In MySQL 5.1 before 5.1.16, if you compare a NULL value to a subquery using ALL, ANY, or SOME, and the subquery returns an empty result, the comparison might evaluate to the non-standard result of NULL rather than to TRUE or FALSE. As of 5.1.16, the comparison evaluates to TRUE or FALSE except for subqueries inside IS NULL, such as this:

SELECT ... WHERE NULL IN (SELECT ...) IS NULL

As of 5.1.32, the IS NULL limitation is removed and the comparison evaluates to TRUE or FALSE.

A subquery's outer statement can be any one of: SELECT, INSERT, UPDATE, DELETE, SET, or DO.

Subquery optimization for IN is not as effective as for the = operator or for the IN(value_list) operator.

A typical case for poor IN subquery performance is when the subquery returns a small number of rows but the outer query returns a large number of rows to be compared to the subquery result.

The problem is that, for a statement that uses an IN subquery, the optimizer rewrites it as a correlated subquery. Consider the following statement that uses an uncorrelated subquery:

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

The optimizer rewrites the statement to a correlated subquery:

SELECT ... FROM t1 WHERE EXISTS (SELECT 1 FROM t2 WHERE t2.b = t1.a);

If the inner and outer queries return M and N rows, respectively, the execution time becomes on the order of O(M×N), rather than O(M+N) as it would be for an uncorrelated subquery.

An implication is that an IN subquery can be much slower than a query written using an IN(value_list) operator that lists the same values that the subquery would return.

In general, you cannot modify a table and select from the same table in a subquery. For example, this limitation applies to statements of the following forms:

DELETE FROM t WHERE ... (SELECT ... FROM t ...);UPDATE t ... WHERE col = (SELECT ... FROM t ...);{INSERT|REPLACE} INTO t (SELECT ... FROM t ...);

Exception: The preceding prohibition does not apply if you are using a subquery for the modified table in the FROM clause. Example:

UPDATE t ... WHERE col = (SELECT * FROM (SELECT ... FROM t...) AS _t ...);

Here the prohibition does not apply because the result from a subquery in the FROM clause is stored as a temporary table, so the relevant rows in t have already been selected by the time the update to t takes place.

Row comparison operations are only partially supported:

In other words, for a subquery that returns rows of n-tuples, this is supported:

(val_1, ..., val_n) IN (subquery)

But this is not supported:

(val_1, ..., val_n) op {ALL|ANY|SOME} (subquery)

The reason for supporting row comparisons for IN but not for the others is that IN is implemented by rewriting it as a sequence of = comparisons and AND operations. This approach cannot be used for ALL, ANY, or SOME.

Row constructors are not well optimized. The following two expressions are equivalent, but only the second can be optimized:

(col1, col2, ...) = (val1, val2, ...)col1 = val1 AND col2 = val2 AND ...

Subqueries in the FROM clause cannot be correlated subqueries. They are materialized (executed to produce a result set) before evaluating the outer query, so they cannot be evaluated per row of the outer query.

The optimizer is more mature for joins than for subqueries, so in many cases a statement that uses a subquery can be executed more efficiently if you rewrite it as a join.

An exception occurs for the case where an IN subquery can be rewritten as a SELECT DISTINCT join. Example:

SELECT col FROM t1 WHERE id_col IN (SELECT id_col2 FROM t2 WHERE condition);

That statement can be rewritten as follows:

SELECT DISTINCT col FROM t1, t2 WHERE t1.id_col = t2.id_col AND condition;

But in this case, the join requires an extra DISTINCT operation and is not more efficient than the subquery.

Possible future optimization: MySQL does not rewrite the join order for subquery evaluation. In some cases, a subquery could be executed more efficiently if MySQL rewrote it as a join. This would give the optimizer a chance to choose between more execution plans. For example, it could decide whether to read one table or the other first.

Example:

SELECT a FROM outer_table AS otWHERE a IN (SELECT a FROM inner_table AS it WHERE ot.b = it.b);

For that query, MySQL always scans outer_table first and then executes the subquery on inner_table for each row. If outer_table has a lot of rows and inner_table has few rows, the query probably will not be as fast as it could be.

The preceding query could be rewritten like this:

SELECT a FROM outer_table AS ot, inner_table AS itWHERE ot.a = it.a AND ot.b = it.b;

In this case, we can scan the small table (inner_table) and look up rows in outer_table, which will be fast if there is an index on (ot.a,ot.b).

Possible future optimization: A correlated subquery is evaluated for each row of the outer query. A better approach is that if the outer row values do not change from the previous row, do not evaluate the subquery again. Instead, use its previous result.

Possible future optimization: A subquery in the FROM clause is evaluated by materializing the result into a temporary table, and this table does not use indexes. This does not allow the use of indexes in comparison with other tables in the query, although that might be useful.

Possible future optimization: If a subquery in the FROM clause resembles a view to which the merge algorithm can be applied, rewrite the query and apply the merge algorithm so that indexes can be used. The following statement contains such a subquery:

SELECT * FROM (SELECT * FROM t1 WHERE t1.t1_col)          AS _t1, t2 WHERE t2.t2_col;

The statement can be rewritten as a join like this:

SELECT * FROM t1, t2 WHERE t1.t1_col AND t2.t2_col;

This type of rewriting would provide two benefits:

Possible future optimization: For IN, = ANY, <> ANY, = ALL, and <> ALL with uncorrelated subqueries, use an in-memory hash for a result or a temporary table with an index for larger results. Example:

SELECT a FROM big_table AS btWHERE non_key_field IN (SELECT non_key_field FROM table WHERE condition)

In this case, we could create a temporary table:

CREATE TABLE t (key (non_key_field))(SELECT non_key_field FROM table WHERE condition)

Then, for each row in big_table, do a key lookup in t based on bt.non_key_field.