mysql 5.1已经到了beta版,官方网站上也陆续有一些文章介绍,比如上次看到的Improving Database Performance with Partitioning。在使用分区的前提下,可以用mysql实现非常大的数据量存储。今天在mysql的站上又看到一篇进阶的文章 ——按日期分区存储。如果能够实现按日期分区,这对某些时效性很强的数据存储是相当实用的功能。下面是从这篇文章中摘录的一些内容。
最直观的方法,就是直接用年月日这种日期格式来进行常规的分区:
mysql> create table rms(d date) -> partition by range (d) ->(partition p0 values less than('1995-01-01'), -> partition p1 VALUES LESS THAN ('2010-01-01'));
上面的例子中,就是直接用"Y-m-d"的格式来对一个table进行分区,可惜想当然往往不能奏效,会得到一个错误信息:
ERROR 1064 (42000): VALUES value must be of same type as partition function near '),
partition p1 VALUES LESS THAN ('2010-01-01'))' at line 3
上述分区方式没有成功,而且明显的不经济,老练的DBA会用整型数值来进行分区:
mysql> CREATE TABLE part_date1 -> ( c1 int default NULL, -> c2 varchar(30) default NULL, -> c3 date default NULL) engine=myisam -> partition by range (cast(date_format(c3,'%Y%m%d') as signed)) ->(PARTITION p0 VALUES LESS THAN(19950101), ->PARTITION p1 VALUES LESS THAN (19960101) , ->PARTITION p2 VALUES LESS THAN (19970101) , ->PARTITION p3 VALUES LESS THAN (19980101) , ->PARTITION p4 VALUES LESS THAN (19990101) , ->PARTITION p5 VALUES LESS THAN (20000101) , ->PARTITION p6 VALUES LESS THAN (20010101) , ->PARTITION p7 VALUES LESS THAN (20020101) , ->PARTITION p8 VALUES LESS THAN (20030101) , ->PARTITION p9 VALUES LESS THAN (20040101) , ->PARTITION p10 VALUES LESS THAN (20100101), ->PARTITION p11 VALUES LESS THAN MAXVALUE ); Query OK,0 rows affected (0.01 sec)
搞定?接着往下分析
mysql> explain partitions ->select count(*) from part_date1 where -> c3> date '1995-01-01' and c3 <date '1995-12-31'\G ***************************1. row *************************** id:1 select_type: SIMPLE table: part_date1 partitions: p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11 type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows:8100000 Extra: Using where 1 row in set(0.00 sec)
万恶的mysql居然对上面的sql使用全表扫描,而不是按照我们的日期分区分块查询。原文中解释到MYSQL的优化器并不认这种日期形式的分区,花了大量的篇幅来引诱俺走上歧路,过分。
mysql优化器支持以下两种内置的日期函数进行分区:
看个例子:
mysql> CREATE TABLE part_date3 -> ( c1 int default NULL, -> c2 varchar(30) default NULL, -> c3 date default NULL) engine=myisam -> partition by range (to_days(c3)) ->(PARTITION p0 VALUES LESS THAN(to_days('1995-01-01')), ->PARTITION p1 VALUES LESS THAN (to_days('1996-01-01')) , ->PARTITION p2 VALUES LESS THAN (to_days('1997-01-01')) , ->PARTITION p3 VALUES LESS THAN (to_days('1998-01-01')) , ->PARTITION p4 VALUES LESS THAN (to_days('1999-01-01')) , ->PARTITION p5 VALUES LESS THAN (to_days('2000-01-01')) , ->PARTITION p6 VALUES LESS THAN (to_days('2001-01-01')) , ->PARTITION p7 VALUES LESS THAN (to_days('2002-01-01')) , ->PARTITION p8 VALUES LESS THAN (to_days('2003-01-01')) , ->PARTITION p9 VALUES LESS THAN (to_days('2004-01-01')) , ->PARTITION p10 VALUES LESS THAN (to_days('2010-01-01')), ->PARTITION p11 VALUES LESS THAN MAXVALUE ); Query OK,0 rows affected (0.00 sec)
以to_days()函数分区成功,我们分析一下看看:
mysql> explain partitions ->select count(*) from part_date3 where -> c3> date '1995-01-01' and c3 <date '1995-12-31'\G ***************************1. row *************************** id:1 select_type: SIMPLE table: part_date3 partitions: p1 type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows:808431 Extra: Using where 1 row in set(0.00 sec)
可以看到,mysql优化器这次不负众望,仅仅在p1分区进行查询。在这种情况下查询,真的能够带来提升查询效率么?下面分别对这次建立的part_date3和之前分区失败的part_date1做一个查询对比:
mysql> select count(*) from part_date3 where -> c3> date '1995-01-01' and c3 <date '1995-12-31'; +----------+ | count(*) | +----------+ | 805114 | +----------+ 1 row in set(4.11 sec) mysql> select count(*) from part_date1 where -> c3> date '1995-01-01' and c3 <date '1995-12-31'; +----------+ | count(*) | +----------+ | 805114 | +----------+ 1 row in set(40.33 sec)
可以看到,分区正确的话query花费时间为4秒,而分区错误则花费时间40秒(相当于没有分区),效率有90%的提升!所以我们千万要正确的使用分区功能,分区后务必用explain验证,这样才能获得真正的性能提升。