '2018-10-28 02:30:00';
+---------------------+ | ts | +---------------------+ | 2018-10-28 02:30:00 | +---------------------+
In this case, the (converted) search value is matched only to index entries, and because the index entries for the distinct stored UTC values are also distinct, the search value can match only one of them.
Due to different optimizer operation for nonindexed and indexed lookups, the query produces different results in each case. The result from the nonindexed lookup returns all values that match in the session time zone. The indexed lookup cannot do so:
It is performed within the storage engine, which knows only about UTC values.
For the two distinct session time zone values that map to the same UTC value, the indexed lookup matches only the corresponding UTC index entry and returns only a single row.
In the preceding discussion, the data set stored in
tstable
happens to consist of distinct UTC
values. In such cases, all index-using queries of the form shown
match at most one index entry.
If the index is not UNIQUE
, it is possible
for the table (and the index) to store multiple instances of a
given UTC value. For example, the ts
column
might contain multiple instances of the UTC value
'2018-10-28 00:30:00'
. In this case, the
index-using query would return each of them (converted to the
MET value '2018-10-28 02:30:00'
in the result
set). It remains true that index-using queries match the
converted search value to a single value in the UTC index
entries, rather than matching multiple UTC values that convert
to the search value in the session time zone.
If it is important to return all ts
values
that match in the session time zone, the workaround is to
suppress use of the index with an IGNORE
INDEX
hint:
mysql>SELECT ts FROM tstable
IGNORE INDEX (ts)
WHERE ts = '2018-10-28 02:30:00';
+---------------------+ | ts | +---------------------+ | 2018-10-28 02:30:00 | | 2018-10-28 02:30:00 | +---------------------+
The same lack of one-to-one mapping for time zone conversions in
both directions occurs in other contexts as well, such as
conversions performed with the
FROM_UNIXTIME()
and
UNIX_TIMESTAMP()
functions. See
Section 12.6, “Date and Time Functions”.
In your role as a database designer, look for the most efficient way to organize your schemas, tables, and columns. As when tuning application code, you minimize I/O, keep related items together, and plan ahead so that performance stays high as the data volume increases. Starting with an efficient database design makes it easier for team members to write high-performing application code, and makes the database likely to endure as applications evolve and are rewritten.
Design your tables to minimize their space on the disk. This can result in huge improvements by reducing the amount of data written to and read from disk. Smaller tables normally require less main memory while their contents are being actively processed during query execution. Any space reduction for table data also results in smaller indexes that can be processed faster.
MySQL supports many different storage engines (table types) and row formats. For each table, you can decide which storage and indexing method to use. Choosing the proper table format for your application can give you a big performance gain. See Chapter 14, The InnoDB Storage Engine, and Chapter 15, Alternative Storage Engines.
You can get better performance for a table and minimize storage space by using the techniques listed here:
Use the most efficient (smallest) data types possible.
MySQL has many specialized types that save disk space and
memory. For example, use the smaller integer types if
possible to get smaller tables.
MEDIUMINT
is often a better
choice than INT
because a
MEDIUMINT
column uses 25%
less space.
Declare columns to be NOT NULL
if
possible. It makes SQL operations faster, by enabling
better use of indexes and eliminating overhead for testing
whether each value is NULL
. You also
save some storage space, one bit per column. If you really
need NULL
values in your tables, use
them. Just avoid the default setting that allows
NULL
values in every column.
InnoDB
tables are created using the
DYNAMIC
row format by default. To use a
row format other than DYNAMIC
,
configure
innodb_default_row_format
,
or specify the ROW_FORMAT
option
explicitly in a CREATE
TABLE
or ALTER
TABLE
statement.
The compact family of row formats, which includes
COMPACT
, DYNAMIC
,
and COMPRESSED
, decreases row storage
space at the cost of increasing CPU use for some
operations. If your workload is a typical one that is
limited by cache hit rates and disk speed it is likely to
be faster. If it is a rare case that is limited by CPU
speed, it might be slower.
The compact family of row formats also optimizes
CHAR
column storage when
using a variable-length character set such as
utf8mb3
or utf8mb4
.
With ROW_FORMAT=REDUNDANT
,
CHAR(
occupies N
)N
× the maximum
byte length of the character set. Many languages can be
written primarily using single-byte
utf8
characters, so a fixed storage
length often wastes space. With the compact family of rows
formats, InnoDB
allocates a variable
amount of storage in the range of
N
to
N
× the maximum byte
length of the character set for these columns by stripping
trailing spaces. The minimum storage length is
N
bytes to facilitate in-place
updates in typical cases. For more information, see
Section 14.11, “InnoDB Row Formats”.
To minimize space even further by storing table data in
compressed form, specify
ROW_FORMAT=COMPRESSED
when creating
InnoDB
tables, or run the
myisampack command on an existing
MyISAM
table.
(InnoDB
compressed tables are readable
and writable, while MyISAM
compressed
tables are read-only.)
For MyISAM
tables, if you do not have
any variable-length columns
(VARCHAR
,
TEXT
, or
BLOB
columns), a fixed-size
row format is used. This is faster but may waste some
space. See Section 15.2.3, “MyISAM Table Storage Formats”. You can
hint that you want to have fixed length rows even if you
have VARCHAR
columns with
the CREATE TABLE
option
ROW_FORMAT=FIXED
.
The primary index of a table should be as short as
possible. This makes identification of each row easy and
efficient. For InnoDB
tables, the
primary key columns are duplicated in each secondary index
entry, so a short primary key saves considerable space if
you have many secondary indexes.
Create only the indexes that you need to improve query performance. Indexes are good for retrieval, but slow down insert and update operations. If you access a table mostly by searching on a combination of columns, create a single composite index on them rather than a separate index for each column. The first part of the index should be the column most used. If you always use many columns when selecting from the table, the first column in the index should be the one with the most duplicates, to obtain better compression of the index.
If it is very likely that a long string column has a unique prefix on the first number of characters, it is better to index only this prefix, using MySQL's support for creating an index on the leftmost part of the column (see Section 13.1.14, “CREATE INDEX Statement”). Shorter indexes are faster, not only because they require less disk space, but because they also give you more hits in the index cache, and thus fewer disk seeks. See Section 5.1.1, “Configuring the Server”.
In some circumstances, it can be beneficial to split into two a table that is scanned very often. This is especially true if it is a dynamic-format table and it is possible to use a smaller static format table that can be used to find the relevant rows when scanning the table.
Declare columns with identical information in different tables with identical data types, to speed up joins based on the corresponding columns.
Keep column names simple, so that you can use the same
name across different tables and simplify join queries.
For example, in a table named customer
,
use a column name of name
instead of
customer_name
. To make your names
portable to other SQL servers, consider keeping them
shorter than 18 characters.
Normally, try to keep all data nonredundant (observing what is referred to in database theory as third normal form). Instead of repeating lengthy values such as names and addresses, assign them unique IDs, repeat these IDs as needed across multiple smaller tables, and join the tables in queries by referencing the IDs in the join clause.
If speed is more important than disk space and the maintenance costs of keeping multiple copies of data, for example in a business intelligence scenario where you analyze all the data from large tables, you can relax the normalization rules, duplicating information or creating summary tables to gain more speed.
For unique IDs or other values that can be represented as either strings or numbers, prefer numeric columns to string columns. Since large numeric values can be stored in fewer bytes than the corresponding strings, it is faster and takes less memory to transfer and compare them.
If you are using numeric data, it is faster in many cases to access information from a database (using a live connection) than to access a text file. Information in the database is likely to be stored in a more compact format than in the text file, so accessing it involves fewer disk accesses. You also save code in your application because you can avoid parsing the text file to find line and column boundaries.
For character and string columns, follow these guidelines:
Use binary collation order for fast comparison and sort
operations, when you do not need language-specific
collation features. You can use the
BINARY
operator to use binary
collation within a particular query.
When comparing values from different columns, declare those columns with the same character set and collation wherever possible, to avoid string conversions while running the query.
For column values less than 8KB in size, use binary
VARCHAR
instead of
BLOB
. The GROUP BY
and ORDER BY
clauses can generate
temporary tables, and these temporary tables can use the
MEMORY
storage engine if the original
table does not contain any BLOB
columns.
If a table contains string columns such as name and address, but many queries do not retrieve those columns, consider splitting the string columns into a separate table and using join queries with a foreign key when necessary. When MySQL retrieves any value from a row, it reads a data block containing all the columns of that row (and possibly other adjacent rows). Keeping each row small, with only the most frequently used columns, allows more rows to fit in each data block. Such compact tables reduce disk I/O and memory usage for common queries.
When you use a randomly generated value as a primary key
in an InnoDB
table, prefix it with an
ascending value such as the current date and time if
possible. When consecutive primary values are physically
stored near each other, InnoDB
can
insert and retrieve them faster.
See Section 8.4.2.1, “Optimizing for Numeric Data” for reasons why a numeric column is usually preferable to an equivalent string column.
When storing a large blob containing textual data,
consider compressing it first. Do not use this technique
when the entire table is compressed by
InnoDB
or MyISAM
.
For a table with several columns, to reduce memory requirements for queries that do not use the BLOB column, consider splitting the BLOB column into a separate table and referencing it with a join query when needed.
Since the performance requirements to retrieve and display a BLOB value might be very different from other data types, you could put the BLOB-specific table on a different storage device or even a separate database instance. For example, to retrieve a BLOB might require a large sequential disk read that is better suited to a traditional hard drive than to an SSD device.
See Section 8.4.2.2, “Optimizing for Character and String Types” for reasons why a
binary VARCHAR
column is sometimes
preferable to an equivalent BLOB column.
Rather than testing for equality against a very long text
string, you can store a hash of the column value in a
separate column, index that column, and test the hashed
value in queries. (Use the MD5()
or
CRC32()
function to produce the hash
value.) Since hash functions can produce duplicate results
for different inputs, you still include a clause
AND
in
the query to guard against false matches; the performance
benefit comes from the smaller, easily scanned index for
the hashed values.
blob_column
=
long_string_value
ANALYSE([
max_elements
[,max_memory
]])
PROCEDURE ANALYSE()
is deprecated as of
MySQL 5.7.18, and is removed in MySQL 8.0.
ANALYSE()
examines the result from a query
and returns an analysis of the results that suggests optimal
data types for each column that may help reduce table sizes.
To obtain this analysis, append PROCEDURE
ANALYSE
to the end of a
SELECT
statement:
SELECT ... FROM ... WHERE ... PROCEDURE ANALYSE([max_elements
,[max_memory
]])
For example:
SELECT col1, col2 FROM table1 PROCEDURE ANALYSE(10, 2000);
The results show some statistics for the values returned by
the query, and propose an optimal data type for the columns.
This can be helpful for checking your existing tables, or
after importing new data. You may need to try different
settings for the arguments so that PROCEDURE
ANALYSE()
does not suggest the
ENUM
data type when it is not
appropriate.
The arguments are optional and are used as follows:
max_elements
(default 256) is
the maximum number of distinct values that
ANALYSE()
notices per column. This is
used by ANALYSE()
to check whether the
optimal data type should be of type
ENUM
; if there are more
than max_elements
distinct
values, then ENUM
is not a
suggested type.
max_memory
(default 8192) is
the maximum amount of memory that
ANALYSE()
should allocate per column
while trying to find all distinct values.
A PROCEDURE
clause is not permitted in a
UNION
statement.
Some techniques for keeping individual queries fast involve splitting data across many tables. When the number of tables runs into the thousands or even millions, the overhead of dealing with all these tables becomes a new performance consideration.
When you execute a mysqladmin status command, you should see something like this:
Uptime: 426 Running threads: 1 Questions: 11082 Reloads: 1 Open tables: 12
The Open tables
value of 12 can be somewhat
puzzling if you have fewer than 12 tables.
MySQL is multithreaded, so there may be many clients issuing
queries for a given table simultaneously. To minimize the
problem with multiple client sessions having different states
on the same table, the table is opened independently by each
concurrent session. This uses additional memory but normally
increases performance. With MyISAM
tables,
one extra file descriptor is required for the data file for
each client that has the table open. (By contrast, the index
file descriptor is shared between all sessions.)
The table_open_cache
and
max_connections
system
variables affect the maximum number of files the server keeps
open. If you increase one or both of these values, you may run
up against a limit imposed by your operating system on the
per-process number of open file descriptors. Many operating
systems permit you to increase the open-files limit, although
the method varies widely from system to system. Consult your
operating system documentation to determine whether it is
possible to increase the limit and how to do so.
table_open_cache
is related
to max_connections
. For
example, for 200 concurrent running connections, specify a
table cache size of at least 200 *
, where
N
N
is the maximum number of tables
per join in any of the queries which you execute. You must
also reserve some extra file descriptors for temporary tables
and files.
Make sure that your operating system can handle the number of
open file descriptors implied by the
table_open_cache
setting. If
table_open_cache
is set too
high, MySQL may run out of file descriptors and exhibit
symptoms such as refusing connections or failing to perform
queries.
Also take into account that the MyISAM
storage engine needs two file descriptors for each unique open
table. For a partitioned MyISAM
table, two
file descriptors are required for each partition of the opened
table. (When MyISAM
opens a partitioned
table, it opens every partition of this table, whether or not
a given partition is actually used. See
MyISAM and partition file descriptor usage.)
To increase the number of file descriptors available to MySQL,
set the open_files_limit
system variable. See
Section B.4.2.17, “File Not Found and Similar Errors”.
The cache of open tables is kept at a level of
table_open_cache
entries. The
server autosizes the cache size at startup. To set the size
explicitly, set the
table_open_cache
system
variable at startup. MySQL may temporarily open more tables
than this to execute queries, as described later in this
section.
MySQL closes an unused table and removes it from the table cache under the following circumstances:
When the cache is full and a thread tries to open a table that is not in the cache.
When the cache contains more than
table_open_cache
entries
and a table in the cache is no longer being used by any
threads.
When a table-flushing operation occurs. This happens when
someone issues a FLUSH
TABLES
statement or executes a
mysqladmin flush-tables or
mysqladmin refresh command.
When the table cache fills up, the server uses the following procedure to locate a cache entry to use:
Tables not currently in use are released, beginning with the table least recently used.
If a new table must be opened, but the cache is full and no tables can be released, the cache is temporarily extended as necessary. When the cache is in a temporarily extended state and a table goes from a used to unused state, the table is closed and released from the cache.
A MyISAM
table is opened for each
concurrent access. This means the table needs to be opened
twice if two threads access the same table or if a thread
accesses the table twice in the same query (for example, by
joining the table to itself). Each concurrent open requires an
entry in the table cache. The first open of any
MyISAM
table takes two file descriptors:
one for the data file and one for the index file. Each
additional use of the table takes only one file descriptor for
the data file. The index file descriptor is shared among all
threads.
If you are opening a table with the HANDLER
statement,
a dedicated table object is allocated for the thread. This
table object is not shared by other threads and is not closed
until the thread calls tbl_name
OPENHANDLER
or the
thread terminates. When this happens, the table is put back in
the table cache (if the cache is not full). See
Section 13.2.4, “HANDLER Statement”.
tbl_name
CLOSE
To determine whether your table cache is too small, check the
Opened_tables
status
variable, which indicates the number of table-opening
operations since the server started:
mysql> SHOW GLOBAL STATUS LIKE 'Opened_tables';
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| Opened_tables | 2741 |
+---------------+-------+
If the value is very large or increases rapidly, even when you
have not issued many FLUSH
TABLES
statements, increase the
table_open_cache
value at
server startup.
If you have many MyISAM
tables in the same
database directory, open, close, and create operations are
slow. If you execute SELECT
statements on many different tables, there is a little
overhead when the table cache is full, because for every table
that has to be opened, another must be closed. You can reduce
this overhead by increasing the number of entries permitted in
the table cache.
In some cases, the server creates internal temporary tables while processing statements. Users have no direct control over when this occurs.
The server creates temporary tables under conditions such as these:
Evaluation of UNION
statements, with some exceptions described later.
Evaluation of some views, such those that use the
TEMPTABLE
algorithm,
UNION
, or aggregation.
Evaluation of derived tables (see Section 13.2.10.8, “Derived Tables”).
Tables created for subquery or semijoin materialization (see Section 8.2.2, “Optimizing Subqueries, Derived Tables, and View References”).
Evaluation of statements that contain an ORDER
BY
clause and a different GROUP
BY
clause, or for which the ORDER
BY
or GROUP BY
contains columns
from tables other than the first table in the join queue.
Evaluation of DISTINCT
combined with
ORDER BY
may require a temporary table.
For queries that use the SQL_SMALL_RESULT
modifier, MySQL uses an in-memory temporary table, unless
the query also contains elements (described later) that
require on-disk storage.
To evaluate
INSERT ...
SELECT
statements that select from and insert into
the same table, MySQL creates an internal temporary table to
hold the rows from the
SELECT
, then inserts those
rows into the target table. See
Section 13.2.5.1, “INSERT ... SELECT Statement”.
Evaluation of multiple-table
UPDATE
statements.
Evaluation of GROUP_CONCAT()
or COUNT(DISTINCT)
expressions.
To determine whether a statement requires a temporary table, use
EXPLAIN
and check the
Extra
column to see whether it says
Using temporary
(see
Section 8.8.1, “Optimizing Queries with EXPLAIN”). EXPLAIN
will not necessarily say Using temporary
for
derived or materialized temporary tables.
When the server creates an internal temporary table (either in
memory or on disk), it increments the
Created_tmp_tables
status
variable. If the server creates the table on disk (either
initially or by converting an in-memory table) it increments the
Created_tmp_disk_tables
status
variable.
Some query conditions prevent the use of an in-memory temporary table, in which case the server uses an on-disk table instead:
Presence of a BLOB
or
TEXT
column in the table.
This includes user-defined variables having a string value
because they are treated as
BLOB
or
TEXT
columns, depending on
whether their value is a binary or nonbinary string,
respectively.
Presence of any string column with a maximum length larger
than 512 (bytes for binary strings, characters for nonbinary
strings) in the SELECT
list,
if UNION
or
UNION ALL
is used.
The SHOW COLUMNS
and
DESCRIBE
statements use
BLOB
as the type for some columns, thus
the temporary table used for the results is an on-disk
table.
The server does not use a temporary table for
UNION
statements that meet
certain qualifications. Instead, it retains from temporary table
creation only the data structures necessary to perform result
column typecasting. The table is not fully instantiated and no
rows are written to or read from it; rows are sent directly to
the client. The result is reduced memory and disk requirements,
and smaller delay before the first row is sent to the client
because the server need not wait until the last query block is
executed. EXPLAIN
and optimizer
trace output reflects this execution strategy: The
UNION RESULT
query block is not present
because that block corresponds to the part that reads from the
temporary table.
These conditions qualify a UNION
for
evaluation without a temporary table:
The union is UNION ALL
, not
UNION
or UNION
DISTINCT
.
There is no global ORDER BY
clause.
The union is not the top-level query block of an
{INSERT | REPLACE} ... SELECT ...
statement.
An internal temporary table can be held in memory and
processed by the MEMORY
storage engine, or
stored on disk by the InnoDB
or
MyISAM
storage engine.
If an internal temporary table is created as an in-memory
table but becomes too large, MySQL automatically converts it
to an on-disk table. The maximum size for in-memory temporary
tables is defined by the
tmp_table_size
or
max_heap_table_size
value,
whichever is smaller. This differs from
MEMORY
tables explicitly created with
CREATE TABLE
. For such tables,
only the max_heap_table_size
variable determines how large a table can grow, and there is
no conversion to on-disk format.
The
internal_tmp_disk_storage_engine
variable defines the storage engine the server uses to manage
on-disk internal temporary tables. Permitted values are
INNODB
(the default) and
MYISAM
.
When using
internal_tmp_disk_storage_engine=INNODB
,
queries that generate on-disk internal temporary tables that
exceed
InnoDB
row
or column limits return Row size too
large or Too many columns
errors. The workaround is to set
internal_tmp_disk_storage_engine
to MYISAM
.
In-memory temporary tables are managed by the
MEMORY
storage engine, which uses
fixed-length row format. VARCHAR
and
VARBINARY
column values are padded to the
maximum column length, in effect storing them as
CHAR
and BINARY
columns.
On-disk temporary tables are managed by the
InnoDB
or MyISAM
storage
engine (depending on the
internal_tmp_disk_storage_engine
setting). Both engines store temporary tables using
dynamic-width row format. Columns take only as much storage as
needed, which reduces disk I/O, space requirements, and
processing time compared to on-disk tables that use
fixed-length rows.
For statements that initially create an internal temporary
table in memory, then convert it to an on-disk table, better
performance might be achieved by skipping the conversion step
and creating the table on disk to begin with. The
big_tables
variable can be
used to force disk storage of internal temporary tables.
MySQL has no limit on the number of databases. The underlying file system may have a limit on the number of directories.
MySQL has no limit on the number of tables. The underlying file
system may have a limit on the number of files that represent
tables. Individual storage engines may impose engine-specific
constraints. InnoDB
permits up to 4 billion
tables.
The effective maximum table size for MySQL databases is usually determined by operating system constraints on file sizes, not by MySQL internal limits. For up-to-date information operating system file size limits, refer to the documentation specific to your operating system.
Windows users, please note that FAT and VFAT (FAT32) are not considered suitable for production use with MySQL. Use NTFS instead.
If you encounter a full-table error, there are several reasons why it might have occurred:
The disk might be full.
You are using InnoDB
tables and have run
out of room in an InnoDB
tablespace file.
The maximum tablespace size is also the maximum size for a
table. For tablespace size limits, see
Section 14.23, “InnoDB Limits”.
Generally, partitioning of tables into multiple tablespace files is recommended for tables larger than 1TB in size.
You have hit an operating system file size limit. For
example, you are using MyISAM
tables on
an operating system that supports files only up to 2GB in
size and you have hit this limit for the data file or index
file.
You are using a MyISAM
table and the
space required for the table exceeds what is permitted by
the internal pointer size. MyISAM
permits
data and index files to grow up to 256TB by default, but
this limit can be changed up to the maximum permissible size
of 65,536TB (2567 − 1
bytes).
If you need a MyISAM
table that is larger
than the default limit and your operating system supports
large files, the CREATE TABLE
statement supports AVG_ROW_LENGTH
and
MAX_ROWS
options. See
Section 13.1.18, “CREATE TABLE Statement”. The server uses these
options to determine how large a table to permit.
If the pointer size is too small for an existing table, you
can change the options with ALTER
TABLE
to increase a table's maximum
permissible size. See Section 13.1.8, “ALTER TABLE Statement”.
ALTER TABLEtbl_name
MAX_ROWS=1000000000 AVG_ROW_LENGTH=nnn
;
You have to specify AVG_ROW_LENGTH
only
for tables with BLOB
or
TEXT
columns; in this case,
MySQL cannot optimize the space required based only on the
number of rows.
To change the default size limit for
MyISAM
tables, set the
myisam_data_pointer_size
,
which sets the number of bytes used for internal row
pointers. The value is used to set the pointer size for new
tables if you do not specify the MAX_ROWS
option. The value of
myisam_data_pointer_size
can be from 2 to 7. A value of 4 permits tables up to 4GB; a
value of 6 permits tables up to 256TB.
You can check the maximum data and index sizes by using this statement:
SHOW TABLE STATUS FROMdb_name
LIKE 'tbl_name
';
You also can use myisamchk -dv /path/to/table-index-file. See Section 13.7.5, “SHOW Statements”, or Section 4.6.3, “myisamchk — MyISAM Table-Maintenance Utility”.
Other ways to work around file-size limits for
MyISAM
tables are as follows:
If your large table is read only, you can use myisampack to compress it. myisampack usually compresses a table by at least 50%, so you can have, in effect, much bigger tables. myisampack also can merge multiple tables into a single table. See Section 4.6.5, “myisampack — Generate Compressed, Read-Only MyISAM Tables”.
MySQL includes a MERGE
library that
enables you to handle a collection of
MyISAM
tables that have identical
structure as a single MERGE
table.
See Section 15.7, “The MERGE Storage Engine”.
You are using the MEMORY
(HEAP
) storage engine; in this case you
need to increase the value of the
max_heap_table_size
system
variable. See Section 5.1.7, “Server System Variables”.
This section describes limits on the number of columns in tables and the size of individual rows.
MySQL has hard limit of 4096 columns per table, but the effective maximum may be less for a given table. The exact column limit depends on several factors:
The maximum row size for a table constrains the number (and possibly size) of columns because the total length of all columns cannot exceed this size. See Row Size Limits.
The storage requirements of individual columns constrain the number of columns that fit within a given maximum row size. Storage requirements for some data types depend on factors such as storage engine, storage format, and character set. See Section 11.7, “Data Type Storage Requirements”.
Storage engines may impose additional restrictions that
limit table column count. For example,
InnoDB
has a limit of 1017
columns per table. See Section 14.23, “InnoDB Limits”.
For information about other storage engines, see
Chapter 15, Alternative Storage Engines.
Each table has an .frm
file that
contains the table definition. The definition affects the
content of this file in ways that may affect the number of
columns permitted in the table. See
Limits Imposed by .frm File Structure.
The maximum row size for a given table is determined by several factors:
The internal representation of a MySQL table has a maximum
row size limit of 65,535 bytes, even if the storage engine
is capable of supporting larger rows.
BLOB
and
TEXT
columns only
contribute 9 to 12 bytes toward the row size limit because
their contents are stored separately from the rest of the
row.
The maximum row size for an InnoDB
table, which applies to data stored locally within a
database page, is slightly less than half a page for 4KB,
8KB, 16KB, and 32KB
innodb_page_size
settings. For example, the maximum row size is slightly
less than 8KB for the default 16KB
InnoDB
page size. For 64KB pages, the
maximum row size is slightly less than 16KB. See
Section 14.23, “InnoDB Limits”.
If a row containing
variable-length
columns exceeds the InnoDB
maximum row size, InnoDB
selects
variable-length columns for external off-page storage
until the row fits within the InnoDB
row size limit. The amount of data stored locally for
variable-length columns that are stored off-page differs
by row format. For more information, see
Section 14.11, “InnoDB Row Formats”.
Different storage formats use different amounts of page header and trailer data, which affects the amount of storage available for rows.
For information about InnoDB
row
formats, see Section 14.11, “InnoDB Row Formats”.
For information about MyISAM
storage formats, see
Section 15.2.3, “MyISAM Table Storage Formats”.
The MySQL maximum row size limit of 65,535 bytes is
demonstrated in the following InnoDB
and MyISAM
examples. The limit is
enforced regardless of storage engine, even though the
storage engine may be capable of supporting larger rows.
mysql>CREATE TABLE t (a VARCHAR(10000), b VARCHAR(10000),
c VARCHAR(10000), d VARCHAR(10000), e VARCHAR(10000),
f VARCHAR(10000), g VARCHAR(6000)) ENGINE=InnoDB CHARACTER SET latin1;
ERROR 1118 (42000): Row size too large. The maximum row size for the used table type, not counting BLOBs, is 65535. This includes storage overhead, check the manual. You have to change some columns to TEXT or BLOBs
mysql>CREATE TABLE t (a VARCHAR(10000), b VARCHAR(10000),
c VARCHAR(10000), d VARCHAR(10000), e VARCHAR(10000),
f VARCHAR(10000), g VARCHAR(6000)) ENGINE=MyISAM CHARACTER SET latin1;
ERROR 1118 (42000): Row size too large. The maximum row size for the used table type, not counting BLOBs, is 65535. This includes storage overhead, check the manual. You have to change some columns to TEXT or BLOBs
In the following MyISAM
example,
changing a column to TEXT
avoids the 65,535-byte row size limit and permits the
operation to succeed because
BLOB
and
TEXT
columns only
contribute 9 to 12 bytes toward the row size.
mysql>CREATE TABLE t (a VARCHAR(10000), b VARCHAR(10000),
c VARCHAR(10000), d VARCHAR(10000), e VARCHAR(10000),
f VARCHAR(10000), g TEXT(6000)) ENGINE=MyISAM CHARACTER SET latin1;
Query OK, 0 rows affected (0.02 sec)
The operation succeeds for an InnoDB
table because changing a column to
TEXT
avoids the MySQL
65,535-byte row size limit, and InnoDB
off-page storage of variable-length columns avoids the
InnoDB
row size limit.
mysql>CREATE TABLE t (a VARCHAR(10000), b VARCHAR(10000),
c VARCHAR(10000), d VARCHAR(10000), e VARCHAR(10000),
f VARCHAR(10000), g TEXT(6000)) ENGINE=InnoDB CHARACTER SET latin1;
Query OK, 0 rows affected (0.02 sec)
Storage for variable-length columns includes length bytes,
which are counted toward the row size. For example, a
VARCHAR(255)
CHARACTER SET utf8mb3
column takes two bytes to
store the length of the value, so each value can take up
to 767 bytes.
The statement to create table t1
succeeds because the columns require 32,765 + 2 bytes and
32,766 + 2 bytes, which falls within the maximum row size
of 65,535 bytes:
mysql>CREATE TABLE t1
(c1 VARCHAR(32765) NOT NULL, c2 VARCHAR(32766) NOT NULL)
ENGINE = InnoDB CHARACTER SET latin1;
Query OK, 0 rows affected (0.02 sec)
The statement to create table t2
fails
because, although the column length is within the maximum
length of 65,535 bytes, two additional bytes are required
to record the length, which causes the row size to exceed
65,535 bytes:
mysql>CREATE TABLE t2
(c1 VARCHAR(65535) NOT NULL)
ENGINE = InnoDB CHARACTER SET latin1;
ERROR 1118 (42000): Row size too large. The maximum row size for the used table type, not counting BLOBs, is 65535. This includes storage overhead, check the manual. You have to change some columns to TEXT or BLOBs
Reducing the column length to 65,533 or less permits the statement to succeed.
mysql>CREATE TABLE t2
(c1 VARCHAR(65533) NOT NULL)
ENGINE = InnoDB CHARACTER SET latin1;
Query OK, 0 rows affected (0.01 sec)
For MyISAM
tables,
NULL
columns require additional space
in the row to record whether their values are
NULL
. Each NULL
column takes one bit extra, rounded up to the nearest
byte.
The statement to create table t3
fails
because MyISAM
requires space
for NULL
columns in addition to the
space required for variable-length column length bytes,
causing the row size to exceed 65,535 bytes:
mysql>CREATE TABLE t3
(c1 VARCHAR(32765) NULL, c2 VARCHAR(32766) NULL)
ENGINE = MyISAM CHARACTER SET latin1;
ERROR 1118 (42000): Row size too large. The maximum row size for the used table type, not counting BLOBs, is 65535. This includes storage overhead, check the manual. You have to change some columns to TEXT or BLOBs
For information about InnoDB
NULL
column storage, see
Section 14.11, “InnoDB Row Formats”.
InnoDB
restricts row size (for data
stored locally within the database page) to slightly less
than half a database page for 4KB, 8KB, 16KB, and 32KB
innodb_page_size
settings, and to slightly less than 16KB for 64KB pages.
The statement to create table t4
fails
because the defined columns exceed the row size limit for
a 16KB InnoDB
page.
mysql>CREATE TABLE t4 (
c1 CHAR(255),c2 CHAR(255),c3 CHAR(255),
c4 CHAR(255),c5 CHAR(255),c6 CHAR(255),
c7 CHAR(255),c8 CHAR(255),c9 CHAR(255),
c10 CHAR(255),c11 CHAR(255),c12 CHAR(255),
c13 CHAR(255),c14 CHAR(255),c15 CHAR(255),
c16 CHAR(255),c17 CHAR(255),c18 CHAR(255),
c19 CHAR(255),c20 CHAR(255),c21 CHAR(255),
c22 CHAR(255),c23 CHAR(255),c24 CHAR(255),
c25 CHAR(255),c26 CHAR(255),c27 CHAR(255),
c28 CHAR(255),c29 CHAR(255),c30 CHAR(255),
c31 CHAR(255),c32 CHAR(255),c33 CHAR(255)
) ENGINE=InnoDB ROW_FORMAT=COMPACT DEFAULT CHARSET latin1;
ERROR 1118 (42000): Row size too large (> 8126). Changing some columns to TEXT or BLOB or using ROW_FORMAT=DYNAMIC or ROW_FORMAT=COMPRESSED may help. In current row format, BLOB prefix of 768 bytes is stored inline.
InnoDB
is the storage engine that
MySQL customers typically use in production databases where
reliability and concurrency are important.
InnoDB
is the default storage engine in MySQL.
This section explains how to optimize database operations for
InnoDB
tables.
Once your data reaches a stable size, or a growing table has
increased by tens or some hundreds of megabytes, consider
using the OPTIMIZE TABLE
statement to
reorganize the table and compact any wasted space. The
reorganized tables require less disk I/O to perform full
table scans. This is a straightforward technique that can
improve performance when other techniques such as improving
index usage or tuning application code are not practical.
OPTIMIZE TABLE
copies the data part of
the table and rebuilds the indexes. The benefits come from
improved packing of data within indexes, and reduced
fragmentation within the tablespaces and on disk. The
benefits vary depending on the data in each table. You may
find that there are significant gains for some and not for
others, or that the gains decrease over time until you next
optimize the table. This operation can be slow if the table
is large or if the indexes being rebuilt do not fit into the
buffer pool. The first run after adding a lot of data to a
table is often much slower than later runs.
In InnoDB
, having a long PRIMARY
KEY
(either a single column with a lengthy value,
or several columns that form a long composite value) wastes
a lot of disk space. The primary key value for a row is
duplicated in all the secondary index records that point to
the same row. (See Section 14.6.2.1, “Clustered and Secondary Indexes”.)
Create an AUTO_INCREMENT
column as the
primary key if your primary key is long, or index a prefix
of a long VARCHAR
column instead of the
entire column.
Use the VARCHAR
data type
instead of CHAR
to store
variable-length strings or for columns with many
NULL
values. A
CHAR(
column always takes N
)N
characters
to store data, even if the string is shorter or its value is
NULL
. Smaller tables fit better in the
buffer pool and reduce disk I/O.
When using COMPACT
row format (the
default InnoDB
format) and
variable-length character sets, such as
utf8
or sjis
,
CHAR(
columns occupy a variable amount of space, but still at
least N
)N
bytes.
For tables that are big, or contain lots of repetitive text
or numeric data, consider using
COMPRESSED
row format. Less disk I/O is
required to bring data into the buffer pool, or to perform
full table scans. Before making a permanent decision,
measure the amount of compression you can achieve by using
COMPRESSED
versus
COMPACT
row format.
To optimize InnoDB
transaction processing,
find the ideal balance between the performance overhead of
transactional features and the workload of your server. For
example, an application might encounter performance issues if it
commits thousands of times per second, and different performance
issues if it commits only every 2-3 hours.
The default MySQL setting AUTOCOMMIT=1
can impose performance limitations on a busy database
server. Where practical, wrap several related data change
operations into a single transaction, by issuing
SET AUTOCOMMIT=0
or a START
TRANSACTION
statement, followed by a
COMMIT
statement after making all the
changes.
InnoDB
must flush the log to disk at each
transaction commit if that transaction made modifications to
the database. When each change is followed by a commit (as
with the default autocommit setting), the I/O throughput of
the storage device puts a cap on the number of potential
operations per second.
Alternatively, for transactions that consist only of a
single SELECT
statement,
turning on AUTOCOMMIT
helps
InnoDB
to recognize read-only
transactions and optimize them. See
Section 8.5.3, “Optimizing InnoDB Read-Only Transactions” for
requirements.
Avoid performing rollbacks after inserting, updating, or deleting huge numbers of rows. If a big transaction is slowing down server performance, rolling it back can make the problem worse, potentially taking several times as long to perform as the original data change operations. Killing the database process does not help, because the rollback starts again on server startup.
To minimize the chance of this issue occurring:
Increase the size of the buffer pool so that all the data change changes can be cached rather than immediately written to disk.
Set
innodb_change_buffering=all
so that update and delete operations are buffered in
addition to inserts.
Consider issuing COMMIT
statements
periodically during the big data change operation,
possibly breaking a single delete or update into
multiple statements that operate on smaller numbers of
rows.
To get rid of a runaway rollback once it occurs, increase
the buffer pool so that the rollback becomes CPU-bound and
runs fast, or kill the server and restart with
innodb_force_recovery=3
, as
explained in Section 14.19.2, “InnoDB Recovery”.
This issue is expected to be infrequent with the default
setting
innodb_change_buffering=all
,
which allows update and delete operations to be cached in
memory, making them faster to perform in the first place,
and also faster to roll back if needed. Make sure to use
this parameter setting on servers that process long-running
transactions with many inserts, updates, or deletes.
If you can afford the loss of some of the latest committed
transactions if a crash occurs, you can set the
innodb_flush_log_at_trx_commit
parameter to 0. InnoDB
tries to flush the
log once per second anyway, although the flush is not
guaranteed. Also, set the value of
innodb_support_xa
to 0,
which will reduce the number of disk flushes due to
synchronizing on disk data and the binary log.
innodb_support_xa
is
deprecated and will be removed in a future release. As of
MySQL 5.7.10, InnoDB
support for
two-phase commit in XA transactions is always enabled and
disabling
innodb_support_xa
is no
longer permitted.
When rows are modified or deleted, the rows and associated
undo logs are not
physically removed immediately, or even immediately after
the transaction commits. The old data is preserved until
transactions that started earlier or concurrently are
finished, so that those transactions can access the previous
state of modified or deleted rows. Thus, a long-running
transaction can prevent InnoDB
from
purging data that was changed by a different transaction.
When rows are modified or deleted within a long-running
transaction, other transactions using the
READ COMMITTED
and
REPEATABLE READ
isolation
levels have to do more work to reconstruct the older data if
they read those same rows.
When a long-running transaction modifies a table, queries against that table from other transactions do not make use of the covering index technique. Queries that normally could retrieve all the result columns from a secondary index, instead look up the appropriate values from the table data.
If secondary index pages are found to have a
PAGE_MAX_TRX_ID
that is too new, or if
records in the secondary index are delete-marked,
InnoDB
may need to look up records using
a clustered index.
InnoDB
can avoid the overhead associated with
setting up the transaction
ID (TRX_ID
field) for transactions
that are known to be read-only. A transaction ID is only needed
for a transaction that
might perform write operations or
locking reads such as
SELECT ... FOR UPDATE
. Eliminating
unnecessary transaction IDs reduces the size of internal data
structures that are consulted each time a query or data change
statement constructs a read
view.
InnoDB
detects read-only transactions when:
The transaction is started with the
START TRANSACTION
READ ONLY
statement. In this case, attempting to
make changes to the database (for InnoDB
,
MyISAM
, or other types of tables) causes
an error, and the transaction continues in read-only state:
ERROR 1792 (25006): Cannot execute statement in a READ ONLY transaction.
You can still make changes to session-specific temporary tables in a read-only transaction, or issue locking queries for them, because those changes and locks are not visible to any other transaction.
The autocommit
setting is
turned on, so that the transaction is guaranteed to be a
single statement, and the single statement making up the
transaction is a “non-locking”
SELECT
statement. That is, a
SELECT
that does not use a FOR
UPDATE
or LOCK IN SHARED MODE
clause.
The transaction is started without the READ
ONLY
option, but no updates or statements that
explicitly lock rows have been executed yet. Until updates
or explicit locks are required, a transaction stays in
read-only mode.
Thus, for a read-intensive application such as a report
generator, you can tune a sequence of InnoDB
queries by grouping them inside
START TRANSACTION READ
ONLY
and
COMMIT
, or by
turning on the autocommit
setting before running the SELECT
statements,
or simply by avoiding any data change statements interspersed
with the queries.
For information about
START
TRANSACTION
and
autocommit
, see
Section 13.3.1, “START TRANSACTION, COMMIT, and ROLLBACK Statements”.
Transactions that qualify as auto-commit, non-locking, and
read-only (AC-NL-RO) are kept out of certain internal
InnoDB
data structures and are therefore
not listed in
SHOW ENGINE
INNODB STATUS
output.
Consider the following guidelines for optimizing redo logging:
Make your redo log files big, even as big as the
buffer pool. When
InnoDB
has written the redo log files
full, it must write the modified contents of the buffer pool
to disk in a
checkpoint. Small
redo log files cause many unnecessary disk writes. Although
historically big redo log files caused lengthy recovery
times, recovery is now much faster and you can confidently
use large redo log files.
The size and number of redo log files are configured using
the innodb_log_file_size
and
innodb_log_files_in_group
configuration options. For information about modifying an
existing redo log file configuration, see
Changing the Number or Size of InnoDB Redo Log Files.
Consider increasing the size of the
log buffer. A large
log buffer enables large
transactions to run
without a need to write the log to disk before the
transactions commit.
Thus, if you have transactions that update, insert, or
delete many rows, making the log buffer larger saves disk
I/O. Log buffer size is configured using the
innodb_log_buffer_size
configuration option.
Configure the
innodb_log_write_ahead_size
configuration option to avoid “read-on-write”.
This option defines the write-ahead block size for the redo
log. Set
innodb_log_write_ahead_size
to match the operating system or file system cache block
size. Read-on-write occurs when redo log blocks are not
entirely cached to the operating system or file system due
to a mismatch between write-ahead block size for the redo
log and operating system or file system cache block size.
Valid values for
innodb_log_write_ahead_size
are multiples of the InnoDB
log file
block size (2n). The minimum
value is the InnoDB
log file block size
(512). Write-ahead does not occur when the minimum value is
specified. The maximum value is equal to the
innodb_page_size
value. If
you specify a value for
innodb_log_write_ahead_size
that is larger than the
innodb_page_size
value, the
innodb_log_write_ahead_size
setting is truncated to the
innodb_page_size
value.
Setting the
innodb_log_write_ahead_size
value too low in relation to the operating system or file
system cache block size results in read-on-write. Setting
the value too high may have a slight impact on
fsync
performance for log file writes due
to several blocks being written at once.
These performance tips supplement the general guidelines for fast inserts in Section 8.2.4.1, “Optimizing INSERT Statements”.
When importing data into InnoDB
, turn off
autocommit mode, because it performs a log flush to disk for
every insert. To disable autocommit during your import
operation, surround it with
SET
autocommit
and
COMMIT
statements:
SET autocommit=0;... SQL import statements ...
COMMIT;
The mysqldump option
--opt
creates dump files
that are fast to import into an InnoDB
table, even without wrapping them with the
SET
autocommit
and
COMMIT
statements.
If you have UNIQUE
constraints on
secondary keys, you can speed up table imports by
temporarily turning off the uniqueness checks during the
import session:
SET unique_checks=0;... SQL import statements ...
SET unique_checks=1;
For big tables, this saves a lot of disk I/O because
InnoDB
can use its change buffer to write
secondary index records in a batch. Be certain that the data
contains no duplicate keys.
If you have FOREIGN KEY
constraints in
your tables, you can speed up table imports by turning off
the foreign key checks for the duration of the import
session:
SET foreign_key_checks=0;... SQL import statements ...
SET foreign_key_checks=1;
For big tables, this can save a lot of disk I/O.
Use the multiple-row INSERT
syntax to reduce communication overhead between the client
and the server if you need to insert many rows:
INSERT INTO yourtable VALUES (1,2), (5,5), ...;
This tip is valid for inserts into any table, not just
InnoDB
tables.
When doing bulk inserts into tables with auto-increment
columns, set
innodb_autoinc_lock_mode
to
2 instead of the default value 1. See
Section 14.6.1.6, “AUTO_INCREMENT Handling in InnoDB” for
details.
When performing bulk inserts, it is faster to insert rows in
PRIMARY KEY
order.
InnoDB
tables use a
clustered index,
which makes it relatively fast to use data in the order of
the PRIMARY KEY
. Performing bulk inserts
in PRIMARY KEY
order is particularly
important for tables that do not fit entirely within the
buffer pool.
For optimal performance when loading data into an
InnoDB
FULLTEXT
index,
follow this set of steps:
Define a column FTS_DOC_ID
at table
creation time, of type BIGINT UNSIGNED NOT
NULL
, with a unique index named
FTS_DOC_ID_INDEX
. For example:
CREATE TABLE t1 ( FTS_DOC_ID BIGINT unsigned NOT NULL AUTO_INCREMENT, title varchar(255) NOT NULL DEFAULT '', text mediumtext NOT NULL, PRIMARY KEY (`FTS_DOC_ID`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1; CREATE UNIQUE INDEX FTS_DOC_ID_INDEX on t1(FTS_DOC_ID);
Load the data into the table.
Create the FULLTEXT
index after the
data is loaded.
When adding FTS_DOC_ID
column at table
creation time, ensure that the
FTS_DOC_ID
column is updated when the
FULLTEXT
indexed column is updated, as
the FTS_DOC_ID
must increase
monotonically with each
INSERT
or
UPDATE
. If you choose not
to add the FTS_DOC_ID
at table creation
time and have InnoDB
manage DOC IDs for
you, InnoDB
will add the
FTS_DOC_ID
as a hidden column with the
next CREATE
FULLTEXT INDEX
call. This approach, however,
requires a table rebuild which will impact performance.
To tune queries for InnoDB
tables, create an
appropriate set of indexes on each table. See
Section 8.3.1, “How MySQL Uses Indexes” for details. Follow these
guidelines for InnoDB
indexes:
Because each InnoDB
table has a
primary key (whether
you request one or not), specify a set of primary key
columns for each table, columns that are used in the most
important and time-critical queries.
Do not specify too many or too long columns in the primary key, because these column values are duplicated in each secondary index. When an index contains unnecessary data, the I/O to read this data and memory to cache it reduce the performance and scalability of the server.
Do not create a separate secondary index for each column, because each query can only make use of one index. Indexes on rarely tested columns or columns with only a few different values might not be helpful for any queries. If you have many queries for the same table, testing different combinations of columns, try to create a small number of concatenated indexes rather than a large number of single-column indexes. If an index contains all the columns needed for the result set (known as a covering index), the query might be able to avoid reading the table data at all.
If an indexed column cannot contain any
NULL
values, declare it as NOT
NULL
when you create the table. The optimizer can
better determine which index is most effective to use for a
query, when it knows whether each column contains
NULL
values.
You can optimize single-query transactions for
InnoDB
tables, using the technique in
Section 8.5.3, “Optimizing InnoDB Read-Only Transactions”.
Many DDL operations on tables and indexes
(CREATE
, ALTER
, and
DROP
statements) can be performed online.
See Section 14.13, “InnoDB and Online DDL” for details.
Online DDL support for adding secondary indexes means that you can generally speed up the process of creating and loading a table and associated indexes by creating the table without secondary indexes, then adding secondary indexes after the data is loaded.
Use TRUNCATE TABLE
to empty a
table, not DELETE FROM
. Foreign key
constraints can make a tbl_name
TRUNCATE
statement
work like a regular DELETE
statement, in
which case a sequence of commands like
DROP TABLE
and
CREATE TABLE
might be
fastest.
Because the primary key is integral to the storage layout of
each InnoDB
table, and changing the
definition of the primary key involves reorganizing the
whole table, always set up the primary key as part of the
CREATE TABLE
statement, and
plan ahead so that you do not need to
ALTER
or DROP
the
primary key afterward.
If you follow best practices for database design and tuning
techniques for SQL operations, but your database is still slow
due to heavy disk I/O activity, consider these disk I/O
optimizations. If the Unix top
tool or the
Windows Task Manager shows that the CPU usage percentage with
your workload is less than 70%, your workload is probably
disk-bound.
Increase buffer pool size
When table data is cached in the InnoDB
buffer pool, it can be accessed repeatedly by queries
without requiring any disk I/O. Specify the size of the
buffer pool with the
innodb_buffer_pool_size
option. This memory area is important enough that it is
typically recommended that
innodb_buffer_pool_size
is
configured to 50 to 75 percent of system memory. For more
information see, Section 8.12.4.1, “How MySQL Uses Memory”.
Adjust the flush method
In some versions of GNU/Linux and Unix, flushing files to
disk with the Unix fsync()
call (which
InnoDB
uses by default) and similar
methods is surprisingly slow. If database write performance
is an issue, conduct benchmarks with the
innodb_flush_method
parameter set to O_DSYNC
.
Use a noop or deadline I/O scheduler with native AIO on Linux
InnoDB
uses the asynchronous I/O
subsystem (native AIO) on Linux to perform read-ahead and
write requests for data file pages. This behavior is
controlled by the
innodb_use_native_aio
configuration option, which is enabled by default. With
native AIO, the type of I/O scheduler has greater influence
on I/O performance. Generally, noop and deadline I/O
schedulers are recommended. Conduct benchmarks to determine
which I/O scheduler provides the best results for your
workload and environment. For more information, see
Section 14.8.7, “Using Asynchronous I/O on Linux”.
Use direct I/O on Solaris 10 for x86_64 architecture
When using the InnoDB
storage engine on
Solaris 10 for x86_64 architecture (AMD Opteron), use direct
I/O for InnoDB
-related files to avoid
degradation of InnoDB
performance. To use
direct I/O for an entire UFS file system used for storing
InnoDB
-related files, mount it with the
forcedirectio
option; see
mount_ufs(1M)
. (The default on Solaris
10/x86_64 is not to use this option.)
To apply direct I/O only to InnoDB
file
operations rather than the whole file system, set
innodb_flush_method =
O_DIRECT
. With this setting,
InnoDB
calls
directio()
instead of
fcntl()
for I/O to data files (not for
I/O to log files).
Use raw storage for data and log files with Solaris 2.6 or later
When using the InnoDB
storage engine with
a large
innodb_buffer_pool_size
value on any release of Solaris 2.6 and up and any platform
(sparc/x86/x64/amd64), conduct benchmarks with
InnoDB
data files and log files on raw
devices or on a separate direct I/O UFS file system, using
the forcedirectio
mount option as
described previously. (It is necessary to use the mount
option rather than setting
innodb_flush_method
if you
want direct I/O for the log files.) Users of the Veritas
file system VxFS should use the
convosync=direct
mount option.
Do not place other MySQL data files, such as those for
MyISAM
tables, on a direct I/O file
system. Executables or libraries must
not be placed on a direct I/O file system.
Use additional storage devices
Additional storage devices could be used to set up a RAID configuration. For related information, see Section 8.12.2, “Optimizing Disk I/O”.
Alternatively, InnoDB
tablespace data
files and log files can be placed on different physical
disks. For more information, refer to the following
sections:
Consider non-rotational storage
Non-rotational storage generally provides better performance for random I/O operations; and rotational storage for sequential I/O operations. When distributing data and log files across rotational and non-rotational storage devices, consider the type of I/O operations that are predominantly performed on each file.
Random I/O-oriented files typically include
file-per-table
and general
tablespace data files,
undo tablespace
files, and
temporary
tablespace files. Sequential I/O-oriented files
include InnoDB
system
tablespace files (due to
doublewrite
buffering and
change buffering)
and log files such as binary
log files and redo
log files.
Review settings for the following configuration options when using non-rotational storage:
The crc32
option uses a faster
checksum algorithm and is recommended for fast storage
systems.
Optimizes I/O for rotational storage devices. Disable it for non-rotational storage or a mix of rotational and non-rotational storage.
The default setting of 200 is generally sufficient for a lower-end non-rotational storage device. For higher-end, bus-attached devices, consider a higher setting such as 1000.
The default value of 2000 is intended for workloads that use non-rotational storage. For a high-end, bus-attached non-rotational storage device, consider a higher setting such as 2500.
If redo logs are on non-rotational storage, consider disabling this option to reduce logging. See Disable logging of compressed pages.
If redo logs are on non-rotational storage, configure this option to maximize caching and write combining.
Consider using a page size that matches the internal
sector size of the disk. Early-generation SSD devices
often have a 4KB sector size. Some newer devices have a
16KB sector size. The default InnoDB
page size is 16KB. Keeping the page size close to the
storage device block size minimizes the amount of
unchanged data that is rewritten to disk.
If binary logs are on non-rotational storage and all
tables have primary keys, consider setting this option
to minimal
to reduce logging.
Ensure that TRIM support is enabled for your operating system. It is typically enabled by default.
Increase I/O capacity to avoid backlogs
If throughput drops periodically because of
InnoDB
checkpoint
operations, consider increasing the value of the
innodb_io_capacity
configuration option. Higher values cause more frequent
flushing, avoiding the
backlog of work that can cause dips in throughput.
Lower I/O capacity if flushing does not fall behind
If the system is not falling behind with
InnoDB
flushing operations,
consider lowering the value of the
innodb_io_capacity
configuration option. Typically, you keep this option value
as low as practical, but not so low that it causes periodic
drops in throughput as mentioned in the preceding bullet. In
a typical scenario where you could lower the option value,
you might see a combination like this in the output from
SHOW ENGINE
INNODB STATUS
:
History list length low, below a few thousand.
Insert buffer merges close to rows inserted.
Modified pages in buffer pool consistently well below
innodb_max_dirty_pages_pct
of the buffer pool. (Measure at a time when the server
is not doing bulk inserts; it is normal during bulk
inserts for the modified pages percentage to rise
significantly.)
Log sequence number - Last checkpoint
is at less than 7/8 or ideally less than 6/8 of the
total size of the InnoDB
log files.
Store system tablespace files on Fusion-io devices
You can take advantage of a doublewrite buffer-related I/O
optimization by storing system tablespace files
(“ibdata files”) on Fusion-io devices that
support atomic writes. In this case, doublewrite buffering
(innodb_doublewrite
) is
automatically disabled and Fusion-io atomic writes are used
for all data files. This feature is only supported on
Fusion-io hardware and is only enabled for Fusion-io NVMFS
on Linux. To take full advantage of this feature, an
innodb_flush_method
setting
of O_DIRECT
is recommended.
Because the doublewrite buffer setting is global, doublewrite buffering is also disabled for data files residing on non-Fusion-io hardware.
Disable logging of compressed pages
When using the InnoDB
table
compression feature,
images of re-compressed
pages are written to the
redo log when changes
are made to compressed data. This behavior is controlled by
innodb_log_compressed_pages
,
which is enabled by default to prevent corruption that can
occur if a different version of the zlib
compression algorithm is used during recovery. If you are
certain that the zlib
version will not
change, disable
innodb_log_compressed_pages
to reduce redo log generation for workloads that modify
compressed data.
Different settings work best for servers with light, predictable loads, versus servers that are running near full capacity all the time, or that experience spikes of high activity.
Because the InnoDB
storage engine performs
many of its optimizations automatically, many performance-tuning
tasks involve monitoring to ensure that the database is
performing well, and changing configuration options when
performance drops. See
Section 14.17, “InnoDB Integration with MySQL Performance Schema” for information
about detailed InnoDB
performance monitoring.
The main configuration steps you can perform include:
Enabling InnoDB
to use high-performance
memory allocators on systems that include them. See
Section 14.8.4, “Configuring the Memory Allocator for InnoDB”.
Controlling the types of data change operations for which
InnoDB
buffers the changed data, to avoid
frequent small disk writes. See
Configuring Change Buffering.
Because the default is to buffer all types of data change
operations, only change this setting if you need to reduce
the amount of buffering.
Turning the adaptive hash indexing feature on and off using
the
innodb_adaptive_hash_index
option. See Section 14.5.3, “Adaptive Hash Index” for more
information. You might change this setting during periods of
unusual activity, then restore it to its original setting.
Setting a limit on the number of concurrent threads that
InnoDB
processes, if context switching is
a bottleneck. See
Section 14.8.5, “Configuring Thread Concurrency for InnoDB”.
Controlling the amount of prefetching that
InnoDB
does with its read-ahead
operations. When the system has unused I/O capacity, more
read-ahead can improve the performance of queries. Too much
read-ahead can cause periodic drops in performance on a
heavily loaded system. See
Section 14.8.3.4, “Configuring InnoDB Buffer Pool Prefetching (Read-Ahead)”.
Increasing the number of background threads for read or write operations, if you have a high-end I/O subsystem that is not fully utilized by the default values. See Section 14.8.6, “Configuring the Number of Background InnoDB I/O Threads”.
Controlling how much I/O InnoDB
performs
in the background. See
Section 14.8.8, “Configuring InnoDB I/O Capacity”. You might
scale back this setting if you observe periodic drops in
performance.
Controlling the algorithm that determines when
InnoDB
performs certain types of
background writes. See
Section 14.8.3.5, “Configuring Buffer Pool Flushing”. The algorithm
works for some types of workloads but not others, so might
turn off this setting if you observe periodic drops in
performance.
Taking advantage of multicore processors and their cache memory configuration, to minimize delays in context switching. See Section 14.8.9, “Configuring Spin Lock Polling”.
Preventing one-time operations such as table scans from
interfering with the frequently accessed data stored in the
InnoDB
buffer cache. See
Section 14.8.3.3, “Making the Buffer Pool Scan Resistant”.
Adjusting log files to a size that makes sense for
reliability and crash recovery. InnoDB
log files have often been kept small to avoid long startup
times after a crash. Optimizations introduced in MySQL 5.5
speed up certain steps of the crash
recovery process.
In particular, scanning the
redo log and applying
the redo log are faster due to improved algorithms for
memory management. If you have kept your log files
artificially small to avoid long startup times, you can now
consider increasing log file size to reduce the I/O that
occurs due recycling of redo log records.
Configuring the size and number of instances for the
InnoDB
buffer pool, especially important
for systems with multi-gigabyte buffer pools. See
Section 14.8.3.2, “Configuring Multiple Buffer Pool Instances”.
Increasing the maximum number of concurrent transactions, which dramatically improves scalability for the busiest databases. See Section 14.6.7, “Undo Logs”.
Moving purge operations (a type of garbage collection) into a background thread. See Section 14.8.10, “Purge Configuration”. To effectively measure the results of this setting, tune the other I/O-related and thread-related configuration settings first.
Reducing the amount of switching that
InnoDB
does between concurrent threads,
so that SQL operations on a busy server do not queue up and
form a “traffic jam”. Set a value for the
innodb_thread_concurrency
option, up to approximately 32 for a high-powered modern
system. Increase the value for the
innodb_concurrency_tickets
option, typically to 5000 or so. This combination of options
sets a cap on the number of threads that
InnoDB
processes at any one time, and
allows each thread to do substantial work before being
swapped out, so that the number of waiting threads stays low
and operations can complete without excessive context
switching.
If you have configured
non-persistent
optimizer statistics (a non-default configuration),
InnoDB
computes index
cardinality values
for a table the first time that table is accessed after
startup, instead of storing such values in the table. This
step can take significant time on systems that partition the
data into many tables. Since this overhead only applies to
the initial table open operation, to “warm up”
a table for later use, access it immediately after startup
by issuing a statement such as SELECT 1 FROM
.
tbl_name
LIMIT 1
Optimizer statistics are persisted to disk by default,
enabled by the
innodb_stats_persistent
configuration option. For information about persistent
optimizer statistics, see
Section 14.8.11.1, “Configuring Persistent Optimizer Statistics Parameters”.
The MyISAM
storage engine performs
best with read-mostly data or with low-concurrency operations,
because table locks limit the ability to perform simultaneous
updates. In MySQL, InnoDB
is the
default storage engine rather than MyISAM
.
Some general tips for speeding up queries on
MyISAM
tables:
To help MySQL better optimize queries, use
ANALYZE TABLE
or run
myisamchk --analyze on a table after it
has been loaded with data. This updates a value for each
index part that indicates the average number of rows that
have the same value. (For unique indexes, this is always 1.)
MySQL uses this to decide which index to choose when you
join two tables based on a nonconstant expression. You can
check the result from the table analysis by using
SHOW INDEX FROM
and examining
the tbl_name
Cardinality
value. myisamchk
--description --verbose shows index distribution
information.
To sort an index and data according to an index, use myisamchk --sort-index --sort-records=1 (assuming that you want to sort on index 1). This is a good way to make queries faster if you have a unique index from which you want to read all rows in order according to the index. The first time you sort a large table this way, it may take a long time.
Try to avoid complex SELECT
queries on MyISAM
tables that are updated
frequently, to avoid problems with table locking that occur
due to contention between readers and writers.
MyISAM
supports concurrent inserts: If a
table has no free blocks in the middle of the data file, you
can INSERT
new rows into it
at the same time that other threads are reading from the
table. If it is important to be able to do this, consider
using the table in ways that avoid deleting rows. Another
possibility is to run OPTIMIZE
TABLE
to defragment the table after you have
deleted a lot of rows from it. This behavior is altered by
setting the
concurrent_insert
variable.
You can force new rows to be appended (and therefore permit
concurrent inserts), even in tables that have deleted rows.
See Section 8.11.3, “Concurrent Inserts”.
For MyISAM
tables that change frequently,
try to avoid all variable-length columns
(VARCHAR
,
BLOB
, and
TEXT
). The table uses dynamic
row format if it includes even a single variable-length
column. See Chapter 15, Alternative Storage Engines.
It is normally not useful to split a table into different
tables just because the rows become large. In accessing a
row, the biggest performance hit is the disk seek needed to
find the first byte of the row. After finding the data, most
modern disks can read the entire row fast enough for most
applications. The only cases where splitting up a table
makes an appreciable difference is if it is a
MyISAM
table using dynamic row format
that you can change to a fixed row size, or if you very
often need to scan the table but do not need most of the
columns. See Chapter 15, Alternative Storage Engines.
Use ALTER TABLE ... ORDER BY
if you
usually retrieve rows in
expr1
,
expr2
, ...
order. By
using this option after extensive changes to the table, you
may be able to get higher performance.
expr1
,
expr2
, ...
If you often need to calculate results such as counts based on information from a lot of rows, it may be preferable to introduce a new table and update the counter in real time. An update of the following form is very fast:
UPDATEtbl_name
SETcount_col
=count_col
+1 WHEREkey_col
=constant
;
This is very important when you use MySQL storage engines
such as MyISAM
that has only table-level
locking (multiple readers with single writers). This also
gives better performance with most database systems, because
the row locking manager in this case has less to do.
Use OPTIMIZE TABLE
periodically to avoid fragmentation with dynamic-format
MyISAM
tables. See
Section 15.2.3, “MyISAM Table Storage Formats”.
Declaring a MyISAM
table with the
DELAY_KEY_WRITE=1
table option makes
index updates faster because they are not flushed to disk
until the table is closed. The downside is that if something
kills the server while such a table is open, you must ensure
that the table is okay by running the server with the
myisam_recover_options
system variable set, or by running
myisamchk before restarting the server.
(However, even in this case, you should not lose anything by
using DELAY_KEY_WRITE
, because the key
information can always be generated from the data rows.)
Strings are automatically prefix- and end-space compressed
in MyISAM
indexes. See
Section 13.1.14, “CREATE INDEX Statement”.
You can increase performance by caching queries or answers in your application and then executing many inserts or updates together. Locking the table during this operation ensures that the index cache is only flushed once after all updates. You can also take advantage of MySQL's query cache to achieve similar results; see Section 8.10.3, “The MySQL Query Cache”.
These performance tips supplement the general guidelines for fast inserts in Section 8.2.4.1, “Optimizing INSERT Statements”.
For a MyISAM
table, you can use
concurrent inserts to add rows at the same time that
SELECT
statements are
running, if there are no deleted rows in middle of the data
file. See Section 8.11.3, “Concurrent Inserts”.
With some extra work, it is possible to make
LOAD DATA
run even faster for
a MyISAM
table when the table has many
indexes. Use the following procedure:
Execute a FLUSH TABLES
statement or a mysqladmin
flush-tables command.
Use myisamchk --keys-used=0 -rq
/path/to/db/tbl_name
to remove all use of indexes for the table.
Insert data into the table with
LOAD DATA
. This does not
update any indexes and therefore is very fast.
If you intend only to read from the table in the future, use myisampack to compress it. See Section 15.2.3.3, “Compressed Table Characteristics”.
Re-create the indexes with myisamchk -rq
/path/to/db/tbl_name
.
This creates the index tree in memory before writing it
to disk, which is much faster than updating the index
during LOAD DATA
because
it avoids lots of disk seeks. The resulting index tree
is also perfectly balanced.
Execute a FLUSH TABLES
statement or a mysqladmin
flush-tables command.
LOAD DATA
performs the
preceding optimization automatically if the
MyISAM
table into which you insert data
is empty. The main difference between automatic optimization
and using the procedure explicitly is that you can let
myisamchk allocate much more temporary
memory for the index creation than you might want the server
to allocate for index re-creation when it executes the
LOAD DATA
statement.
You can also disable or enable the nonunique indexes for a
MyISAM
table by using the following
statements rather than myisamchk. If you
use these statements, you can skip the
FLUSH TABLES
operations:
ALTER TABLEtbl_name
DISABLE KEYS; ALTER TABLEtbl_name
ENABLE KEYS;
To speed up INSERT
operations
that are performed with multiple statements for
nontransactional tables, lock your tables:
LOCK TABLES a WRITE; INSERT INTO a VALUES (1,23),(2,34),(4,33); INSERT INTO a VALUES (8,26),(6,29); ... UNLOCK TABLES;
This benefits performance because the index buffer is
flushed to disk only once, after all
INSERT
statements have
completed. Normally, there would be as many index buffer
flushes as there are INSERT
statements. Explicit locking statements are not needed if
you can insert all rows with a single
INSERT
.
Locking also lowers the total time for multiple-connection tests, although the maximum wait time for individual connections might go up because they wait for locks. Suppose that five clients attempt to perform inserts simultaneously as follows:
Connection 1 does 1000 inserts
Connections 2, 3, and 4 do 1 insert
Connection 5 does 1000 inserts
If you do not use locking, connections 2, 3, and 4 finish before 1 and 5. If you use locking, connections 2, 3, and 4 probably do not finish before 1 or 5, but the total time should be about 40% faster.
INSERT
,
UPDATE
, and
DELETE
operations are very
fast in MySQL, but you can obtain better overall performance
by adding locks around everything that does more than about
five successive inserts or updates. If you do very many
successive inserts, you could do a LOCK
TABLES
followed by an
UNLOCK
TABLES
once in a while (each 1,000 rows or so) to
permit other threads to access table. This would still
result in a nice performance gain.
INSERT
is still much slower
for loading data than LOAD
DATA
, even when using the strategies just
outlined.
To increase performance for MyISAM
tables, for both LOAD DATA
and INSERT
, enlarge the key
cache by increasing the
key_buffer_size
system
variable. See Section 5.1.1, “Configuring the Server”.
REPAIR TABLE
for
MyISAM
tables is similar to using
myisamchk for repair operations, and some of
the same performance optimizations apply:
myisamchk has variables that control memory allocation. You may be able to its improve performance by setting these variables, as described in Section 4.6.3.6, “myisamchk Memory Usage”.
For REPAIR TABLE
, the same
principle applies, but because the repair is done by the
server, you set server system variables instead of
myisamchk variables. Also, in addition to
setting memory-allocation variables, increasing the
myisam_max_sort_file_size
system variable increases the likelihood that the repair
will use the faster filesort method and avoid the slower
repair by key cache method. Set the variable to the maximum
file size for your system, after checking to be sure that
there is enough free space to hold a copy of the table
files. The free space must be available in the file system
containing the original table files.
Suppose that a myisamchk table-repair operation is done using the following options to set its memory-allocation variables:
--key_buffer_size=128M --myisam_sort_buffer_size=256M --read_buffer_size=64M --write_buffer_size=64M
Some of those myisamchk variables correspond to server system variables:
myisamchk Variable | System Variable |
---|---|
key_buffer_size |
key_buffer_size |
myisam_sort_buffer_size |
myisam_sort_buffer_size |
read_buffer_size |
read_buffer_size |
write_buffer_size |
none |
Each of the server system variables can be set at runtime, and
some of them
(myisam_sort_buffer_size
,
read_buffer_size
) have a
session value in addition to a global value. Setting a session
value limits the effect of the change to your current session
and does not affect other users. Changing a global-only variable
(key_buffer_size
,
myisam_max_sort_file_size
)
affects other users as well. For
key_buffer_size
, you must take
into account that the buffer is shared with those users. For
example, if you set the myisamchk
key_buffer_size
variable to 128MB, you could
set the corresponding
key_buffer_size
system variable
larger than that (if it is not already set larger), to permit
key buffer use by activity in other sessions. However, changing
the global key buffer size invalidates the buffer, causing
increased disk I/O and slowdown for other sessions. An
alternative that avoids this problem is to use a separate key
cache, assign to it the indexes from the table to be repaired,
and deallocate it when the repair is complete. See
Section 8.10.2.2, “Multiple Key Caches”.
Based on the preceding remarks, a REPAIR
TABLE
operation can be done as follows to use settings
similar to the myisamchk command. Here a
separate 128MB key buffer is allocated and the file system is
assumed to permit a file size of at least 100GB.
SET SESSION myisam_sort_buffer_size = 256*1024*1024; SET SESSION read_buffer_size = 64*1024*1024; SET GLOBAL myisam_max_sort_file_size = 100*1024*1024*1024; SET GLOBAL repair_cache.key_buffer_size = 128*1024*1024; CACHE INDEXtbl_name
IN repair_cache; LOAD INDEX INTO CACHEtbl_name
; REPAIR TABLEtbl_name
; SET GLOBAL repair_cache.key_buffer_size = 0;
If you intend to change a global variable but want to do so only
for the duration of a REPAIR
TABLE
operation to minimally affect other users, save
its value in a user variable and restore it afterward. For
example:
SET @old_myisam_sort_buffer_size = @@GLOBAL.myisam_max_sort_file_size; SET GLOBAL myisam_max_sort_file_size = 100*1024*1024*1024; REPAIR TABLE tbl_name ; SET GLOBAL myisam_max_sort_file_size = @old_myisam_max_sort_file_size;
The system variables that affect REPAIR
TABLE
can be set globally at server startup if you
want the values to be in effect by default. For example, add
these lines to the server my.cnf
file:
[mysqld] myisam_sort_buffer_size=256M key_buffer_size=1G myisam_max_sort_file_size=100G
These settings do not include
read_buffer_size
. Setting
read_buffer_size
globally to a
large value does so for all sessions and can cause performance
to suffer due to excessive memory allocation for a server with
many simultaneous sessions.
Consider using MEMORY
tables for noncritical
data that is accessed often, and is read-only or rarely updated.
Benchmark your application against equivalent
InnoDB
or MyISAM
tables
under a realistic workload, to confirm that any additional
performance is worth the risk of losing data, or the overhead of
copying data from a disk-based table at application start.
For best performance with MEMORY
tables,
examine the kinds of queries against each table, and specify the
type to use for each associated index, either a B-tree index or a
hash index. On the CREATE INDEX
statement, use the clause USING BTREE
or
USING HASH
. B-tree indexes are fast for queries
that do greater-than or less-than comparisons through operators
such as >
or BETWEEN
.
Hash indexes are only fast for queries that look up single values
through the =
operator, or a restricted set of
values through the IN
operator. For why
USING BTREE
is often a better choice than the
default USING HASH
, see
Section 8.2.1.20, “Avoiding Full Table Scans”. For implementation details
of the different types of MEMORY
indexes, see
Section 8.3.8, “Comparison of B-Tree and Hash Indexes”.
Depending on the details of your tables, columns, indexes, and the
conditions in your WHERE
clause, the MySQL
optimizer considers many techniques to efficiently perform the
lookups involved in an SQL query. A query on a huge table can be
performed without reading all the rows; a join involving several
tables can be performed without comparing every combination of
rows. The set of operations that the optimizer chooses to perform
the most efficient query is called the “query execution
plan”, also known as the
EXPLAIN
plan. Your goals are to
recognize the aspects of the
EXPLAIN
plan that indicate a query
is optimized well, and to learn the SQL syntax and indexing
techniques to improve the plan if you see some inefficient
operations.
The EXPLAIN
statement provides
information about how MySQL executes statements:
EXPLAIN
works with
SELECT
,
DELETE
,
INSERT
,
REPLACE
, and
UPDATE
statements.
When EXPLAIN
is used with an
explainable statement, MySQL displays information from the
optimizer about the statement execution plan. That is, MySQL
explains how it would process the statement, including
information about how tables are joined and in which order.
For information about using
EXPLAIN
to obtain execution
plan information, see Section 8.8.2, “EXPLAIN Output Format”.
When EXPLAIN
is used with
FOR CONNECTION
rather
than an explainable statement, it displays the execution
plan for the statement executing in the named connection.
See Section 8.8.4, “Obtaining Execution Plan Information for a Named Connection”.
connection_id
For SELECT
statements,
EXPLAIN
produces additional
execution plan information that can be displayed using
SHOW WARNINGS
. See
Section 8.8.3, “Extended EXPLAIN Output Format”.
EXPLAIN
is useful for
examining queries involving partitioned tables. See
Section 22.3.5, “Obtaining Information About Partitions”.
The FORMAT
option can be used to select
the output format. TRADITIONAL
presents
the output in tabular format. This is the default if no
FORMAT
option is present.
JSON
format displays the information in
JSON format.
With the help of EXPLAIN
, you can
see where you should add indexes to tables so that the statement
executes faster by using indexes to find rows. You can also use
EXPLAIN
to check whether the
optimizer joins the tables in an optimal order. To give a hint
to the optimizer to use a join order corresponding to the order
in which the tables are named in a
SELECT
statement, begin the
statement with SELECT STRAIGHT_JOIN
rather
than just SELECT
. (See
Section 13.2.9, “SELECT Statement”.) However,
STRAIGHT_JOIN
may prevent indexes from being
used because it disables semijoin transformations. See
Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin
Transformations”.
The optimizer trace may sometimes provide information
complementary to that of EXPLAIN
.
However, the optimizer trace format and content are subject to
change between versions. For details, see
MySQL
Internals: Tracing the Optimizer.
If you have a problem with indexes not being used when you
believe that they should be, run ANALYZE
TABLE
to update table statistics, such as cardinality
of keys, that can affect the choices the optimizer makes. See
Section 13.7.2.1, “ANALYZE TABLE Statement”.
EXPLAIN
can also be used to
obtain information about the columns in a table.
EXPLAIN
is synonymous
with tbl_name
DESCRIBE
and
tbl_name
SHOW COLUMNS FROM
. For more
information, see Section 13.8.1, “DESCRIBE Statement”, and
Section 13.7.5.5, “SHOW COLUMNS Statement”.
tbl_name
The EXPLAIN
statement provides
information about how MySQL executes statements.
EXPLAIN
works with
SELECT
,
DELETE
,
INSERT
,
REPLACE
, and
UPDATE
statements.
EXPLAIN
returns a row of
information for each table used in the
SELECT
statement. It lists the
tables in the output in the order that MySQL would read them
while processing the statement. MySQL resolves all joins using a
nested-loop join method. This means that MySQL reads a row from
the first table, and then finds a matching row in the second
table, the third table, and so on. When all tables are
processed, MySQL outputs the selected columns and backtracks
through the table list until a table is found for which there
are more matching rows. The next row is read from this table and
the process continues with the next table.
EXPLAIN
output includes partition
information. Also, for SELECT
statements, EXPLAIN
generates
extended information that can be displayed with
SHOW WARNINGS
following the
EXPLAIN
(see
Section 8.8.3, “Extended EXPLAIN Output Format”).
In older MySQL releases, partition and extended information
was produced using
EXPLAIN
PARTITIONS
and
EXPLAIN
EXTENDED
. Those syntaxes are still recognized for
backward compatibility but partition and extended output is
now enabled by default, so the PARTITIONS
and EXTENDED
keywords are superfluous and
deprecated. Their use results in a warning, and they will be
removed from EXPLAIN
syntax in
a future MySQL release.
You cannot use the deprecated PARTITIONS
and EXTENDED
keywords together in the same
EXPLAIN
statement. In addition,
neither of these keywords can be used together with the
FORMAT
option.
MySQL Workbench has a Visual Explain capability that provides a
visual representation of
EXPLAIN
output. See
Tutorial: Using Explain to Improve Query Performance.
This section describes the output columns produced by
EXPLAIN
. Later sections provide
additional information about the
type
and
Extra
columns.
Each output row from EXPLAIN
provides information about one table. Each row contains the
values summarized in
Table 8.1, “EXPLAIN Output Columns”, and described
in more detail following the table. Column names are shown in
the table's first column; the second column provides the
equivalent property name shown in the output when
FORMAT=JSON
is used.
Table 8.1 EXPLAIN Output Columns
Column | JSON Name | Meaning |
---|---|---|
id |
select_id |
The SELECT identifier |
select_type |
None | The SELECT type |
table |
table_name |
The table for the output row |
partitions |
partitions |
The matching partitions |
type |
access_type |
The join type |
possible_keys |
possible_keys |
The possible indexes to choose |
key |
key |
The index actually chosen |
key_len |
key_length |
The length of the chosen key |
ref |
ref |
The columns compared to the index |
rows |
rows |
Estimate of rows to be examined |
filtered |
filtered |
Percentage of rows filtered by table condition |
Extra |
None | Additional information |
JSON properties which are NULL
are not
displayed in JSON-formatted EXPLAIN
output.
The SELECT
identifier. This
is the sequential number of the
SELECT
within the query.
The value can be NULL
if the row refers
to the union result of other rows. In this case, the
table
column shows a value like
<union
to indicate that the row refers to the union of the rows
with M
,N
>id
values of
M
and
N
.
The type of SELECT
, which
can be any of those shown in the following table. A
JSON-formatted EXPLAIN
exposes the
SELECT
type as a property of a
query_block
, unless it is
SIMPLE
or PRIMARY
.
The JSON names (where applicable) are also shown in the
table.
select_type Value |
JSON Name | Meaning |
---|---|---|
SIMPLE |
None | Simple SELECT (not using
UNION or subqueries) |
PRIMARY |
None | Outermost SELECT |
UNION |
None | Second or later SELECT statement in a
UNION |
DEPENDENT UNION |
dependent (true ) |
Second or later SELECT statement in a
UNION , dependent on
outer query |
UNION RESULT |
union_result |
Result of a UNION . |
SUBQUERY |
None | First SELECT in subquery |
DEPENDENT SUBQUERY |
dependent (true ) |
First SELECT in subquery, dependent on
outer query |
DERIVED |
None | Derived table |
MATERIALIZED |
materialized_from_subquery |
Materialized subquery |
UNCACHEABLE SUBQUERY |
cacheable (false ) |
A subquery for which the result cannot be cached and must be re-evaluated for each row of the outer query |
UNCACHEABLE UNION |
cacheable (false ) |
The second or later select in a UNION
that belongs to an uncacheable subquery (see
UNCACHEABLE SUBQUERY ) |
DEPENDENT
typically signifies the use
of a correlated subquery. See
Section 13.2.10.7, “Correlated Subqueries”.
DEPENDENT SUBQUERY
evaluation differs
from UNCACHEABLE SUBQUERY
evaluation.
For DEPENDENT SUBQUERY
, the subquery is
re-evaluated only once for each set of different values of
the variables from its outer context. For
UNCACHEABLE SUBQUERY
, the subquery is
re-evaluated for each row of the outer context.
Cacheability of subqueries differs from caching of query results in the query cache (which is described in Section 8.10.3.1, “How the Query Cache Operates”). Subquery caching occurs during query execution, whereas the query cache is used to store results only after query execution finishes.
When you specify FORMAT=JSON
with
EXPLAIN
, the output has no single
property directly equivalent to
select_type
; the
query_block
property corresponds to a
given SELECT
. Properties equivalent to
most of the SELECT
subquery types just
shown are available (an example being
materialized_from_subquery
for
MATERIALIZED
), and are displayed when
appropriate. There are no JSON equivalents for
SIMPLE
or PRIMARY
.
The select_type
value for
non-SELECT
statements
displays the statement type for affected tables. For
example, select_type
is
DELETE
for
DELETE
statements.
The name of the table to which the row of output refers. This can also be one of the following values:
<union
:
The row refers to the union of the rows with
M
,N
>id
values of
M
and
N
.
<derived
:
The row refers to the derived table result for the row
with an N
>id
value of
N
. A derived table may
result, for example, from a subquery in the
FROM
clause.
<subquery
:
The row refers to the result of a materialized
subquery for the row with an N
>id
value of N
. See
Section 8.2.2.2, “Optimizing Subqueries with Materialization”.
partitions
(JSON name:
partitions
)
The partitions from which records would be matched by the
query. The value is NULL
for
nonpartitioned tables. See
Section 22.3.5, “Obtaining Information About Partitions”.
The join type. For descriptions of the different types,
see
EXPLAIN
Join Types.
possible_keys
(JSON name:
possible_keys
)
The possible_keys
column indicates the
indexes from which MySQL can choose to find the rows in
this table. Note that this column is totally independent
of the order of the tables as displayed in the output from
EXPLAIN
. That means that
some of the keys in possible_keys
might
not be usable in practice with the generated table order.
If this column is NULL
(or undefined in
JSON-formatted output), there are no relevant indexes. In
this case, you may be able to improve the performance of
your query by examining the WHERE
clause to check whether it refers to some column or
columns that would be suitable for indexing. If so, create
an appropriate index and check the query with
EXPLAIN
again. See
Section 13.1.8, “ALTER TABLE Statement”.
To see what indexes a table has, use SHOW INDEX
FROM
.
tbl_name
The key
column indicates the key
(index) that MySQL actually decided to use. If MySQL
decides to use one of the possible_keys
indexes to look up rows, that index is listed as the key
value.
It is possible that key
will name an
index that is not present in the
possible_keys
value. This can happen if
none of the possible_keys
indexes are
suitable for looking up rows, but all the columns selected
by the query are columns of some other index. That is, the
named index covers the selected columns, so although it is
not used to determine which rows to retrieve, an index
scan is more efficient than a data row scan.
For InnoDB
, a secondary index might
cover the selected columns even if the query also selects
the primary key because InnoDB
stores
the primary key value with each secondary index. If
key
is NULL
, MySQL
found no index to use for executing the query more
efficiently.
To force MySQL to use or ignore an index listed in the
possible_keys
column, use
FORCE INDEX
, USE
INDEX
, or IGNORE INDEX
in
your query. See Section 8.9.4, “Index Hints”.
For MyISAM
tables, running
ANALYZE TABLE
helps the
optimizer choose better indexes. For
MyISAM
tables, myisamchk
--analyze does the same. See
Section 13.7.2.1, “ANALYZE TABLE Statement”, and
Section 7.6, “MyISAM Table Maintenance and Crash Recovery”.
key_len
(JSON name:
key_length
)
The key_len
column indicates the length
of the key that MySQL decided to use. The value of
key_len
enables you to determine how
many parts of a multiple-part key MySQL actually uses. If
the key
column says
NULL
, the key_len
column also says NULL
.
Due to the key storage format, the key length is one
greater for a column that can be NULL
than for a NOT NULL
column.
The ref
column shows which columns or
constants are compared to the index named in the
key
column to select rows from the
table.
If the value is func
, the value used is
the result of some function. To see which function, use
SHOW WARNINGS
following
EXPLAIN
to see the extended
EXPLAIN
output. The
function might actually be an operator such as an
arithmetic operator.
The rows
column indicates the number of
rows MySQL believes it must examine to execute the query.
For InnoDB
tables, this
number is an estimate, and may not always be exact.
filtered
(JSON name:
filtered
)
The filtered
column indicates an
estimated percentage of table rows that will be filtered
by the table condition. The maximum value is 100, which
means no filtering of rows occurred. Values decreasing
from 100 indicate increasing amounts of filtering.
rows
shows the estimated number of rows
examined and rows
×
filtered
shows the number of rows that
will be joined with the following table. For example, if
rows
is 1000 and
filtered
is 50.00 (50%), the number of
rows to be joined with the following table is 1000 ×
50% = 500.
This column contains additional information about how
MySQL resolves the query. For descriptions of the
different values, see
EXPLAIN
Extra Information.
There is no single JSON property corresponding to the
Extra
column; however, values that can
occur in this column are exposed as JSON properties, or as
the text of the message
property.
The type
column of
EXPLAIN
output describes how
tables are joined. In JSON-formatted output, these are found
as values of the access_type
property. The
following list describes the join types, ordered from the best
type to the worst:
The table has only one row (= system table). This is a
special case of the
const
join type.
The table has at most one matching row, which is read at
the start of the query. Because there is only one row,
values from the column in this row can be regarded as
constants by the rest of the optimizer.
const
tables are very
fast because they are read only once.
const
is used when you
compare all parts of a PRIMARY KEY
or
UNIQUE
index to constant values. In the
following queries, tbl_name
can
be used as a const
table:
SELECT * FROMtbl_name
WHEREprimary_key
=1; SELECT * FROMtbl_name
WHEREprimary_key_part1
=1 ANDprimary_key_part2
=2;
One row is read from this table for each combination of
rows from the previous tables. Other than the
system
and
const
types, this is
the best possible join type. It is used when all parts of
an index are used by the join and the index is a
PRIMARY KEY
or UNIQUE NOT
NULL
index.
eq_ref
can be used for
indexed columns that are compared using the
=
operator. The comparison value can be
a constant or an expression that uses columns from tables
that are read before this table. In the following
examples, MySQL can use an
eq_ref
join to process
ref_table
:
SELECT * FROMref_table
,other_table
WHEREref_table
.key_column
=other_table
.column
; SELECT * FROMref_table
,other_table
WHEREref_table
.key_column_part1
=other_table
.column
ANDref_table
.key_column_part2
=1;
All rows with matching index values are read from this
table for each combination of rows from the previous
tables. ref
is used if
the join uses only a leftmost prefix of the key or if the
key is not a PRIMARY KEY
or
UNIQUE
index (in other words, if the
join cannot select a single row based on the key value).
If the key that is used matches only a few rows, this is a
good join type.
ref
can be used for
indexed columns that are compared using the
=
or <=>
operator. In the following examples, MySQL can use a
ref
join to process
ref_table
:
SELECT * FROMref_table
WHEREkey_column
=expr
; SELECT * FROMref_table
,other_table
WHEREref_table
.key_column
=other_table
.column
; SELECT * FROMref_table
,other_table
WHEREref_table
.key_column_part1
=other_table
.column
ANDref_table
.key_column_part2
=1;
The join is performed using a FULLTEXT
index.
This join type is like
ref
, but with the
addition that MySQL does an extra search for rows that
contain NULL
values. This join type
optimization is used most often in resolving subqueries.
In the following examples, MySQL can use a
ref_or_null
join to
process ref_table
:
SELECT * FROMref_table
WHEREkey_column
=expr
ORkey_column
IS NULL;
This join type indicates that the Index Merge optimization
is used. In this case, the key
column
in the output row contains a list of indexes used, and
key_len
contains a list of the longest
key parts for the indexes used. For more information, see
Section 8.2.1.3, “Index Merge Optimization”.
This type replaces
eq_ref
for some
IN
subqueries of the following form:
value
IN (SELECTprimary_key
FROMsingle_table
WHEREsome_expr
)
unique_subquery
is just
an index lookup function that replaces the subquery
completely for better efficiency.
This join type is similar to
unique_subquery
. It
replaces IN
subqueries, but it works
for nonunique indexes in subqueries of the following form:
value
IN (SELECTkey_column
FROMsingle_table
WHEREsome_expr
)
Only rows that are in a given range are retrieved, using
an index to select the rows. The key
column in the output row indicates which index is used.
The key_len
contains the longest key
part that was used. The ref
column is
NULL
for this type.
range
can be used when
a key column is compared to a constant using any of the
=
,
<>
,
>
,
>=
,
<
,
<=
,
IS NULL
,
<=>
,
BETWEEN
,
LIKE
, or
IN()
operators:
SELECT * FROMtbl_name
WHEREkey_column
= 10; SELECT * FROMtbl_name
WHEREkey_column
BETWEEN 10 and 20; SELECT * FROMtbl_name
WHEREkey_column
IN (10,20,30); SELECT * FROMtbl_name
WHEREkey_part1
= 10 ANDkey_part2
IN (10,20,30);
The index
join type is the same as
ALL
, except that the
index tree is scanned. This occurs two ways:
If the index is a covering index for the queries and
can be used to satisfy all data required from the
table, only the index tree is scanned. In this case,
the Extra
column says
Using index
. An index-only scan
usually is faster than
ALL
because the
size of the index usually is smaller than the table
data.
A full table scan is performed using reads from the
index to look up data rows in index order.
Uses index
does not appear in the
Extra
column.
MySQL can use this join type when the query uses only columns that are part of a single index.
A full table scan is done for each combination of rows
from the previous tables. This is normally not good if the
table is the first table not marked
const
, and usually
very bad in all other cases.
Normally, you can avoid
ALL
by adding indexes
that enable row retrieval from the table based on constant
values or column values from earlier tables.
The Extra
column of
EXPLAIN
output contains
additional information about how MySQL resolves the query. The
following list explains the values that can appear in this
column. Each item also indicates for JSON-formatted output
which property displays the Extra
value.
For some of these, there is a specific property. The others
display as the text of the message
property.
If you want to make your queries as fast as possible, look out
for Extra
column values of Using
filesort
and Using temporary
, or,
in JSON-formatted EXPLAIN
output, for
using_filesort
and
using_temporary_table
properties equal to
true
.
Child of '
(JSON: table
'
pushed join@1message
text)
This table is referenced as the child of
table
in a join that can be
pushed down to the NDB kernel. Applies only in NDB
Cluster, when pushed-down joins are enabled. See the
description of the
ndb_join_pushdown
server
system variable for more information and examples.
const row not found
(JSON property:
const_row_not_found
)
For a query such as SELECT ... FROM
, the table
was empty.
tbl_name
Deleting all rows
(JSON property:
message
)
For DELETE
, some storage
engines (such as MyISAM
)
support a handler method that removes all table rows in a
simple and fast way. This Extra
value
is displayed if the engine uses this optimization.
Distinct
(JSON property:
distinct
)
MySQL is looking for distinct values, so it stops searching for more rows for the current row combination after it has found the first matching row.
FirstMatch(
(JSON property: tbl_name
)first_match
)
The semijoin FirstMatch join shortcutting strategy is used
for tbl_name
.
Full scan on NULL key
(JSON property:
message
)
This occurs for subquery optimization as a fallback strategy when the optimizer cannot use an index-lookup access method.
Impossible HAVING
(JSON property:
message
)
The HAVING
clause is always false and
cannot select any rows.
Impossible WHERE
(JSON property:
message
)
The WHERE
clause is always false and
cannot select any rows.
Impossible WHERE noticed after reading const
tables
(JSON property:
message
)
MySQL has read all
const
(and
system
) tables and
notice that the WHERE
clause is always
false.
LooseScan(
(JSON property: m
..n
)message
)
The semijoin LooseScan strategy is used.
m
and
n
are key part numbers.
No matching min/max row
(JSON property:
message
)
No row satisfies the condition for a query such as
SELECT MIN(...) FROM ... WHERE
.
condition
no matching row in const table
(JSON
property: message
)
For a query with a join, there was an empty table or a table with no rows satisfying a unique index condition.
No matching rows after partition
pruning
(JSON property:
message
)
For DELETE
or
UPDATE
, the optimizer found
nothing to delete or update after partition pruning. It is
similar in meaning to Impossible WHERE
for SELECT
statements.
No tables used
(JSON property:
message
)
The query has no FROM
clause, or has a
FROM DUAL
clause.
For INSERT
or
REPLACE
statements,
EXPLAIN
displays this value
when there is no SELECT
part. For example, it appears for EXPLAIN INSERT
INTO t VALUES(10)
because that is equivalent to
EXPLAIN INSERT INTO t SELECT 10 FROM
DUAL
.
Not exists
(JSON property:
message
)
MySQL was able to do a LEFT JOIN
optimization on the query and does not examine more rows
in this table for the previous row combination after it
finds one row that matches the LEFT
JOIN
criteria. Here is an example of the type of
query that can be optimized this way:
SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id WHERE t2.id IS NULL;
Assume that t2.id
is defined as
NOT NULL
. In this case, MySQL scans
t1
and looks up the rows in
t2
using the values of
t1.id
. If MySQL finds a matching row in
t2
, it knows that
t2.id
can never be
NULL
, and does not scan through the
rest of the rows in t2
that have the
same id
value. In other words, for each
row in t1
, MySQL needs to do only a
single lookup in t2
, regardless of how
many rows actually match in t2
.
Plan isn't ready yet
(JSON property:
none)
This value occurs with EXPLAIN FOR
CONNECTION
when the optimizer has not finished
creating the execution plan for the statement executing in
the named connection. If execution plan output comprises
multiple lines, any or all of them could have this
Extra
value, depending on the progress
of the optimizer in determining the full execution plan.
Range checked for each record (index map:
(JSON property:
N
)message
)
MySQL found no good index to use, but found that some of
indexes might be used after column values from preceding
tables are known. For each row combination in the
preceding tables, MySQL checks whether it is possible to
use a range
or
index_merge
access
method to retrieve rows. This is not very fast, but is
faster than performing a join with no index at all. The
applicability criteria are as described in
Section 8.2.1.2, “Range Optimization”, and
Section 8.2.1.3, “Index Merge Optimization”, with the
exception that all column values for the preceding table
are known and considered to be constants.
Indexes are numbered beginning with 1, in the same order
as shown by SHOW INDEX
for
the table. The index map value
N
is a bitmask value that
indicates which indexes are candidates. For example, a
value of 0x19
(binary 11001) means that
indexes 1, 4, and 5 will be considered.
Scanned
(JSON property:
N
databasesmessage
)
This indicates how many directory scans the server
performs when processing a query for
INFORMATION_SCHEMA
tables, as described
in Section 8.2.3, “Optimizing INFORMATION_SCHEMA Queries”. The
value of N
can be 0, 1, or
all
.
Select tables optimized away
(JSON
property: message
)
The optimizer determined 1) that at most one row should be returned, and 2) that to produce this row, a deterministic set of rows must be read. When the rows to be read can be read during the optimization phase (for example, by reading index rows), there is no need to read any tables during query execution.
The first condition is fulfilled when the query is
implicitly grouped (contains an aggregate function but no
GROUP BY
clause). The second condition
is fulfilled when one row lookup is performed per index
used. The number of indexes read determines the number of
rows to read.
Consider the following implicitly grouped query:
SELECT MIN(c1), MIN(c2) FROM t1;
Suppose that MIN(c1)
can be retrieved
by reading one index row and MIN(c2)
can be retrieved by reading one row from a different
index. That is, for each column c1
and
c2
, there exists an index where the
column is the first column of the index. In this case, one
row is returned, produced by reading two deterministic
rows.
This Extra
value does not occur if the
rows to read are not deterministic. Consider this query:
SELECT MIN(c2) FROM t1 WHERE c1 <= 10;
Suppose that (c1, c2)
is a covering
index. Using this index, all rows with c1 <=
10
must be scanned to find the minimum
c2
value. By contrast, consider this
query:
SELECT MIN(c2) FROM t1 WHERE c1 = 10;
In this case, the first index row with c1 =
10
contains the minimum c2
value. Only one row must be read to produce the returned
row.
For storage engines that maintain an exact row count per
table (such as MyISAM
, but not
InnoDB
), this Extra
value can occur for COUNT(*)
queries
for which the WHERE
clause is missing
or always true and there is no GROUP BY
clause. (This is an instance of an implicitly grouped
query where the storage engine influences whether a
deterministic number of rows can be read.)
Skip_open_table
,
Open_frm_only
,
Open_full_table
(JSON property:
message
)
These values indicate file-opening optimizations that
apply to queries for INFORMATION_SCHEMA
tables, as described in
Section 8.2.3, “Optimizing INFORMATION_SCHEMA Queries”.
Skip_open_table
: Table files do not
need to be opened. The information has already become
available within the query by scanning the database
directory.
Open_frm_only
: Only the
table's .frm
file need be
opened.
Open_full_table
: The unoptimized
information lookup. The .frm
,
.MYD
, and
.MYI
files must be opened.
Start temporary
, End
temporary
(JSON property:
message
)
This indicates temporary table use for the semijoin Duplicate Weedout strategy.
unique row not found
(JSON property:
message
)
For a query such as SELECT ... FROM
, no rows
satisfy the condition for a tbl_name
UNIQUE
index or PRIMARY KEY
on the table.
Using filesort
(JSON property:
using_filesort
)
MySQL must do an extra pass to find out how to retrieve
the rows in sorted order. The sort is done by going
through all rows according to the join type and storing
the sort key and pointer to the row for all rows that
match the WHERE
clause. The keys then
are sorted and the rows are retrieved in sorted order. See
Section 8.2.1.14, “ORDER BY Optimization”.
Using index
(JSON property:
using_index
)
The column information is retrieved from the table using only information in the index tree without having to do an additional seek to read the actual row. This strategy can be used when the query uses only columns that are part of a single index.
For InnoDB
tables that have a
user-defined clustered index, that index can be used even
when Using index
is absent from the
Extra
column. This is the case if
type
is
index
and
key
is PRIMARY
.
Using index condition
(JSON property:
using_index_condition
)
Tables are read by accessing index tuples and testing them first to determine whether to read full table rows. In this way, index information is used to defer (“push down”) reading full table rows unless it is necessary. See Section 8.2.1.5, “Index Condition Pushdown Optimization”.
Using index for group-by
(JSON
property: using_index_for_group_by
)
Similar to the Using index
table access
method, Using index for group-by
indicates that MySQL found an index that can be used to
retrieve all columns of a GROUP BY
or
DISTINCT
query without any extra disk
access to the actual table. Additionally, the index is
used in the most efficient way so that for each group,
only a few index entries are read. For details, see
Section 8.2.1.15, “GROUP BY Optimization”.
Using join buffer (Block Nested Loop)
,
Using join buffer (Batched Key Access)
(JSON property: using_join_buffer
)
Tables from earlier joins are read in portions into the
join buffer, and then their rows are used from the buffer
to perform the join with the current table.
(Block Nested Loop)
indicates use of
the Block Nested-Loop algorithm and (Batched Key
Access)
indicates use of the Batched Key Access
algorithm. That is, the keys from the table on the
preceding line of the
EXPLAIN
output will be
buffered, and the matching rows will be fetched in batches
from the table represented by the line in which
Using join buffer
appears.
In JSON-formatted output, the value of
using_join_buffer
is always either one
of Block Nested Loop
or
Batched Key Access
.
Using MRR
(JSON property:
message
)
Tables are read using the Multi-Range Read optimization strategy. See Section 8.2.1.10, “Multi-Range Read Optimization”.
Using sort_union(...)
, Using
union(...)
, Using
intersect(...)
(JSON property:
message
)
These indicate the particular algorithm showing how index
scans are merged for the
index_merge
join type.
See Section 8.2.1.3, “Index Merge Optimization”.
Using temporary
(JSON property:
using_temporary_table
)
To resolve the query, MySQL needs to create a temporary
table to hold the result. This typically happens if the
query contains GROUP BY
and
ORDER BY
clauses that list columns
differently.
Using where
(JSON property:
attached_condition
)
A WHERE
clause is used to restrict
which rows to match against the next table or send to the
client. Unless you specifically intend to fetch or examine
all rows from the table, you may have something wrong in
your query if the Extra
value is not
Using where
and the table join type is
ALL
or
index
.
Using where
has no direct counterpart
in JSON-formatted output; the
attached_condition
property contains
any WHERE
condition used.
Using where with pushed condition
(JSON
property: message
)
This item applies to NDB
tables only. It means that NDB
Cluster is using the Condition Pushdown optimization to
improve the efficiency of a direct comparison between a
nonindexed column and a constant. In such cases, the
condition is “pushed down” to the
cluster's data nodes and is evaluated on all data
nodes simultaneously. This eliminates the need to send
nonmatching rows over the network, and can speed up such
queries by a factor of 5 to 10 times over cases where
Condition Pushdown could be but is not used. For more
information, see
Section 8.2.1.4, “Engine Condition Pushdown Optimization”.
Zero limit
(JSON property:
message
)
The query had a LIMIT 0
clause and
cannot select any rows.
You can get a good indication of how good a join is by taking
the product of the values in the rows
column of the EXPLAIN
output.
This should tell you roughly how many rows MySQL must examine
to execute the query. If you restrict queries with the
max_join_size
system
variable, this row product also is used to determine which
multiple-table SELECT
statements to execute and which to abort. See
Section 5.1.1, “Configuring the Server”.
The following example shows how a multiple-table join can be
optimized progressively based on the information provided by
EXPLAIN
.
Suppose that you have the
SELECT
statement shown here and
that you plan to examine it using
EXPLAIN
:
EXPLAIN SELECT tt.TicketNumber, tt.TimeIn, tt.ProjectReference, tt.EstimatedShipDate, tt.ActualShipDate, tt.ClientID, tt.ServiceCodes, tt.RepetitiveID, tt.CurrentProcess, tt.CurrentDPPerson, tt.RecordVolume, tt.DPPrinted, et.COUNTRY, et_1.COUNTRY, do.CUSTNAME FROM tt, et, et AS et_1, do WHERE tt.SubmitTime IS NULL AND tt.ActualPC = et.EMPLOYID AND tt.AssignedPC = et_1.EMPLOYID AND tt.ClientID = do.CUSTNMBR;
For this example, make the following assumptions:
The columns being compared have been declared as follows.
Table | Column | Data Type |
---|---|---|
tt |
ActualPC |
CHAR(10) |
tt |
AssignedPC |
CHAR(10) |
tt |
ClientID |
CHAR(10) |
et |
EMPLOYID |
CHAR(15) |
do |
CUSTNMBR |
CHAR(15) |
The tables have the following indexes.
Table | Index |
---|---|
tt |
ActualPC |
tt |
AssignedPC |
tt |
ClientID |
et |
EMPLOYID (primary key) |
do |
CUSTNMBR (primary key) |
The tt.ActualPC
values are not evenly
distributed.
Initially, before any optimizations have been performed, the
EXPLAIN
statement produces the
following information:
table type possible_keys key key_len ref rows Extra et ALL PRIMARY NULL NULL NULL 74 do ALL PRIMARY NULL NULL NULL 2135 et_1 ALL PRIMARY NULL NULL NULL 74 tt ALL AssignedPC, NULL NULL NULL 3872 ClientID, ActualPC Range checked for each record (index map: 0x23)
Because type
is
ALL
for each table, this
output indicates that MySQL is generating a Cartesian product
of all the tables; that is, every combination of rows. This
takes quite a long time, because the product of the number of
rows in each table must be examined. For the case at hand,
this product is 74 × 2135 × 74 × 3872 =
45,268,558,720 rows. If the tables were bigger, you can only
imagine how long it would take.
One problem here is that MySQL can use indexes on columns more
efficiently if they are declared as the same type and size. In
this context, VARCHAR
and
CHAR
are considered the same if
they are declared as the same size.
tt.ActualPC
is declared as
CHAR(10)
and et.EMPLOYID
is CHAR(15)
, so there is a length mismatch.
To fix this disparity between column lengths, use
ALTER TABLE
to lengthen
ActualPC
from 10 characters to 15
characters:
mysql> ALTER TABLE tt MODIFY ActualPC VARCHAR(15);
Now tt.ActualPC
and
et.EMPLOYID
are both
VARCHAR(15)
. Executing the
EXPLAIN
statement again
produces this result:
table type possible_keys key key_len ref rows Extra tt ALL AssignedPC, NULL NULL NULL 3872 Using ClientID, where ActualPC do ALL PRIMARY NULL NULL NULL 2135 Range checked for each record (index map: 0x1) et_1 ALL PRIMARY NULL NULL NULL 74 Range checked for each record (index map: 0x1) et eq_ref PRIMARY PRIMARY 15 tt.ActualPC 1
This is not perfect, but is much better: The product of the
rows
values is less by a factor of 74. This
version executes in a couple of seconds.
A second alteration can be made to eliminate the column length
mismatches for the tt.AssignedPC =
et_1.EMPLOYID
and tt.ClientID =
do.CUSTNMBR
comparisons:
mysql>ALTER TABLE tt MODIFY AssignedPC VARCHAR(15),
MODIFY ClientID VARCHAR(15);
After that modification,
EXPLAIN
produces the output
shown here:
table type possible_keys key key_len ref rows Extra et ALL PRIMARY NULL NULL NULL 74 tt ref AssignedPC, ActualPC 15 et.EMPLOYID 52 Using ClientID, where ActualPC et_1 eq_ref PRIMARY PRIMARY 15 tt.AssignedPC 1 do eq_ref PRIMARY PRIMARY 15 tt.ClientID 1
At this point, the query is optimized almost as well as
possible. The remaining problem is that, by default, MySQL
assumes that values in the tt.ActualPC
column are evenly distributed, and that is not the case for
the tt
table. Fortunately, it is easy to
tell MySQL to analyze the key distribution:
mysql> ANALYZE TABLE tt;
With the additional index information, the join is perfect and
EXPLAIN
produces this result:
table type possible_keys key key_len ref rows Extra tt ALL AssignedPC NULL NULL NULL 3872 Using ClientID, where ActualPC et eq_ref PRIMARY PRIMARY 15 tt.ActualPC 1 et_1 eq_ref PRIMARY PRIMARY 15 tt.AssignedPC 1 do eq_ref PRIMARY PRIMARY 15 tt.ClientID 1
The rows
column in the output from
EXPLAIN
is an educated guess
from the MySQL join optimizer. Check whether the numbers are
even close to the truth by comparing the
rows
product with the actual number of rows
that the query returns. If the numbers are quite different,
you might get better performance by using
STRAIGHT_JOIN
in your
SELECT
statement and trying to
list the tables in a different order in the
FROM
clause. (However,
STRAIGHT_JOIN
may prevent indexes from
being used because it disables semijoin transformations. See
Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin
Transformations”.)
It is possible in some cases to execute statements that modify
data when EXPLAIN
SELECT
is used with a subquery; for more
information, see Section 13.2.10.8, “Derived Tables”.
For SELECT
statements, the
EXPLAIN
statement produces extra
(“extended”) information that is not part of
EXPLAIN
output but can be viewed
by issuing a SHOW WARNINGS
statement following EXPLAIN
. The
Message
value in SHOW
WARNINGS
output displays how the optimizer qualifies
table and column names in the
SELECT
statement, what the
SELECT
looks like after the
application of rewriting and optimization rules, and possibly
other notes about the optimization process.
The extended information displayable with a
SHOW WARNINGS
statement following
EXPLAIN
is produced only for
SELECT
statements.
SHOW WARNINGS
displays an empty
result for other explainable statements
(DELETE
,
INSERT
,
REPLACE
, and
UPDATE
).
In older MySQL releases, extended information was produced
using EXPLAIN
EXTENDED
. That syntax is still recognized for
backward compatibility but extended output is now enabled by
default, so the EXTENDED
keyword is
superfluous and deprecated. Its use results in a warning, and
it will be removed from EXPLAIN
syntax in a future MySQL release.
Here is an example of extended
EXPLAIN
output:
mysql>EXPLAIN
SELECT t1.a, t1.a IN (SELECT t2.a FROM t2) FROM t1\G
*************************** 1. row *************************** id: 1 select_type: PRIMARY table: t1 type: index possible_keys: NULL key: PRIMARY key_len: 4 ref: NULL rows: 4 filtered: 100.00 Extra: Using index *************************** 2. row *************************** id: 2 select_type: SUBQUERY table: t2 type: index possible_keys: a key: a key_len: 5 ref: NULL rows: 3 filtered: 100.00 Extra: Using index 2 rows in set, 1 warning (0.00 sec) mysql>SHOW WARNINGS\G
*************************** 1. row *************************** Level: Note Code: 1003 Message: /* select#1 */ select `test`.`t1`.`a` AS `a`, <in_optimizer>(`test`.`t1`.`a`,`test`.`t1`.`a` in ( <materialize> (/* select#2 */ select `test`.`t2`.`a` from `test`.`t2` where 1 having 1 ), <primary_index_lookup>(`test`.`t1`.`a` in <temporary table> on <auto_key> where ((`test`.`t1`.`a` = `materialized-subquery`.`a`))))) AS `t1.a IN (SELECT t2.a FROM t2)` from `test`.`t1` 1 row in set (0.00 sec)
Because the statement displayed by SHOW
WARNINGS
may contain special markers to provide
information about query rewriting or optimizer actions, the
statement is not necessarily valid SQL and is not intended to be
executed. The output may also include rows with
Message
values that provide additional
non-SQL explanatory notes about actions taken by the optimizer.
The following list describes special markers that can appear in
the extended output displayed by SHOW
WARNINGS
:
<auto_key>
An automatically generated key for a temporary table.
<cache>(
expr
)
The expression (such as a scalar subquery) is executed once
and the resulting value is saved in memory for later use.
For results consisting of multiple values, a temporary table
may be created and you will see <temporary
table>
instead.
<exists>(
query
fragment
)
The subquery predicate is converted to an
EXISTS
predicate and the subquery is
transformed so that it can be used together with the
EXISTS
predicate.
<in_optimizer>(
query
fragment
)
This is an internal optimizer object with no user significance.
<index_lookup>(
query
fragment
)
The query fragment is processed using an index lookup to find qualifying rows.
<if>(
condition
,
expr1
,
expr2
)
If the condition is true, evaluate to
expr1
, otherwise
expr2
.
<is_not_null_test>(
expr
)
A test to verify that the expression does not evaluate to
NULL
.
<materialize>(
query
fragment
)
Subquery materialization is used.
`materialized-subquery`.
col_name
A reference to the column
col_name
in an internal temporary
table materialized to hold the result from evaluating a
subquery.
<primary_index_lookup>(
query
fragment
)
The query fragment is processed using a primary key lookup to find qualifying rows.
<ref_null_helper>(
expr
)
This is an internal optimizer object with no user significance.
/* select#
N
*/
select_stmt
The SELECT
is associated with the row in
non-extended EXPLAIN
output
that has an id
value of
N
.
outer_tables
semi join
(inner_tables
)
A semijoin operation.
inner_tables
shows the tables
that were not pulled out. See Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin
Transformations”.
<temporary table>
This represents an internal temporary table created to cache an intermediate result.
When some tables are of const
or system
type, expressions
involving columns from these tables are evaluated early by the
optimizer and are not part of the displayed statement. However,
with FORMAT=JSON
, some
const
table accesses are
displayed as a ref
access
that uses a const value.
To obtain the execution plan for an explainable statement executing in a named connection, use this statement:
EXPLAIN [options
] FOR CONNECTIONconnection_id
;
EXPLAIN FOR CONNECTION
returns
the EXPLAIN
information that is
currently being used to execute a query in a given connection.
Because of changes to data (and supporting statistics) it may
produce a different result from running
EXPLAIN
on the equivalent query
text. This difference in behavior can be useful in diagnosing
more transient performance problems. For example, if you are
running a statement in one session that is taking a long time to
complete, using EXPLAIN FOR
CONNECTION
in another session may yield useful
information about the cause of the delay.
connection_id
is the connection
identifier, as obtained from the
INFORMATION_SCHEMA
PROCESSLIST
table or the
SHOW PROCESSLIST
statement. If
you have the PROCESS
privilege,
you can specify the identifier for any connection. Otherwise,
you can specify the identifier only for your own connections.
If the named connection is not executing a statement, the result
is empty. Otherwise, EXPLAIN FOR CONNECTION
applies only if the statement being executed in the named
connection is explainable. This includes
SELECT
,
DELETE
,
INSERT
,
REPLACE
, and
UPDATE
. (However,
EXPLAIN FOR CONNECTION
does not work for
prepared statements, even prepared statements of those types.)
If the named connection is executing an explainable statement,
the output is what you would obtain by using
EXPLAIN
on the statement itself.
If the named connection is executing a statement that is not
explainable, an error occurs. For example, you cannot name the
connection identifier for your current session because
EXPLAIN
is not explainable:
mysql>SELECT CONNECTION_ID();
+-----------------+ | CONNECTION_ID() | +-----------------+ | 373 | +-----------------+ 1 row in set (0.00 sec) mysql>EXPLAIN FOR CONNECTION 373;
ERROR 1889 (HY000): EXPLAIN FOR CONNECTION command is supported only for SELECT/UPDATE/INSERT/DELETE/REPLACE
The Com_explain_other
status variable
indicates the number of
EXPLAIN FOR
CONNECTION
statements executed.
In most cases, you can estimate query performance by counting
disk seeks. For small tables, you can usually find a row in one
disk seek (because the index is probably cached). For bigger
tables, you can estimate that, using B-tree indexes, you need
this many seeks to find a row:
log(
.
row_count
) /
log(index_block_length
/ 3 * 2 /
(index_length
+
data_pointer_length
)) + 1
In MySQL, an index block is usually 1,024 bytes and the data
pointer is usually four bytes. For a 500,000-row table with a
key value length of three bytes (the size of
MEDIUMINT
), the formula indicates
log(500,000)/log(1024/3*2/(3+4)) + 1
=
4
seeks.
This index would require storage of about 500,000 * 7 * 3/2 = 5.2MB (assuming a typical index buffer fill ratio of 2/3), so you probably have much of the index in memory and so need only one or two calls to read data to find the row.
For writes, however, you need four seek requests to find where to place a new index value and normally two seeks to update the index and write the row.
The preceding discussion does not mean that your application
performance slowly degenerates by log
N
. As long as everything is cached by
the OS or the MySQL server, things become only marginally slower
as the table gets bigger. After the data gets too big to be
cached, things start to go much slower until your applications
are bound only by disk seeks (which increase by log
N
). To avoid this, increase the key
cache size as the data grows. For MyISAM
tables, the key cache size is controlled by the
key_buffer_size
system
variable. See Section 5.1.1, “Configuring the Server”.
MySQL provides optimizer control through system variables that affect how query plans are evaluated, switchable optimizations, optimizer and index hints, and the optimizer cost model.
The task of the query optimizer is to find an optimal plan for executing an SQL query. Because the difference in performance between “good” and “bad” plans can be orders of magnitude (that is, seconds versus hours or even days), most query optimizers, including that of MySQL, perform a more or less exhaustive search for an optimal plan among all possible query evaluation plans. For join queries, the number of possible plans investigated by the MySQL optimizer grows exponentially with the number of tables referenced in a query. For small numbers of tables (typically less than 7 to 10) this is not a problem. However, when larger queries are submitted, the time spent in query optimization may easily become the major bottleneck in the server's performance.
A more flexible method for query optimization enables the user to control how exhaustive the optimizer is in its search for an optimal query evaluation plan. The general idea is that the fewer plans that are investigated by the optimizer, the less time it spends in compiling a query. On the other hand, because the optimizer skips some plans, it may miss finding an optimal plan.
The behavior of the optimizer with respect to the number of plans it evaluates can be controlled using two system variables:
The optimizer_prune_level
variable tells the optimizer to skip certain plans based on
estimates of the number of rows accessed for each table. Our
experience shows that this kind of “educated
guess” rarely misses optimal plans, and may
dramatically reduce query compilation times. That is why
this option is on
(optimizer_prune_level=1
) by default.
However, if you believe that the optimizer missed a better
query plan, this option can be switched off
(optimizer_prune_level=0
) with the risk
that query compilation may take much longer. Note that, even
with the use of this heuristic, the optimizer still explores
a roughly exponential number of plans.
The optimizer_search_depth
variable tells how far into the “future” of
each incomplete plan the optimizer should look to evaluate
whether it should be expanded further. Smaller values of
optimizer_search_depth
may
result in orders of magnitude smaller query compilation
times. For example, queries with 12, 13, or more tables may
easily require hours and even days to compile if
optimizer_search_depth
is
close to the number of tables in the query. At the same
time, if compiled with
optimizer_search_depth
equal to 3 or 4, the optimizer may compile in less than a
minute for the same query. If you are unsure of what a
reasonable value is for
optimizer_search_depth
,
this variable can be set to 0 to tell the optimizer to
determine the value automatically.
The optimizer_switch
system
variable enables control over optimizer behavior. Its value is a
set of flags, each of which has a value of on
or off
to indicate whether the corresponding
optimizer behavior is enabled or disabled. This variable has
global and session values and can be changed at runtime. The
global default can be set at server startup.
To see the current set of optimizer flags, select the variable value:
mysql> SELECT @@optimizer_switch\G
*************************** 1. row ***************************
@@optimizer_switch: index_merge=on,index_merge_union=on,
index_merge_sort_union=on,
index_merge_intersection=on,
engine_condition_pushdown=on,
index_condition_pushdown=on,
mrr=on,mrr_cost_based=on,
block_nested_loop=on,batched_key_access=off,
materialization=on,semijoin=on,loosescan=on,
firstmatch=on,duplicateweedout=on,
subquery_materialization_cost_based=on,
use_index_extensions=on,
condition_fanout_filter=on,derived_merge=on
To change the value of
optimizer_switch
, assign a
value consisting of a comma-separated list of one or more
commands:
SET [GLOBAL|SESSION] optimizer_switch='command
[,command
]...';
Each command
value should have one of
the forms shown in the following table.
Command Syntax | Meaning |
---|---|
default |
Reset every optimization to its default value |
|
Set the named optimization to its default value |
|
Disable the named optimization |
|
Enable the named optimization |
The order of the commands in the value does not matter, although
the default
command is executed first if
present. Setting an opt_name
flag to
default
sets it to whichever of
on
or off
is its default
value. Specifying any given opt_name
more than once in the value is not permitted and causes an
error. Any errors in the value cause the assignment to fail with
an error, leaving the value of
optimizer_switch
unchanged.
The following list describes the permissible
opt_name
flag names, grouped by
optimization strategy:
Batched Key Access Flags
batched_key_access
(default
off
)
Controls use of BKA join algorithm.
For batched_key_access
to have any effect
when set to on
, the
mrr
flag must also be
on
. Currently, the cost estimation for
MRR is too pessimistic. Hence, it is also necessary for
mrr_cost_based
to be
off
for BKA to be used.
For more information, see Section 8.2.1.11, “Block Nested-Loop and Batched Key Access Joins”.
Block Nested-Loop Flags
block_nested_loop
(default
on
)
Controls use of BNL join algorithm.
For more information, see Section 8.2.1.11, “Block Nested-Loop and Batched Key Access Joins”.
Condition Filtering Flags
condition_fanout_filter
(default
on
)
Controls use of condition filtering.
For more information, see Section 8.2.1.12, “Condition Filtering”.
Derived Table Merging Flags
derived_merge
(default
on
)
Controls merging of derived tables and views into outer query block.
The derived_merge
flag controls whether
the optimizer attempts to merge derived tables and view
references into the outer query block, assuming that no
other rule prevents merging; for example, an
ALGORITHM
directive for a view takes
precedence over the derived_merge
setting. By default, the flag is on
to
enable merging.
For more information, see Section 8.2.2.4, “Optimizing Derived Tables and View References with Merging or Materialization”.
Engine Condition Pushdown Flags
engine_condition_pushdown
(default
on
)
Controls engine condition pushdown.
For more information, see Section 8.2.1.4, “Engine Condition Pushdown Optimization”.
Index Condition Pushdown Flags
index_condition_pushdown
(default
on
)
Controls index condition pushdown.
For more information, see Section 8.2.1.5, “Index Condition Pushdown Optimization”.
Index Extensions Flags
use_index_extensions
(default
on
)
Controls use of index extensions.
For more information, see Section 8.3.9, “Use of Index Extensions”.
Index Merge Flags
index_merge
(default
on
)
Controls all Index Merge optimizations.
index_merge_intersection
(default
on
)
Controls the Index Merge Intersection Access optimization.
index_merge_sort_union
(default
on
)
Controls the Index Merge Sort-Union Access optimization.
index_merge_union
(default
on
)
Controls the Index Merge Union Access optimization.
For more information, see Section 8.2.1.3, “Index Merge Optimization”.
Multi-Range Read Flags
mrr
(default on
)
Controls the Multi-Range Read strategy.
mrr_cost_based
(default
on
)
Controls use of cost-based MRR if
mrr=on
.
For more information, see Section 8.2.1.10, “Multi-Range Read Optimization”.
Semijoin Flags
semijoin
(default
on
)
Controls all semijoin strategies.
duplicateweedout
(default
on
)
Controls the semijoin Duplicate Weedout strategy.
firstmatch
(default
on
)
Controls the semijoin FirstMatch strategy.
loosescan
(default
on
)
Controls the semijoin LooseScan strategy (not to be
confused with Loose Index Scan for GROUP
BY
).
The semijoin
,
firstmatch
, loosescan
,
and duplicateweedout
flags enable control
over semijoin strategies. The semijoin
flag controls whether semijoins are used. If it is set to
on
, the firstmatch
and
loosescan
flags enable finer control over
the permitted semijoin strategies.
If the duplicateweedout
semijoin strategy
is disabled, it is not used unless all other applicable
strategies are also disabled.
If semijoin
and
materialization
are both
on
, semijoins also use materialization
where applicable. These flags are on
by
default.
For more information, see Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin Transformations”.
Subquery Materialization Flags
materialization
(default
on
)
Controls materialization (including semijoin materialization).
subquery_materialization_cost_based
(default on
)
Use cost-based materialization choice.
The materialization
flag controls whether
subquery materialization is used. If
semijoin
and
materialization
are both
on
, semijoins also use materialization
where applicable. These flags are on
by
default.
The subquery_materialization_cost_based
flag enables control over the choice between subquery
materialization and
IN
-to-EXISTS
subquery
transformation. If the flag is on
(the
default), the optimizer performs a cost-based choice between
subquery materialization and
IN
-to-EXISTS
subquery
transformation if either method could be used. If the flag
is off
, the optimizer chooses subquery
materialization over
IN
-to-EXISTS
subquery
transformation.
For more information, see Section 8.2.2, “Optimizing Subqueries, Derived Tables, and View References”.
When you assign a value to
optimizer_switch
, flags that
are not mentioned keep their current values. This makes it
possible to enable or disable specific optimizer behaviors in a
single statement without affecting other behaviors. The
statement does not depend on what other optimizer flags exist
and what their values are. Suppose that all Index Merge
optimizations are enabled:
mysql> SELECT @@optimizer_switch\G
*************************** 1. row ***************************
@@optimizer_switch: index_merge=on,index_merge_union=on,
index_merge_sort_union=on,
index_merge_intersection=on,
engine_condition_pushdown=on,
index_condition_pushdown=on,
mrr=on,mrr_cost_based=on,
block_nested_loop=on,batched_key_access=off,
materialization=on,semijoin=on,loosescan=on,
firstmatch=on,
subquery_materialization_cost_based=on,
use_index_extensions=on,
condition_fanout_filter=on
If the server is using the Index Merge Union or Index Merge Sort-Union access methods for certain queries and you want to check whether the optimizer will perform better without them, set the variable value like this:
mysql>SET optimizer_switch='index_merge_union=off,index_merge_sort_union=off';
mysql>SELECT @@optimizer_switch\G
*************************** 1. row *************************** @@optimizer_switch: index_merge=on,index_merge_union=off, index_merge_sort_union=off, index_merge_intersection=on, engine_condition_pushdown=on, index_condition_pushdown=on, mrr=on,mrr_cost_based=on, block_nested_loop=on,batched_key_access=off, materialization=on,semijoin=on,loosescan=on, firstmatch=on, subquery_materialization_cost_based=on, use_index_extensions=on, condition_fanout_filter=on
One means of control over optimizer strategies is to set the
optimizer_switch
system
variable (see Section 8.9.2, “Switchable Optimizations”).
Changes to this variable affect execution of all subsequent
queries; to affect one query differently from another, it is
necessary to change
optimizer_switch
before each
one.
another way to control the optimizer is by using optimizer
hints, which can be specified within individual statements.
Because optimizer hints apply on a per-statement basis, they
provide finer control over statement execution plans than can be
achieved using
optimizer_switch
. For example,
you can enable an optimization for one table in a statement and
disable the optimization for a different table. Hints within a
statement take precedence over
optimizer_switch
flags.
Examples:
SELECT /*+ NO_RANGE_OPTIMIZATION(t3 PRIMARY, f2_idx) */ f1 FROM t3 WHERE f1 > 30 AND f1 < 33; SELECT /*+ BKA(t1) NO_BKA(t2) */ * FROM t1 INNER JOIN t2 WHERE ...; SELECT /*+ NO_ICP(t1, t2) */ * FROM t1 INNER JOIN t2 WHERE ...; SELECT /*+ SEMIJOIN(FIRSTMATCH, LOOSESCAN) */ * FROM t1 ...; EXPLAIN SELECT /*+ NO_ICP(t1) */ * FROM t1 WHERE ...;
The mysql client by default strips comments
from SQL statements sent to the server (including optimizer
hints) until MySQL 5.7.7, when it was changed to pass
optimizer hints to the server. To ensure that optimizer hints
are not stripped if you are using an older version of the
mysql client with a version of the server
that understands optimizer hints, invoke
mysql with the
--comments
option.
Optimizer hints, described here, differ from index hints, described in Section 8.9.4, “Index Hints”. Optimizer and index hints may be used separately or together.
Optimizer hints apply at different scope levels:
Global: The hint affects the entire statement
Query block: The hint affects a particular query block within a statement
Table-level: The hint affects a particular table within a query block
Index-level: The hint affects a particular index within a table
The following table summarizes the available optimizer hints, the optimizer strategies they affect, and the scope or scopes at which they apply. More details are given later.
Table 8.2 Optimizer Hints Available
Hint Name | Description | Applicable Scopes |
---|---|---|
BKA ,
NO_BKA |
Affects Batched Key Access join processing | Query block, table |
BNL ,
NO_BNL |
Affects Block Nested-Loop join processing | Query block, table |
MAX_EXECUTION_TIME |
Limits statement execution time | Global |
MRR ,
NO_MRR |
Affects Multi-Range Read optimization | Table, index |
NO_ICP |
Affects Index Condition Pushdown optimization | Table, index |
NO_RANGE_OPTIMIZATION |
Affects range optimization | Table, index |
QB_NAME |
Assigns name to query block | Query block |
SEMIJOIN ,
NO_SEMIJOIN |
Affects semijoin strategies | Query block |
SUBQUERY |
Affects materialization,
IN -to-EXISTS
subquery stratgies |
Query block |
Disabling an optimization prevents the optimizer from using it. Enabling an optimization means the optimizer is free to use the strategy if it applies to statement execution, not that the optimizer necessarily will use it.
MySQL supports comments in SQL statements as described in
Section 9.6, “Comment Syntax”. Optimizer hints must be specified
within /*+ ... */
comments. That is,
optimizer hints use a variant of /* ... */
C-style comment syntax, with a +
character
following the /*
comment opening sequence.
Examples:
/*+ BKA(t1) */ /*+ BNL(t1, t2) */ /*+ NO_RANGE_OPTIMIZATION(t4 PRIMARY) */ /*+ QB_NAME(qb2) */
Whitespace is permitted after the +
character.
The parser recognizes optimizer hint comments after the
initial keyword of SELECT
,
UPDATE
,
INSERT
,
REPLACE
, and
DELETE
statements. Hints are
permitted in these contexts:
At the beginning of query and data change statements:
SELECT /*+ ... */ ... INSERT /*+ ... */ ... REPLACE /*+ ... */ ... UPDATE /*+ ... */ ... DELETE /*+ ... */ ...
At the beginning of query blocks:
(SELECT /*+ ... */ ... ) (SELECT ... ) UNION (SELECT /*+ ... */ ... ) (SELECT /*+ ... */ ... ) UNION (SELECT /*+ ... */ ... ) UPDATE ... WHERE x IN (SELECT /*+ ... */ ...) INSERT ... SELECT /*+ ... */ ...
In hintable statements prefaced by
EXPLAIN
. For example:
EXPLAIN SELECT /*+ ... */ ... EXPLAIN UPDATE ... WHERE x IN (SELECT /*+ ... */ ...)
The implication is that you can use
EXPLAIN
to see how
optimizer hints affect execution plans. Use
SHOW WARNINGS
immediately
after EXPLAIN
to see how
hints are used. The extended EXPLAIN
output displayed by a following SHOW
WARNINGS
indicates which hints were used.
Ignored hints are not displayed.
A hint comment may contain multiple hints, but a query block cannot contain multiple hint comments. This is valid:
SELECT /*+ BNL(t1) BKA(t2) */ ...
But this is invalid:
SELECT /*+ BNL(t1) */ /* BKA(t2) */ ...
When a hint comment contains multiple hints, the possibility of duplicates and conflicts exists. The following general guidelines apply. For specific hint types, additional rules may apply, as indicated in the hint descriptions.
Duplicate hints: For a hint such as /*+ MRR(idx1)
MRR(idx1) */
, MySQL uses the first hint and
issues a warning about the duplicate hint.
Conflicting hints: For a hint such as /*+
MRR(idx1) NO_MRR(idx1) */
, MySQL uses the first
hint and issues a warning about the second conflicting
hint.
Query block names are identifiers and follow the usual rules about what names are valid and how to quote them (see Section 9.2, “Schema Object Names”).
Hint names, query block names, and strategy names are not case-sensitive. References to table and index names follow the usual identifier case sensitivity rules (see Section 9.2.3, “Identifier Case Sensitivity”).
Table-level hints affect use of the Block Nested-Loop (BNL) and Batched Key Access (BKA) join-processing algorithms (see Section 8.2.1.11, “Block Nested-Loop and Batched Key Access Joins”). These hint types apply to specific tables, or all tables in a query block.
Syntax of table-level hints:
hint_name
([@query_block_name
] [tbl_name
[,tbl_name
] ...])hint_name
([tbl_name
@query_block_name
[,tbl_name
@query_block_name
] ...])
The syntax refers to these terms:
hint_name
: These hint names are
permitted:
To use a BNL or BKA hint to enable join buffering for any inner table of an outer join, join buffering must be enabled for all inner tables of the outer join.
tbl_name
: The name of a table
used in the statement. The hint applies to all tables that
it names. If the hint names no tables, it applies to all
tables of the query block in which it occurs.
If a table has an alias, hints must refer to the alias, not the table name.
Table names in hints cannot be qualified with schema names.
query_block_name
: The query
block to which the hint applies. If the hint includes no
leading
@
,
the hint applies to the query block in which it occurs.
For
query_block_name
syntax, the hint applies to the named table in the named
query block. To assign a name to a query block, see
Optimizer Hints for Naming Query Blocks.
tbl_name
@query_block_name
Examples:
SELECT /*+ NO_BKA(t1, t2) */ t1.* FROM t1 INNER JOIN t2 INNER JOIN t3; SELECT /*+ NO_BNL() BKA(t1) */ t1.* FROM t1 INNER JOIN t2 INNER JOIN t3;
A table-level hint applies to tables that receive records from previous tables, not sender tables. Consider this statement:
SELECT /*+ BNL(t2) */ FROM t1, t2;
If the optimizer chooses to process t1
first, it applies a Block Nested-Loop join to
t2
by buffering the rows from
t1
before starting to read from
t2
. If the optimizer instead chooses to
process t2
first, the hint has no effect
because t2
is a sender table.
Index-level hints affect which index-processing strategies the optimizer uses for particular tables or indexes. These hint types affect use of Index Condition Pushdown (ICP), Multi-Range Read (MRR), and range optimizations (see Section 8.2.1, “Optimizing SELECT Statements”).
Syntax of index-level hints:
hint_name
([@query_block_name
]tbl_name
[index_name
[,index_name
] ...])hint_name
(tbl_name
@query_block_name
[index_name
[,index_name
] ...])
The syntax refers to these terms:
hint_name
: These hint names are
permitted:
MRR
,
NO_MRR
: Enable or
disable MRR for the specified table or indexes. MRR
hints apply only to InnoDB
and
MyISAM
tables.
NO_ICP
: Disable ICP
for the specified table or indexes. By default, ICP is
a candidate optimization strategy, so there is no hint
for enabling it.
NO_RANGE_OPTIMIZATION
:
Disable index range access for the specified table or
indexes. This hint also disables Index Merge and Loose
Index Scan for the table or indexes. By default, range
access is a candidate optimization strategy, so there
is no hint for enabling it.
This hint may be useful when the number of ranges may be high and range optimization would require many resources.
tbl_name
: The table to which
the hint applies.
index_name
: The name of an
index in the named table. The hint applies to all indexes
that it names. If the hint names no indexes, it applies to
all indexes in the table.
To refer to a primary key, use the name
PRIMARY
. To see the index names for a
table, use SHOW INDEX
.
query_block_name
: The query
block to which the hint applies. If the hint includes no
leading
@
,
the hint applies to the query block in which it occurs.
For
query_block_name
syntax, the hint applies to the named table in the named
query block. To assign a name to a query block, see
Optimizer Hints for Naming Query Blocks.
tbl_name
@query_block_name
Examples:
SELECT /*+ MRR(t1) */ * FROM t1 WHERE f2 <= 3 AND 3 <= f3; SELECT /*+ NO_RANGE_OPTIMIZATION(t3 PRIMARY, f2_idx) */ f1 FROM t3 WHERE f1 > 30 AND f1 < 33; INSERT INTO t3(f1, f2, f3) (SELECT /*+ NO_ICP(t2) */ t2.f1, t2.f2, t2.f3 FROM t1,t2 WHERE t1.f1=t2.f1 AND t2.f2 BETWEEN t1.f1 AND t1.f2 AND t2.f2 + 1 >= t1.f1 + 1);
Subquery hints affect whether to use semijoin transformations
and which semijoin strategies to permit, and, when semijoins
are not used, whether to use subquery materialization or
IN
-to-EXISTS
transformations. For more information about these
optimizations, see Section 8.2.2, “Optimizing Subqueries, Derived Tables, and View References”.
Syntax of hints that affect semijoin strategies:
hint_name
([@query_block_name
] [strategy
[,strategy
] ...])
The syntax refers to these terms:
hint_name
: These hint names are
permitted:
SEMIJOIN
,
NO_SEMIJOIN
: Enable
or disable the named semijoin strategies.
strategy
: A semijoin strategy
to be enabled or disabled. These strategy names are
permitted: DUPSWEEDOUT
,
FIRSTMATCH
,
LOOSESCAN
,
MATERIALIZATION
.
For SEMIJOIN
hints, if
no strategies are named, semijoin is used if possible
based on the strategies enabled according to the
optimizer_switch
system
variable. If strategies are named but inapplicable for the
statement, DUPSWEEDOUT
is used.
For NO_SEMIJOIN
hints,
if no strategies are named, semijoin is not used. If
strategies are named that rule out all applicable
strategies for the statement,
DUPSWEEDOUT
is used.
If one subquery is nested within another and both are merged
into a semijoin of an outer query, any specification of
semijoin strategies for the innermost query are ignored.
SEMIJOIN
and
NO_SEMIJOIN
hints can still
be used to enable or disable semijoin transformations for such
nested subqueries.
If DUPSWEEDOUT
is disabled, on occasion the
optimizer may generate a query plan that is far from optimal.
This occurs due to heuristic pruning during greedy search,
which can be avoided by setting
optimizer_prune_level=0
.
Examples:
SELECT /*+ NO_SEMIJOIN(@subq1 FIRSTMATCH, LOOSESCAN) */ * FROM t2 WHERE t2.a IN (SELECT /*+ QB_NAME(subq1) */ a FROM t3); SELECT /*+ SEMIJOIN(@subq1 MATERIALIZATION, DUPSWEEDOUT) */ * FROM t2 WHERE t2.a IN (SELECT /*+ QB_NAME(subq1) */ a FROM t3);
Syntax of hints that affect whether to use subquery
materialization or
IN
-to-EXISTS
transformations:
SUBQUERY([@query_block_name
]strategy
)
The hint name is always
SUBQUERY
.
For SUBQUERY
hints, these
strategy
values are permitted:
INTOEXISTS
,
MATERIALIZATION
.
Examples:
SELECT id, a IN (SELECT /*+ SUBQUERY(MATERIALIZATION) */ a FROM t1) FROM t2; SELECT * FROM t2 WHERE t2.a IN (SELECT /*+ SUBQUERY(INTOEXISTS) */ a FROM t1);
For semijoin and SUBQUERY
hints, a leading
@
specifies the query block to which the hint applies. If the
hint includes no leading
query_block_name
@
,
the hint applies to the query block in which it occurs. To
assign a name to a query block, see
Optimizer Hints for Naming Query Blocks.
query_block_name
If a hint comment contains multiple subquery hints, the first is used. If there are other following hints of that type, they produce a warning. Following hints of other types are silently ignored.
The MAX_EXECUTION_TIME
hint
is permitted only for SELECT
statements. It places a limit N
(a
timeout value in milliseconds) on how long a statement is
permitted to execute before the server terminates it:
MAX_EXECUTION_TIME(N
)
Example with a timeout of 1 second (1000 milliseconds):
SELECT /*+ MAX_EXECUTION_TIME(1000) */ * FROM t1 INNER JOIN t2 WHERE ...
The
MAX_EXECUTION_TIME(
hint sets a statement execution timeout of
N
)N
milliseconds. If this option is
absent or N
is 0, the statement
timeout established by the
max_execution_time
system
variable applies.
The MAX_EXECUTION_TIME
hint
is applicable as follows:
For statements with multiple SELECT
keywords, such as unions or statements with subqueries,
MAX_EXECUTION_TIME
applies to the entire statement and must appear after the
first SELECT
.
It applies to read-only
SELECT
statements.
Statements that are not read only are those that invoke a
stored function that modifies data as a side effect.
It does not apply to SELECT
statements in stored programs and is ignored.
Table-level, index-level, and subquery optimizer hints permit
specific query blocks to be named as part of their argument
syntax. To create these names, use the
QB_NAME
hint, which assigns
a name to the query block in which it occurs:
QB_NAME(name
)
QB_NAME
hints can be used to
make explicit in a clear way which query blocks other hints
apply to. They also permit all non-query block name hints to
be specified within a single hint comment for easier
understanding of complex statements. Consider the following
statement:
SELECT ... FROM (SELECT ... FROM (SELECT ... FROM ...)) ...
QB_NAME
hints assign names
to query blocks in the statement:
SELECT /*+ QB_NAME(qb1) */ ... FROM (SELECT /*+ QB_NAME(qb2) */ ... FROM (SELECT /*+ QB_NAME(qb3) */ ... FROM ...)) ...
Then other hints can use those names to refer to the appropriate query blocks:
SELECT /*+ QB_NAME(qb1) MRR(@qb1 t1) BKA(@qb2) NO_MRR(@qb3t1 idx1, id2) */ ... FROM (SELECT /*+ QB_NAME(qb2) */ ... FROM (SELECT /*+ QB_NAME(qb3) */ ... FROM ...)) ...
The resulting effect is as follows:
MRR(@qb1 t1)
applies to
table t1
in query block
qb1
.
BKA(@qb2)
applies to
query block qb2
.
NO_MRR(@qb3 t1 idx1,
id2)
applies to indexes idx1
and idx2
in table t1
in query block qb3
.
Query block names are identifiers and follow the usual rules about what names are valid and how to quote them (see Section 9.2, “Schema Object Names”). For example, a query block name that contains spaces must be quoted, which can be done using backticks:
SELECT /*+ BKA(@`my hint name`) */ ... FROM (SELECT /*+ QB_NAME(`my hint name`) */ ...) ...
If the ANSI_QUOTES
SQL mode
is enabled, it is also possible to quote query block names
within double quotation marks:
SELECT /*+ BKA(@"my hint name") */ ... FROM (SELECT /*+ QB_NAME("my hint name") */ ...) ...
Index hints give the optimizer information about how to choose indexes during query processing. Index hints, described here, differ from optimizer hints, described in Section 8.9.3, “Optimizer Hints”. Index and optimizer hints may be used separately or together.
Index hints apply only to SELECT
and UPDATE
statements.
Index hints are specified following a table name. (For the
general syntax for specifying tables in a
SELECT
statement, see
Section 13.2.9.2, “JOIN Clause”.) The syntax for referring to an
individual table, including index hints, looks like this:
tbl_name
[[AS]alias
] [index_hint_list
]index_hint_list
:index_hint
[index_hint
] ...index_hint
: USE {INDEX|KEY} [FOR {JOIN|ORDER BY|GROUP BY}] ([index_list
]) | {IGNORE|FORCE} {INDEX|KEY} [FOR {JOIN|ORDER BY|GROUP BY}] (index_list
)index_list
:index_name
[,index_name
] ...
The USE INDEX
(
hint tells
MySQL to use only one of the named indexes to find rows in the
table. The alternative syntax index_list
)IGNORE INDEX
(
tells MySQL to
not use some particular index or indexes. These hints are useful
if index_list
)EXPLAIN
shows that MySQL is
using the wrong index from the list of possible indexes.
The FORCE INDEX
hint acts like USE
INDEX (
, with
the addition that a table scan is assumed to be
very expensive. In other words, a table
scan is used only if there is no way to use one of the named
indexes to find rows in the table.
index_list
)
Each hint requires index names, not column names. To refer to a
primary key, use the name PRIMARY
. To see the
index names for a table, use the SHOW
INDEX
statement or the
INFORMATION_SCHEMA.STATISTICS
table.
An index_name
value need not be a
full index name. It can be an unambiguous prefix of an index
name. If a prefix is ambiguous, an error occurs.
Examples:
SELECT * FROM table1 USE INDEX (col1_index,col2_index) WHERE col1=1 AND col2=2 AND col3=3; SELECT * FROM table1 IGNORE INDEX (col3_index) WHERE col1=1 AND col2=2 AND col3=3;
The syntax for index hints has the following characteristics:
It is syntactically valid to omit
index_list
for USE
INDEX
, which means “use no indexes.”
Omitting index_list
for
FORCE INDEX
or IGNORE
INDEX
is a syntax error.
You can specify the scope of an index hint by adding a
FOR
clause to the hint. This provides
more fine-grained control over optimizer selection of an
execution plan for various phases of query processing. To
affect only the indexes used when MySQL decides how to find
rows in the table and how to process joins, use FOR
JOIN
. To influence index usage for sorting or
grouping rows, use FOR ORDER BY
or
FOR GROUP BY
.
You can specify multiple index hints:
SELECT * FROM t1 USE INDEX (i1) IGNORE INDEX FOR ORDER BY (i2) ORDER BY a;
It is not an error to name the same index in several hints (even within the same hint):
SELECT * FROM t1 USE INDEX (i1) USE INDEX (i1,i1);
However, it is an error to mix USE INDEX
and FORCE INDEX
for the same table:
SELECT * FROM t1 USE INDEX FOR JOIN (i1) FORCE INDEX FOR JOIN (i2);
If an index hint includes no FOR
clause, the
scope of the hint is to apply to all parts of the statement. For
example, this hint:
IGNORE INDEX (i1)
is equivalent to this combination of hints:
IGNORE INDEX FOR JOIN (i1) IGNORE INDEX FOR ORDER BY (i1) IGNORE INDEX FOR GROUP BY (i1)
In MySQL 5.0, hint scope with no FOR
clause
was to apply only to row retrieval. To cause the server to use
this older behavior when no FOR
clause is
present, enable the old
system
variable at server startup. Take care about enabling this
variable in a replication setup. With statement-based binary
logging, having different modes for the master and slaves might
lead to replication errors.
When index hints are processed, they are collected in a single
list by type (USE
, FORCE
,
IGNORE
) and by scope (FOR
JOIN
, FOR ORDER BY
, FOR
GROUP BY
). For example:
SELECT * FROM t1 USE INDEX () IGNORE INDEX (i2) USE INDEX (i1) USE INDEX (i2);
is equivalent to:
SELECT * FROM t1 USE INDEX (i1,i2) IGNORE INDEX (i2);
The index hints then are applied for each scope in the following order:
{USE|FORCE} INDEX
is applied if present.
(If not, the optimizer-determined set of indexes is used.)
IGNORE INDEX
is applied over the result
of the previous step. For example, the following two queries
are equivalent:
SELECT * FROM t1 USE INDEX (i1) IGNORE INDEX (i2) USE INDEX (i2); SELECT * FROM t1 USE INDEX (i1);
For FULLTEXT
searches, index hints work as
follows:
For natural language mode searches, index hints are silently
ignored. For example, IGNORE INDEX(i1)
is
ignored with no warning and the index is still used.
For boolean mode searches, index hints with FOR
ORDER BY
or FOR GROUP BY
are
silently ignored. Index hints with FOR
JOIN
or no FOR
modifier are
honored. In contrast to how hints apply for
non-FULLTEXT
searches, the hint is used
for all phases of query execution (finding rows and
retrieval, grouping, and ordering). This is true even if the
hint is given for a non-FULLTEXT
index.
For example, the following two queries are equivalent:
SELECT * FROM t USE INDEX (index1) IGNORE INDEX (index1) FOR ORDER BY IGNORE INDEX (index1) FOR GROUP BY WHERE ... IN BOOLEAN MODE ... ; SELECT * FROM t USE INDEX (index1) WHERE ... IN BOOLEAN MODE ... ;
To generate execution plans, the optimizer uses a cost model that is based on estimates of the cost of various operations that occur during query execution. The optimizer has a set of compiled-in default “cost constants” available to it to make decisions regarding execution plans.
The optimizer also has a database of cost estimates to use
during execution plan construction. These estimates are stored
in the server_cost
and
engine_cost
tables in the
mysql
system database and are configurable at
any time. The intent of these tables is to make it possible to
easily adjust the cost estimates that the optimizer uses when it
attempts to arrive at query execution plans.
The configurable optimizer cost model works like this:
The server reads the cost model tables into memory at
startup and uses the in-memory values at runtime. Any
non-NULL
cost estimate specified in the
tables takes precedence over the corresponding compiled-in
default cost constant. Any NULL
estimate indicates to the optimizer to use the compiled-in
default.
At runtime, the server may reread the cost tables. This
occurs when a storage engine is dynamically loaded or when
a FLUSH OPTIMIZER_COSTS
statement is executed.
Cost tables enable server administrators to easily adjust
cost estimates by changing entries in the tables. It is
also easy to revert to a default by setting an
entry's cost to NULL
. The
optimizer uses the in-memory cost values, so changes to
the tables should be followed by
FLUSH OPTIMIZER_COSTS
to
take effect.
The in-memory cost estimates that are current when a client session begins apply throughout that session until it ends. In particular, if the server rereads the cost tables, any changed estimates apply only to subsequently started sessions. Existing sessions are unaffected.
Cost tables are specific to a given server instance. The server does not replicate cost table changes to replication slaves.
The optimizer cost model database consists of two tables in
the mysql
system database that contain cost
estimate information for operations that occur during query
execution:
The server_cost
table contains these
columns:
cost_name
The name of a cost estimate used in the cost model. The name is not case-sensitive. If the server does not recognize the cost name when it reads this table, it writes a warning to the error log.
cost_value
The cost estimate value. If the value is
non-NULL
, the server uses it as the
cost. Otherwise, it uses the default estimate (the
compiled-in value). DBAs can change a cost estimate by
updating this column. If the server finds that the cost
value is invalid (nonpositive) when it reads this table,
it writes a warning to the error log.
To override a default cost estimate (for an entry that
specifies NULL
), set the cost to a
non-NULL
value. To revert to the
default, set the value to NULL
. Then
execute FLUSH
OPTIMIZER_COSTS
to tell the server to reread the
cost tables.
last_update
The time of the last row update.
comment
A descriptive comment associated with the cost estimate. DBAs can use this column to provide information about why a cost estimate row stores a particular value.
The primary key for the server_cost
table
is the cost_name
column, so it is not
possible to create multiple entries for any cost estimate.
The server recognizes these cost_name
values for the server_cost
table:
disk_temptable_create_cost
(default
40.0), disk_temptable_row_cost
(default
1.0)
The cost estimates for internally created temporary tables
stored in a disk-based storage engine (either
InnoDB
or MyISAM
).
Increasing these values increases the cost estimate of
using internal temporary tables and makes the optimizer
prefer query plans with less use of them. For information
about such tables, see
Section 8.4.4, “Internal Temporary Table Use in MySQL”.
The larger default values for these disk parameters
compared to the default values for the corresponding
memory parameters
(memory_temptable_create_cost
,
memory_temptable_row_cost
) reflects the
greater cost of processing disk-based tables.
key_compare_cost
(default 0.1)
The cost of comparing record keys. Increasing this value
causes a query plan that compares many keys to become more
expensive. For example, a query plan that performs a
filesort
becomes relatively more
expensive compared to a query plan that avoids sorting by
using an index.
memory_temptable_create_cost
(default
2.0), memory_temptable_row_cost
(default 0.2)
The cost estimates for internally created temporary tables
stored in the MEMORY
storage engine.
Increasing these values increases the cost estimate of
using internal temporary tables and makes the optimizer
prefer query plans with less use of them. For information
about such tables, see
Section 8.4.4, “Internal Temporary Table Use in MySQL”.
The smaller default values for these memory parameters
compared to the default values for the corresponding disk
parameters (disk_temptable_create_cost
,
disk_temptable_row_cost
) reflects the
lesser cost of processing memory-based tables.
row_evaluate_cost
(default 0.2)
The cost of evaluating record conditions. Increasing this value causes a query plan that examines many rows to become more expensive compared to a query plan that examines fewer rows. For example, a table scan becomes relatively more expensive compared to a range scan that reads fewer rows.
The engine_cost
table contains these
columns:
engine_name
The name of the storage engine to which this cost estimate
applies. The name is not case-sensitive. If the value is
default
, it applies to all storage
engines that have no named entry of their own. If the
server does not recognize the engine name when it reads
this table, it writes a warning to the error log.
device_type
The device type to which this cost estimate applies. The column is intended for specifying different cost estimates for different storage device types, such as hard disk drives versus solid state drives. Currently, this information is not used and 0 is the only permitted value.
cost_name
Same as in the server_cost
table.
cost_value
Same as in the server_cost
table.
last_update
Same as in the server_cost
table.
comment
Same as in the server_cost
table.
The primary key for the engine_cost
table
is a tuple comprising the (cost_name
,
engine_name
,
device_type
) columns, so it is not possible
to create multiple entries for any combination of values in
those columns.
The server recognizes these cost_name
values for the engine_cost
table:
io_block_read_cost
(default 1.0)
The cost of reading an index or data block from disk. Increasing this value causes a query plan that reads many disk blocks to become more expensive compared to a query plan that reads fewer disk blocks. For example, a table scan becomes relatively more expensive compared to a range scan that reads fewer blocks.
memory_block_read_cost
(default 1.0)
Similar to io_block_read_cost
, but
represents the cost of reading an index or data block from
an in-memory database buffer.
If the io_block_read_cost
and
memory_block_read_cost
values differ, the
execution plan may change between two runs of the same query.
Suppose that the cost for memory access is less than the cost
for disk access. In that case, at server startup before data
has been read into the buffer pool, you may get a different
plan than after the query has been run because then the data
will be in memory.
For DBAs who wish to change the cost model parameters from their defaults, try doubling or halving the value and measuring the effect.
Changes to the io_block_read_cost
and
memory_block_read_cost
parameters are most
likely to yield worthwhile results. These parameter values
enable cost models for data access methods to take into
account the costs of reading information from different
sources; that is, the cost of reading information from disk
versus reading information already in a memory buffer. For
example, all other things being equal, setting
io_block_read_cost
to a value larger than
memory_block_read_cost
causes the optimizer
to prefer query plans that read information already held in
memory to plans that must read from disk.
This example shows how to change the default value for
io_block_read_cost
:
UPDATE mysql.engine_cost SET cost_value = 2.0 WHERE cost_name = 'io_block_read_cost'; FLUSH OPTIMIZER_COSTS;
This example shows how to change the value of
io_block_read_cost
only for the
InnoDB
storage engine:
INSERT INTO mysql.engine_cost VALUES ('InnoDB', 0, 'io_block_read_cost', 3.0, CURRENT_TIMESTAMP, 'Using a slower disk for InnoDB'); FLUSH OPTIMIZER_COSTS;
MySQL uses several strategies that cache information in memory buffers to increase performance.
InnoDB
maintains a storage area
called the buffer pool
for caching data and indexes in memory. Knowing how the
InnoDB
buffer pool works, and taking
advantage of it to keep frequently accessed data in memory, is
an important aspect of MySQL tuning.
For an explanation of the inner workings of the
InnoDB
buffer pool, an overview of its LRU
replacement algorithm, and general configuration information,
see Section 14.5.1, “Buffer Pool”.
For additional InnoDB
buffer pool
configuration and tuning information, see these sections:
To minimize disk I/O, the MyISAM
storage
engine exploits a strategy that is used by many database
management systems. It employs a cache mechanism to keep the
most frequently accessed table blocks in memory:
For index blocks, a special structure called the key cache (or key buffer) is maintained. The structure contains a number of block buffers where the most-used index blocks are placed.
For data blocks, MySQL uses no special cache. Instead it relies on the native operating system file system cache.
This section first describes the basic operation of the
MyISAM
key cache. Then it discusses features
that improve key cache performance and that enable you to better
control cache operation:
Multiple sessions can access the cache concurrently.
You can set up multiple key caches and assign table indexes to specific caches.
To control the size of the key cache, use the
key_buffer_size
system
variable. If this variable is set equal to zero, no key cache is
used. The key cache also is not used if the
key_buffer_size
value is too
small to allocate the minimal number of block buffers (8).
When the key cache is not operational, index files are accessed using only the native file system buffering provided by the operating system. (In other words, table index blocks are accessed using the same strategy as that employed for table data blocks.)
An index block is a contiguous unit of access to the
MyISAM
index files. Usually the size of an
index block is equal to the size of nodes of the index B-tree.
(Indexes are represented on disk using a B-tree data structure.
Nodes at the bottom of the tree are leaf nodes. Nodes above the
leaf nodes are nonleaf nodes.)
All block buffers in a key cache structure are the same size. This size can be equal to, greater than, or less than the size of a table index block. Usually one these two values is a multiple of the other.
When data from any table index block must be accessed, the server first checks whether it is available in some block buffer of the key cache. If it is, the server accesses data in the key cache rather than on disk. That is, it reads from the cache or writes into it rather than reading from or writing to disk. Otherwise, the server chooses a cache block buffer containing a different table index block (or blocks) and replaces the data there by a copy of required table index block. As soon as the new index block is in the cache, the index data can be accessed.
If it happens that a block selected for replacement has been modified, the block is considered “dirty.” In this case, prior to being replaced, its contents are flushed to the table index from which it came.
Usually the server follows an LRU (Least Recently Used) strategy: When choosing a block for replacement, it selects the least recently used index block. To make this choice easier, the key cache module maintains all used blocks in a special list (LRU chain) ordered by time of use. When a block is accessed, it is the most recently used and is placed at the end of the list. When blocks need to be replaced, blocks at the beginning of the list are the least recently used and become the first candidates for eviction.
The InnoDB
storage engine also uses an LRU
algorithm, to manage its buffer pool. See
Section 14.5.1, “Buffer Pool”.
Threads can access key cache buffers simultaneously, subject to the following conditions:
A buffer that is not being updated can be accessed by multiple sessions.
A buffer that is being updated causes sessions that need to use it to wait until the update is complete.
Multiple sessions can initiate requests that result in cache block replacements, as long as they do not interfere with each other (that is, as long as they need different index blocks, and thus cause different cache blocks to be replaced).
Shared access to the key cache enables the server to improve throughput significantly.
Shared access to the key cache improves performance but does not eliminate contention among sessions entirely. They still compete for control structures that manage access to the key cache buffers. To reduce key cache access contention further, MySQL also provides multiple key caches. This feature enables you to assign different table indexes to different key caches.
Where there are multiple key caches, the server must know
which cache to use when processing queries for a given
MyISAM
table. By default, all
MyISAM
table indexes are cached in the
default key cache. To assign table indexes to a specific key
cache, use the CACHE INDEX
statement (see Section 13.7.6.2, “CACHE INDEX Statement”). For example,
the following statement assigns indexes from the tables
t1
, t2
, and
t3
to the key cache named
hot_cache
:
mysql> CACHE INDEX t1, t2, t3 IN hot_cache;
+---------+--------------------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+---------+--------------------+----------+----------+
| test.t1 | assign_to_keycache | status | OK |
| test.t2 | assign_to_keycache | status | OK |
| test.t3 | assign_to_keycache | status | OK |
+---------+--------------------+----------+----------+
The key cache referred to in a CACHE
INDEX
statement can be created by setting its size
with a SET
GLOBAL
parameter setting statement or by using
server startup options. For example:
mysql> SET GLOBAL keycache1.key_buffer_size=128*1024;
To destroy a key cache, set its size to zero:
mysql> SET GLOBAL keycache1.key_buffer_size=0;
You cannot destroy the default key cache. Any attempt to do this is ignored:
mysql>SET GLOBAL key_buffer_size = 0;
mysql>SHOW VARIABLES LIKE 'key_buffer_size';
+-----------------+---------+ | Variable_name | Value | +-----------------+---------+ | key_buffer_size | 8384512 | +-----------------+---------+
Key cache variables are structured system variables that have
a name and components. For
keycache1.key_buffer_size
,
keycache1
is the cache variable name and
key_buffer_size
is the cache
component. See Section 5.1.8.3, “Structured System Variables”,
for a description of the syntax used for referring to
structured key cache system variables.
By default, table indexes are assigned to the main (default) key cache created at the server startup. When a key cache is destroyed, all indexes assigned to it are reassigned to the default key cache.
For a busy server, you can use a strategy that involves three key caches:
A “hot” key cache that takes up 20% of the space allocated for all key caches. Use this for tables that are heavily used for searches but that are not updated.
A “cold” key cache that takes up 20% of the space allocated for all key caches. Use this cache for medium-sized, intensively modified tables, such as temporary tables.
A “warm” key cache that takes up 60% of the key cache space. Employ this as the default key cache, to be used by default for all other tables.
One reason the use of three key caches is beneficial is that access to one key cache structure does not block access to the others. Statements that access tables assigned to one cache do not compete with statements that access tables assigned to another cache. Performance gains occur for other reasons as well:
The hot cache is used only for retrieval queries, so its contents are never modified. Consequently, whenever an index block needs to be pulled in from disk, the contents of the cache block chosen for replacement need not be flushed first.
For an index assigned to the hot cache, if there are no queries requiring an index scan, there is a high probability that the index blocks corresponding to nonleaf nodes of the index B-tree remain in the cache.
An update operation most frequently executed for temporary tables is performed much faster when the updated node is in the cache and need not be read from disk first. If the size of the indexes of the temporary tables are comparable with the size of cold key cache, the probability is very high that the updated node is in the cache.
The CACHE INDEX
statement sets
up an association between a table and a key cache, but the
association is lost each time the server restarts. If you want
the association to take effect each time the server starts,
one way to accomplish this is to use an option file: Include
variable settings that configure your key caches, and an
init_file
system variable
that names a file containing CACHE
INDEX
statements to be executed. For example:
key_buffer_size = 4G hot_cache.key_buffer_size = 2G cold_cache.key_buffer_size = 2G init_file=/path
/to
/data-directory
/mysqld_init.sql
The statements in mysqld_init.sql
are
executed each time the server starts. The file should contain
one SQL statement per line. The following example assigns
several tables each to hot_cache
and
cold_cache
:
CACHE INDEX db1.t1, db1.t2, db2.t3 IN hot_cache CACHE INDEX db1.t4, db2.t5, db2.t6 IN cold_cache
By default, the key cache management system uses a simple LRU strategy for choosing key cache blocks to be evicted, but it also supports a more sophisticated method called the midpoint insertion strategy.
When using the midpoint insertion strategy, the LRU chain is
divided into two parts: a hot sublist and a warm sublist. The
division point between two parts is not fixed, but the key
cache management system takes care that the warm part is not
“too short,” always containing at least
key_cache_division_limit
percent of the key cache blocks.
key_cache_division_limit
is a
component of structured key cache variables, so its value is a
parameter that can be set per cache.
When an index block is read from a table into the key cache, it is placed at the end of the warm sublist. After a certain number of hits (accesses of the block), it is promoted to the hot sublist. At present, the number of hits required to promote a block (3) is the same for all index blocks.
A block promoted into the hot sublist is placed at the end of
the list. The block then circulates within this sublist. If
the block stays at the beginning of the sublist for a long
enough time, it is demoted to the warm sublist. This time is
determined by the value of the
key_cache_age_threshold
component of the key cache.
The threshold value prescribes that, for a key cache
containing N
blocks, the block at
the beginning of the hot sublist not accessed within the last
hits is to be moved to
the beginning of the warm sublist. It then becomes the first
candidate for eviction, because blocks for replacement always
are taken from the beginning of the warm sublist.
N
*
key_cache_age_threshold / 100
The midpoint insertion strategy enables you to keep
more-valued blocks always in the cache. If you prefer to use
the plain LRU strategy, leave the
key_cache_division_limit
value set to its default of 100.
The midpoint insertion strategy helps to improve performance
when execution of a query that requires an index scan
effectively pushes out of the cache all the index blocks
corresponding to valuable high-level B-tree nodes. To avoid
this, you must use a midpoint insertion strategy with the
key_cache_division_limit
set
to much less than 100. Then valuable frequently hit nodes are
preserved in the hot sublist during an index scan operation as
well.
If there are enough blocks in a key cache to hold blocks of an entire index, or at least the blocks corresponding to its nonleaf nodes, it makes sense to preload the key cache with index blocks before starting to use it. Preloading enables you to put the table index blocks into a key cache buffer in the most efficient way: by reading the index blocks from disk sequentially.
Without preloading, the blocks are still placed into the key cache as needed by queries. Although the blocks will stay in the cache, because there are enough buffers for all of them, they are fetched from disk in random order, and not sequentially.
To preload an index into a cache, use the
LOAD INDEX INTO
CACHE
statement. For example, the following
statement preloads nodes (index blocks) of indexes of the
tables t1
and t2
:
mysql> LOAD INDEX INTO CACHE t1, t2 IGNORE LEAVES;
+---------+--------------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+---------+--------------+----------+----------+
| test.t1 | preload_keys | status | OK |
| test.t2 | preload_keys | status | OK |
+---------+--------------+----------+----------+
The IGNORE LEAVES
modifier causes only
blocks for the nonleaf nodes of the index to be preloaded.
Thus, the statement shown preloads all index blocks from
t1
, but only blocks for the nonleaf nodes
from t2
.
If an index has been assigned to a key cache using a
CACHE INDEX
statement,
preloading places index blocks into that cache. Otherwise, the
index is loaded into the default key cache.
It is possible to specify the size of the block buffers for an
individual key cache using the
key_cache_block_size
variable. This permits tuning of the performance of I/O
operations for index files.
The best performance for I/O operations is achieved when the size of read buffers is equal to the size of the native operating system I/O buffers. But setting the size of key nodes equal to the size of the I/O buffer does not always ensure the best overall performance. When reading the big leaf nodes, the server pulls in a lot of unnecessary data, effectively preventing reading other leaf nodes.
To control the size of blocks in the .MYI
index file of MyISAM
tables, use the
--myisam-block-size
option at
server startup.
A key cache can be restructured at any time by updating its parameter values. For example:
mysql> SET GLOBAL cold_cache.key_buffer_size=4*1024*1024;
If you assign to either the
key_buffer_size
or
key_cache_block_size
key
cache component a value that differs from the component's
current value, the server destroys the cache's old
structure and creates a new one based on the new values. If
the cache contains any dirty blocks, the server saves them to
disk before destroying and re-creating the cache.
Restructuring does not occur if you change other key cache
parameters.
When restructuring a key cache, the server first flushes the contents of any dirty buffers to disk. After that, the cache contents become unavailable. However, restructuring does not block queries that need to use indexes assigned to the cache. Instead, the server directly accesses the table indexes using native file system caching. File system caching is not as efficient as using a key cache, so although queries execute, a slowdown can be anticipated. After the cache has been restructured, it becomes available again for caching indexes assigned to it, and the use of file system caching for the indexes ceases.
The query cache is deprecated as of MySQL 5.7.20, and is removed in MySQL 8.0.
The query cache stores the text of a
SELECT
statement together with
the corresponding result that was sent to the client. If an
identical statement is received later, the server retrieves the
results from the query cache rather than parsing and executing
the statement again. The query cache is shared among sessions,
so a result set generated by one client can be sent in response
to the same query issued by another client.
The query cache can be useful in an environment where you have tables that do not change very often and for which the server receives many identical queries. This is a typical situation for many Web servers that generate many dynamic pages based on database content.
The query cache does not return stale data. When tables are modified, any relevant entries in the query cache are flushed.
The query cache does not work in an environment where you have
multiple mysqld servers updating the same
MyISAM
tables.
The query cache is used for prepared statements under the conditions described in Section 8.10.3.1, “How the Query Cache Operates”.
The query cache is not supported for partitioned tables, and is automatically disabled for queries involving partitioned tables. The query cache cannot be enabled for such queries.
Some performance data for the query cache follows. These results were generated by running the MySQL benchmark suite on a Linux Alpha 2×500MHz system with 2GB RAM and a 64MB query cache.
If all the queries you are performing are simple (such as selecting a row from a table with one row), but still differ so that the queries cannot be cached, the overhead for having the query cache active is 13%. This could be regarded as the worst case scenario. In real life, queries tend to be much more complicated, so the overhead normally is significantly lower.
Searches for a single row in a single-row table are 238% faster with the query cache than without it. This can be regarded as close to the minimum speedup to be expected for a query that is cached.
To disable the query cache at server startup, set the
query_cache_size
system
variable to 0. By disabling the query cache code, there is no
noticeable overhead.
The query cache offers the potential for substantial performance improvement, but do not assume that it will do so under all circumstances. With some query cache configurations or server workloads, you might actually see a performance decrease:
Be cautious about sizing the query cache excessively large, which increases the overhead required to maintain the cache, possibly beyond the benefit of enabling it. Sizes in tens of megabytes are usually beneficial. Sizes in the hundreds of megabytes might not be.
Server workload has a significant effect on query cache
efficiency. A query mix consisting almost entirely of a
fixed set of SELECT
statements is much more likely to benefit from enabling the
cache than a mix in which frequent
INSERT
statements cause
continual invalidation of results in the cache. In some
cases, a workaround is to use the
SQL_NO_CACHE
option to prevent results
from even entering the cache for
SELECT
statements that use
frequently modified tables. (See
Section 8.10.3.2, “Query Cache SELECT Options”.)
To verify that enabling the query cache is beneficial, test the operation of your MySQL server with the cache enabled and disabled. Then retest periodically because query cache efficiency may change as server workload changes.
The query cache is deprecated as of MySQL 5.7.20, and is removed in MySQL 8.0.
This section describes how the query cache works when it is operational. Section 8.10.3.3, “Query Cache Configuration”, describes how to control whether it is operational.
Incoming queries are compared to those in the query cache before parsing, so the following two queries are regarded as different by the query cache:
SELECT * FROMtbl_name
Select * fromtbl_name
Queries must be exactly the same (byte for byte) to be seen as identical. In addition, query strings that are identical may be treated as different for other reasons. Queries that use different databases, different protocol versions, or different default character sets are considered different queries and are cached separately.
The cache is not used for queries of the following types:
Queries that are a subquery of an outer query
Queries executed within the body of a stored function, trigger, or event
Before a query result is fetched from the query cache, MySQL
checks whether the user has
SELECT
privilege for all
databases and tables involved. If this is not the case, the
cached result is not used.
If a query result is returned from query cache, the server
increments the Qcache_hits
status variable, not Com_select
. See
Section 8.10.3.4, “Query Cache Status and Maintenance”.
If a table changes, all cached queries that use the table
become invalid and are removed from the cache. This includes
queries that use MERGE
tables that map to
the changed table. A table can be changed by many types of
statements, such as INSERT
,
UPDATE
,
DELETE
,
TRUNCATE TABLE
,
ALTER TABLE
,
DROP TABLE
, or
DROP DATABASE
.
The query cache also works within transactions when using
InnoDB
tables.
The result from a SELECT
query
on a view is cached.
The query cache works for SELECT SQL_CALC_FOUND_ROWS
...
queries and stores a value that is returned by a
following SELECT FOUND_ROWS()
query.
FOUND_ROWS()
returns the
correct value even if the preceding query was fetched from the
cache because the number of found rows is also stored in the
cache. The SELECT FOUND_ROWS()
query itself
cannot be cached.
Prepared statements that are issued using the binary protocol
using mysql_stmt_prepare()
and
mysql_stmt_execute()
(see
Section 27.7.7, “C API Prepared Statements”), are subject to
limitations on caching. Comparison with statements in the
query cache is based on the text of the statement after
expansion of ?
parameter markers. The
statement is compared only with other cached statements that
were executed using the binary protocol. That is, for query
cache purposes, prepared statements issued using the binary
protocol are distinct from prepared statements issued using
the text protocol (see
Section 13.5, “Prepared Statements”).
A query cannot be cached if it uses any of the following functions:
ENCRYPT()
with one
parameter
UNIX_TIMESTAMP()
with no
parameters
A query also is not cached under these conditions:
It refers to user-defined functions (UDFs) or stored functions.
It refers to user variables or local stored program variables.
It refers to tables in the mysql
,
INFORMATION_SCHEMA
, or
performance_schema
database.
It refers to any partitioned tables.
It is of any of the following forms:
SELECT ... LOCK IN SHARE MODE SELECT ... FOR UPDATE SELECT ... INTO OUTFILE ... SELECT ... INTO DUMPFILE ... SELECT * FROM ... WHERE autoincrement_col IS NULL
The last form is not cached because it is used as the ODBC workaround for obtaining the last insert ID value. See the Connector/ODBC section of Chapter 27, Connectors and APIs.
Statements within transactions that use
SERIALIZABLE
isolation
level also cannot be cached because they use LOCK
IN SHARE MODE
locking.
It uses TEMPORARY
tables.
It does not use any tables.
It generates warnings.
The user has a column-level privilege for any of the involved tables.
The query cache is deprecated as of MySQL 5.7.20, and is removed in MySQL 8.0.
Two query cache-related options may be specified in
SELECT
statements:
The query result is cached if it is cacheable and the
value of the
query_cache_type
system
variable is ON
or
DEMAND
.
SQL_NO_CACHE
The server does not use the query cache. It neither checks the query cache to see whether the result is already cached, nor does it cache the query result.
Examples:
SELECT SQL_CACHE id, name FROM customer; SELECT SQL_NO_CACHE id, name FROM customer;
The query cache is deprecated as of MySQL 5.7.20, and is removed in MySQL 8.0.
The have_query_cache
server
system variable indicates whether the query cache is
available:
mysql> SHOW VARIABLES LIKE 'have_query_cache';
+------------------+-------+
| Variable_name | Value |
+------------------+-------+
| have_query_cache | YES |
+------------------+-------+
When using a standard MySQL binary, this value is always
YES
, even if query caching is disabled.
Several other system variables control query cache operation.
These can be set in an option file or on the command line when
starting mysqld. The query cache system
variables all have names that begin with
query_cache_
. They are described briefly in
Section 5.1.7, “Server System Variables”, with additional
configuration information given here.
To set the size of the query cache, set the
query_cache_size
system
variable. Setting it to 0 disables the query cache, as does
setting query_cache_type=0
.
By default, the query cache is disabled. This is achieved
using a default size of 1M, with a default for
query_cache_type
of 0.
To reduce overhead significantly, start the server with
query_cache_type=0
if you
will not be using the query cache.
When using the Windows Configuration Wizard to install or
configure MySQL, the default value for
query_cache_size
will be
configured automatically for you based on the different
configuration types available. When using the Windows
Configuration Wizard, the query cache may be enabled (that
is, set to a nonzero value) due to the selected
configuration. The query cache is also controlled by the
setting of the
query_cache_type
variable.
Check the values of these variables as set in your
my.ini
file after configuration has
taken place.
When you set query_cache_size
to a nonzero value, keep in mind that the query cache needs a
minimum size of about 40KB to allocate its structures. (The
exact size depends on system architecture.) If you set the
value too small, you'll get a warning, as in this example:
mysql>SET GLOBAL query_cache_size = 40000;
Query OK, 0 rows affected, 1 warning (0.00 sec) mysql>SHOW WARNINGS\G
*************************** 1. row *************************** Level: Warning Code: 1282 Message: Query cache failed to set size 39936; new query cache size is 0 mysql>SET GLOBAL query_cache_size = 41984;
Query OK, 0 rows affected (0.00 sec) mysql>SHOW VARIABLES LIKE 'query_cache_size';
+------------------+-------+ | Variable_name | Value | +------------------+-------+ | query_cache_size | 41984 | +------------------+-------+
For the query cache to actually be able to hold any query results, its size must be set larger:
mysql>SET GLOBAL query_cache_size = 1000000;
Query OK, 0 rows affected (0.04 sec) mysql>SHOW VARIABLES LIKE 'query_cache_size';
+------------------+--------+ | Variable_name | Value | +------------------+--------+ | query_cache_size | 999424 | +------------------+--------+ 1 row in set (0.00 sec)
The query_cache_size
value is
aligned to the nearest 1024 byte block. The value reported may
therefore be different from the value that you assign.
If the query cache size is greater than 0, the
query_cache_type
variable
influences how it works. This variable can be set to the
following values:
A value of 0
or OFF
prevents caching or retrieval of cached results.
A value of 1
or ON
enables caching except of those statements that begin with
SELECT SQL_NO_CACHE
.
A value of 2
or
DEMAND
causes caching of only those
statements that begin with SELECT
SQL_CACHE
.
If query_cache_size
is 0, you
should also set
query_cache_type
variable to
0. In this case, the server does not acquire the query cache
mutex at all, which means that the query cache cannot be
enabled at runtime and there is reduced overhead in query
execution.
Setting the GLOBAL
query_cache_type
value
determines query cache behavior for all clients that connect
after the change is made. Individual clients can control cache
behavior for their own connection by setting the
SESSION
query_cache_type
value. For
example, a client can disable use of the query cache for its
own queries like this:
mysql> SET SESSION query_cache_type = OFF;
If you set query_cache_type
at server startup (rather than at runtime with a
SET
statement), only the numeric values are permitted.
To control the maximum size of individual query results that
can be cached, set the
query_cache_limit
system
variable. The default value is 1MB.
Be careful not to set the size of the cache too large. Due to the need for threads to lock the cache during updates, you may see lock contention issues with a very large cache.
You can set the maximum size that can be specified for the
query cache at runtime with the
SET
statement by using the
--maximum-query_cache_size=
option on the command line or in the configuration file.
32M
When a query is to be cached, its result (the data sent to the
client) is stored in the query cache during result retrieval.
Therefore the data usually is not handled in one big chunk.
The query cache allocates blocks for storing this data on
demand, so when one block is filled, a new block is allocated.
Because memory allocation operation is costly (timewise), the
query cache allocates blocks with a minimum size given by the
query_cache_min_res_unit
system variable. When a query is executed, the last result
block is trimmed to the actual data size so that unused memory
is freed. Depending on the types of queries your server
executes, you might find it helpful to tune the value of
query_cache_min_res_unit
:
The default value of
query_cache_min_res_unit
is 4KB. This should be adequate for most cases.
If you have a lot of queries with small results, the
default block size may lead to memory fragmentation, as
indicated by a large number of free blocks. Fragmentation
can force the query cache to prune (delete) queries from
the cache due to lack of memory. In this case, decrease
the value of
query_cache_min_res_unit
.
The number of free blocks and queries removed due to
pruning are given by the values of the
Qcache_free_blocks
and
Qcache_lowmem_prunes
status variables.
If most of your queries have large results (check the
Qcache_total_blocks
and
Qcache_queries_in_cache
status variables), you can increase performance by
increasing
query_cache_min_res_unit
.
However, be careful to not make it too large (see the
previous item).
The query cache is deprecated as of MySQL 5.7.20, and is removed in MySQL 8.0.
To check whether the query cache is present in your MySQL server, use the following statement:
mysql> SHOW VARIABLES LIKE 'have_query_cache';
+------------------+-------+
| Variable_name | Value |
+------------------+-------+
| have_query_cache | YES |
+------------------+-------+
You can defragment the query cache to better utilize its
memory with the FLUSH QUERY
CACHE
statement. The statement does not remove any
queries from the cache.
The RESET QUERY CACHE
statement removes all
query results from the query cache. The
FLUSH TABLES
statement also
does this.
To monitor query cache performance, use
SHOW STATUS
to view the cache
status variables:
mysql> SHOW STATUS LIKE 'Qcache%';
+-------------------------+--------+
| Variable_name | Value |
+-------------------------+--------+
| Qcache_free_blocks | 36 |
| Qcache_free_memory | 138488 |
| Qcache_hits | 79570 |
| Qcache_inserts | 27087 |
| Qcache_lowmem_prunes | 3114 |
| Qcache_not_cached | 22989 |
| Qcache_queries_in_cache | 415 |
| Qcache_total_blocks | 912 |
+-------------------------+--------+
Descriptions of each of these variables are given in Section 5.1.9, “Server Status Variables”. Some uses for them are described here.
The total number of SELECT
queries is given by this formula:
Com_select + Qcache_hits + queries with errors found by parser
The Com_select
value is given by this
formula:
Qcache_inserts + Qcache_not_cached + queries with errors found during the column-privileges check
The query cache uses variable-length blocks, so
Qcache_total_blocks
and
Qcache_free_blocks
may
indicate query cache memory fragmentation. After
FLUSH QUERY CACHE
, only a
single free block remains.
Every cached query requires a minimum of two blocks (one for the query text and one or more for the query results). Also, every table that is used by a query requires one block. However, if two or more queries use the same table, only one table block needs to be allocated.
The information provided by the
Qcache_lowmem_prunes
status
variable can help you tune the query cache size. It counts the
number of queries that have been removed from the cache to
free up memory for caching new queries. The query cache uses a
least recently used (LRU) strategy to decide which queries to
remove from the cache. Tuning information is given in
Section 8.10.3.3, “Query Cache Configuration”.
For certain statements that a client might execute multiple times during a session, the server converts the statement to an internal structure and caches that structure to be used during execution. Caching enables the server to perform more efficiently because it avoids the overhead of reconverting the statement should it be needed again during the session. Conversion and caching occurs for these statements:
Prepared statements, both those processed at the SQL level
(using the PREPARE
statement)
and those processed using the binary client/server protocol
(using the
mysql_stmt_prepare()
C API
function). The
max_prepared_stmt_count
system variable controls the total number of statements the
server caches. (The sum of the number of prepared statements
across all sessions.)
Stored programs (stored procedures and functions, triggers,
and events). In this case, the server converts and caches
the entire program body. The
stored_program_cache
system
variable indicates the approximate number of stored programs
the server caches per session.
The server maintains caches for prepared statements and stored programs on a per-session basis. Statements cached for one session are not accessible to other sessions. When a session ends, the server discards any statements cached for it.
When the server uses a cached internal statement structure, it
must take care that the structure does not go out of date.
Metadata changes can occur for an object used by the statement,
causing a mismatch between the current object definition and the
definition as represented in the internal statement structure.
Metadata changes occur for DDL statements such as those that
create, drop, alter, rename, or truncate tables, or that
analyze, optimize, or repair tables. Table content changes (for
example, with INSERT
or
UPDATE
) do not change metadata,
nor do SELECT
statements.
Here is an illustration of the problem. Suppose that a client prepares this statement:
PREPARE s1 FROM 'SELECT * FROM t1';
The SELECT *
expands in the internal
structure to the list of columns in the table. If the set of
columns in the table is modified with ALTER
TABLE
, the prepared statement goes out of date. If the
server does not detect this change the next time the client
executes s1
, the prepared statement will
return incorrect results.
To avoid problems caused by metadata changes to tables or views
referred to by the prepared statement, the server detects these
changes and automatically reprepares the statement when it is
next executed. That is, the server reparses the statement and
rebuilds the internal structure. Reparsing also occurs after
referenced tables or views are flushed from the table definition
cache, either implicitly to make room for new entries in the
cache, or explicitly due to FLUSH
TABLES
.
Similarly, if changes occur to objects used by a stored program, the server reparses affected statements within the program.
The server also detects metadata changes for objects in
expressions. These might be used in statements specific to
stored programs, such as DECLARE CURSOR
or
flow-control statements such as
IF
,
CASE
, and
RETURN
.
To avoid reparsing entire stored programs, the server reparses affected statements or expressions within a program only as needed. Examples:
Suppose that metadata for a table or view is changed.
Reparsing occurs for a SELECT *
within
the program that accesses the table or view, but not for a
SELECT *
that does not access the table
or view.
When a statement is affected, the server reparses it only
partially if possible. Consider this
CASE
statement:
CASEcase_expr
WHENwhen_expr1
... WHENwhen_expr2
... WHENwhen_expr3
... ... END CASE
If a metadata change affects only WHEN
, that
expression is reparsed. when_expr3
case_expr
and the other WHEN
expressions are not
reparsed.
Reparsing uses the default database and SQL mode that were in effect for the original conversion to internal form.
The server attempts reparsing up to three times. An error occurs if all attempts fail.
Reparsing is automatic, but to the extent that it occurs, diminishes prepared statement and stored program performance.
For prepared statements, the
Com_stmt_reprepare
status variable tracks the number of repreparations.
MySQL manages contention for table contents using locking:
Internal locking is performed within the MySQL server itself to manage contention for table contents by multiple threads. This type of locking is internal because it is performed entirely by the server and involves no other programs. See Section 8.11.1, “Internal Locking Methods”.
External locking occurs when the server and other programs
lock MyISAM
table files to
coordinate among themselves which program can access the
tables at which time. See Section 8.11.5, “External Locking”.
This section discusses internal locking; that is, locking performed within the MySQL server itself to manage contention for table contents by multiple sessions. This type of locking is internal because it is performed entirely by the server and involves no other programs. For locking performed on MySQL files by other programs, see Section 8.11.5, “External Locking”.
MySQL uses row-level
locking for InnoDB
tables to support
simultaneous write access by multiple sessions, making them
suitable for multi-user, highly concurrent, and OLTP
applications.
To avoid deadlocks when
performing multiple concurrent write operations on a single
InnoDB
table, acquire necessary locks at
the start of the transaction by issuing a SELECT ...
FOR UPDATE
statement for each group of rows expected
to be modified, even if the data change statements come later
in the transaction. If transactions modify or lock more than
one table, issue the applicable statements in the same order
within each transaction. Deadlocks affect performance rather
than representing a serious error, because
InnoDB
automatically
detects
deadlock conditions and rolls back one of the affected
transactions.
On high concurrency systems, deadlock detection can cause a
slowdown when numerous threads wait for the same lock. At
times, it may be more efficient to disable deadlock detection
and rely on the
innodb_lock_wait_timeout
setting for transaction rollback when a deadlock occurs.
Deadlock detection can be disabled using the
innodb_deadlock_detect
configuration option.
Advantages of row-level locking:
Fewer lock conflicts when different sessions access different rows.
Fewer changes for rollbacks.
Possible to lock a single row for a long time.
MySQL uses table-level
locking for MyISAM
,
MEMORY
, and MERGE
tables, permitting only one session to update those tables at
a time. This locking level makes these storage engines more
suitable for read-only, read-mostly, or single-user
applications.
These storage engines avoid deadlocks by always requesting all needed locks at once at the beginning of a query and always locking the tables in the same order. The tradeoff is that this strategy reduces concurrency; other sessions that want to modify the table must wait until the current data change statement finishes.
Advantages of table-level locking:
Relatively little memory required (row locking requires memory per row or group of rows locked)
Fast when used on a large part of the table because only a single lock is involved.
Fast if you often do GROUP BY
operations on a large part of the data or must scan the
entire table frequently.
MySQL grants table write locks as follows:
If there are no locks on the table, put a write lock on it.
Otherwise, put the lock request in the write lock queue.
MySQL grants table read locks as follows:
If there are no write locks on the table, put a read lock on it.
Otherwise, put the lock request in the read lock queue.
Table updates are given higher priority than table retrievals.
Therefore, when a lock is released, the lock is made available
to the requests in the write lock queue and then to the
requests in the read lock queue. This ensures that updates to
a table are not “starved” even when there is
heavy SELECT
activity for the
table. However, if there are many updates for a table,
SELECT
statements wait until
there are no more updates.
For information on altering the priority of reads and writes, see Section 8.11.2, “Table Locking Issues”.
You can analyze the table lock contention on your system by
checking the
Table_locks_immediate
and
Table_locks_waited
status
variables, which indicate the number of times that requests
for table locks could be granted immediately and the number
that had to wait, respectively:
mysql> SHOW STATUS LIKE 'Table%';
+-----------------------+---------+
| Variable_name | Value |
+-----------------------+---------+
| Table_locks_immediate | 1151552 |
| Table_locks_waited | 15324 |
+-----------------------+---------+
The Performance Schema lock tables also provide locking information. See Section 25.12.12, “Performance Schema Lock Tables”.
The MyISAM
storage engine supports
concurrent inserts to reduce contention between readers and
writers for a given table: If a MyISAM
table has no free blocks in the middle of the data file, rows
are always inserted at the end of the data file. In this case,
you can freely mix concurrent
INSERT
and
SELECT
statements for a
MyISAM
table without locks. That is, you
can insert rows into a MyISAM
table at the
same time other clients are reading from it. Holes can result
from rows having been deleted from or updated in the middle of
the table. If there are holes, concurrent inserts are disabled
but are enabled again automatically when all holes have been
filled with new data. To control this behavior, use the
concurrent_insert
system
variable. See Section 8.11.3, “Concurrent Inserts”.
If you acquire a table lock explicitly with
LOCK TABLES
, you can request a
READ LOCAL
lock rather than a
READ
lock to enable other sessions to
perform concurrent inserts while you have the table locked.
To perform many INSERT
and
SELECT
operations on a table
t1
when concurrent inserts are not
possible, you can insert rows into a temporary table
temp_t1
and update the real table with the
rows from the temporary table:
mysql>LOCK TABLES t1 WRITE, temp_t1 WRITE;
mysql>INSERT INTO t1 SELECT * FROM temp_t1;
mysql>DELETE FROM temp_t1;
mysql>UNLOCK TABLES;
Generally, table locks are superior to row-level locks in the following cases:
Most statements for the table are reads.
Statements for the table are a mix of reads and writes, where writes are updates or deletes for a single row that can be fetched with one key read:
UPDATEtbl_name
SETcolumn
=value
WHEREunique_key_col
=key_value
; DELETE FROMtbl_name
WHEREunique_key_col
=key_value
;
SELECT
combined with
concurrent INSERT
statements, and very few
UPDATE
or
DELETE
statements.
Many scans or GROUP BY
operations on
the entire table without any writers.
With higher-level locks, you can more easily tune applications by supporting locks of different types, because the lock overhead is less than for row-level locks.
Options other than row-level locking:
Versioning (such as that used in MySQL for concurrent inserts) where it is possible to have one writer at the same time as many readers. This means that the database or table supports different views for the data depending on when access begins. Other common terms for this are “time travel,” “copy on write,” or “copy on demand.”
Copy on demand is in many cases superior to row-level locking. However, in the worst case, it can use much more memory than using normal locks.
Instead of using row-level locks, you can employ
application-level locks, such as those provided by
GET_LOCK()
and
RELEASE_LOCK()
in MySQL.
These are advisory locks, so they work only with
applications that cooperate with each other. See
Section 12.14, “Locking Functions”.
InnoDB
tables use row-level locking so that
multiple sessions and applications can read from and write to
the same table simultaneously, without making each other wait or
producing inconsistent results. For this storage engine, avoid
using the LOCK TABLES
statement,
because it does not offer any extra protection, but instead
reduces concurrency. The automatic row-level locking makes these
tables suitable for your busiest databases with your most
important data, while also simplifying application logic since
you do not need to lock and unlock tables. Consequently, the
InnoDB
storage engine is the default in
MySQL.
MySQL uses table locking (instead of page, row, or column
locking) for all storage engines except
InnoDB
. The locking operations themselves do
not have much overhead. But because only one session can write
to a table at any one time, for best performance with these
other storage engines, use them primarily for tables that are
queried often and rarely inserted into or updated.
When choosing whether to create a table using
InnoDB
or a different storage engine, keep
in mind the following disadvantages of table locking:
Table locking enables many sessions to read from a table at the same time, but if a session wants to write to a table, it must first get exclusive access, meaning it might have to wait for other sessions to finish with the table first. During the update, all other sessions that want to access this particular table must wait until the update is done.
Table locking causes problems when a session is waiting because the disk is full and free space needs to become available before the session can proceed. In this case, all sessions that want to access the problem table are also put in a waiting state until more disk space is made available.
A SELECT
statement that
takes a long time to run prevents other sessions from
updating the table in the meantime, making the other
sessions appear slow or unresponsive. While a session is
waiting to get exclusive access to the table for updates,
other sessions that issue
SELECT
statements will
queue up behind it, reducing concurrency even for
read-only sessions.
The following items describe some ways to avoid or reduce contention caused by table locking:
Consider switching the table to the
InnoDB
storage engine, either using
CREATE TABLE ... ENGINE=INNODB
during
setup, or using ALTER TABLE ...
ENGINE=INNODB
for an existing table. See
Chapter 14, The InnoDB Storage Engine for more details
about this storage engine.
Optimize SELECT
statements
to run faster so that they lock tables for a shorter time.
You might have to create some summary tables to do this.
Start mysqld with
--low-priority-updates
. For
storage engines that use only table-level locking (such as
MyISAM
, MEMORY
, and
MERGE
), this gives all statements that
update (modify) a table lower priority than
SELECT
statements. In this
case, the second SELECT
statement in the preceding scenario would execute before
the UPDATE
statement, and
would not wait for the first
SELECT
to finish.
To specify that all updates issued in a specific
connection should be done with low priority, set the
low_priority_updates
server system variable equal to 1.
To give a specific INSERT
,
UPDATE
, or
DELETE
statement lower
priority, use the LOW_PRIORITY
attribute.
To give a specific SELECT
statement higher priority, use the
HIGH_PRIORITY
attribute. See
Section 13.2.9, “SELECT Statement”.
Start mysqld with a low value for the
max_write_lock_count
system variable to force MySQL to temporarily elevate the
priority of all SELECT
statements that are waiting for a table after a specific
number of inserts to the table occur. This permits
READ
locks after a certain number of
WRITE
locks.
If you have problems with
INSERT
combined with
SELECT
, consider switching
to MyISAM
tables, which support
concurrent SELECT
and
INSERT
statements. (See
Section 8.11.3, “Concurrent Inserts”.)
If you have problems with mixed
SELECT
and
DELETE
statements, the
LIMIT
option to
DELETE
may help. See
Section 13.2.2, “DELETE Statement”.
Using SQL_BUFFER_RESULT
with
SELECT
statements can help
to make the duration of table locks shorter. See
Section 13.2.9, “SELECT Statement”.
Splitting table contents into separate tables may help, by allowing queries to run against columns in one table, while updates are confined to columns in a different table.
You could change the locking code in
mysys/thr_lock.c
to use a single
queue. In this case, write locks and read locks would have
the same priority, which might help some applications.
The MyISAM
storage engine supports concurrent
inserts to reduce contention between readers and writers for a
given table: If a MyISAM
table has no holes
in the data file (deleted rows in the middle), an
INSERT
statement can be executed
to add rows to the end of the table at the same time that
SELECT
statements are reading
rows from the table. If there are multiple
INSERT
statements, they are
queued and performed in sequence, concurrently with the
SELECT
statements. The results of
a concurrent INSERT
may not be
visible immediately.
The concurrent_insert
system
variable can be set to modify the concurrent-insert processing.
By default, the variable is set to AUTO
(or
1) and concurrent inserts are handled as just described. If
concurrent_insert
is set to
NEVER
(or 0), concurrent inserts are
disabled. If the variable is set to ALWAYS
(or 2), concurrent inserts at the end of the table are permitted
even for tables that have deleted rows. See also the description
of the concurrent_insert
system
variable.
If you are using the binary log, concurrent inserts are
converted to normal inserts for CREATE ...
SELECT
or
INSERT ...
SELECT
statements. This is done to ensure that you can
re-create an exact copy of your tables by applying the log
during a backup operation. See Section 5.4.4, “The Binary Log”. In
addition, for those statements a read lock is placed on the
selected-from table such that inserts into that table are
blocked. The effect is that concurrent inserts for that table
must wait as well.
With LOAD DATA
, if you specify
CONCURRENT
with a MyISAM
table that satisfies the condition for concurrent inserts (that
is, it contains no free blocks in the middle), other sessions
can retrieve data from the table while LOAD
DATA
is executing. Use of the
CONCURRENT
option affects the performance of
LOAD DATA
a bit, even if no other
session is using the table at the same time.
If you specify HIGH_PRIORITY
, it overrides
the effect of the
--low-priority-updates
option if
the server was started with that option. It also causes
concurrent inserts not to be used.
For LOCK
TABLE
, the difference between READ
LOCAL
and READ
is that
READ LOCAL
permits nonconflicting
INSERT
statements (concurrent
inserts) to execute while the lock is held. However, this cannot
be used if you are going to manipulate the database using
processes external to the server while you hold the lock.
MySQL uses metadata locking to manage concurrent access to
database objects and to ensure data consistency. Metadata
locking applies not just to tables, but also to schemas, stored
programs (procedures, functions, triggers, scheduled events),
tablespaces, user locks acquired with the
GET_LOCK()
function (see
Section 12.14, “Locking Functions”), and locks acquired with
the locking service described in
Section 28.3.1, “The Locking Service”.
The Performance Schema
metadata_locks
table exposes
metadata lock information, which can be useful for seeing which
sessions hold locks, are blocked waiting for locks, and so
forth. For details, see Section 25.12.12.1, “The metadata_locks Table”.
Metadata locking does involve some overhead, which increases as query volume increases. Metadata contention increases the more that multiple queries attempt to access the same objects.
Metadata locking is not a replacement for the table definition
cache, and its mutexes and locks differ from the
LOCK_open
mutex. The following discussion
provides some information about how metadata locking works.
If there are multiple waiters for a given lock, the
highest-priority lock request is satisfied first, with an
exception related to the
max_write_lock_count
system
variable. Write lock requests have higher priority than read
lock requests. However, if
max_write_lock_count
is set
to some low value (say, 10), read lock requests may be
preferred over pending write lock requests if the read lock
requests have already been passed over in favor of 10 write
lock requests. Normally this behavior does not occur because
max_write_lock_count
by
default has a very large value.
Statements acquire metadata locks one by one, not simultaneously, and perform deadlock detection in the process.
DML statements normally acquire locks in the order in which tables are mentioned in the statement.
DDL statements, LOCK TABLES
,
and other similar statements try to reduce the number of
possible deadlocks between concurrent DDL statements by
acquiring locks on explicitly named tables in name order.
Locks might be acquired in a different order for implicitly
used tables (such as tables in foreign key relationships that
also must be locked).
For example, RENAME TABLE
is a
DDL statement that acquires locks in name order:
This RENAME TABLE
statement
renames tbla
to something else, and
renames tblc
to
tbla
:
RENAME TABLE tbla TO tbld, tblc TO tbla;
The statement acquires metadata locks, in order, on
tbla
, tblc
, and
tbld
(because tbld
follows tblc
in name order):
This slightly different statement also renames
tbla
to something else, and renames
tblc
to tbla
:
RENAME TABLE tbla TO tblb, tblc TO tbla;
In this case, the statement acquires metadata locks, in
order, on tbla
,
tblb
, and tblc
(because tblb
precedes
tblc
in name order):
Both statements acquire locks on tbla
and
tblc
, in that order, but differ in whether
the lock on the remaining table name is acquired before or
after tblc
.
Metadata lock acquisition order can make a difference in operation outcome when multiple transactions execute concurrently, as the following example illustrates.
Begin with two tables x
and
x_new
that have identical structure. Three
clients issue statements that involve these tables:
Client 1:
LOCK TABLE x WRITE, x_new WRITE;
The statement requests and acquires write locks in name order
on x
and x_new
.
Client 2:
INSERT INTO x VALUES(1);
The statement requests and blocks waiting for a write lock on
x
.
Client 3:
RENAME TABLE x TO x_old, x_new TO x;
The statement requests exclusive locks in name order on
x
, x_new
, and
x_old
, but blocks waiting for the lock on
x
.
Client 1:
UNLOCK TABLES;
The statement releases the write locks on x
and x_new
. The exclusive lock request for
x
by Client 3 has higher priority than the
write lock request by Client 2, so Client 3 acquires its lock
on x
, then also on x_new
and x_old
, performs the renaming, and
releases its locks. Client 2 then acquires its lock on
x
, performs the insert, and releases its
lock.
Lock acquisition order results in the
RENAME TABLE
executing before
the INSERT
. The
x
into which the insert occurs is the table
that was named x_new
when Client 2 issued
the insert and was renamed to x
by Client
3:
mysql>SELECT * FROM x;
+------+ | i | +------+ | 1 | +------+ mysql>SELECT * FROM x_old;
Empty set (0.01 sec)
Now begin instead with tables named x
and
new_x
that have identical structure. Again,
three clients issue statements that involve these tables:
Client 1:
LOCK TABLE x WRITE, new_x WRITE;
The statement requests and acquires write locks in name order
on new_x
and x
.
Client 2:
INSERT INTO x VALUES(1);
The statement requests and blocks waiting for a write lock on
x
.
Client 3:
RENAME TABLE x TO old_x, new_x TO x;
The statement requests exclusive locks in name order on
new_x
, old_x
, and
x
, but blocks waiting for the lock on
new_x
.
Client 1:
UNLOCK TABLES;
The statement releases the write locks on x
and new_x
. For x
, the
only pending request is by Client 2, so Client 2 acquires its
lock, performs the insert, and releases the lock. For
new_x
, the only pending request is by
Client 3, which is permitted to acquire that lock (and also
the lock on old_x
). The rename operation
still blocks for the lock on x
until the
Client 2 insert finishes and releases its lock. Then Client 3
acquires the lock on x
, performs the
rename, and releases its lock.
In this case, lock acquisition order results in the
INSERT
executing before the
RENAME TABLE
. The
x
into which the insert occurs is the
original x
, now renamed to
old_x
by the rename operation:
mysql>SELECT * FROM x;
Empty set (0.01 sec) mysql>SELECT * FROM old_x;
+------+ | i | +------+ | 1 | +------+
If order of lock acquisition in concurrent statements makes a difference to an application in operation outcome, as in the preceding example, you may be able to adjust the table names to affect the order of lock acquisition.
To ensure transaction serializability, the server must not permit one session to perform a data definition language (DDL) statement on a table that is used in an uncompleted explicitly or implicitly started transaction in another session. The server achieves this by acquiring metadata locks on tables used within a transaction and deferring release of those locks until the transaction ends. A metadata lock on a table prevents changes to the table's structure. This locking approach has the implication that a table that is being used by a transaction within one session cannot be used in DDL statements by other sessions until the transaction ends.
This principle applies not only to transactional tables, but
also to nontransactional tables. Suppose that a session begins
a transaction that uses transactional table
t
and nontransactional table
nt
as follows:
START TRANSACTION; SELECT * FROM t; SELECT * FROM nt;
The server holds metadata locks on both t
and nt
until the transaction ends. If
another session attempts a DDL or write lock operation on
either table, it blocks until metadata lock release at
transaction end. For example, a second session blocks if it
attempts any of these operations:
DROP TABLE t; ALTER TABLE t ...; DROP TABLE nt; ALTER TABLE nt ...; LOCK TABLE t ... WRITE;
The same behavior applies for The
LOCK TABLES ...
READ
. That is, explicitly or implicitly started
transactions that update any table (transactional or
nontransactional) will block and be blocked by LOCK
TABLES ... READ
for that table.
If the server acquires metadata locks for a statement that is syntactically valid but fails during execution, it does not release the locks early. Lock release is still deferred to the end of the transaction because the failed statement is written to the binary log and the locks protect log consistency.
In autocommit mode, each statement is in effect a complete transaction, so metadata locks acquired for the statement are held only to the end of the statement.
Metadata locks acquired during a
PREPARE
statement are released
once the statement has been prepared, even if preparation
occurs within a multiple-statement transaction.
External locking is the use of file system locking to manage
contention for MyISAM
database
tables by multiple processes. External locking is used in
situations where a single process such as the MySQL server
cannot be assumed to be the only process that requires access to
tables. Here are some examples:
If you run multiple servers that use the same database directory (not recommended), each server must have external locking enabled.
If you use myisamchk to perform table
maintenance operations on
MyISAM
tables, you must either
ensure that the server is not running, or that the server
has external locking enabled so that it locks table files as
necessary to coordinate with myisamchk
for access to the tables. The same is true for use of
myisampack to pack
MyISAM
tables.
If the server is run with external locking enabled, you can use myisamchk at any time for read operations such a checking tables. In this case, if the server tries to update a table that myisamchk is using, the server will wait for myisamchk to finish before it continues.
If you use myisamchk for write operations such as repairing or optimizing tables, or if you use myisampack to pack tables, you must always ensure that the mysqld server is not using the table. If you do not stop mysqld, at least do a mysqladmin flush-tables before you run myisamchk. Your tables may become corrupted if the server and myisamchk access the tables simultaneously.
With external locking in effect, each process that requires access to a table acquires a file system lock for the table files before proceeding to access the table. If all necessary locks cannot be acquired, the process is blocked from accessing the table until the locks can be obtained (after the process that currently holds the locks releases them).
External locking affects server performance because the server must sometimes wait for other processes before it can access tables.
External locking is unnecessary if you run a single server to access a given data directory (which is the usual case) and if no other programs such as myisamchk need to modify tables while the server is running. If you only read tables with other programs, external locking is not required, although myisamchk might report warnings if the server changes tables while myisamchk is reading them.
With external locking disabled, to use
myisamchk, you must either stop the server
while myisamchk executes or else lock and
flush the tables before running myisamchk.
(See Section 8.12.1, “System Factors”.) To avoid this
requirement, use the CHECK TABLE
and REPAIR TABLE
statements to
check and repair MyISAM
tables.
For mysqld, external locking is controlled by
the value of the
skip_external_locking
system
variable. When this variable is enabled, external locking is
disabled, and vice versa. External locking is disabled by
default.
Use of external locking can be controlled at server startup by
using the --external-locking
or
--skip-external-locking
option.
If you do use external locking option to enable updates to
MyISAM
tables from many MySQL
processes, you must ensure that the following conditions are
satisfied:
Do not use the query cache for queries that use tables that are updated by another process.
Do not start the server with the
delay_key_write
system
variable set to ALL
or use the
DELAY_KEY_WRITE=1
table option for any
shared tables. Otherwise, index corruption can occur.
The easiest way to satisfy these conditions is to always use
--external-locking
together with
--delay-key-write=OFF
and
--query-cache-size=0
. (This is
not done by default because in many setups it is useful to have
a mixture of the preceding options.)
This section discusses optimization techniques for the database server, primarily dealing with system configuration rather than tuning SQL statements. The information in this section is appropriate for DBAs who want to ensure performance and scalability across the servers they manage; for developers constructing installation scripts that include setting up the database; and people running MySQL themselves for development, testing, and so on who want to maximize their own productivity.
Some system-level factors can affect performance in a major way:
If you have enough RAM, you could remove all swap devices. Some operating systems use a swap device in some contexts even if you have free memory.
Avoid external locking for
MyISAM
tables. The default is
for external locking to be disabled. The
--external-locking
and
--skip-external-locking
options explicitly enable and disable external locking.
Disabling external locking does not affect MySQL's functionality as long as you run only one server. Just remember to take down the server (or lock and flush the relevant tables) before you run myisamchk. On some systems it is mandatory to disable external locking because it does not work, anyway.
The only case in which you cannot disable external locking is when you run multiple MySQL servers (not clients) on the same data, or if you run myisamchk to check (not repair) a table without telling the server to flush and lock the tables first. Note that using multiple MySQL servers to access the same data concurrently is generally not recommended, except when using NDB Cluster.
The LOCK TABLES
and
UNLOCK
TABLES
statements use internal locking, so you can
use them even if external locking is disabled.
This section describes ways to configure storage devices when
you can devote more and faster storage hardware to the
database server. For information about optimizing an
InnoDB
configuration to improve I/O
performance, see Section 8.5.8, “Optimizing InnoDB Disk I/O”.
Disk seeks are a huge performance bottleneck. This problem becomes more apparent when the amount of data starts to grow so large that effective caching becomes impossible. For large databases where you access data more or less randomly, you can be sure that you need at least one disk seek to read and a couple of disk seeks to write things. To minimize this problem, use disks with low seek times.
Increase the number of available disk spindles (and thereby reduce the seek overhead) by either symlinking files to different disks or striping the disks:
Using symbolic links
This means that, for MyISAM
tables,
you symlink the index file and data files from their
usual location in the data directory to another disk
(that may also be striped). This makes both the seek and
read times better, assuming that the disk is not used
for other purposes as well. See
Section 8.12.3, “Using Symbolic Links”.
Symbolic links are not supported for use with
InnoDB
tables. However, it is
possible to place InnoDB
data and log
files on different physical disks. For more information,
see Section 8.5.8, “Optimizing InnoDB Disk I/O”.
Striping means that you have many disks and put the
first block on the first disk, the second block on the
second disk, and the N
-th
block on the (
)
disk, and so on. This means if your normal data size is
less than the stripe size (or perfectly aligned), you
get much better performance. Striping is very dependent
on the operating system and the stripe size, so
benchmark your application with different stripe sizes.
See Section 8.13.2, “Using Your Own Benchmarks”.
N
MOD
number_of_disks
The speed difference for striping is very dependent on the parameters. Depending on how you set the striping parameters and number of disks, you may get differences measured in orders of magnitude. You have to choose to optimize for random or sequential access.
For reliability, you may want to use RAID 0+1 (striping plus
mirroring), but in this case, you need 2 ×
N
drives to hold
N
drives of data. This is
probably the best option if you have the money for it.
However, you may also have to invest in some
volume-management software to handle it efficiently.
A good option is to vary the RAID level according to how
critical a type of data is. For example, store
semi-important data that can be regenerated on a RAID 0
disk, but store really important data such as host
information and logs on a RAID 0+1 or RAID
N
disk. RAID
N
can be a problem if you have
many writes, due to the time required to update the parity
bits.
You can also set the parameters for the file system that the database uses:
If you do not need to know when files were last accessed
(which is not really useful on a database server), you can
mount your file systems with the -o noatime
option. That skips updates to the last access time in inodes
on the file system, which avoids some disk seeks.
On many operating systems, you can set a file system to be
updated asynchronously by mounting it with the -o
async
option. If your computer is reasonably
stable, this should give you better performance without
sacrificing too much reliability. (This flag is on by
default on Linux.)
Caution is advised when considering using NFS with MySQL. Potential issues, which vary by operating system and NFS version, include:
MySQL data and log files placed on NFS volumes becoming locked and unavailable for use. Locking issues may occur in cases where multiple instances of MySQL access the same data directory or where MySQL is shut down improperly, due to a power outage, for example. NFS version 4 addresses underlying locking issues with the introduction of advisory and lease-based locking. However, sharing a data directory among MySQL instances is not recommended.
Data inconsistencies introduced due to messages received
out of order or lost network traffic. To avoid this issue,
use TCP with hard
and
intr
mount options.
Maximum file size limitations. NFS Version 2 clients can only access the lowest 2GB of a file (signed 32 bit offset). NFS Version 3 clients support larger files (up to 64 bit offsets). The maximum supported file size also depends on the local file system of the NFS server.
Using NFS within a professional SAN environment or other storage system tends to offer greater reliability than using NFS outside of such an environment. However, NFS within a SAN environment may be slower than directly attached or bus-attached non-rotational storage.
If you choose to use NFS, NFS Version 4 or later is recommended, as is testing your NFS setup thoroughly before deploying into a production environment.
You can move databases or tables from the database directory to other locations and replace them with symbolic links to the new locations. You might want to do this, for example, to move a database to a file system with more free space or increase the speed of your system by spreading your tables to different disks.
For InnoDB
tables, use the DATA
DIRECTORY
clause of the CREATE
TABLE
statement instead of symbolic links, as
explained in Section 14.6.1.2, “Creating Tables Externally”.
This new feature is a supported, cross-platform technique.
The recommended way to do this is to symlink entire database
directories to a different disk. Symlink
MyISAM
tables only as a last resort.
To determine the location of your data directory, use this statement:
SHOW VARIABLES LIKE 'datadir';
On Unix, the way to symlink a database is first to create a directory on some disk where you have free space and then to create a soft link to it from the MySQL data directory.
shell>mkdir /dr1/databases/test
shell>ln -s /dr1/databases/test
/path/to/datadir
MySQL does not support linking one directory to multiple
databases. Replacing a database directory with a symbolic link
works as long as you do not make a symbolic link between
databases. Suppose that you have a database
db1
under the MySQL data directory, and
then make a symlink db2
that points to
db1
:
shell>cd
shell>/path/to/datadir
ln -s db1 db2
The result is that, for any table tbl_a
in
db1
, there also appears to be a table
tbl_a
in db2
. If one
client updates db1.tbl_a
and another client
updates db2.tbl_a
, problems are likely to
occur.
Symlinks are fully supported only for
MyISAM
tables. For files used by tables for
other storage engines, you may get strange problems if you try
to use symbolic links. For InnoDB
tables,
use the alternative technique explained in
Section 14.6.1.2, “Creating Tables Externally” instead.
Do not symlink tables on systems that do not have a fully
operational realpath()
call. (Linux and
Solaris support realpath()
). To determine
whether your system supports symbolic links, check the value
of the have_symlink
system
variable using this statement:
SHOW VARIABLES LIKE 'have_symlink';
The handling of symbolic links for MyISAM
tables works as follows:
In the data directory, you always have the table format
(.frm
) file, the data
(.MYD
) file, and the index
(.MYI
) file. The data file and index
file can be moved elsewhere and replaced in the data
directory by symlinks. The format file cannot.
You can symlink the data file and the index file independently to different directories.
To instruct a running MySQL server to perform the
symlinking, use the DATA DIRECTORY
and
INDEX DIRECTORY
options to
CREATE TABLE
. See
Section 13.1.18, “CREATE TABLE Statement”. Alternatively, if
mysqld is not running, symlinking can
be accomplished manually using ln -s
from the command line.
The path used with either or both of the DATA
DIRECTORY
and INDEX
DIRECTORY
options may not include the MySQL
data
directory. (Bug #32167)
myisamchk does not replace a symlink
with the data file or index file. It works directly on the
file to which the symlink points. Any temporary files are
created in the directory where the data file or index file
is located. The same is true for the
ALTER TABLE
,
OPTIMIZE TABLE
, and
REPAIR TABLE
statements.
When you drop a table that is using symlinks,
both the symlink and the file to which the
symlink points are dropped. This is an
extremely good reason not to run
mysqld as the root
operating system user or permit operating system users
to have write access to MySQL database directories.
If you rename a table with
ALTER TABLE
... RENAME
or RENAME
TABLE
and you do not move the table to another
database, the symlinks in the database directory are
renamed to the new names and the data file and index file
are renamed accordingly.
If you use
ALTER TABLE
... RENAME
or RENAME
TABLE
to move a table to another database, the
table is moved to the other database directory. If the
table name changed, the symlinks in the new database
directory are renamed to the new names and the data file
and index file are renamed accordingly.
If you are not using symlinks, start
mysqld with the
--skip-symbolic-links
option to ensure that no one can use