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 < ((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。
好在最后这个SQL有惊无险的执行成功,总耗时2911秒。
# Query_time: 2911.426483 Lock_time: 0.000251 Rows_sent: 1 Rows_examined: 76586082
当然了,这个SQL执行过程中的代价也确实非常大,产生了大量的磁盘(不可见)临时文件。
我每隔几秒钟就统计一次所有临时文件的总大小,并且观察磁盘空间剩余量。
/data 分区最开始可用空间是 373GB,这条SQL在峰值吃掉了约170GB,着实可怕。
# 刚开始
/dev/vdb 524032000 132967368 391064632 26% /data
# 峰值时
/dev/vdb 524032000 319732288 204299712 62% /data
CPU的负载从监控上看倒是还算能接受,最高约38.4%
4. 补充测试
上面的测试中,优化器"擅自"修改了驱动顺序,那加上straight_join看看会怎样
[root@yejr.run]> EXPLAIN STRAIGHT_JOIN select count(*)
from
customer straight_join
orders straight_join
lineitem straight_join
supplier straight_join
nation straight_join
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 | rows | filtered | Extra |
+----------+----------+----------+----------------------------------------------------+
| customer | 1485216 | 100.00 | NULL |
| orders | 14932433 | 1.11 | Using where; Using join buffer (Block Nested Loop) |
| lineitem | 59386314 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| supplier | 98705 | 1.00 | Using where; Using join buffer (Block Nested Loop) |
| nation | 25 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| region | 5 | 20.00 | Using where; Using join buffer (Block Nested Loop) |
+----------+----------+----------+----------------------------------------------------+
#format=tree模式下
| -> Aggregate: count(0)
-> Inner hash join (region.R_REGIONKEY = nation.N_REGIONKEY) (cost=204565289351994015744.00 rows=8021527039324357632)
-> Filter: (region.R_NAME = "AMERICA") (cost=0.00 rows=1)
-> Table scan on region (cost=0.00 rows=5)
-> Hash
-> Inner hash join (nation.N_NATIONKEY = customer.C_NATIONKEY) (cost=200554431911464173568.00 rows=-9223372036854775808)
-> Table scan on nation (cost=0.00 rows=25)
-> Hash
-> Inner hash join (supplier.S_NATIONKEY = customer.C_NATIONKEY), (supplier.S_SUPPKEY = lineitem.L_SUPPKEY) (cost=160446786739199049728.00 rows=-9223372036854775808)
-> Table scan on supplier (cost=0.00 rows=98705)
-> Hash
-> Inner hash join (lineitem.L_ORDERKEY = orders.O_ORDERKEY) (cost=16253562153466286.00 rows=16253535510797654)
-> Table scan on lineitem (cost=0.01 rows=59386314)
-> Hash
-> Inner hash join (orders.O_CUSTKEY = customer.C_CUSTKEY) (cost=24638698342.46 rows=2736915995)
-> Filter: ((orders.O_ORDERDATE >= DATE"1993-01-01") and (orders.O_ORDERDATE < ((DATE"1993-01-01" + interval "1" year)))) (cost=0.94 rows=165883)
-> Table scan on orders (cost=0.94 rows=14932433)
-> Hash
-> Table scan on customer (cost=153126.35 rows=1485216)
最后实际执行耗时
[root@yejr.run]> mysql> select
STRAIGHT_JOIN count(*)
...
+----------+
| count(*) |
+----------+
| 72033 |
+----------+
1 row in set (4 min 12.31 sec)
这个SQL执行过程中,只产生了很少几个临时文件,影响几乎可以忽略不计的那种。
这次之所以会比较快,是因为 orders 表在第二顺序执行,对它还附加了WHERE条件,过滤后数据量变小了(全表1500万,过滤后227万),因此整体执行时间缩短了。
靠着 straight_join 拯救了危机。
此外,在测试的过程中,我还做过一次只有3个表的全表join,下面是执行计划
[root@yejr.run]> desc select count(*) from orders o , lineitem l, partsupp ps where
o.O_CUSTKEY = l.L_SUPPKEY and l.L_PARTKEY = ps.PS_AVAILQTY;
+-------+----------+----------+----------------------------------------------------+
| table | rows | filtered | Extra |
+-------+----------+----------+----------------------------------------------------+
| ps | 7697248 | 100.00 | NULL |
| l | 59386314 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| o | 14932433 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
+-------+----------+----------+----------------------------------------------------+
在这个执行计划中,就不会出现徐老师说的问题,不再简单的把最小的表作为驱动表,最大的表放在最后面。
这条SQL耗时304秒,还好吧。
# Query_time: 304.889654 Lock_time: 0.000178 Rows_sent: 1 Rows_examined: 82986052
写在最后
在前几天我的文章《MySQL没前途了吗?》中,其实已经说了MySQL目前不适合做OLAP业务,即便有Hash Join也不行,毕竟其适用的场景很有限。
本案中几个表完全没任何索引,这属于很极端的场景,不应该允许此类现象发生。
另外,在已经明确需要走Hash Join的情况下,就应该人为干预,提前加大join_buffer_size,减少执行过程中生成的临时文件。
当然了,如果遇到多表JOIN不符合预期时,还可以用STRAIGHT_JOIN强制设定驱动顺序,也可以规避这个问题。
不过,MySQL在偏OLAP场景上的性能的确还有很大提升空间,对此我持谨慎乐观态度,比如把ClickHouse给直接收编了呢 :)
对于本文,我心里不是很有底气,毕竟不是啥源码大神,如果理解上的错误,还请留言指正,不吝感激。
SQL优化大神郑松华对本文亦有贡献,谢谢二位老师。
全文完。
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