Chapter 8 Optimization

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.

You might be tempted to rewrite your queries to make arithmetic operations faster, while sacrificing readability. Because MySQL does similar optimizations automatically, you can often avoid this work, and leave the query in a more understandable and maintainable form. Some of the optimizations performed by MySQL follow:

Some examples of queries that are very fast:

SELECT COUNT(*) FROM tbl_name;

SELECT MIN(key_part1),MAX(key_part1) FROM tbl_name;

SELECT MAX(key_part2) FROM tbl_name
  WHERE key_part1=constant;

SELECT ... FROM tbl_name
  ORDER BY key_part1,key_part2,... LIMIT 10;

SELECT ... FROM tbl_name
  ORDER BY key_part1 DESC, key_part2 DESC, ... LIMIT 10;

MySQL resolves the following queries using only the index tree, assuming that the indexed columns are numeric:

SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;

SELECT COUNT(*) FROM tbl_name
  WHERE key_part1=val1 AND key_part2=val2;

SELECT key_part2 FROM tbl_name GROUP BY key_part1;

The following queries use indexing to retrieve the rows in sorted order without a separate sorting pass:

SELECT ... FROM tbl_name
  ORDER BY key_part1,key_part2,... ;

SELECT ... FROM tbl_name
  ORDER BY key_part1 DESC, key_part2 DESC, ... ;

The range access method uses a single index to retrieve a subset of table rows that are contained within one or several index value intervals. It can be used for a single-part or multiple-part index. The following sections describe conditions under which the optimizer uses range access.

SELECT * FROM t1
  WHERE key_col > 1
  AND key_col < 10;

SELECT * FROM t1
  WHERE key_col = 1
  OR key_col IN (15,18,20);

SELECT * FROM t1
  WHERE key_col LIKE 'ab%'
  OR key_col BETWEEN 'bar' AND 'foo';

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');

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'

(1,-inf,-inf) <= (key_part1,key_part2,key_part3) < (1,+inf,+inf)

col_name IN(val1, ..., valN)
col_name = val1 OR ... OR col_name = valN

CREATE TABLE t1 (f1 INT NOT NULL, f2 INT NOT NULL, PRIMARY KEY(f1, f2));
INSERT INTO t1 VALUES
  (1,1), (1,2), (1,3), (1,4), (1,5),
  (2,1), (2,2), (2,3), (2,4), (2,5);
INSERT INTO t1 SELECT f1, f2 + 5 FROM t1;
INSERT INTO t1 SELECT f1, f2 + 10 FROM t1;
INSERT INTO t1 SELECT f1, f2 + 20 FROM t1;
INSERT INTO t1 SELECT f1, f2 + 40 FROM t1;
ANALYZE TABLE t1;

EXPLAIN SELECT f1, f2 FROM t1 WHERE f2 > 40;

"skip_scan_range": {
  "type": "skip_scan",
  "index": index_used_for_skip_scan,
  "key_parts_used_for_access": [key_parts_used_for_access],
  "range": [range]
}

SELECT ... FROM t1 WHERE ( col_1, col_2 ) IN (( 'a', 'b' ), ( 'c', 'd' ));

SELECT ... FROM t1 WHERE ( col_1 = 'a' AND col_2 = 'b' )
OR ( col_1 = 'c' AND col_2 = 'd' );

The Index Merge access method retrieves rows with multiple range scans and merges their results into one. This access method merges index scans from a single table only, not scans across multiple tables. The merge can produce unions, intersections, or unions-of-intersections of its underlying scans.

Example queries for which Index Merge may be used:

SELECT * FROM tbl_name WHERE key1 = 10 OR key2 = 20;

SELECT * FROM tbl_name
  WHERE (key1 = 10 OR key2 = 20) AND non_key = 30;

SELECT * FROM t1, t2
  WHERE (t1.key1 IN (1,2) OR t1.key2 LIKE 'value%')
  AND t2.key1 = t1.some_col;

SELECT * FROM t1, t2
  WHERE t1.key1 = 1
  AND (t2.key1 = t1.some_col OR t2.key2 = t1.some_col2);

In EXPLAIN output, the Index Merge method appears as index_merge in the type column. In this case, the key column contains a list of indexes used, and key_len contains a list of the longest key parts for those indexes.

The Index Merge access method has several algorithms, which are displayed in the Extra field of EXPLAIN output:

The following sections describe these algorithms in greater detail. The optimizer chooses between different possible Index Merge algorithms and other access methods based on cost estimates of the various available options.

SELECT * FROM innodb_table
  WHERE primary_key < 10 AND key_col1 = 20;

SELECT * FROM tbl_name
  WHERE key1_part1 = 1 AND key1_part2 = 2 AND key2 = 2;

SELECT COUNT(*) FROM t1 WHERE key1 = 1 AND key2 = 1;

SELECT * FROM t1
  WHERE key1 = 1 OR key2 = 2 OR key3 = 3;

SELECT * FROM innodb_table
  WHERE (key1 = 1 AND key2 = 2)
     OR (key3 = 'foo' AND key4 = 'bar') AND key5 = 5;

SELECT * FROM tbl_name
  WHERE key_col1 < 10 OR key_col2 < 20;

SELECT * FROM tbl_name
  WHERE (key_col1 > 10 OR key_col2 = 20) AND nonkey_col = 30;

Beginning with MySQL 8.0.18, MySQL employs a hash join for any query for which each join has an equi-join condition and uses no indexes, such as this one:

SELECT * 
    FROM t1 
    JOIN t2 
        ON t1.c1=t2.c1;

A hash join is usually faster than and is intended to be used in such cases instead of the block nested loop algorithm (see Block Nested-Loop Join Algorithm) employed in previous versions of MySQL.

In the example just shown and the remaining examples in this section, we assume that the three tables t1, t2, and t3 have been created using the following statements:

CREATE TABLE t1 (c1 INT, c2 INT);
CREATE TABLE t2 (c1 INT, c2 INT);
CREATE TABLE t3 (c1 INT, c2 INT);

You can see that a hash join is being employed by using EXPLAIN FORMAT=TREE, like this:

mysql> EXPLAIN FORMAT=TREE
    -> SELECT * 
    ->     FROM t1 
    ->     JOIN t2 
    ->         ON t1.c1=t2.c1\G
*************************** 1. row ***************************
EXPLAIN: -> Inner hash join (t2.c1 = t1.c1)  (cost=0.70 rows=1)
    -> Table scan on t2  (cost=0.35 rows=1)
    -> Hash
        -> Table scan on t1  (cost=0.35 rows=1)

To see whether hash joins are being used for a given join, you must use EXPLAIN with the FORMAT=TREE option. EXPLAIN ANALYZE also displays information about hash joins used.

The hash join is used for queries involving multiple joins as well, as long as at least one join condition for each pair of tables is an equi-join, like the query shown here:

SELECT * 
    FROM t1
    JOIN t2 
        ON (t1.c1 = t2.c1 AND t1.c2 < t2.c2)
    JOIN t3 
        ON (t2.c1 = t3.c1);

In cases like the one just shown, any extra conditions which are not equi-joins are applied as filters after the join is executed. This can be seen here in the output of EXPLAIN FORMAT=TREE:

mysql> EXPLAIN FORMAT=TREE
    -> SELECT * 
    ->     FROM t1
    ->     JOIN t2 
    ->         ON (t1.c1 = t2.c1 AND t1.c2 < t2.c2)
    ->     JOIN t3 
    ->         ON (t2.c1 = t3.c1)\G
*************************** 1. row ***************************
EXPLAIN: -> Inner hash join (t3.c1 = t1.c1)  (cost=1.05 rows=1)
    -> Table scan on t3  (cost=0.35 rows=1)
    -> Hash
        -> Filter: (t1.c2 < t2.c2)  (cost=0.70 rows=1)
            -> Inner hash join (t2.c1 = t1.c1)  (cost=0.70 rows=1)
                -> Table scan on t2  (cost=0.35 rows=1)
                -> Hash
                    -> Table scan on t1  (cost=0.35 rows=1)

As also can be seen from the output just shown, multiple hash joins can be (and are) used for joins having multiple equi-join conditions.

A hash join cannot be used if any pair of joined tables does not have at least one equi-join condition, as can be seen here:

mysql> EXPLAIN FORMAT=TREE
    ->     SELECT * 
    ->         FROM t1
    ->         JOIN t2 
    ->             ON (t1.c1 = t2.c1)
    ->         JOIN t3 
    ->             ON (t2.c1 < t3.c1)\G
*************************** 1. row ***************************
EXPLAIN: <not executable by iterator executor>

In this case, the slower block nested loop algorithm is employed, as in versions previous to MySQL 8.0.18 where no indexes are available:

mysql> EXPLAIN
    ->     SELECT * 
    ->         FROM t1
    ->         JOIN t2 
    ->             ON (t1.c1 = t2.c1)
    ->         JOIN t3 
    ->             ON (t2.c1 < t3.c1)\G             
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: t1
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 1
     filtered: 100.00
        Extra: NULL
*************************** 2. row ***************************
           id: 1
  select_type: SIMPLE
        table: t2
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 1
     filtered: 100.00
        Extra: Using where; Using join buffer (Block Nested Loop)
*************************** 3. row ***************************
           id: 1
  select_type: SIMPLE
        table: t3
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 1
     filtered: 100.00
        Extra: Using where; Using join buffer (Block Nested Loop)

A hash join is also applied for a Cartesian product—that is, when no join condition is specified, as shown here:

mysql> EXPLAIN FORMAT=TREE
    -> SELECT *
    ->     FROM t1
    ->     JOIN t2
    ->     WHERE t1.c2 > 50\G
*************************** 1. row ***************************
EXPLAIN: -> Inner hash join  (cost=0.70 rows=1)
    -> Table scan on t2  (cost=0.35 rows=1)
    -> Hash
        -> Filter: (t1.c2 > 50)  (cost=0.35 rows=1)
            -> Table scan on t1  (cost=0.35 rows=1)

By default, MySQL employs hash joins whenever possible. It is possible to control whether hash joins are used in either of two ways:

Memory usage by hash joins can be controlled using the join_buffer_size system variable; a hash join cannot use more memory than this amount. When the memory required for a hash join exceeds the amount available, MySQL handles this by using files on disk. If this happens, you should be aware that the join may not succeed if a hash join cannot fit into memory and it creates more files than set for open_files_limit. To avoid such problems, make either of the following changes:

This optimization improves the efficiency of direct comparisons between a nonindexed column and a constant. In such cases, the condition is “pushed down” to the storage engine for evaluation. This optimization can be used only by the NDB storage engine.

For NDB Cluster, this optimization can eliminate the need to send nonmatching rows over the network between the cluster's data nodes and the MySQL server that issued the query, and can speed up queries where it is used by a factor of 5 to 10 times over cases where condition pushdown could be but is not used.

Suppose that an NDB Cluster table is defined as follows:

CREATE TABLE t1 (
    a INT,
    b INT,
    KEY(a)
) ENGINE=NDB;

Condition pushdown can be used with queries such as the one shown here, which includes a comparison between a nonindexed column and a constant:

SELECT a, b FROM t1 WHERE b = 10;

The use of condition pushdown can be seen in the output of EXPLAIN:

mysql> EXPLAIN SELECT a,b FROM t1 WHERE b = 10\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: t1
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 10
        Extra: Using where with pushed condition

However, condition pushdown cannot be used with the following query:

SELECT a,b FROM t1 WHERE a = 10;

Condition pushdown is not applicable here because an index exists on column a. (An index access method would be more efficient and so would be chosen in preference to condition pushdown.)

Condition pushdown may also be employed when an indexed column is compared with a constant using a > or < operator:

mysql> EXPLAIN SELECT a, b FROM t1 WHERE a < 2\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: t1
         type: range
possible_keys: a
          key: a
      key_len: 5
          ref: NULL
         rows: 2
        Extra: Using where with pushed condition

Other supported comparisons for condition pushdown include the following:

In all of the cases in the preceding list, it is possible for the condition to be converted into the form of one or more direct comparisons between a column and a constant.

Engine condition pushdown is enabled by default. To disable it at server startup, set the optimizer_switch system variable. For example, in a my.cnf file, use these lines:

[mysqld]
optimizer_switch=engine_condition_pushdown=off

At runtime, disable condition pushdown like this:

SET optimizer_switch='engine_condition_pushdown=off';

Limitations.  Engine condition pushdown is subject to the following limitations:

Previously, condition pushdown was limited to terms referring to column values from the same table to which the condition was being pushed. Beginning with NDB 8.0.16, column values from tables earlier in the query plan can also be referred to from pushed conditions. This reduces the number of rows which must be handled by the SQL node during join processing. Filtering can be also performed in parallel in the LDM threads, rather than in a single mysqld process. This has the potential to improve performance of queries by a significant margin.

Join algorithms cannot be combined with referring columns from previous tables in the following two situations:

Index Condition Pushdown (ICP) is an optimization for the case where MySQL retrieves rows from a table using an index. Without ICP, the storage engine traverses the index to locate rows in the base table and returns them to the MySQL server which evaluates the WHERE condition for the rows. With ICP enabled, and if parts of the WHERE condition can be evaluated by using only columns from the index, the MySQL server pushes this part of the WHERE condition down to the storage engine. The storage engine then evaluates the pushed index condition by using the index entry and only if this is satisfied is the row read from the table. ICP can reduce the number of times the storage engine must access the base table and the number of times the MySQL server must access the storage engine.

Applicability of the Index Condition Pushdown optimization is subject to these conditions:

To understand how this optimization works, first consider how an index scan proceeds when Index Condition Pushdown is not used:

Using Index Condition Pushdown, the scan proceeds like this instead:

EXPLAIN output shows Using index condition in the Extra column when Index Condition Pushdown is used. It does not show Using index because that does not apply when full table rows must be read.

Suppose that a table contains information about people and their addresses and that the table has an index defined as INDEX (zipcode, lastname, firstname). If we know a person's zipcode value but are not sure about the last name, we can search like this:

SELECT * FROM people
  WHERE zipcode='95054'
  AND lastname LIKE '%etrunia%'
  AND address LIKE '%Main Street%';

MySQL can use the index to scan through people with zipcode='95054'. The second part (lastname LIKE '%etrunia%') cannot be used to limit the number of rows that must be scanned, so without Index Condition Pushdown, this query must retrieve full table rows for all people who have zipcode='95054'.

With Index Condition Pushdown, MySQL checks the lastname LIKE '%etrunia%' part before reading the full table row. This avoids reading full rows corresponding to index tuples that match the zipcode condition but not the lastname condition.

Index Condition Pushdown is enabled by default. It can be controlled with the optimizer_switch system variable by setting the index_condition_pushdown flag:

SET optimizer_switch = 'index_condition_pushdown=off';
SET optimizer_switch = 'index_condition_pushdown=on';

See Section 8.9.2, “Switchable Optimizations”.

MySQL executes joins between tables using a nested-loop algorithm or variations on it.

Table   Join Type
t1      range
t2      ref
t3      ALL

for each row in t1 matching range {
  for each row in t2 matching reference key {
    for each row in t3 {
      if row satisfies join conditions, send to client
    }
  }
}

for each row in t1 matching range {
  for each row in t2 matching reference key {
    store used columns from t1, t2 in join buffer
    if buffer is full {
      for each row in t3 {
        for each t1, t2 combination in join buffer {
          if row satisfies join conditions, send to client
        }
      }
      empty join buffer
    }
  }
}

if buffer is not empty {
  for each row in t3 {
    for each t1, t2 combination in join buffer {
      if row satisfies join conditions, send to client
    }
  }
}

(S * C)/join_buffer_size + 1

The syntax for expressing joins permits nested joins. The following discussion refers to the join syntax described in Section 13.2.10.2, “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 syntactically equivalent to INNER JOIN; they can replace each other. In standard SQL, they are not equivalent. INNER JOIN is used with an ON clause; CROSS JOIN is used otherwise.

In general, parentheses can be ignored in join expressions containing only inner join operations. Consider this join expression:

t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
   ON t1.a=t2.a

After removing parentheses and grouping operations to the left, that join expression transforms into this expression:

(t1 LEFT JOIN t2 ON t1.a=t2.a) LEFT JOIN t3
    ON t2.b=t3.b OR t2.b IS NULL

Yet, the two expressions are not equivalent. To see this, suppose that the tables t1, t2, and t3 have the following state:

In this case, the first expression returns a result set including the rows (1,1,101,101), (2,NULL,NULL,NULL), whereas the second expression returns the rows (1,1,101,101), (2,NULL,NULL,101):

mysql> SELECT *
       FROM t1
            LEFT JOIN
            (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
            ON t1.a=t2.a;
+------+------+------+------+
| a    | a    | b    | b    |
+------+------+------+------+
|    1 |    1 |  101 |  101 |
|    2 | NULL | NULL | NULL |
+------+------+------+------+

mysql> SELECT *
       FROM (t1 LEFT JOIN t2 ON t1.a=t2.a)
            LEFT JOIN t3
            ON t2.b=t3.b OR t2.b IS NULL;
+------+------+------+------+
| a    | a    | b    | b    |
+------+------+------+------+
|    1 |    1 |  101 |  101 |
|    2 | NULL | NULL |  101 |
+------+------+------+------+

In the following example, an outer join operation is used together with an inner join operation:

t1 LEFT JOIN (t2, t3) ON t1.a=t2.a

That expression cannot be transformed into the following expression:

t1 LEFT JOIN t2 ON t1.a=t2.a, t3

For the given table states, the two expressions return different sets of rows:

mysql> SELECT *
       FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a;
+------+------+------+------+
| a    | a    | b    | b    |
+------+------+------+------+
|    1 |    1 |  101 |  101 |
|    2 | NULL | NULL | NULL |
+------+------+------+------+

mysql> SELECT *
       FROM t1 LEFT JOIN t2 ON t1.a=t2.a, t3;
+------+------+------+------+
| a    | a    | b    | b    |
+------+------+------+------+
|    1 |    1 |  101 |  101 |
|    2 | NULL | NULL |  101 |
+------+------+------+------+

Therefore, if we omit parentheses in a join expression with outer join operators, we might change the result set for the original expression.

More exactly, we cannot ignore parentheses in the right operand of the left outer join operation and in the left operand of a right join operation. In other words, we cannot ignore parentheses for the inner table expressions of outer join operations. Parentheses for the other operand (operand for the outer table) can be ignored.

The following expression:

(t1,t2) LEFT JOIN t3 ON P(t2.b,t3.b)

Is equivalent to this expression for any tables t1,t2,t3 and any condition P over attributes t2.b and t3.b:

t1, t2 LEFT JOIN t3 ON P(t2.b,t3.b)

Whenever the order of execution of join operations in a join expression (joined_table) is not from left to right, we talk about nested joins. Consider the following queries:

SELECT * FROM t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b) ON t1.a=t2.a
  WHERE t1.a > 1

SELECT * FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a
  WHERE (t2.b=t3.b OR t2.b IS NULL) AND t1.a > 1

Those queries are considered to contain these nested joins:

t2 LEFT JOIN t3 ON t2.b=t3.b
t2, t3

In the first query, the nested join is formed with a left join operation. In the second query, it is formed with an inner join operation.

In the first query, the parentheses can be omitted: The grammatical structure of the join expression will dictate the same order of execution for join operations. For the second query, the parentheses cannot be omitted, although the join expression here can be interpreted unambiguously without them. In our extended syntax, the parentheses in (t2, t3) of the second query are required, although theoretically the query could be parsed without them: We still would have unambiguous syntactical structure for the query because LEFT JOIN and ON play the role of the left and right delimiters for the expression (t2,t3).

The preceding examples demonstrate these points:

Queries with nested outer joins are executed in the same pipeline manner as queries with inner joins. More exactly, a variation of the nested-loop join algorithm is exploited. Recall the algorithm by which the nested-loop join executes a query (see Section 8.2.1.7, “Nested-Loop Join Algorithms”). Suppose that a join query over 3 tables T1,T2,T3 has this form:

SELECT * FROM T1 INNER JOIN T2 ON P1(T1,T2)
                 INNER JOIN T3 ON P2(T2,T3)
  WHERE P(T1,T2,T3)

Here, P1(T1,T2) and P2(T3,T3) are some join conditions (on expressions), whereas P(T1,T2,T3) is a condition over columns of tables T1,T2,T3.

The nested-loop join algorithm would execute this query in the following manner:

FOR each row t1 in T1 {
  FOR each row t2 in T2 such that P1(t1,t2) {
    FOR each row t3 in T3 such that P2(t2,t3) {
      IF P(t1,t2,t3) {
         t:=t1||t2||t3; OUTPUT t;
      }
    }
  }
}

The notation t1||t2||t3 indicates a row constructed by concatenating the columns of rows t1, t2, and t3. In some of the following examples, NULL where a table name appears means a row in which NULL is used for each column of that table. For example, t1||t2||NULL indicates a row constructed by concatenating the columns of rows t1 and t2, and NULL for each column of t3. Such a row is said to be NULL-complemented.

Now consider a query with nested outer joins:

SELECT * FROM T1 LEFT JOIN
              (T2 LEFT JOIN T3 ON P2(T2,T3))
              ON P1(T1,T2)
  WHERE P(T1,T2,T3)

For this query, modify the nested-loop pattern to obtain:

FOR each row t1 in T1 {
  BOOL f1:=FALSE;
  FOR each row t2 in T2 such that P1(t1,t2) {
    BOOL f2:=FALSE;
    FOR each row t3 in T3 such that P2(t2,t3) {
      IF P(t1,t2,t3) {
        t:=t1||t2||t3; OUTPUT t;
      }
      f2=TRUE;
      f1=TRUE;
    }
    IF (!f2) {
      IF P(t1,t2,NULL) {
        t:=t1||t2||NULL; OUTPUT t;
      }
      f1=TRUE;
    }
  }
  IF (!f1) {
    IF P(t1,NULL,NULL) {
      t:=t1||NULL||NULL; OUTPUT t;
    }
  }
}

In general, for any nested loop for the first inner table in an outer join operation, a flag is introduced that is turned off before the loop and is checked after the loop. The flag is turned on when for the current row from the outer table a match from the table representing the inner operand is found. If at the end of the loop cycle the flag is still off, no match has been found for the current row of the outer table. In this case, the row is complemented by NULL values for the columns of the inner tables. The result row is passed to the final check for the output or into the next nested loop, but only if the row satisfies the join condition of all embedded outer joins.

In the example, the outer join table expressed by the following expression is embedded:

(T2 LEFT JOIN T3 ON P2(T2,T3))

For the query with inner joins, the optimizer could choose a different order of nested loops, such as this one:

FOR each row t3 in T3 {
  FOR each row t2 in T2 such that P2(t2,t3) {
    FOR each row t1 in T1 such that P1(t1,t2) {
      IF P(t1,t2,t3) {
         t:=t1||t2||t3; OUTPUT t;
      }
    }
  }
}

For queries with outer joins, the optimizer can choose only such an order where loops for outer tables precede loops for inner tables. Thus, for our query with outer joins, only one nesting order is possible. For the following query, the optimizer evaluates two different nestings. In both nestings, T1 must be processed in the outer loop because it is used in an outer join. T2 and T3 are used in an inner join, so that join must be processed in the inner loop. However, because the join is an inner join, T2 and T3 can be processed in either order.

SELECT * T1 LEFT JOIN (T2,T3) ON P1(T1,T2) AND P2(T1,T3)
  WHERE P(T1,T2,T3)

One nesting evaluates T2, then T3:

FOR each row t1 in T1 {
  BOOL f1:=FALSE;
  FOR each row t2 in T2 such that P1(t1,t2) {
    FOR each row t3 in T3 such that P2(t1,t3) {
      IF P(t1,t2,t3) {
        t:=t1||t2||t3; OUTPUT t;
      }
      f1:=TRUE
    }
  }
  IF (!f1) {
    IF P(t1,NULL,NULL) {
      t:=t1||NULL||NULL; OUTPUT t;
    }
  }
}

The other nesting evaluates T3, then T2:

FOR each row t1 in T1 {
  BOOL f1:=FALSE;
  FOR each row t3 in T3 such that P2(t1,t3) {
    FOR each row t2 in T2 such that P1(t1,t2) {
      IF P(t1,t2,t3) {
        t:=t1||t2||t3; OUTPUT t;
      }
      f1:=TRUE
    }
  }
  IF (!f1) {
    IF P(t1,NULL,NULL) {
      t:=t1||NULL||NULL; OUTPUT t;
    }
  }
}

When discussing the nested-loop algorithm for inner joins, we omitted some details whose impact on the performance of query execution may be huge. We did not mention so-called “pushed-down” conditions. Suppose that our WHERE condition P(T1,T2,T3) can be represented by a conjunctive formula:

P(T1,T2,T2) = C1(T1) AND C2(T2) AND C3(T3).

In this case, MySQL actually uses the following nested-loop algorithm for the execution of the query with inner joins:

FOR each row t1 in T1 such that C1(t1) {
  FOR each row t2 in T2 such that P1(t1,t2) AND C2(t2)  {
    FOR each row t3 in T3 such that P2(t2,t3) AND C3(t3) {
      IF P(t1,t2,t3) {
         t:=t1||t2||t3; OUTPUT t;
      }
    }
  }
}

You see that each of the conjuncts C1(T1), C2(T2), C3(T3) are pushed out of the most inner loop to the most outer loop where it can be evaluated. If C1(T1) is a very restrictive condition, this condition pushdown may greatly reduce the number of rows from table T1 passed to the inner loops. As a result, the execution time for the query may improve immensely.

For a query with outer joins, the WHERE condition is to be checked only after it has been found that the current row from the outer table has a match in the inner tables. Thus, the optimization of pushing conditions out of the inner nested loops cannot be applied directly to queries with outer joins. Here we must introduce conditional pushed-down predicates guarded by the flags that are turned on when a match has been encountered.

Recall this example with outer joins:

P(T1,T2,T3)=C1(T1) AND C(T2) AND C3(T3)

For that example, the nested-loop algorithm using guarded pushed-down conditions looks like this:

FOR each row t1 in T1 such that C1(t1) {
  BOOL f1:=FALSE;
  FOR each row t2 in T2
      such that P1(t1,t2) AND (f1?C2(t2):TRUE) {
    BOOL f2:=FALSE;
    FOR each row t3 in T3
        such that P2(t2,t3) AND (f1&&f2?C3(t3):TRUE) {
      IF (f1&&f2?TRUE:(C2(t2) AND C3(t3))) {
        t:=t1||t2||t3; OUTPUT t;
      }
      f2=TRUE;
      f1=TRUE;
    }
    IF (!f2) {
      IF (f1?TRUE:C2(t2) && P(t1,t2,NULL)) {
        t:=t1||t2||NULL; OUTPUT t;
      }
      f1=TRUE;
    }
  }
  IF (!f1 && P(t1,NULL,NULL)) {
      t:=t1||NULL||NULL; OUTPUT t;
  }
}

In general, pushed-down predicates can be extracted from join conditions such as P1(T1,T2) and P(T2,T3). In this case, a pushed-down predicate is guarded also by a flag that prevents checking the predicate for the NULL-complemented row generated by the corresponding outer join operation.

Access by key from one inner table to another in the same nested join is prohibited if it is induced by a predicate from the WHERE condition.

Outer joins include LEFT JOIN and RIGHT JOIN.

MySQL implements an A LEFT JOIN B join_specification as follows:

The RIGHT JOIN implementation is analogous to that of LEFT JOIN with the table roles reversed. Right joins are converted to equivalent left joins, as described in Section 8.2.1.10, “Outer Join Simplification”.

For a LEFT JOIN, if the WHERE condition is always false for the generated NULL row, the LEFT JOIN is changed to an inner join. For example, the WHERE clause would be false in the following query if t2.column1 were NULL:

SELECT * FROM t1 LEFT JOIN t2 ON (column1) WHERE t2.column2=5;

Therefore, it is safe to convert the query to an inner join:

SELECT * FROM t1, t2 WHERE t2.column2=5 AND t1.column1=t2.column1;

In MySQL 8.0.14 and later, trivial WHERE conditions arising from constant literal expressions are removed during preparation, rather than at a later stage in optimization, by which time joins have already been simplified. Earlier removal of trivial conditions allows the optimizer to convert outer joins to inner joins; this can result in improved plans for queries with outer joins containing trivial conditions in the WHERE clause, such as this one:

SELECT * FROM t1 LEFT JOIN t2 ON condition_1 WHERE condition_2 OR 0 = 1

The optimizer now sees during preparation that 0 = 1 is always false, making OR 0 = 1 redundant, and removes it, leaving this:

SELECT * FROM t1 LEFT JOIN t2 ON condition_1 where condition_2

Now the optimizer can rewrite the query as an inner join, like this:

SELECT * FROM t1 JOIN t2 WHERE condition_1 AND condition_2

Now the optimizer can use table t2 before table t1 if doing so would result in a better query plan. To provide a hint about the table join order, use optimizer hints; see Section 8.9.3, “Optimizer Hints”. Alternatively, use STRAIGHT_JOIN; see Section 13.2.10, “SELECT Syntax”. However, STRAIGHT_JOIN may prevent indexes from being used because it disables semijoin transformations; see Section 8.2.2.1, “Optimizing IN and EXISTS Subquery predicates with Semijoin Transformations”.

Table expressions in the FROM clause of a query are simplified in many cases.

At the parser stage, queries with right outer join operations are converted to equivalent queries containing only left join operations. In the general case, the conversion is performed such that this right join:

(T1, ...) RIGHT JOIN (T2, ...) ON P(T1, ..., T2, ...)

Becomes this equivalent left join:

(T2, ...) LEFT JOIN (T1, ...) ON P(T1, ..., T2, ...)

All inner join expressions of the form T1 INNER JOIN T2 ON P(T1,T2) are replaced by the list T1,T2, P(T1,T2) being joined as a conjunct to the WHERE condition (or to the join condition of the embedding join, if there is any).

When the optimizer evaluates plans for outer join operations, it takes into consideration only plans where, for each such operation, the outer tables are accessed before the inner tables. The optimizer choices are limited because only such plans enable outer joins to be executed using the nested-loop algorithm.

Consider a query of this form, where R(T2) greatly narrows the number of matching rows from table T2:

SELECT * T1 LEFT JOIN T2 ON P1(T1,T2)
  WHERE P(T1,T2) AND R(T2)

If the query is executed as written, the optimizer has no choice but to access the less-restricted table T1 before the more-restricted table T2, which may produce a very inefficient execution plan.

Instead, MySQL converts the query to a query with no outer join operation if the WHERE condition is null-rejected. (That is, it converts the outer join to an inner join.) A condition is said to be null-rejected for an outer join operation if it evaluates to FALSE or UNKNOWN for any NULL-complemented row generated for the operation.

Thus, for this outer join:

T1 LEFT JOIN T2 ON T1.A=T2.A

Conditions such as these are null-rejected because they cannot be true for any NULL-complemented row (with T2 columns set to NULL):

T2.B IS NOT NULL
T2.B > 3
T2.C <= T1.C
T2.B < 2 OR T2.C > 1

Conditions such as these are not null-rejected because they might be true for a NULL-complemented row:

T2.B IS NULL
T1.B < 3 OR T2.B IS NOT NULL
T1.B < 3 OR T2.B > 3

The general rules for checking whether a condition is null-rejected for an outer join operation are simple:

A condition can be null-rejected for one outer join operation in a query and not null-rejected for another. In this query, the WHERE condition is null-rejected for the second outer join operation but is not null-rejected for the first one:

SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                 LEFT JOIN T3 ON T3.B=T1.B
  WHERE T3.C > 0

If the WHERE condition is null-rejected for an outer join operation in a query, the outer join operation is replaced by an inner join operation.

For example, in the preceding query, the second outer join is null-rejected and can be replaced by an inner join:

SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                 INNER JOIN T3 ON T3.B=T1.B
  WHERE T3.C > 0

For the original query, the optimizer evaluates only plans compatible with the single table-access order T1,T2,T3. For the rewritten query, it additionally considers the access order T3,T1,T2.

A conversion of one outer join operation may trigger a conversion of another. Thus, the query:

SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                 LEFT JOIN T3 ON T3.B=T2.B
  WHERE T3.C > 0

Is first converted to the query:

SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                 INNER JOIN T3 ON T3.B=T2.B
  WHERE T3.C > 0

Which is equivalent to the query:

SELECT * FROM (T1 LEFT JOIN T2 ON T2.A=T1.A), T3
  WHERE T3.C > 0 AND T3.B=T2.B

The remaining outer join operation can also be replaced by an inner join because the condition T3.B=T2.B is null-rejected. This results in a query with no outer joins at all:

SELECT * FROM (T1 INNER JOIN T2 ON T2.A=T1.A), T3
  WHERE T3.C > 0 AND T3.B=T2.B

Sometimes the optimizer succeeds in replacing an embedded outer join operation, but cannot convert the embedding outer join. The following query:

SELECT * FROM T1 LEFT JOIN
              (T2 LEFT JOIN T3 ON T3.B=T2.B)
              ON T2.A=T1.A
  WHERE T3.C > 0

Is converted to:

SELECT * FROM T1 LEFT JOIN
              (T2 INNER JOIN T3 ON T3.B=T2.B)
              ON T2.A=T1.A
  WHERE T3.C > 0

That can be rewritten only to the form still containing the embedding outer join operation:

SELECT * FROM T1 LEFT JOIN
              (T2,T3)
              ON (T2.A=T1.A AND T3.B=T2.B)
  WHERE T3.C > 0

Any attempt to convert an embedded outer join operation in a query must take into account the join condition for the embedding outer join together with the WHERE condition. In this query, the WHERE condition is not null-rejected for the embedded outer join, but the join condition of the embedding outer join T2.A=T1.A AND T3.C=T1.C is null-rejected:

SELECT * FROM T1 LEFT JOIN
              (T2 LEFT JOIN T3 ON T3.B=T2.B)
              ON T2.A=T1.A AND T3.C=T1.C
  WHERE T3.D > 0 OR T1.D > 0

Consequently, the query can be converted to:

SELECT * FROM T1 LEFT JOIN
              (T2, T3)
              ON T2.A=T1.A AND T3.C=T1.C AND T3.B=T2.B
  WHERE T3.D > 0 OR T1.D > 0

Reading rows using a range scan on a secondary index can result in many random disk accesses to the base table when the table is large and not stored in the storage engine's cache. With the Disk-Sweep Multi-Range Read (MRR) optimization, MySQL tries to reduce the number of random disk access for range scans by first scanning the index only and collecting the keys for the relevant rows. Then the keys are sorted and finally the rows are retrieved from the base table using the order of the primary key. The motivation for Disk-sweep MRR is to reduce the number of random disk accesses and instead achieve a more sequential scan of the base table data.

The Multi-Range Read optimization provides these benefits:

The MRR optimization is not supported with secondary indexes created on virtual generated columns. InnoDB supports secondary indexes on virtual generated columns.

The following scenarios illustrate when MRR optimization can be advantageous:

Scenario A: MRR can be used for InnoDB and MyISAM tables for index range scans and equi-join operations.

Scenario B: MRR can be used for NDB tables for multiple-range index scans or when performing an equi-join by an attribute.

When MRR is used, the Extra column in EXPLAIN output shows Using MRR.

InnoDB and MyISAM do not use MRR if full table rows need not be accessed to produce the query result. This is the case if results can be produced entirely on the basis on information in the index tuples (through a covering index); MRR provides no benefit.

Two optimizer_switch system variable flags provide an interface to the use of MRR optimization. The mrr flag controls whether MRR is enabled. If mrr is enabled (on), the mrr_cost_based flag controls whether the optimizer attempts to make a cost-based choice between using and not using MRR (on) or uses MRR whenever possible (off). By default, mrr is on and mrr_cost_based is on. See Section 8.9.2, “Switchable Optimizations”.

For MRR, a storage engine uses the value of the read_rnd_buffer_size system variable as a guideline for how much memory it can allocate for its buffer. The engine uses up to read_rnd_buffer_size bytes and determines the number of ranges to process in a single pass.

In MySQL, a Batched Key Access (BKA) Join algorithm is available that uses both index access to the joined table and a join buffer. The BKA algorithm supports inner join, outer join, and semijoin operations, including nested outer joins. Benefits of BKA include improved join performance due to more efficient table scanning. Also, the Block Nested-Loop (BNL) Join algorithm previously used only for inner joins is extended and can be employed for outer join and semijoin operations, including nested outer joins.

The following sections discuss the join buffer management that underlies the extension of the original BNL algorithm, the extended BNL algorithm, and the BKA algorithm. For information about semijoin strategies, see Section 8.2.2.1, “Optimizing IN and EXISTS Subquery predicates with Semijoin Transformations”

mysql> SET optimizer_switch='mrr=on,mrr_cost_based=off,batched_key_access=on';

In join processing, prefix rows are those rows passed from one table in a join to the next. In general, the optimizer attempts to put tables with low prefix counts early in the join order to keep the number of row combinations from increasing rapidly. To the extent that the optimizer can use information about conditions on rows selected from one table and passed to the next, the more accurately it can compute row estimates and choose the best execution plan.

Without condition filtering, the prefix row count for a table is based on the estimated number of rows selected by the WHERE clause according to whichever access method the optimizer chooses. Condition filtering enables the optimizer to use other relevant conditions in the WHERE clause not taken into account by the access method, and thus improve its prefix row count estimates. For example, even though there might be an index-based access method that can be used to select rows from the current table in a join, there might also be additional conditions for the table in the WHERE clause that can filter (further restrict) the estimate for qualifying rows passed to the next table.

A condition contributes to the filtering estimate only if:

In EXPLAIN output, the rows column indicates the row estimate for the chosen access method, and the filtered column reflects the effect of condition filtering. filtered values are expressed as percentages. The maximum value is 100, which means no filtering of rows occurred. Values decreasing from 100 indicate increasing amounts of filtering.

The prefix row count (the number of rows estimated to be passed from the current table in a join to the next) is the product of the rows and filtered values. That is, the prefix row count is the estimated row count, reduced by the estimated filtering effect. For example, if rows is 1000 and filtered is 20%, condition filtering reduces the estimated row count of 1000 to a prefix row count of 1000 × 20% = 1000 × .2 = 200.

Consider the following query:

SELECT *
  FROM employee JOIN department ON employee.dept_no = department.dept_no
  WHERE employee.first_name = 'John'
  AND employee.hire_date BETWEEN '2018-01-01' AND '2018-06-01';

Suppose that the data set has these characteristics:

Without condition filtering, EXPLAIN produces output like this:

+----+------------+--------+------------------+---------+---------+------+----------+
| id | table      | type   | possible_keys    | key     | ref     | rows | filtered |
+----+------------+--------+------------------+---------+---------+------+----------+
| 1  | employee   | ref    | name,h_date,dept | name    | const   | 8    | 100.00   |
| 1  | department | eq_ref | PRIMARY          | PRIMARY | dept_no | 1    | 100.00   |
+----+------------+--------+------------------+---------+---------+------+----------+

For employee, the access method on the name index picks up the 8 rows that match a name of 'John'. No filtering is done (filtered is 100%), so all rows are prefix rows for the next table: The prefix row count is rows × filtered = 8 × 100% = 8.

With condition filtering, the optimizer additionally takes into account conditions from the WHERE clause not taken into account by the access method. In this case, the optimizer uses heuristics to estimate a filtering effect of 16.31% for the BETWEEN condition on employee.hire_date. As a result, EXPLAIN produces output like this:

+----+------------+--------+------------------+---------+---------+------+----------+
| id | table      | type   | possible_keys    | key     | ref     | rows | filtered |
+----+------------+--------+------------------+---------+---------+------+----------+
| 1  | employee   | ref    | name,h_date,dept | name    | const   | 8    | 16.31    |
| 1  | department | eq_ref | PRIMARY          | PRIMARY | dept_no | 1    | 100.00   |
+----+------------+--------+------------------+---------+---------+------+----------+

Now the prefix row count is rows × filtered = 8 × 16.31% = 1.3, which more closely reflects actual data set.

Normally, the optimizer does not calculate the condition filtering effect (prefix row count reduction) for the last joined table because there is no next table to pass rows to. An exception occurs for EXPLAIN: To provide more information, the filtering effect is calculated for all joined tables, including the last one.

To control whether the optimizer considers additional filtering conditions, use the condition_fanout_filter flag of the optimizer_switch system variable (see Section 8.9.2, “Switchable Optimizations”). This flag is enabled by default but can be disabled to suppress condition filtering (for example, if a particular query is found to yield better performance without it).

If the optimizer overestimates the effect of condition filtering, performance may be worse than if condition filtering is not used. In such cases, these techniques may help:

Comparisons between constants and column values in which the constant value is out of range or of the wrong type with respect to the column type are now handled once during query optimization rather row-by-row than during execution. The comparisons that can be treated in this manner are >, >=, <, <=, <>/!=, =, and <=>.

Consider the table created by the following statement:

CREATE TABLE t (c TINYINT UNSIGNED NOT NULL);

The WHERE condition in the query SELECT * FROM t WHERE c < 256 contains the integral constant 256 which is out of range for a TINYINT UNSIGNED column. Previously, this was handled by treating both operands as the larger type, but now, since any allowed value for c is less than the constant, the WHERE expression can instead be folded as WHERE 1, so that the query is rewritten as SELECT * FROM t WHERE 1.

This makes it possible for the optimizer to remove the WHERE expression altogether. If the column c were nullable (that is, defined only as TINYINT UNSIGNED) the query would be rewritten like this:

SELECT * FROM t WHERE ti IS NOT NULL

Folding is performed for constants compared to supported MySQL column types as follows:

Limitations.  This optimization cannot be used in the following cases:

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 * FROM tbl_name WHERE key_col IS NULL;

SELECT * FROM tbl_name WHERE key_col <=> NULL;

SELECT * FROM tbl_name
  WHERE key_col=const1 OR key_col=const2 OR key_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 OR 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.

The optimization can handle only one IS NULL level. In the following query, MySQL uses key lookups only on the expression (t1.a=t2.a AND t2.a IS NULL) and is not able to use the key part on b:

SELECT * FROM t1, t2
  WHERE (t1.a=t2.a AND t2.a IS NULL)
  OR (t1.b=t2.b AND t2.b IS NULL);

This section describes when MySQL can use an index to satisfy an ORDER BY clause, the filesort operation used when an index cannot be used, and execution plan information available from the optimizer about ORDER BY.

An ORDER BY with and without LIMIT may return rows in different orders, as discussed in Section 8.2.1.19, “LIMIT Query Optimization”.

SELECT a FROM t1 ORDER BY a;

SELECT ABS(a) AS a FROM t1 ORDER BY a;

SELECT ABS(a) AS b FROM t1 ORDER BY a;

SELECT ... FROM single_table ... ORDER BY non_index_column [DESC] LIMIT [M,]N;

SELECT col1, ... FROM t1 ... ORDER BY name LIMIT 10;
SELECT col1, ... FROM t1 ... ORDER BY RAND() LIMIT 15;

"filesort_summary": {
  "rows": 100,
  "examined_rows": 100,
  "number_of_tmp_files": 0,
  "peak_memory_used": 25192,
  "sort_mode": "<sort_key, packed_additional_fields>"
}

The most general way to satisfy a GROUP BY clause is to scan the whole table and create a new temporary table where all rows from each group are consecutive, and then use this temporary table to discover groups and apply aggregate functions (if any). In some cases, MySQL is able to do much better than that and avoid creation of temporary tables by using index access.

The most important preconditions for using indexes for GROUP BY are that all GROUP BY columns reference attributes from the same index, and that the index stores its keys in order (as is true, for example, for a BTREE index, but not for a HASH index). Whether use of temporary tables can be replaced by index access also depends on which parts of an index are used in a query, the conditions specified for these parts, and the selected aggregate functions.

There are two ways to execute a GROUP BY query through index access, as detailed in the following sections. The first method applies the grouping operation together with all range predicates (if any). The second method first performs a range scan, and then groups the resulting tuples.

Loose Index Scan can also be used in the absence of GROUP BY under some conditions. See Skip Scan Range Access Method.

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;

SELECT COUNT(DISTINCT c1), SUM(DISTINCT c1) FROM t1;

SELECT COUNT(DISTINCT c1, c2), COUNT(DISTINCT c2, c1) FROM t1;

DISTINCT combined with ORDER BY needs a temporary table in many cases.

Because DISTINCT may use GROUP BY, learn how MySQL works with columns in ORDER BY or HAVING clauses that are not part of the selected columns. See Section 12.20.3, “MySQL Handling of GROUP BY”.

In most cases, a DISTINCT clause can be considered as a special case of GROUP BY. For example, the following two queries are equivalent:

SELECT DISTINCT c1, c2, c3 FROM t1
WHERE c1 > const;

SELECT c1, c2, c3 FROM t1
WHERE c1 > const GROUP BY c1, c2, c3;

Due to this equivalence, the optimizations applicable to GROUP BY queries can be also applied to queries with a DISTINCT clause. Thus, for more details on the optimization possibilities for DISTINCT queries, see Section 8.2.1.17, “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;

If you need only a specified number of rows from a result set, use a LIMIT clause in the query, rather than fetching the whole result set and throwing away the extra data.

MySQL sometimes optimizes a query that has a LIMIT row_count clause and no HAVING clause:

If multiple rows have identical values in the ORDER BY columns, the server is free to return those rows in any order, and may do so differently depending on the overall execution plan. In other words, the sort order of those rows is nondeterministic with respect to the nonordered columns.

One factor that affects the execution plan is LIMIT, so an ORDER BY query with and without LIMIT may return rows in different orders. Consider this query, which is sorted by the category column but nondeterministic with respect to the id and rating columns:

mysql> SELECT * FROM ratings ORDER BY category;
+----+----------+--------+
| id | category | rating |
+----+----------+--------+
|  1 |        1 |    4.5 |
|  5 |        1 |    3.2 |
|  3 |        2 |    3.7 |
|  4 |        2 |    3.5 |
|  6 |        2 |    3.5 |
|  2 |        3 |    5.0 |
|  7 |        3 |    2.7 |
+----+----------+--------+

Including LIMIT may affect order of rows within each category value. For example, this is a valid query result:

mysql> SELECT * FROM ratings ORDER BY category LIMIT 5;
+----+----------+--------+
| id | category | rating |
+----+----------+--------+
|  1 |        1 |    4.5 |
|  5 |        1 |    3.2 |
|  4 |        2 |    3.5 |
|  3 |        2 |    3.7 |
|  6 |        2 |    3.5 |
+----+----------+--------+

In each case, the rows are sorted by the ORDER BY column, which is all that is required by the SQL standard.

If it is important to ensure the same row order with and without LIMIT, include additional columns in the ORDER BY clause to make the order deterministic. For example, if id values are unique, you can make rows for a given category value appear in id order by sorting like this:

mysql> SELECT * FROM ratings ORDER BY category, id;
+----+----------+--------+
| id | category | rating |
+----+----------+--------+
|  1 |        1 |    4.5 |
|  5 |        1 |    3.2 |
|  3 |        2 |    3.7 |
|  4 |        2 |    3.5 |
|  6 |        2 |    3.5 |
|  2 |        3 |    5.0 |
|  7 |        3 |    2.7 |
+----+----------+--------+

mysql> SELECT * FROM ratings ORDER BY category, id LIMIT 5;
+----+----------+--------+
| id | category | rating |
+----+----------+--------+
|  1 |        1 |    4.5 |
|  5 |        1 |    3.2 |
|  3 |        2 |    3.7 |
|  4 |        2 |    3.5 |
|  6 |        2 |    3.5 |
+----+----------+--------+

MySQL functions are tagged internally as deterministic or nondeterministic. A function is nondeterministic if, given fixed values for its arguments, it can return different results for different invocations. Examples of nondeterministic functions: RAND(), UUID().

If a function is tagged nondeterministic, a reference to it in a WHERE clause is evaluated for every row (when selecting from one table) or combination of rows (when selecting from a multiple-table join).

MySQL also determines when to evaluate functions based on types of arguments, whether the arguments are table columns or constant values. A deterministic function that takes a table column as argument must be evaluated whenever that column changes value.

Nondeterministic functions may affect query performance. For example, some optimizations may not be available, or more locking might be required. The following discussion uses RAND() but applies to other nondeterministic functions as well.

Suppose that a table t has this definition:

CREATE TABLE t (id INT NOT NULL PRIMARY KEY, col_a VARCHAR(100));

Consider these two queries:

SELECT * FROM t WHERE id = POW(1,2);
SELECT * FROM t WHERE id = FLOOR(1 + RAND() * 49);

Both queries appear to use a primary key lookup because of the equality comparison against the primary key, but that is true only for the first of them:

The effects of nondeterminism are not limited to SELECT statements. This UPDATE statement uses a nondeterministic function to select rows to be modified:

UPDATE t SET col_a = some_expr WHERE id = FLOOR(1 + RAND() * 49);

Presumably the intent is to update at most a single row for which the primary key matches the expression. However, it might update zero, one, or multiple rows, depending on the id column values and the values in the RAND() sequence.

The behavior just described has implications for performance and replication:

The difficulties stem from the fact that the RAND() function is evaluated once for every row of the table. To avoid multiple function evaluations, use one of these techniques:

As mentioned previously, a nondeterministic expression in the WHERE clause might prevent optimizations and result in a table scan. However, it may be possible to partially optimize the WHERE clause if other expressions are deterministic. For example:

SELECT * FROM t WHERE partial_key=5 AND some_column=RAND();

If the optimizer can use partial_key to reduce the set of rows selected, RAND() is executed fewer times, which diminishes the effect of nondeterminism on optimization.

Window functions affect the strategies the optimizer considers:

An aggregate function not used as a window function is aggregated in the outermost possible query. For example, in this query, MySQL sees that COUNT(t1.b) is something that cannot exist in the outer query because of its placement in the WHERE clause:

SELECT * FROM t1 WHERE t1.a = (SELECT COUNT(t1.b) FROM t2);

Consequently, MySQL aggregates inside the subquery, treating t1.b as a constant and returning the count of rows of t2.

Replacing WHERE with HAVING results in an error:

mysql> SELECT * FROM t1 HAVING t1.a = (SELECT COUNT(t1.b) FROM t2);
ERROR 1140 (42000): In aggregated query without GROUP BY, expression #1
of SELECT list contains nonaggregated column 'test.t1.a'; this is
incompatible with sql_mode=only_full_group_by

The error occurs because COUNT(t1.b) can exist in the HAVING, and so makes the outer query aggregated.

Window functions (including aggregate functions used as window functions) do not have the preceding complexity. They always aggregate in the subquery where they are written, never in the outer query.

Window function evaluation may be affected by the value of the windowing_use_high_precision system variable, which determines whether to compute window operations without loss of precision. By default, windowing_use_high_precision is enabled.

For some moving frame aggregates, the inverse aggregate function can be applied to remove values from the aggregate. This can improve performance but possibly with a loss of precision. For example, adding a very small floating-point value to a very large value causes the very small value to be “hidden” by the large value. When inverting the large value later, the effect of the small value is lost.

Loss of precision due to inverse aggregation is a factor only for operations on floating-point (approximate-value) data types. For other types, inverse aggregation is safe; this includes DECIMAL, which permits a fractional part but is an exact-value type.

For faster execution, MySQL always uses inverse aggregation when it is safe:

For evaluation of the variance functions STDDEV_POP(), STDDEV_SAMP(), VAR_POP(), VAR_SAMP(), and their synonyms, evaluation can occur in optimized mode or default mode. Optimized mode may produce slightly different results in the last significant digits. If such differences are permissible, windowing_use_high_precision can be disabled to permit optimized mode.

For EXPLAIN, windowing execution plan information is too extensive to display in traditional output format. To see windowing information, use EXPLAIN FORMAT=JSON and look for the windowing element.

Row constructors permit simultaneous comparisons of multiple values. For example, these two statements are semantically equivalent:

SELECT * FROM t1 WHERE (column1,column2) = (1,1);
SELECT * FROM t1 WHERE column1 = 1 AND column2 = 1;

In addition, the optimizer handles both expressions the same way.

The optimizer is less likely to use available indexes if the row constructor columns do not cover the prefix of an index. Consider the following table, which has a primary key on (c1, c2, c3):

CREATE TABLE t1 (
  c1 INT, c2 INT, c3 INT, c4 CHAR(100),
  PRIMARY KEY(c1,c2,c3)
);

In this query, the WHERE clause uses all columns in the index. However, the row constructor itself does not cover an index prefix, with the result that the optimizer uses only c1 (key_len=4, the size of c1):

mysql> EXPLAIN SELECT * FROM t1
       WHERE c1=1 AND (c2,c3) > (1,1)\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: t1
   partitions: NULL
         type: ref
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 4
          ref: const
         rows: 3
     filtered: 100.00
        Extra: Using where

In such cases, rewriting the row constructor expression using an equivalent nonconstructor expression may result in more complete index use. For the given query, the row constructor and equivalent nonconstructor expressions are:

(c2,c3) > (1,1)
c2 > 1 OR ((c2 = 1) AND (c3 > 1))

Rewriting the query to use the nonconstructor expression results in the optimizer using all three columns in the index (key_len=12):

mysql> EXPLAIN SELECT * FROM t1
       WHERE c1 = 1 AND (c2 > 1 OR ((c2 = 1) AND (c3 > 1)))\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: t1
   partitions: NULL
         type: range
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 12
          ref: NULL
         rows: 3
     filtered: 100.00
        Extra: Using where

Thus, for better results, avoid mixing row constructors with AND/OR expressions. Use one or the other.

Under certain conditions, the optimizer can apply the range access method to IN() expressions that have row constructor arguments. See Range Optimization of Row Constructor Expressions.

The output from EXPLAIN shows ALL in the type column when MySQL uses a full table scan to resolve a query. This usually happens under the following conditions:

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:

A semijoin is a preparation-time transformation that enables multiple execution strategies such as table pullout, duplicate weedout, first match, loose scan, and materialization. The optimizer uses semijoin strategies to improve subquery execution, as described in this section.

For an inner join between two tables, the join returns a row from one table as many times as there are matches in the other table. But for some questions, the only information that matters is whether there is a match, not the number of matches. Suppose that there are tables named class and roster that list classes in a course curriculum and class rosters (students enrolled in each class), respectively. To list the classes that actually have students enrolled, you could use this join:

SELECT class.class_num, class.class_name
FROM class INNER JOIN roster
WHERE class.class_num = roster.class_num;

However, the result lists each class once for each enrolled student. For the question being asked, this is unnecessary duplication of information.

Assuming that class_num is a primary key in the class table, duplicate suppression is possible by using SELECT DISTINCT, but it is inefficient to generate all matching rows first only to eliminate duplicates later.

The same duplicate-free result can be obtained by using a subquery:

SELECT class_num, class_name
FROM class
WHERE class_num IN (SELECT class_num FROM roster);

Here, the optimizer can recognize that the IN clause requires the subquery to return only one instance of each class number from the roster table. In this case, the query can use a semijoin; that is, an operation that returns only one instance of each row in class that is matched by rows in roster.

In MySQL 8.0.16 and later, this strategy can also be employed with EXISTS subqueries by transforming an EXISTS subquery that appears at the top level of a WHERE or ON clause of an IN or EXISTS predicate that refers to a subquery:

WHERE ... EXISTS (SELECT ... FROM ...)

-> WHERE ... 1 IN (SELECT 1 FROM ...)

After this, the subquery operation can be handled as a semijoin.

Beginning with MySQL 8.0.17, the following subqueries are transformed into antijoins:

In short, any case in which a subquery of the form IN (SELECT ... FROM ...) or EXISTS (SELECT ... FROM ...) is negated is transformed into an antijoin.

An antijoin is an operation that returns only rows for which there is no match. Consider the query shown here:

SELECT class_num, class_name
FROM class
WHERE class_num NOT IN (SELECT class_num FROM roster);

This query is rewritten internally as the antijoin SELECT class_num, class_name FROM class ANTIJOIN roster ON class_num, which returns one instance of each row in class that is not matched by any rows in roster. This means that, for each row in class, as soon as a match is found in roster, the row in class can be discarded.

Antijoin transformations cannot in most cases be applied if the expressions being compared are nullable. An exception to this rule is that (... NOT IN (SELECT ...)) IS NOT FALSE and its equivalent (... IN (SELECT ...)) IS NOT TRUE can be transformed into antijoins.

Outer join and inner join syntax is permitted in the outer query specification, and table references may be base tables, derived tables, view references, or common table expressions.

In MySQL, a subquery must satisfy these criteria to be handled as a semijoin (or, in MySQL 8.0.17 and later, an antijoin if NOT modifies the subquery):

The subquery may be correlated or uncorrelated. In MySQL 8.0.16 and later, decorrelation looks at equality predicates in the WHERE clause of a subquery used as the argument to EXISTS, and makes it possible to optimize it as if it was used within IN (SELECT b FROM ...). The term trivially correlated means that the predicate is an equality predicate, that it is the sole predicate in the WHERE clause (or is combined with AND), and that one operand is from a table referenced in the subquery and the other operand is from the outer query block.

DISTINCT is permitted, as is LIMIT unless ORDER BY is also used.

If a subquery meets the preceding criteria, MySQL converts it to a semijoin (or, in MySQL 8.0.17 or later, an antijoin if applicable) and makes a cost-based choice from these strategies:

Each of these strategies can be enabled or disabled using the following optimizer_switch system variable flags:

These flags are enabled by default. See Section 8.9.2, “Switchable Optimizations”.

The optimizer minimizes differences in handling of views and derived tables. This affects queries that use the STRAIGHT_JOIN modifier and a view with an IN subquery that can be converted to a semijoin. The following query illustrates this because the change in processing causes a change in transformation, and thus a different execution strategy:

CREATE VIEW v AS
SELECT *
FROM t1
WHERE a IN (SELECT b
           FROM t2);

SELECT STRAIGHT_JOIN *
FROM t3 JOIN v ON t3.x = v.a;

The optimizer first looks at the view and converts the IN subquery to a semijoin, then checks whether it is possible to merge the view into the outer query. Because the STRAIGHT_JOIN modifier in the outer query prevents semijoin, the optimizer refuses the merge, causing derived table evaluation using a materialized table.

EXPLAIN output indicates the use of semijoin strategies as follows:

The optimizer uses materialization to enable more efficient subquery processing. Materialization speeds up query execution by generating a subquery result as a temporary table, normally in memory. The first time MySQL needs the subquery result, it materializes that result into a temporary table. Any subsequent time the result is needed, MySQL refers again to the temporary table. The optimizer may index the table with a hash index to make lookups fast and inexpensive. The index contains unique values to eliminate duplicates and make the table smaller.

Subquery materialization uses an in-memory temporary table when possible, falling back to on-disk storage if the table becomes too large. See Section 8.4.4, “Internal Temporary Table Use in MySQL”.

If materialization is not used, the optimizer sometimes rewrites a noncorrelated subquery as a correlated subquery. For example, the following IN subquery is noncorrelated (where_condition involves only columns from t2 and not t1):

SELECT * FROM t1
WHERE t1.a IN (SELECT t2.b FROM t2 WHERE where_condition);

The optimizer might rewrite this as an EXISTS correlated subquery:

SELECT * FROM t1
WHERE EXISTS (SELECT t2.b FROM t2 WHERE where_condition AND t1.a=t2.b);

Subquery materialization using a temporary table avoids such rewrites and makes it possible to execute the subquery only once rather than once per row of the outer query.

For subquery materialization to be used in MySQL, the optimizer_switch system variable materialization flag must be enabled. (See Section 8.9.2, “Switchable Optimizations”.) With the materialization flag enabled, materialization applies to subquery predicates that appear anywhere (in the select list, WHERE, ON, GROUP BY, HAVING, or ORDER BY), for predicates that fall into any of these use cases:

The following examples illustrate how the requirement for equivalence of UNKNOWN and FALSE predicate evaluation affects whether subquery materialization can be used. Assume that where_condition involves columns only from t2 and not t1 so that the subquery is noncorrelated.

This query is subject to materialization:

SELECT * FROM t1
WHERE t1.a IN (SELECT t2.b FROM t2 WHERE where_condition);

Here, it does not matter whether the IN predicate returns UNKNOWN or FALSE. Either way, the row from t1 is not included in the query result.

An example where subquery materialization is not used is the following query, where t2.b is a nullable column:

SELECT * FROM t1
WHERE (t1.a,t1.b) NOT IN (SELECT t2.a,t2.b FROM t2
                          WHERE where_condition);

The following restrictions apply to the use of subquery materialization:

Use of EXPLAIN with a query provides some indication of whether the optimizer uses subquery materialization:

Certain optimizations are applicable to comparisons that use the IN (or =ANY) operator to test subquery results. This section discusses these optimizations, particularly with regard to the challenges that NULL values present. The last part of the discussion suggests how you can help the optimizer.

Consider the following subquery comparison:

outer_expr IN (SELECT inner_expr FROM ... WHERE subquery_where)

MySQL evaluates queries “from outside to inside.” That is, it first obtains the value of the outer expression outer_expr, and then runs the subquery and captures the rows that it produces.

A very useful optimization is to “inform” the subquery that the only rows of interest are those where the inner expression inner_expr is equal to outer_expr. This is done by pushing down an appropriate equality into the subquery's WHERE clause to make it more restrictive. The converted comparison looks like this:

EXISTS (SELECT 1 FROM ... WHERE subquery_where AND outer_expr=inner_expr)

After the conversion, MySQL can use the pushed-down equality to limit the number of rows it must examine to evaluate the subquery.

More generally, a comparison of N values to a subquery that returns N-value rows is subject to the same conversion. If oe_i and ie_i represent corresponding outer and inner expression values, this subquery comparison:

(oe_1, ..., oe_N) IN
  (SELECT ie_1, ..., ie_N FROM ... WHERE subquery_where)

Becomes:

EXISTS (SELECT 1 FROM ... WHERE subquery_where
                          AND oe_1 = ie_1
                          AND ...
                          AND oe_N = ie_N)

For simplicity, the following discussion assumes a single pair of outer and inner expression values.

The conversion just described has its limitations. It is valid only if we ignore possible NULL values. That is, the “pushdown” strategy works as long as both of these conditions are true:

When either or both of those conditions do not hold, optimization is more complex.

Suppose that outer_expr is known to be a non-NULL value but the subquery does not produce a row such that outer_expr = inner_expr. Then outer_expr IN (SELECT ...) evaluates as follows:

In this situation, the approach of looking for rows with outer_expr = inner_expr is no longer valid. It is necessary to look for such rows, but if none are found, also look for rows where inner_expr is NULL. Roughly speaking, the subquery can be converted to something like this:

EXISTS (SELECT 1 FROM ... WHERE subquery_where AND
        (outer_expr=inner_expr OR inner_expr IS NULL))

The need to evaluate the extra IS NULL condition is why MySQL has the ref_or_null access method:

mysql> EXPLAIN
       SELECT outer_expr IN (SELECT t2.maybe_null_key
                             FROM t2, t3 WHERE ...)
       FROM t1;
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        table: t1
...
*************************** 2. row ***************************
           id: 2
  select_type: DEPENDENT SUBQUERY
        table: t2
         type: ref_or_null
possible_keys: maybe_null_key
          key: maybe_null_key
      key_len: 5
          ref: func
         rows: 2
        Extra: Using where; Using index
...

The unique_subquery and index_subquery subquery-specific access methods also have “or NULL” variants.

The additional OR ... IS NULL condition makes query execution slightly more complicated (and some optimizations within the subquery become inapplicable), but generally this is tolerable.

The situation is much worse when outer_expr can be NULL. According to the SQL interpretation of NULL as “unknown value,” NULL IN (SELECT inner_expr ...) should evaluate to:

For proper evaluation, it is necessary to be able to check whether the SELECT has produced any rows at all, so outer_expr = inner_expr cannot be pushed down into the subquery. This is a problem because many real world subqueries become very slow unless the equality can be pushed down.

Essentially, there must be different ways to execute the subquery depending on the value of outer_expr.

The optimizer chooses SQL compliance over speed, so it accounts for the possibility that outer_expr might be NULL:

To solve the dilemma of whether or not to push down conditions into the subquery, the conditions are wrapped within “trigger” functions. Thus, an expression of the following form:

outer_expr IN (SELECT inner_expr FROM ... WHERE subquery_where)

Is converted into:

EXISTS (SELECT 1 FROM ... WHERE subquery_where
                          AND trigcond(outer_expr=inner_expr))

More generally, if the subquery comparison is based on several pairs of outer and inner expressions, the conversion takes this comparison:

(oe_1, ..., oe_N) IN (SELECT ie_1, ..., ie_N FROM ... WHERE subquery_where)

And converts it to this expression:

EXISTS (SELECT 1 FROM ... WHERE subquery_where
                          AND trigcond(oe_1=ie_1)
                          AND ...
                          AND trigcond(oe_N=ie_N)
       )

Each trigcond(X) is a special function that evaluates to the following values:

Equalities that are wrapped within trigcond() functions are not first class predicates for the query optimizer. Most optimizations cannot deal with predicates that may be turned on and off at query execution time, so they assume any trigcond(X) to be an unknown function and ignore it. Triggered equalities can be used by those optimizations:

When the optimizer uses a triggered condition to create some kind of index lookup-based access (as for the first two items of the preceding list), it must have a fallback strategy for the case when the condition is turned off. This fallback strategy is always the same: Do a full table scan. In EXPLAIN output, the fallback shows up as Full scan on NULL key in the Extra column:

mysql> EXPLAIN SELECT t1.col1,
       t1.col1 IN (SELECT t2.key1 FROM t2 WHERE t2.col2=t1.col2) FROM t1\G
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        table: t1
        ...
*************************** 2. row ***************************
           id: 2
  select_type: DEPENDENT SUBQUERY
        table: t2
         type: index_subquery
possible_keys: key1
          key: key1
      key_len: 5
          ref: func
         rows: 2
        Extra: Using where; Full scan on NULL key

If you run EXPLAIN followed by SHOW WARNINGS, you can see the triggered condition:

*************************** 1. row ***************************
  Level: Note
   Code: 1003
Message: select `test`.`t1`.`col1` AS `col1`,
         <in_optimizer>(`test`.`t1`.`col1`,
         <exists>(<index_lookup>(<cache>(`test`.`t1`.`col1`) in t2
         on key1 checking NULL
         where (`test`.`t2`.`col2` = `test`.`t1`.`col2`) having
         trigcond(<is_not_null_test>(`test`.`t2`.`key1`))))) AS
         `t1.col1 IN (select t2.key1 from t2 where t2.col2=t1.col2)`
         from `test`.`t1`

The use of triggered conditions has some performance implications. A NULL IN (SELECT ...) expression now may cause a full table scan (which is slow) when it previously did not. This is the price paid for correct results (the goal of the trigger-condition strategy is to improve compliance, not speed).

For multiple-table subqueries, execution of NULL IN (SELECT ...) is particularly slow because the join optimizer does not optimize for the case where the outer expression is NULL. It assumes that subquery evaluations with NULL on the left side are very rare, even if there are statistics that indicate otherwise. On the other hand, if the outer expression might be NULL but never actually is, there is no performance penalty.

To help the query optimizer better execute your queries, use these suggestions:

The subquery_materialization_cost_based flag of the optimizer_switch system variable enables control over the choice between subquery materialization and IN-to-EXISTS subquery transformation. See Section 8.9.2, “Switchable Optimizations”.

The optimizer can handle derived table references using two strategies (which also apply to view references and common table expressions):

Example 1:

SELECT * FROM (SELECT * FROM t1) AS derived_t1;

With merging of the derived table derived_t1, that query is executed similar to:

SELECT * FROM t1;

Example 2:

SELECT *
  FROM t1 JOIN (SELECT t2.f1 FROM t2) AS derived_t2 ON t1.f2=derived_t2.f1
  WHERE t1.f1 > 0;

With merging of the derived table derived_t2, that query is executed similar to:

SELECT t1.*, t2.f1
  FROM t1 JOIN t2 ON t1.f2=t2.f1
  WHERE t1.f1 > 0;

With materialization, derived_t1 and derived_t2 are each treated as a separate table within their respective queries.

The optimizer handles derived tables, view references, and common table expressions the same way: It avoids unnecessary materialization whenever possible, which enables pushing down conditions from the outer query to derived tables and produces more efficient execution plans. (For an example, see Section 8.2.2.2, “Optimizing Subqueries with Materialization”.)

If merging would result in an outer query block that references more than 61 base tables, the optimizer chooses materialization instead.

The optimizer propagates an ORDER BY clause in a derived table or view reference to the outer query block if these conditions are all true:

Otherwise, the optimizer ignores the ORDER BY clause.

The following means are available to influence whether the optimizer attempts to merge derived tables, view references, and common table expressions into the outer query block:

If the optimizer chooses the materialization strategy rather than merging for a derived table, it handles the query as follows:

Consider the following EXPLAIN statement, for a SELECT query that contains a derived table:

EXPLAIN SELECT * FROM (SELECT * FROM t1) AS derived_t1;

The optimizer avoids materializing the derived table by delaying it until the result is needed during SELECT execution. In this case, the query is not executed (because it occurs in an EXPLAIN statement), so the result is never needed.

Even for queries that are executed, delay of derived table materialization may enable the optimizer to avoid materialization entirely. When this happens, query execution is quicker by the time needed to perform materialization. Consider the following query, which joins the result of a derived table to another table:

SELECT *
  FROM t1 JOIN (SELECT t2.f1 FROM t2) AS derived_t2
          ON t1.f2=derived_t2.f1
  WHERE t1.f1 > 0;

If the optimization processes t1 first and the WHERE clause produces an empty result, the join must necessarily be empty and the derived table need not be materialized.

For cases when a derived table requires materialization, the optimizer may add an index to the materialized table to speed up access to it. If such an index enables ref access to the table, it can greatly reduce amount of data read during query execution. Consider the following query:

SELECT *
 FROM t1 JOIN (SELECT DISTINCT f1 FROM t2) AS derived_t2
         ON t1.f1=derived_t2.f1;

The optimizer constructs an index over column f1 from derived_t2 if doing so would enable use of ref access for the lowest cost execution plan. After adding the index, the optimizer can treat the materialized derived table the same as a regular table with an index, and it benefits similarly from the generated index. The overhead of index creation is negligible compared to the cost of query execution without the index. If ref access would result in higher cost than some other access method, the optimizer creates no index and loses nothing.

For optimizer trace output, a merged derived table or view reference is not shown as a node. Only its underlying tables appear in the top query's plan.

What is true for materialization of derived tables is also true for common table expressions (CTEs). In addition, the following considerations pertain specifically to CTEs.

If a CTE is materialized by a query, it is materialized once for the query, even if the query references it several times.

A recursive CTE is always materialized.

If a CTE is materialized, the optimizer automatically adds relevant indexes if it estimates that indexing will speed up access by the top-level statement to the CTE. This is similar to automatic indexing of derived tables, except that if the CTE is referenced multiple times, the optimizer may create multiple indexes, to speed up access by each reference in the most appropriate way.

The MERGE and NO_MERGE optimizer hints can be applied to CTEs. Each CTE reference in the top-level statement can have its own hint, permitting CTE references to be selectively merged or materialized. The following statement uses hints to indicate that cte1 should be merged and cte2 should be materialized:

WITH
  cte1 AS (SELECT a, b FROM table1),
  cte2 AS (SELECT c, d FROM table2)
SELECT /*+ MERGE(cte1) NO_MERGE(cte2) */ cte1.b, cte2.d
FROM cte1 JOIN cte2
WHERE cte1.a = cte2.c;

The ALGORITHM clause for CREATE VIEW does not affect materialization for any WITH clause preceding the SELECT statement in the view definition. Consider this statement:

CREATE ALGORITHM={TEMPTABLE|MERGE} VIEW v1 AS WITH ... SELECT ...

The ALGORITHM value affects materialization only of the SELECT, not the WITH clause.

Prior to MySQL 8.0.16, if internal_tmp_disk_storage_engine=MYISAM, an error occurred for any attempt to materialize a CTE using an on-disk temporary table, since for CTEs, the storage engine used for on-disk internal temporary tables could not be MyISAM. Beginning with MySQL 8.0.16, this is no longer an issue, since TempTable now always uses InnoDB for on-disk internal temporary tables.

As mentioned previously, a CTE, if materialized, is materialized once, even if referenced multiple times. To indicate one-time materialization, optimizer trace output contains an occurrence of creating_tmp_table plus one or more occurrences of reusing_tmp_table.

CTEs are similar to derived tables, for which the materialized_from_subquery node follows the reference. This is true for a CTE that is referenced multiple times, so there is no duplication of materialized_from_subquery nodes (which would give the impression that the subquery is executed multiple times, and produce unnecessarily verbose output). Only one reference to the CTE has a complete materialized_from_subquery node with the description of its subquery plan. Other references have a reduced materialized_from_subquery node. The same idea applies to EXPLAIN output in TRADITIONAL format: Subqueries for other references are not shown.

To optimize insert speed, combine many small operations into a single large operation. Ideally, you make a single connection, send the data for many new rows at once, and delay all index updates and consistency checking until the very end.

The time required for inserting a row is determined by the following factors, where the numbers indicate approximate proportions:

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:

An update statement is optimized like a SELECT query with the additional overhead of a write. The speed of the write depends on the amount of data being updated and the number of indexes that are updated. Indexes that are not changed do not get updated.

Another way to get fast updates is to delay updates and then do many updates in a row later. Performing multiple updates together is much quicker than doing one at a time if you lock the table.

For a MyISAM table that uses dynamic row format, updating a row to a longer total length may split the row. If you do this often, it is very important to use OPTIMIZE TABLE occasionally. See Section 13.7.3.4, “OPTIMIZE TABLE Syntax”.

The time required to delete individual rows in a MyISAM table is exactly proportional to the number of indexes. To delete rows more quickly, you can increase the size of the key cache by increasing the key_buffer_size system variable. See Section 5.1.1, “Configuring the Server”.

To delete all rows from a MyISAM table, TRUNCATE TABLE tbl_name is faster than DELETE FROM tbl_name. Truncate operations are not transaction-safe; an error occurs when attempting one in the course of an active transaction or active table lock. See Section 13.1.37, “TRUNCATE TABLE Syntax”.

For character and string columns, follow these guidelines:

When you execute a mysqladmin status command, you should see something like this:

Uptime: 426 Running threads: 1 Questions: 11082
Reloads: 1 Open tables: 12

The Open tables value of 12 can be somewhat puzzling if you have fewer than 12 tables.

MySQL is multithreaded, so there may be many clients issuing queries for a given table simultaneously. To minimize the problem with multiple client sessions having different states on the same table, the table is opened independently by each concurrent session. This uses additional memory but normally increases performance. With MyISAM tables, one extra file descriptor is required for the data file for each client that has the table open. (By contrast, the index file descriptor is shared between all sessions.)

The table_open_cache and max_connections system variables affect the maximum number of files the server keeps open. If you increase one or both of these values, you may run up against a limit imposed by your operating system on the per-process number of open file descriptors. Many operating systems permit you to increase the open-files limit, although the method varies widely from system to system. Consult your operating system documentation to determine whether it is possible to increase the limit and how to do so.

table_open_cache is related to max_connections. For example, for 200 concurrent running connections, specify a table cache size of at least 200 * N, where N is the maximum number of tables per join in any of the queries which you execute. You must also reserve some extra file descriptors for temporary tables and files.

Make sure that your operating system can handle the number of open file descriptors implied by the table_open_cache setting. If table_open_cache is set too high, MySQL may run out of file descriptors and exhibit symptoms such as refusing connections or failing to perform queries.

Also take into account that the MyISAM storage engine needs two file descriptors for each unique open table. To increase the number of file descriptors available to MySQL, set the open_files_limit system variable. See Section B.4.2.17, “File Not Found and Similar Errors”.

The cache of open tables is kept at a level of table_open_cache entries. The server autosizes the cache size at startup. To set the size explicitly, set the table_open_cache system variable at startup. MySQL may temporarily open more tables than this to execute queries, as described later in this section.

MySQL closes an unused table and removes it from the table cache under the following circumstances:

When the table cache fills up, the server uses the following procedure to locate a cache entry to use:

A MyISAM table is opened for each concurrent access. This means the table needs to be opened twice if two threads access the same table or if a thread accesses the table twice in the same query (for example, by joining the table to itself). Each concurrent open requires an entry in the table cache. The first open of any MyISAM table takes two file descriptors: one for the data file and one for the index file. Each additional use of the table takes only one file descriptor for the data file. The index file descriptor is shared among all threads.

If you are opening a table with the HANDLER tbl_name OPEN statement, a dedicated table object is allocated for the thread. This table object is not shared by other threads and is not closed until the thread calls HANDLER tbl_name CLOSE or the thread terminates. When this happens, the table is put back in the table cache (if the cache is not full). See Section 13.2.4, “HANDLER Syntax”.

To determine whether your table cache is too small, check the Opened_tables status variable, which indicates the number of table-opening operations since the server started:

mysql> SHOW GLOBAL STATUS LIKE 'Opened_tables';
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| Opened_tables | 2741  |
+---------------+-------+

If the value is very large or increases rapidly, even when you have not issued many FLUSH TABLES statements, increase the table_open_cache value at server startup.

If you have many MyISAM tables in the same database directory, open, close, and create operations are slow. If you execute SELECT statements on many different tables, there is a little overhead when the table cache is full, because for every table that has to be opened, another must be closed. You can reduce this overhead by increasing the number of entries permitted in the table cache.