徐春阳老师发文爆MySQL 8.0 hash join有重大缺陷。
文章核心观点如下：多表（比如3个个表）join时，只会简单的把表数据量小的放在前面作为驱动表，大表放在最后面，从而导致可能产生极大结果集的笛卡尔积，甚至耗尽CPU和磁盘空间。
就此现象，我也做了个测试。
1. 利用TPC-H工具准备测试环境
TPC-H工具在这里下载 http://www.tpc.org/tpch/default5.asp。默认并不支持MySQL，需要自己手动做些调整，参见 https://imysql.com/2012/12/21/tpch-for-mysql-manual.html。
在本案中，我指定的 Scale Factor 参数是10，即：
[root@yejr.run dbgen]# ./dbgen -s 10 && ls -l *tbl -rw-r--r-- 1 root root 244847642 Apr 14 09:52 customer.tbl -rw-r--r-- 1 root root 7775727688 Apr 14 09:52 lineitem.tbl -rw-r--r-- 1 root root 2224 Apr 14 09:52 nation.tbl -rw-r--r-- 1 root root 1749195031 Apr 14 09:52 orders.tbl -rw-r--r-- 1 root root 243336157 Apr 14 09:52 part.tbl -rw-r--r-- 1 root root 1204850769 Apr 14 09:52 partsupp.tbl -rw-r--r-- 1 root root 389 Apr 14 09:52 region.tbl -rw-r--r-- 1 root root 14176368 Apr 14 09:52 supplier.tbl
2. 创建测试表，导入测试数据。
查看几个表的数据量分别是：
+----------+------------+----------+----------------+-------------+--------------+
| Name | Row_format | Rows | Avg_row_length | Data_length | Index_length |+----------+------------+----------+----------------+-------------+--------------+
| customer | Dynamic | 1476605 | 197 | 291258368 | 0 |
| lineitem | Dynamic | 59431418 | 152 | 9035579392 | 0 |
| nation | Dynamic | 25 | 655 | 16384 | 0 |
| orders | Dynamic | 14442405 | 137 | 1992294400 | 0 |
| part | Dynamic | 1980917 | 165 | 327991296 | 0 |
| partsupp | Dynamic | 9464104 | 199 | 1885339648 | 0 |
| region | Dynamic | 5 | 3276 | 16384 | 0 |
| supplier | Dynamic | 99517 | 184 | 18366464 | 0 |
+----------+------------+----------+----------------+-------------+--------------+
提醒：几个测试表都不要加任何索引，包括主键，上表中 Index_length的值均为0。
3. 运行测试SQL
本案选用的MySQL版本是8.0.19：
[root@yejr.run]> \s
...
Server version: 8.0.19-commercial MySQL Enterprise Server - Commercial
...
徐老师是在用TPC-H中的Q5时遇到的问题，本案也同样选择这个SQL。
不过，本案主要测试Hash Join，因此去掉了其中的GROUP BY和ORDER BY子句。
先看下执行计划吧，都是全表扫描，好可怕...
[root@yejr.run]> desc select count(*)
-> from
-> customer,
-> orders,
-> lineitem,
-> supplier,
-> nation,
-> region
-> where
-> c_custkey = o_custkey
-> and l_orderkey = o_orderkey
-> and l_suppkey = s_suppkey
-> and c_nationkey = s_nationkey
-> and s_nationkey = n_nationkey
-> and n_regionkey = r_regionkey
-> and r_name = 'AMERICA'
-> and o_orderdate >= date '1993-01-01'
-> and o_orderdate < date '1993-01-01' + interval '1' year;
+----------+------+----------+----------+----------------------------------------------------+
| table | type | rows | filtered | Extra |
+----------+------+----------+----------+----------------------------------------------------+
| region | ALL | 5 | 20.00 | Using where |
| nation | ALL | 25 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| supplier | ALL | 98705 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| customer | ALL | 1485216 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| orders | ALL | 14932433 | 1.11 | Using where; Using join buffer (Block Nested Loop) |
| lineitem | ALL | 59386314 | 1.00 | Using where; Using join buffer (Block Nested Loop) |
+----------+------+----------+----------+----------------------------------------------------+
加上 format=tree 再看下（真壮观啊。。。）
1. row **
EXPLAIN: -> Aggregate: count(0)
-> Inner hash join (lineitem.L_SUPPKEY = supplier.S_SUPPKEY), (lineitem.L_ORDERKEY = orders.O_ORDERKEY) (cost=40107736685515472896.00 rows=4010763818487343104)
-> Table scan on lineitem (cost=0.07 rows=59386314)
-> Hash
-> Inner hash join (orders.O_CUSTKEY = customer.C_CUSTKEY) (cost=60799566599072.12 rows=6753683238538)
-> Filter: ((orders.O_ORDERDATE >= DATE'1993-01-01') and (orders.O_ORDERDATE < <cache>((DATE'1993-01-01' + interval '1' year)))) (cost=0.16 rows=165883)
-> Table scan on orders (cost=0.16 rows=14932433)
-> Hash
-> Inner hash join (customer.C_NATIONKEY = nation.N_NATIONKEY) (cost=3664985889.79 rows=3664956624)
-> Table scan on customer (cost=0.79 rows=1485216)
-> Hash
-> Inner hash join (supplier.S_NATIONKEY = nation.N_NATIONKEY) (cost=24976.50 rows=24676)
-> Table scan on supplier (cost=513.52 rows=98705)
-> Hash
-> Inner hash join (nation.N_REGIONKEY = region.R_REGIONKEY) (cost=3.50 rows=3)
-> Table scan on nation (cost=0.50 rows=25)
-> Hash
-> Filter: (region.R_NAME = 'AMERICA') (cost=0.75 rows=1)
-> Table scan on region (cost=0.75 rows=5)
看起来的确是把最小的表放在最前面，把最大的放在最后面。
在开始跑之前，我们先看一眼手册中关于Hash Join的描述，其中有一段是这样的：
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 thishappens, 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:
- Increase join_buffer_size so that the hash join does not spill over to disk.
- Increase open_files_limit.
简言之，当 join_buffer_size 不够时，会在hash join的过程中转储大量的磁盘表（把一个hash表切分成多个小文件放在磁盘上，再逐个读入内存进行hash join），因此建议加大 join_buffer_size，或者加大 open_files_limit 上限。
所以，正式开跑前，我先把join_buffer_size调大到1GB，并顺便看下其他几个参数值：
[root@yejr.run]> select @@join_buffer_size, @@tmp_table_size, @@innodb_buffer_pool_size;
+--------------------+------------------+---------------------------+
| @@join_buffer_size | @@tmp_table_size | @@innodb_buffer_pool_size |
+--------------------+------------------+---------------------------+
| 1073741824 | 16777216 | 10737418240 |
+--------------------+------------------+---------------------------+
并且为了保险起见，在执行SQL时也用 SET_VAR（8.0新特性） 设置了 join_bufer_size，走起。
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