First, one factor affects all statements: The more complex your
permissions setup, the more overhead you have. Using simpler
permissions when you issue 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, it may be worth the time to use a
simplified grant structure to reduce permission-checking overhead.
If your problem is with a specific MySQL expression or function,
you can perform a timing test by invoking the
BENCHMARK()
function using the
mysql client program. Its syntax is
BENCHMARK(
loop_count
,expression
).
The return value is always zero, but mysql
prints a line displaying approximately how long the statement took
to execute. For example:
mysql> SELECT BENCHMARK(1000000,1+1);
+------------------------+
| BENCHMARK(1000000,1+1) |
+------------------------+
| 0 |
+------------------------+
1 row in set (0.32 sec)
This result was obtained on a Pentium II 400MHz system. It shows that MySQL can execute 1,000,000 simple addition expressions in 0.32 seconds on that system.
All MySQL functions should be highly optimized, but there may be
some exceptions. BENCHMARK()
is an excellent
tool for finding out if some function is a problem for your
queries.
EXPLAIN tbl_name
Or:
EXPLAIN [EXTENDED] SELECT select_options
The EXPLAIN
statement can be used either as a
synonym for DESCRIBE
or as a way to obtain
information about how MySQL executes a SELECT
statement:
-
EXPLAIN
tbl_name
is synonymous withDESCRIBE
tbl_name
orSHOW COLUMNS FROM
tbl_name
. -
When you precede a
SELECT
statement with the keywordEXPLAIN
, MySQL displays information from the optimizer about the query execution plan. That is, MySQL explains how it would process theSELECT
, including information about how tables are joined and in which order.
This section describes the second use of
EXPLAIN
for obtaining query execution plan
information. For a description of the
DESCRIBE
and SHOW COLUMNS
statements, see Section 13.3.1, “DESCRIBE
Syntax”, and
Section 13.5.4.3, “SHOW COLUMNS
Syntax”.
With the help of EXPLAIN
, you can see where
you should add indexes to tables to get a faster
SELECT
that uses indexes to find rows. You
can also use EXPLAIN
to check whether the
optimizer joins the tables in an optimal order. To force the
optimizer to use a join order corresponding to the order in
which the tables are named in the SELECT
statement, begin the statement with SELECT
STRAIGHT_JOIN
rather than just
SELECT
.
If you have a problem with indexes not being used when you
believe that they should be, you should run ANALYZE
TABLE
to update table statistics such as cardinality
of keys, that can affect the choices the optimizer makes. See
Section 13.5.2.1, “ANALYZE TABLE
Syntax”.
EXPLAIN
returns a row of information for each
table used in the SELECT
statement. The
tables are listed in the output in the order that MySQL would
read them while processing the query. MySQL resolves all joins
using a single-sweep multi-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.
When the EXTENDED
keyword is used,
EXPLAIN
produces extra information that can
be viewed by issuing a SHOW WARNINGS
statement following the EXPLAIN
statement.
This information 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.
Each output row from EXPLAIN
provides
information about one table, and each row contains the following
columns:
-
id
The
SELECT
identifier. This is the sequential number of theSELECT
within the query. -
select_type
The type of
SELECT
, which can be any of those shown in the following table:SIMPLE
Simple SELECT
(not usingUNION
or subqueries)PRIMARY
Outermost SELECT
UNION
Second or later SELECT
statement in aUNION
DEPENDENT UNION
Second or later SELECT
statement in aUNION
, dependent on outer queryUNION RESULT
Result of a UNION
.SUBQUERY
First SELECT
in subqueryDEPENDENT SUBQUERY
First SELECT
in subquery, dependent on outer queryDERIVED
Derived table SELECT
(subquery inFROM
clause)DEPENDENT
typically signifies the use of a correlated subquery. See Section 13.2.8.7, “Correlated Subqueries”. -
table
The table to which the row of output refers.
-
type
The join type. The different join types are listed here, 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 aPRIMARY KEY
orUNIQUE
index to constant values. In the following queries,tbl_name
can be used as aconst
table:SELECT * FROM
tbl_name
WHEREprimary_key
=1; SELECT * FROMtbl_name
WHEREprimary_key_part1
=1 ANDprimary_key_part2
=2; -
eq_ref
One row is read from this table for each combination of rows from the previous tables. Other than the
system
andconst
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 aPRIMARY KEY
orUNIQUE
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 aneq_ref
join to processref_table
:SELECT * FROM
ref_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; -
ref
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 aPRIMARY KEY
orUNIQUE
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 aref
join to processref_table
:SELECT * FROM
ref_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; -
ref_or_null
This join type is like
ref
, but with the addition that MySQL does an extra search for rows that containNULL
values. This join type optimization is used most often in resolving subqueries. In the following examples, MySQL can use aref_or_null
join to processref_table
:SELECT * FROM
ref_table
WHEREkey_column
=expr
ORkey_column
IS NULL; -
index_merge
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, andkey_len
contains a list of the longest key parts for the indexes used. For more information, see Section 7.2.6, “Index Merge Optimization”. -
unique_subquery
This type replaces
ref
for someIN
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. -
index_subquery
This join type is similar to
unique_subquery
. It replacesIN
subqueries, but it works for non-unique indexes in subqueries of the following form:value
IN (SELECTkey_column
FROMsingle_table
WHEREsome_expr
) -
range
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. Thekey_len
contains the longest key part that was used. Theref
column isNULL
for this type.range
can be used when a key column is compared to a constant using any of the=
,<>
,>
,>=
,<
,<=
,IS NULL
,<=>
,BETWEEN
, orIN
operators:SELECT * FROM
tbl_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); -
index
This join type is the same as
ALL
, except that only the index tree is scanned. This usually is faster thanALL
because the index file usually is smaller than the data file.MySQL can use this join type when the query uses only columns that are part of a single index.
-
ALL
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 avoidALL
by adding indexes that allow row retrieval from the table based on constant values or column values from earlier tables.
-
-
possible_keys
The
possible_keys
column indicates which indexes MySQL can choose from use 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 fromEXPLAIN
. That means that some of the keys inpossible_keys
might not be usable in practice with the generated table order.If this column is
NULL
, there are no relevant indexes. In this case, you may be able to improve the performance of your query by examining theWHERE
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 withEXPLAIN
again. See Section 13.1.2, “ALTER TABLE
Syntax”.To see what indexes a table has, use
SHOW INDEX FROM
tbl_name
. -
key
The
key
column indicates the key (index) that MySQL actually decided to use. The key isNULL
if no index was chosen. To force MySQL to use or ignore an index listed in thepossible_keys
column, useFORCE INDEX
,USE INDEX
, orIGNORE INDEX
in your query. See Section 13.2.7, “SELECT
Syntax”.For
MyISAM
andBDB
tables, runningANALYZE TABLE
helps the optimizer choose better indexes. ForMyISAM
tables, myisamchk --analyze does the same. See Section 13.5.2.1, “ANALYZE TABLE
Syntax”, and Section 5.10.4, “Table Maintenance and Crash Recovery”. -
key_len
The
key_len
column indicates the length of the key that MySQL decided to use. The length isNULL
if thekey
column saysNULL
. Note that the value ofkey_len
enables you to determine how many parts of a multiple-part key MySQL actually uses. -
ref
The
ref
column shows which columns or constants are compared to the index named in thekey
column to select rows from the table. -
rows
The
rows
column indicates the number of rows MySQL believes it must examine to execute the query. -
Extra
This column contains additional information about how MySQL resolves the query. Here is an explanation of the values that can appear in this column:
-
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.
-
Not exists
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 theLEFT 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 asNOT NULL
. In this case, MySQL scanst1
and looks up the rows int2
using the values oft1.id
. If MySQL finds a matching row int2
, it knows thatt2.id
can never beNULL
, and does not scan through the rest of the rows int2
that have the sameid
value. In other words, for each row int1
, MySQL needs to do only a single lookup int2
, regardless of how many rows actually match int2
. -
range checked for each record (index map:
N
)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
orindex_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 7.2.5, “Range Optimization”, and Section 7.2.6, “Index Merge Optimization”, with the exception that all column values for the preceding table are known and considered to be constants. -
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 7.2.12, “ORDER BY
Optimization”. -
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.
-
Using temporary
To resolve the query, MySQL needs to create a temporary table to hold the result. This typically happens if the query contains
GROUP BY
andORDER BY
clauses that list columns differently. -
Using where
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 theExtra
value is notUsing where
and the table join type isALL
orindex
.If you want to make your queries as fast as possible, you should look out for
Extra
values ofUsing filesort
andUsing temporary
. -
Using sort_union(...)
,Using union(...)
,Using intersect(...)
These indicate how index scans are merged for the
index_merge
join type. See Section 7.2.6, “Index Merge Optimization”, for more information. -
Using index for group-by
Similar to the
Using index
way of accessing a table,Using index for group-by
indicates that MySQL found an index that can be used to retrieve all columns of aGROUP BY
orDISTINCT
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 7.2.13, “GROUP BY
Optimization”. -
Using where with pushed condition
This item applies to
NDB Cluster
tables only. It means that MySQL Cluster is using condition pushdown to improve the efficiency of a direct comparison (=
) between a non-indexed column and a constant. In such cases, the condition is “pushed down” to the cluster's data nodes where it is evaluated in all partitions simultaneously. This eliminates the need to send non-matching 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.Suppose that you have a Cluster table defined as follows:
CREATE TABLE t1 ( a INT, b INT, KEY(a) ) ENGINE=NDBCLUSTER;
In this case, condition pushdown can be used with a query such as this one:
SELECT a,b FROM t1 WHERE b = 10;
This can be seen in the output of
EXPLAIN SELECT
, as shown here: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 conditionCondition 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;
With regard to the first of these two queries, condition pushdown is not applicable because an index exists on column
a
. In the case of the second query, a condition pushdown cannot be employed because the comparison involving the non-indexed columnb
is an indirect one. (However, it would apply if you were to reduceb + 1 = 10
tob = 9
in theWHERE
clause.)However, a condition pushdown may also be employed when an indexed column 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 conditionWith regard to condition pushdown, keep in mind that:
-
Condition pushdown is relevant to MySQL Cluster only, and does not occur when executing queries against tables using any other storage engine.
-
Condition pushdown capability is not used by default. To enable it, you can start mysqld with the
--engine-condition-pushdown
option, or execute the following statement:SET engine_condition_pushdown=On;
Note: Condition pushdown is not supported for columns of any of the
BLOB
orTEXT
types.
Condition pushdown,
Using where with pushed condition
, andengine_condition_pushdown
were all introduced in MySQL 5.0 Cluster. -
-
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 7.5.2, “Tuning Server Parameters”.
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 (key map: 35)
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 (key map: 1) et_1 ALL PRIMARY NULL NULL NULL 74 range checked for each record (key map: 1) 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
Note that the rows
column in the output from
EXPLAIN
is an educated guess from the MySQL
join optimizer. You should 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.
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 an
index 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.
Note that 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 7.5.2, “Tuning Server Parameters”.
In general, when you want to make a slow SELECT ...
WHERE
query faster, the first thing to check is
whether you can add an index. All references between different
tables should usually be done with indexes. You can use the
EXPLAIN
statement to determine which indexes
are used for a SELECT
. See
Section 7.2.1, “Optimizing Queries with EXPLAIN
”, and Section 7.4.5, “How MySQL Uses Indexes”.
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 non-constant expression. You can check the result from the table analysis by usingSHOW INDEX FROM
tbl_name
and examining theCardinality
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.
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.
Work on the MySQL optimizer is ongoing, so this section is incomplete. MySQL performs a great many optimizations, not all of which are documented here.
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 (needed because of constant folding):
(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 aWHERE
is retrieved directly from the table information forMyISAM
andMEMORY
tables. This is also done for anyNOT 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 withWHERE
if you do not useGROUP BY
or aggregate functions (COUNT()
,MIN()
, and so on). -
For each table in a join, a simpler
WHERE
is constructed to get a fastWHERE
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 aPRIMARY KEY
or aUNIQUE
index, where all index parts are compared to constant expressions and are defined asNOT NULL
.
All of the following tables are used as constant tables:
SELECT * FROM t WHERE
primary_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
andGROUP BY
clauses come from the same table, that table is preferred first when joining. -
If there is an
ORDER BY
clause and a differentGROUP BY
clause, or if theORDER BY
orGROUP 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
option, 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 give
a detailed description of how intervals are extracted from the
WHERE
clause.
For a single-part index, index value intervals can be
conveniently represented by corresponding conditions in the
WHERE
clause, so we speak of
range conditions rather than
“intervals.”
The definition of a range condition for a single-part index is as follows:
-
For both
BTREE
andHASH
indexes, comparison of a key part with a constant value is a range condition when using the=
,<=>
,IN
,IS NULL
, orIS NOT NULL
operators. -
For
BTREE
indexes, comparison of a key part with a constant value is a range condition when using the>
,<
,>=
,<=
,BETWEEN
,!=
, or<>
operators, orLIKE '
pattern
' (where'
pattern
' does not start with a wildcard). -
For all types of indexes, multiple range conditions combined with
OR
orAND
form a range condition.
“Constant value” in the preceding descriptions means one of the following:
-
A constant from the query string
-
A column of a
const
orsystem
table from the same join -
The result of an uncorrelated subquery
-
Any expression composed entirely from subexpressions of the preceding types
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';
Note that some non-constant values may be converted to constants during the 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
andkey1 LIKE '%b'
because they cannot be used for a range scan. The correct way to remove them is to replace them withTRUE
, so that we do not miss any matching rows when doing the range scan. Having replaced them withTRUE
, we get:(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, we get:
(key1 < 'abc' AND TRUE) OR (key1 < 'bar' AND TRUE) OR (FALSE)
Removing unnecessary
TRUE
andFALSE
constants, we obtain:(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.
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(
key_part1
,
key_part2
,
key_part3
), and the
following set of key tuples listed in key order:
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 key_part1
=
1 defines this interval:
(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
key_part3
=
'abc' does not define a single interval and cannot
be used by the range access method.
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=
,<=>
, orIS NULL
comparison operators, and the conditions cover all index parts. (That is, there areN
conditions, one for each part of anN
-part index.) For example, the following is a range condition for a three-partHASH
index:key_part1
= 1 ANDkey_part2
IS NULL ANDkey_part3
= 'foo'For the definition of what is considered to be a constant, see Section 7.2.5.1, “The Range Access Method for Single-Part Indexes”.
-
For a
BTREE
index, an interval might be usable for conditions combined withAND
, where each condition compares a key part with a constant value using=
,<=>
,IS NULL
,>
,<
,>=
,<=
,!=
,<>
,BETWEEN
, orLIKE '
pattern
' (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<>
or!=
is used). For example, for this condition:key_part1
= 'foo' ANDkey_part2
>= 10 ANDkey_part3
> 10The single interval is:
('foo',10,10) < (
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 withAND
, 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 theEXPLAIN
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 thatkey_part1
andkey_part2
can beNULL
. Then thekey_len
column displays two key part lengths for the following condition:key_part1
>= 1 ANDkey_part2
< 2But, in fact, the condition is converted to this:
key_part1
>= 1 ANDkey_part2
IS NOT NULL
Section 7.2.5.1, “The Range Access Method for Single-Part Indexes”, describes how optimizations are performed to combine or eliminate intervals for range conditions on a single-part index. Analogous steps are performed for range conditions on multiple-part indexes.
The Index Merge method is used to
retrieve rows with several range
scans and to
merge their results into one. The merge can produce unions,
intersections, or unions-of-intersections of its underlying
scans.
Note: If you have upgraded from a previous version of MySQL, you should be aware that this type of join optimization is first introduced in MySQL 5.0, and represents a significant change in behavior with regard to indexes. (Formerly, MySQL was able to use at most only one index for each referenced table.)
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.
Examples:
SELECT * FROMtbl_name
WHEREkey_part1
= 10 ORkey_part2
= 20; SELECT * FROMtbl_name
WHERE (key_part1
= 10 ORkey_part2
= 20) ANDnon_key_part
=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 method has several access algorithms (seen in
the Extra
field of EXPLAIN
output):
-
Using intersect(...)
-
Using union(...)
-
Using sort_union(...)
The following sections describe these methods in greater detail.
Note: The Index Merge optimization algorithm has the following known deficiencies:
-
If a range scan is possible on some key, an Index Merge is not considered. For example, consider this query:
SELECT * FROM t1 WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;
For this query, two plans are possible:
-
An Index Merge scan using the
(goodkey1 < 10 OR goodkey2 < 20)
condition. -
A range scan using the
badkey < 30
condition.
However, the optimizer considers only the second plan. If that is not what you want, you can make the optimizer consider Index Merge by using
IGNORE INDEX
orFORCE INDEX
. The following queries are executed using Index Merge:SELECT * FROM t1 FORCE INDEX(index_for_goodkey1,index_for_goodkey2) WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30; SELECT * FROM t1 IGNORE INDEX(index_for_badkey) WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;
-
-
If your query has a complex
WHERE
clause with deepAND
/OR
nesting and MySQL doesn't choose the optimal plan, try distributing terms using the following identity laws:(
x
ANDy
) ORz
= (x
ORz
) AND (y
ORz
) (x
ORy
) ANDz
= (x
ANDz
) OR (y
ANDz
) -
Index Merge is not applicable to fulltext indexes. We plan to extend it to cover these in a future MySQL release.
The choice between different possible variants of the Index Merge access method and other access methods is based on cost estimates of various available options.
This access algorithm can be employed when a
WHERE
clause was converted to several range
conditions on different keys combined with
AND
, and each condition is one of the
following:
-
In 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
orBDB
table.
Examples:
SELECT * FROMinnodb_table
WHEREprimary_key
< 10 ANDkey_col1
=20; SELECT * FROMtbl_name
WHERE (key1_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 don't 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 a primary
key of an InnoDB
or BDB
table, it is not used for row retrieval, but is used to filter
out rows retrieved using other conditions.
The applicability criteria for this algorithm are similar to
those for the Index Merge method intersection algorithm. The
algorithm can be employed when the table's
WHERE
clause was converted to several range
conditions on different keys combined with
OR
, and each condition is one of the
following:
-
In 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
orBDB
table. -
A condition for which the Index Merge method 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 employed when the
WHERE
clause was converted to several range
conditions combined by OR
, but for which
the Index Merge method 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.
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
col_name
=
expr
AND
col_name
IS NULL, a form
that is common in resolved subqueries.
EXPLAIN
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.
Note that 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);
DISTINCT
combined with ORDER
BY
needs a temporary table in many cases.
Because DISTINCT
may use GROUP
BY
, you should be aware of how MySQL works with
columns in ORDER BY
or
HAVING
clauses that are not part of the
selected columns. See Section 12.10.3, “GROUP BY
and HAVING
with Hidden
Fields”.
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 7.2.13, “GROUP BY
Optimization”.
When combining LIMIT
row_count
with
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;
MySQL implements an A
LEFT
JOIN B
join_condition as
follows:
-
Table
B
is set to depend on tableA
and all tables on whichA
depends. -
Table
A
is set to depend on all tables (exceptB
) that are used in theLEFT JOIN
condition. -
The
LEFT JOIN
condition is used to decide how to retrieve rows from tableB
. (In other words, any condition in theWHERE
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 dependence, MySQL issues an error.
-
All standard
WHERE
optimizations are performed. -
If there is a row in
A
that matches theWHERE
clause, but there is no row inB
that matches theON
condition, an extraB
row is generated with all columns set toNULL
. -
If you use
LEFT JOIN
to find rows that do not exist in some table and you have the following test:col_name
IS NULL in theWHERE
part, wherecol_name
is a column that is declared asNOT NULL
, MySQL stops searching for more rows (for a particular key combination) after it has found one row that matches theLEFT JOIN
condition.
The implementation of RIGHT JOIN
is analogous
to that of LEFT JOIN
with the roles of the
tables reversed.
The join optimizer calculates the order in which tables should
be joined. The table read order forced by LEFT
JOIN
or STRAIGHT_JOIN
helps the
join optimizer do its work much more quickly, because there are
fewer table permutations to check. Note that this means that if
you do a query of the following type, MySQL does a full scan on
b
because the LEFT JOIN
forces it to be read before d
:
SELECT * FROM a JOIN b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key) WHERE b.key=d.key;
The fix in this case is reverse the order in which
a
and b
are listed in the
FROM
clause:
SELECT * FROM b JOIN a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key) WHERE b.key=d.key;
For a LEFT JOIN
, if the
WHERE
condition is always false for the
generated NULL
row, the LEFT
JOIN
is changed to a normal 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 a normal join:
SELECT * FROM t1, t2 WHERE t2.column2=5 AND t1.column1=t2.column1;
This can be made faster because MySQL can use table
t2
before table t1
if
doing so would result in a better query plan. To force a
specific table order, use STRAIGHT_JOIN
.
As of MySQL 5.0.1, the syntax for expressing joins allows nested
joins. The following discussion refers to the join syntax
described in Section 13.2.7.1, “JOIN
Syntax”.
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 a syntactic
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 versions of MySQL prior to 5.0.1, parentheses in
table_references
were just omitted
and all join operations were grouped to the left. In general,
parentheses can be ignored in join expressions containing only
inner join operations.
After removing parentheses and grouping operations to the left, the join expression:
t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL) ON t1.a=t2.a
transforms into the 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:
t1, t2 LEFT JOIN t3 ON P(t2.b,t3.b)
for any tables t1,t2,t3
and any condition
P
over attributes t2.b
and
t3.b
.
Whenever the order of execution of the join operations in a join
expression (join_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
The nested join is formed in the first query with a left join operation, whereas 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
would 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. You can remove parentheses and evaluate left to right (or, in fact, you can evaluate the tables 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
by what algorithmic schema the nested-loop join executes a
query. Suppose that we have a join query over 3 tables
T1,T2,T3
of the 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
means “a row
constructed by concatenating the columns of rows
t1
, t2
, and
t3
.” In some of the following
examples, NULL
where a row name appears means
that NULL
is used for each column of that
row. For example, t1||t2||NULL
means “a
row constructed by concatenating the columns of rows
t1
and t2
, and
NULL
for each column of
t3
.”
Now let's 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, we modify the nested-loop pattern to get:
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 our example, the outer join table expressed by the following expression is embedded:
(T2 LEFT JOIN T3 ON P2(T2,T3))
Note that 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 the 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 will evaluate two different nestings:
SELECT * T1 LEFT JOIN (T2,T3) ON P1(T1,T2) AND P2(T1,T3) WHERE P(T1,T2,T3)
The nestings are these:
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; } } }
and:
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; } } }
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.
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 schema 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 have to introduce conditional pushed-down
predicates guarded by the flags that are turned on when a match
has been encountered.
For our example with outer joins with:
P(T1,T2,T3)=C1(T1) AND C(T2) AND C3(T3)
the nested-loop schema 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.
Note that 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. (We could use
conditional key access in this case, but this technique is not
employed yet in MySQL 5.0.)
Table expressions in the FROM
clause of a
query are simplified in many cases.
At the parser stage, queries with right outer joins operations are converted to equivalent queries containing only left join operations. In the general case, the conversion is performed according to the following rule:
(T1, ...) RIGHT JOIN (T2,...) ON P(T1,...,T2,...) = (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 join queries with outer join operation, it takes into consideration only the plans where, for each such operation, the outer tables are accessed before the inner tables. The optimizer options are limited because only such plans enables us to execute queries with outer joins operations by the nested loop schema.
Suppose that we have a query of the form:
SELECT * T1 LEFT JOIN T2 ON P1(T1,T2) WHERE P(T1,T2) AND R(T2)
with R(T2)
narrowing greatly the number of
matching rows from table T2
. If we executed
the query as it is, the optimizer would have no other choice
besides to access table T1
before table
T2
that may lead to a very inefficient
execution plan.
Fortunately, MySQL converts such a query into a query without an
outer join operation if the WHERE
condition
is null-rejected. A condition is called null-rejected for an
outer join operation if it evaluates to FALSE
or to UNKNOWN
for any
NULL
-complemented row built for the
operation.
Thus, for this outer join:
T1 LEFT JOIN T2 ON T1.A=T2.A
Conditions such as these are null-rejected:
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:
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. A condition is null-rejected in the following cases:
-
If it is of the form
A IS NOT NULL
, whereA
is an attribute of any of the inner tables -
If it is a predicate containing a reference to an inner table that evaluates to
UNKNOWN
when one of its arguments isNULL
-
If it is a conjunction containing a null-rejected condition as a conjunct
-
If 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 the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A LEFT JOIN T3 ON T3.B=T1.B WHERE T3.C > 0
the WHERE
condition is null-rejected for the
second outer join operation but is not null-rejected for the
first one.
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, the preceding query is replaced with the query:
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 would evaluate plans
compatible with only one access order
T1,T2,T3
. For the replacing query, it
additionally considers the access sequence
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
will be 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
Now the remaining outer join operation can be replaced by an
inner join, too, because the condition
T3.B=T2.B
is null-rejected and we get a query
without 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 we succeed 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.
When trying to convert an embedded outer join operation in a
query, we must take into account the join condition for the
embedding outer join together with the WHERE
condition. In the query:
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
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. So 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
The algorithm that converts outer join operations into inner joins was implemented in full measure, as it has been described here, in MySQL 5.0.1. MySQL 4.1 performs only some simple conversions.
In some cases, MySQL can use an index to satisfy an
ORDER BY
clause without doing any extra
sorting.
The index can also be used even if the ORDER
BY
does not match the index exactly, as long as all of
the unused portions of the index and all the extra
ORDER BY
columns are constants in the
WHERE
clause. The following queries use the
index to resolve the ORDER BY
part:
SELECT * FROM t1 ORDER BYkey_part1
,key_part2
,... ; SELECT * FROM t1 WHEREkey_part1
=constant
ORDER BYkey_part2
; SELECT * FROM t1 ORDER BYkey_part1
DESC,key_part2
DESC; SELECT * FROM t1 WHEREkey_part1
=1 ORDER BYkey_part1
DESC,key_part2
DESC;
In some cases, MySQL cannot use indexes to
resolve the ORDER BY
, although it still uses
indexes to find the rows that match the WHERE
clause. These cases include the following:
-
You use
ORDER BY
on different keys:SELECT * FROM t1 ORDER BY
key1
,key2
; -
You use
ORDER BY
on non-consecutive parts of a key:SELECT * FROM t1 WHERE
key2
=constant
ORDER BYkey_part2
; -
You mix
ASC
andDESC
:SELECT * FROM t1 ORDER BY
key_part1
DESC,key_part2
ASC; -
The key used to fetch the rows is not the same as the one used in the
ORDER BY
:SELECT * FROM t1 WHERE
key2
=constant
ORDER BYkey1
; -
You are joining many tables, and the columns in the
ORDER BY
are not all from the first non-constant table that is used to retrieve rows. (This is the first table in theEXPLAIN
output that does not have aconst
join type.) -
You have different
ORDER BY
andGROUP BY
expressions. -
The type of table index used does not store rows in order. For example, this is true for a
HASH
index in aMEMORY
table.
With EXPLAIN SELECT ... ORDER BY
, you can
check whether MySQL can use indexes to resolve the query. It
cannot if you see Using filesort
in the
Extra
column. See Section 7.2.1, “Optimizing Queries with EXPLAIN
”.
A filesort
optimization is used that records
not only the sort key value and row position, but the columns
required for the query as well. This avoids reading the rows
twice. The filesort
algorithm works like
this:
-
Read the rows that match the
WHERE
clause. -
For each row, record a tuple of values consisting of the sort key value and row position, and also the columns required for the query.
-
Sort the tuples by sort key value
-
Retrieve the rows in sorted order, but read the required columns directly from the sorted tuples rather than by accessing the table a second time.
This algorithm represents a significant improvement over that used in some older versions of MySQL.
To avoid a slowdown, this optimization is used only if the total
size of the extra columns in the sort tuple does not exceed the
value of the max_length_for_sort_data
system
variable. (A symptom of setting the value of this variable too
high is that you should see high disk activity and low CPU
activity.)
If you want 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, you can try the
following strategies:
-
Increase the size of the
sort_buffer_size
variable. -
Increase the size of the
read_rnd_buffer_size
variable. -
Change
tmpdir
to point to a dedicated filesystem with large amounts of empty space. This option accepts several paths that are used in round-robin fashion. Paths should be separated by colon characters (‘:
’) on Unix and semicolon characters (‘;
’) on Windows, NetWare, and OS/2. You can use this feature to spread the load across several directories. Note: The paths should be for directories in filesystems that are located on different physical disks, not different partitions on the same disk.
By default, MySQL sorts all GROUP BY
col1
,
col2
, ... queries as if you
specified ORDER BY
col1
,
col2
, ... in the query as
well. If you include an ORDER BY
clause
explicitly 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 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 to 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 (for example, this
is a BTREE
index and not 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 via index access, as detailed in the following sections.
In the first method, the grouping operation is applied together
with all range predicates (if any). The second method first
performs a range scan, and then groups the resulting tuples.
The most efficient way to process GROUP BY
is when the index is used to directly retrieve the group
fields. 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 7.2.1, “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 least possible number of keys.
This is possible under the following conditions:
-
The query is over a single table.
-
The
GROUP BY
includes the first consecutive parts of the index. (If, instead ofGROUP BY
, the query has aDISTINCT
clause, all distinct attributes refer to the beginning of the index.) -
The only aggregate functions used (if any) are
MIN()
andMAX()
, and all of them refer to the same column. -
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 ofMIN()
orMAX()
functions.
The EXPLAIN
output for such queries shows
Using index for group-by
in the
Extra
column.
The following queries fall into this category, assuming that
there is an index idx(c1,c2,c3)
on table
t1(c1,c2,c3,c4)
:
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()
orMAX()
, for example:SELECT c1, SUM(c2) FROM t1 GROUP BY c1;
-
The fields in the
GROUP BY
clause do not refer to the beginning of the index, as shown here: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, an example being:SELECT c1,c3 FROM t1 GROUP BY c1, c2;
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 is
still 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, we
term it a tight index scan. Notice that
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 is 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 we require sorting of the GROUP
BY
result, 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.
The following queries do not work with the loose index scan
access method described earlier, but still work with the tight
index scan access method (assuming that there is an index
idx(c1,c2,c3)
on table
t1(c1,c2,c3,c4)
).
-
There is a gap in the
GROUP BY
, but it is covered by the conditionc2 = '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;
In some cases, MySQL handles a query differently when you are
using LIMIT
row_count
and not using
HAVING
:
-
If you are selecting 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 use
LIMIT
row_count
withORDER BY
, MySQL ends the sorting as soon as it has found the firstrow_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 theLIMIT
clause must be selected, and most or all of them must be sorted, before it can be ascertained that the firstrow_count
rows have been found. In either case, after the initial rows have been found, there is no need to sort any remainder of the result set, and MySQL does not do so. -
When combining
LIMIT
row_count
withDISTINCT
, MySQL stops as soon as it findsrow_count
unique rows. -
In some cases, a
GROUP BY
can be resolved by reading the key in order (or doing a sort on the key) and then calculating summaries until the key value changes. In this case,LIMIT
row_count
does not calculate any unnecessaryGROUP 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
. -
LIMIT 0
quickly returns an empty set. This can be useful for checking the validity of a query. When using one of the MySQL APIs, it can also be employed for obtaining the types of the result columns. (This trick does not work in the MySQL Monitor (the mysql program), which merely displaysEmpty set
in such cases; you should instead useSHOW COLUMNS
orDESCRIBE
for this purpose.) -
When the server uses temporary tables to resolve the query, it uses the
LIMIT
row_count
clause to calculate how much space is required.
The output from EXPLAIN
shows
ALL
in the type
column
when MySQL uses a 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
orWHERE
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 7.2.4, “
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
tbl_name
to update the key distributions for the scanned table. See Section 13.5.2.1, “ANALYZE TABLE
Syntax”. -
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 useSET max_seeks_for_key=1000
to tell the optimizer to assume that no key scan causes more than 1,000 key seeks. See Section 5.2.2, “Server System Variables”.
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 multipleVALUES
lists to insert several rows at a time. This is considerably faster (many times faster in some cases) than using separate single-rowINSERT
statements. If you are adding data to a non-empty table, you can tune thebulk_insert_buffer_size
variable to make data insertion even faster. See Section 5.2.2, “Server System Variables”. -
If you are inserting a lot of rows from different clients, you can get higher speed by using the
INSERT DELAYED
statement. See Section 13.2.4.2, “INSERT DELAYED
Syntax”. -
For a
MyISAM
table, you can use concurrent inserts to add rows at the same time thatSELECT
statements are running if there are no deleted rows in middle of the table. See Section 7.3.3, “Concurrent Inserts”. -
When loading a table from a text file, use
LOAD DATA INFILE
. This is usually 20 times faster than usingINSERT
statements. See Section 13.2.5, “LOAD DATA INFILE
Syntax”. -
With some extra work, it is possible to make
LOAD DATA INFILE
run even faster for aMyISAM
table when the table has many indexes. Use the following procedure:-
Optionally create the table with
CREATE TABLE
. -
Execute a
FLUSH TABLES
statement or a mysqladmin flush-tables command. -
Use myisamchk --keys-used=0 -rq
/path/to/db/tbl_name
. This removes all use of indexes for the table. -
Insert data into the table with
LOAD DATA INFILE
. 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 14.1.3.3, “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 that updating the index duringLOAD DATA INFILE
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.
Note that
LOAD DATA INFILE
performs the preceding optimization automatically if theMyISAM
table into which you insert data is empty. The main difference 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 theLOAD DATA INFILE
statement.You can also disable or enable the indexes for a
MyISAM
table by using the following statements rather than myisamchk. If you use these statements, you can skip theFLUSH TABLE
operations:ALTER TABLE
tbl_name
DISABLE KEYS; ALTER TABLEtbl_name
ENABLE KEYS; -
-
To speed up
INSERT
operations that are performed with multiple statements for non-transactional 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 areINSERT
statements. Explicit locking statements are not needed if you can insert all rows with a singleINSERT
.To obtain faster insertions, for transactional tables, you should use
START TRANSACTION
andCOMMIT
instead ofLOCK TABLES
.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. For example:
-
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
, andDELETE
operations are very fast in MySQL, but you can obtain better overall performance by adding locks around everything that does more than about five inserts or updates in a row. If you do very many inserts in a row, you could do aLOCK TABLES
followed by anUNLOCK TABLES
once in a while (each 1,000 rows or so) to allow other threads access to the table. This would still result in a nice performance gain.INSERT
is still much slower for loading data thanLOAD DATA INFILE
, even when using the strategies just outlined. -
-
To increase performance for
MyISAM
tables, for bothLOAD DATA INFILE
andINSERT
, enlarge the key cache by increasing thekey_buffer_size
system variable. See Section 7.5.2, “Tuning Server Parameters”.
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 13.5.2.5, “OPTIMIZE TABLE
Syntax”.
The time required to delete individual rows 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 7.5.2, “Tuning Server Parameters”.
To delete all rows from a table, TRUNCATE TABLE
tbl_name
if faster than
than DELETE FROM
tbl_name
. See
Section 13.2.9, “TRUNCATE
Syntax”.
This section lists a number of miscellaneous tips for improving query processing speed:
-
Use persistent connections to the database to avoid connection overhead. If you cannot use persistent connections and you are initiating many new connections to the database, you may want to change the value of the
thread_cache_size
variable. See Section 7.5.2, “Tuning Server Parameters”. -
Always check whether all your queries really use the indexes that you have created in the tables. In MySQL, you can do this with the
EXPLAIN
statement. See Section 7.2.1, “Optimizing Queries withEXPLAIN
”. -
Try to avoid complex
SELECT
queries onMyISAM
tables that are updated frequently, to avoid problems with table locking that occur due to contention between readers and writers. -
With
MyISAM
tables that have no deleted rows in the middle, you can insert rows at the end at the same time that another query is reading from the table. If it is important to be able to do this, you should consider using the table in ways that avoid deleting rows. Another possibility is to runOPTIMIZE TABLE
to defragment the table after you have deleted a lot of rows from it. See Section 14.1, “TheMyISAM
Storage Engine”. -
To fix any compression issues that may have occurred with
ARCHIVE
tables, you can useOPTIMIZE TABLE
. See Section 14.8, “TheARCHIVE
Storage Engine”. -
Use
ALTER TABLE ... ORDER BY
expr1
,expr2
, ... if you usually retrieve rows inexpr1
,expr2
, ... order. By using this option after extensive changes to the table, you may be able to get higher performance. -
In some cases, it may make sense to introduce a column that is “hashed” based on information from other columns. If this column is short and reasonably unique, it may be much faster than a “wide” index on many columns. In MySQL, it is very easy to use this extra column:
SELECT * FROM
tbl_name
WHEREhash_col
=MD5(CONCAT(col1
,col2
)) ANDcol1
='constant
' ANDcol2
='constant
'; -
For
MyISAM
tables that change frequently, you should try to avoid all variable-length columns (VARCHAR
,BLOB
, andTEXT
). The table uses dynamic row format if it includes even a single variable-length column. See Chapter 14, Storage Engines and Table Types. -
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 14, Storage Engines and Table Types. -
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:
UPDATE
tbl_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. -
If you need to collect statistics from large log tables, use summary tables instead of scanning the entire log table. Maintaining the summaries should be much faster than trying to calculate statistics “live.” Regenerating new summary tables from the logs when things change (depending on business decisions) is faster than changing the running application.
-
If possible, you should 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.
-
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.
-
In some cases, it is convenient to 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 may save a lot of accesses at some stage. This is practical when you have data that does not conform well to a rows-and-columns table structure. -
Normally, you should try to keep all data non-redundant (observing what is referred to in database theory as third normal form). However, there may be situations in which it can be advantageous to duplicate information or create summary tables to gain more speed.
-
Stored routines or UDFs (user-defined functions) may be a good way to gain performance for some tasks. See Chapter 17, Stored Procedures and Functions, and Section 24.2, “Adding New Functions to MySQL”, for more information.
-
You can always gain something by caching queries or answers in your application and then performing many inserts or updates together. If your database system supports table locks (as do MySQL and Oracle), this should help to ensure 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 5.14, “The MySQL Query Cache”.
-
Use
INSERT DELAYED
when you do not need to know when your data is written. This reduces the overall insertion impact because many rows can be written with a single disk write. -
Use
INSERT LOW_PRIORITY
when you want to giveSELECT
statements higher priority than your inserts. -
Use
SELECT HIGH_PRIORITY
to get retrievals that jump the queue. That is, theSELECT
is executed even if there is another client waiting to do a write. -
Use multiple-row
INSERT
statements to store many rows with one SQL statement. Many SQL servers support this, including MySQL. -
Use
LOAD DATA INFILE
to load large amounts of data. This is faster than usingINSERT
statements. -
Use
AUTO_INCREMENT
columns to generate unique values. -
Use
OPTIMIZE TABLE
once in a while to avoid fragmentation with dynamic-formatMyISAM
tables. See Section 14.1.3, “MyISAM
Table Storage Formats”. -
Use
MEMORY
(HEAP
) tables when possible to get more speed. See Section 14.4, “TheMEMORY
(HEAP
) Storage Engine”.MEMORY
tables are useful for non-critical data that is accessed often, such as information about the last displayed banner for users who don't have cookies enabled in their Web browser. User sessions are another alternative available in many Web application environments for handling volatile state data. -
With Web servers, images and other binary assets should normally be stored as files. That is, store only a reference to the file rather than the file itself in the database. Most Web servers are better at caching files than database contents, so using files is generally faster.
-
Columns with identical information in different tables should be declared to have identical data types so that joins based on the corresponding columns will be faster.
-
Try to keep column names simple. For example, in a table named
customer
, use a column name ofname
instead ofcustomer_name
. To make your names portable to other SQL servers, you should keep them shorter than 18 characters. -
If you need really high speed, you should take a look at the low-level interfaces for data storage that the different SQL servers support. For example, by accessing the MySQL
MyISAM
storage engine directly, you could get a speed increase of two to five times compared to using the SQL interface. To be able to do this, the data must be on the same server as the application, and usually it should only be accessed by one process (because external file locking is really slow). One could eliminate these problems by introducing low-levelMyISAM
commands in the MySQL server (this could be one easy way to get more performance if needed). By carefully designing the database interface, it should be quite easy to support this type of optimization. -
If you are using numerical 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 need not parse your text files to find line and column boundaries.
-
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 6, Replication.
-
Declaring a
MyISAM
table with theDELAY_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 should ensure that the table is okay by running the server with the--myisam-recover
option, or by running myisamchk before restarting the server. (However, even in this case, you should not lose anything by usingDELAY_KEY_WRITE
, because the key information can always be generated from the data rows.)