MySQL 8.0 新特性之哈希连接（Hash Join）

MySQL 开发组于 2019 年 10 月 14 日正式发布了 MySQL 8.0.18 GA 版本，带来了一些新特性和增强功能。其中最引人注目的莫过于多表连接查询支持 hash join 方式了。我们先来看看官方的描述：

MySQL 实现了用于内连接查询的 hash join 方式。例如，从 MySQL 8.0.18 开始以下查询可以使用 hash join 进行连接查询：

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

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

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)

多个表之间使用等值连接的的查询也会进行这种优化。例如以下查询：

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

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)

但是，如果任何连接语句（ON）中没有使用等值连接条件，将不会采用 hash join 连接方式。例如：

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>

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: ALLpossible_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: ALLpossible_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: ALLpossible_keys: NULL     key: NULL   key_len: NULL     ref: NULL     rows: 1   filtered: 100.00    Extra: Using where; Using join buffer (Block Nested Loop)

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)

在全局或者会话级别设置服务器系统变量 optimizer_switch 中的 hash_join=on 或者 hash_join=off 选项。默认为 hash_join=on。

在语句级别为特定的连接指定优化器提示 HASH_JOIN 或者 NO_HASH_JOIN。

可以通过系统变量 join_buffer_size 控制 hash join 允许使用的内存数量；hash join 不会使用超过该变量设置的内存数量。如果 hash join 所需的内存超过该阈值，MySQL 将会在磁盘中执行操作。需要注意的是，如果 hash join 无法在内存中完成，并且打开的文件数量超过系统变量 open_files_limit 的值，连接操作可能会失败。为了解决这个问题，可以使用以下方法之一：

增加 join_buffer_size 的值，确保 hash join 可以在内存中完成。

增加 open_files_limit 的值。

接下来他们比较一下 hash join 和 block nested loop 的性能，首先分别为 t1、t2 和 t3 生成 1000000 条记录：

set join_buffer_size=2097152000;SET @@cte_max_recursion_depth = 99999999;INSERT INTO t1-- INSERT INTO t2-- INSERT INTO t3WITH RECURSIVE t AS ( SELECT 1 AS c1, 1 AS c2 UNION ALL SELECT t.c1 + 1, t.c1 * 2  FROM t  WHERE t.c1 < 1000000)SELECT * FROM t;

mysql> EXPLAIN ANALYZE  -> SELECT COUNT(*)  ->  FROM t1  ->  JOIN t2   ->   ON (t1.c1 = t2.c1)  ->  JOIN t3   ->   ON (t2.c1 = t3.c1)\G*************************** 1. row ***************************EXPLAIN: -> Aggregate: count(0) (actual time=22993.098..22993.099 rows=1 loops=1)  -> Inner hash join (t3.c1 = t1.c1) (cost=9952535443663536.00 rows=9952435908880402) (actual time=14489.176..21737.032 rows=1000000 loops=1)    -> Table scan on t3 (cost=0.00 rows=998412) (actual time=0.103..3973.892 rows=1000000 loops=1)    -> Hash      -> Inner hash join (t2.c1 = t1.c1) (cost=99682753413.67 rows=99682653660) (actual time=5663.592..12236.984 rows=1000000 loops=1)        -> Table scan on t2 (cost=0.01 rows=998412) (actual time=0.067..3364.105 rows=1000000 loops=1)        -> Hash          -> Table scan on t1 (cost=100539.40 rows=998412) (actual time=0.133..3395.799 rows=1000000 loops=1)1 row in set (23.22 sec)mysql> SELECT COUNT(*)  ->  FROM t1  ->  JOIN t2   ->   ON (t1.c1 = t2.c1)  ->  JOIN t3   ->   ON (t2.c1 = t3.c1);+----------+| COUNT(*) |+----------+| 1000000 |+----------+1 row in set (12.98 sec)

mysql> EXPLAIN FORMAT=TREE  -> SELECT /*+ NO_HASH_JOIN(t1, t2, t3) */ COUNT(*)  ->  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>1 row in set (0.00 sec)SELECT /*+ NO_HASH_JOIN(t1, t2, t3) */ COUNT(*) FROM t1 JOIN t2   ON (t1.c1 = t2.c1) JOIN t3   ON (t2.c1 = t3.c1);

再看有索引时的 block nested loop 方法，增加索引：

mysql> CREATE index idx1 ON t1(c1);Query OK, 0 rows affected (7.39 sec)Records: 0 Duplicates: 0 Warnings: 0mysql> CREATE index idx2 ON t2(c1);Query OK, 0 rows affected (6.77 sec)Records: 0 Duplicates: 0 Warnings: 0mysql> CREATE index idx3 ON t3(c1);Query OK, 0 rows affected (7.23 sec)Records: 0 Duplicates: 0 Warnings: 0

mysql> EXPLAIN ANALYZE  -> SELECT COUNT(*)  ->  FROM t1  ->  JOIN t2   ->   ON (t1.c1 = t2.c1)  ->  JOIN t3   ->   ON (t2.c1 = t3.c1)\G*************************** 1. row ***************************EXPLAIN: -> Aggregate: count(0) (actual time=47684.034..47684.035 rows=1 loops=1)  -> Nested loop inner join (cost=2295573.22 rows=998412) (actual time=0.116..46363.599 rows=1000000 loops=1)    -> Nested loop inner join (cost=1198056.31 rows=998412) (actual time=0.087..25788.696 rows=1000000 loops=1)      -> Filter: (t1.c1 is not null) (cost=100539.40 rows=998412) (actual time=0.050..5557.847 rows=1000000 loops=1)        -> Index scan on t1 using idx1 (cost=100539.40 rows=998412) (actual time=0.043..3253.769 rows=1000000 loops=1)      -> Index lookup on t2 using idx2 (c1=t1.c1) (cost=1.00 rows=1) (actual time=0.012..0.015 rows=1 loops=1000000)    -> Index lookup on t3 using idx3 (c1=t1.c1) (cost=1.00 rows=1) (actual time=0.012..0.015 rows=1 loops=1000000)1 row in set (47.68 sec)mysql> SELECT COUNT(*)  ->  FROM t1  ->  JOIN t2   ->   ON (t1.c1 = t2.c1)  ->  JOIN t3   ->   ON (t2.c1 = t3.c1);+----------+| COUNT(*) |+----------+| 1000000 |+----------+1 row in set (19.56 sec)

Hash Join（无索引）	Block Nested Loop（无索引）	Block Nested Loop（有索引）
12.98 s	未返回	19.56 s

再增加一个 Oracle 12c 中无索引时 hash join 结果：1.282 s。

再增加一个 PostgreSQL 11.5 中无索引时 hash join 结果：6.234 s。

再增加一个 SQL 2017 中无索引时 hash join 结果：5.207 s。

总结

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