As of MySQL 5.6.5, the optimizer uses semi-join 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 could be achieved 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 be executed as a semi-join—that is, an operation that returns only one instance of each row in class that is matched by rows in roster.

Before MySQL 5.6.6, the outer query specification was limited to simple table scans or inner joins using comma syntax, and view references were not possible. As of 5.6.6, outer join and inner join syntax is permitted in the outer query specification, and the restriction that table references must be base tables has been lifted.

In MySQL, a subquery must satisfy these criteria to be handled as a semi-join:

The subquery may be correlated or uncorrelated. DISTINCT is permitted, as is LIMIT unless ORDER BY is also used.

If a subquery meets the preceding criteria, MySQL converts it to a semi-join and makes a cost-based choice from these strategies:

Each of these strategies except Duplicate Weedout can be enabled or disabled using the optimizer_switch system variable. The semijoin flag controls whether semi-joins are used. If it is set to on, the firstmatch, loosescan, and materialization flags enable finer control over the permitted semi-join strategies. These flags are on by default. See Section 8.9.2, “Controlling Switchable Optimizations”.

The use of semi-join strategies is indicated in EXPLAIN output as follows:

As of MySQL 5.6.5, the optimizer uses subquery materialization as a strategy that enables 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 table is indexed with a hash index to make lookups fast and inexpensive. The index is unique, which makes the table smaller because it has no duplicates.

Subquery materialization attempts to use 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 materialization flag of the optimizer_switch system variable must be on. Materialization then 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 will not be 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 can give some indication of whether the optimizer uses subquery materialization. Compared to query execution that does not use materialization, select_type may change from DEPENDENT SUBQUERY to SUBQUERY. This indicates that, for a subquery that would be executed once per outer row, materialization enables the subquery to be executed just once. In addition, for extended EXPLAIN output, the text displayed by a following SHOW WARNINGS includes materialize materialize and materialized-subquery (materialized subselect before MySQL 5.6.6).

As of MySQL 5.6.3, the optimizer more efficiently handles derived tables (subqueries in the FROM clause):

Consider the following EXPLAIN statement, for which a subquery appears in the FROM clause of a SELECT query:

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

The optimizer avoids materializing the subquery by delaying it until the result is needed during SELECT execution. In this case, the query is not executed, so the result is never needed.

Even for queries that are executed, delay of subquery materialization may enable the optimizer to avoid materialization entirely. Consider the following query, which joins the result of a subquery in the FROM clause 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 subquery need not be materialized.

In the worst case (derived tables are materialized), query execution takes the same time as before MySQL 5.6.3 because no additional work is done. In the best case (derived tables are not materialized), query execution is quicker by the time needed to perform materialization.

For cases when a derived table requires materialization, the optimizer may speed up access to the result by adding an index to the materialized table. If such an index enables ref access to the table, it can greatly reduce amount of data that must be 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 usual 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.

Certain optimizations are applicable to comparisons that use the IN operator to test subquery results (or that use =ANY, which is equivalent). This section discusses these optimizations, particularly with regard to the challenges that NULL values present. The last part of the discussion includes suggestions on what you can do to 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. That is, the comparison is converted to 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 that it must examine when evaluating 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 two 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. However, they are not visible in EXPLAIN output, so you must use EXPLAIN EXTENDED followed by SHOW WARNINGS (note the checking NULL in the warning message):

mysql> EXPLAIN EXTENDED
    -> SELECT outer_expr IN (SELECT maybe_null_key FROM t2) 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: maybe_null_key
          key: maybe_null_key
      key_len: 5
          ref: func
         rows: 2
        Extra: Using index

mysql> SHOW WARNINGS\G
*************************** 1. row ***************************
  Level: Note
   Code: 1003
Message: select (`test`.`t1`.`outer_expr`,
         (((`test`.`t1`.`outer_expr`) in t2 on
         maybe_null_key checking NULL))) AS `outer_expr IN (SELECT
         maybe_null_key FROM t2)` from `test`.`t1`

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.

If outer_expr is NULL, to evaluate the following expression, it is necessary to run the SELECT to determine whether it produces any rows:

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

It is necessary to run the original SELECT here, without any pushed-down equalities of the kind mentioned earlier.

On the other hand, when outer_expr is not NULL, it is absolutely essential that this comparison:

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

be converted to this expression that uses a pushed-down condition:

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

Without this conversion, subqueries will be slow. To solve the dilemma of whether to push down or not push down conditions into the subquery, the conditions are wrapped in “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 into 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. At the moment, 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 EXTENDED 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 was to improve compliance and not speed).

For multiple-table subqueries, execution of NULL IN (SELECT ...) will be 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 tips:

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