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Pendragon
V2EX  ›  MySQL

为什么数据库分了表后性能会直线下降?

  •  2
     
  •   Pendragon · 2020-06-17 23:10:58 +08:00 · 3215 次点击
    这是一个创建于 1623 天前的主题,其中的信息可能已经有所发展或是发生改变。

    原先有按月分的 12 张表,每张表里大概有 2000w 左右的数据

    ...
    CREATE TABLE `parcel_with_no_partition_m05` (
    ...
    )
    CREATE TABLE `parcel_with_no_partition_m06` (
      `id` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '数据主键,毫无意义',
      `equip_id` int(11) DEFAULT NULL,
      `scan_id` int(11) DEFAULT NULL,
      `create_datetime` datetime DEFAULT NULL,
      `minute_of_hour` int(11) DEFAULT NULL,
      `hour_of_day` int(11) DEFAULT NULL,
      `day_of_month` int(11) DEFAULT NULL,
      `week_of_year` int(11) DEFAULT NULL,
      `month_of_year` int(11) DEFAULT NULL,
      `parcel_length` int(11) DEFAULT NULL
      PRIMARY KEY (`id`)
    ) ENGINE=innodb AUTO_INCREMENT=1 DEFAULT CHARSET=utf8;
    
    CREATE TABLE `parcel_with_no_partition_m07` (
    ...
    )
    ...
    

    现在想尝试用 mysql8 的 partition 来进行分表,集中存储了 12 个月的一共 2 亿多条数据,每个分区 2000w 条左右的数据,与之前的表数据一致

    CREATE TABLE `parcel_with_partition` (
      `equip_id` int(11) DEFAULT NULL,
      `scan_id` int(11) DEFAULT NULL,
      `create_datetime` datetime DEFAULT NULL,
      `minute_of_hour` int(11) DEFAULT NULL,
      `hour_of_day` int(11) DEFAULT NULL,
      `day_of_month` int(11) DEFAULT NULL,
      `week_of_year` int(11) DEFAULT NULL,
      `month_of_year` int(11) DEFAULT NULL,
      `parcel_length` int(11) DEFAULT NULL
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8 ENGINE=InnoDB PARTITION BY RANGE(`month_of_year`) (
    PARTITION `JAN` VALUES LESS THAN (2),
    PARTITION `FEB` VALUES LESS THAN (3),
    PARTITION `MAR` VALUES LESS THAN (4),
    PARTITION `APR` VALUES LESS THAN (5),
    PARTITION `MAY` VALUES LESS THAN (6),
    PARTITION `JUN` VALUES LESS THAN (7),
    PARTITION `JUL` VALUES LESS THAN (8),
    PARTITION `AUG` VALUES LESS THAN (9),
    PARTITION `SEP` VALUES LESS THAN (10),
    PARTITION `OCT` VALUES LESS THAN (11),
    PARTITION `NOV` VALUES LESS THAN (12),
    PARTITION `DEC` VALUES LESS THAN (13)
    );
    

    我尝试执行了一条业务 sql,发现效率下降了一倍

    #从 mysql 自带的分区中取数据
    select sql_no_cache count(*) as num,`day_of_month` as day,`hour_of_day` as hour,`scan_id` as scanId from `parcel_with_partition` partition(`JUN`) where `equip_id` = 3 and `create_datetime` >'2020-06-01' and `create_datetime` <= '2020-06-29' group by `scan_id`,`day_of_month`,`hour_of_day`
    2018 rows in set (59.56 sec)
    
    #从原始分表中取数据
     select sql_no_cache count(*) as num,`day_of_month` as day,`hour_of_day` as hour,`scan_id` as scanId from `parcel_with_no_partition_m06` where `equip_id` = 3 and `create_datetime` >'2020-06-01' and `sort_datetime` <= '2020-06-29' group by `scan_id`,`day_of_month`,`hour_of_day`
     2018 rows in set, 1 warning (29.13 sec)
    

    又进一步用 explain 和 performance schema 分别观察了两种 sql

    mysql 自带分区查询详情

    explain:
    +----+-------------+-----------------------+------------+------+---------------+------+---------+------+----------+----------+------------------------------+
    | id | select_type | table                 | partitions | type | possible_keys | key  | key_len | ref  | rows     | filtered | Extra                        |
    +----+-------------+-----------------------+------------+------+---------------+------+---------+------+----------+----------+------------------------------+
    |  1 | SIMPLE      | parcel_with_partition | JUN        | ALL  | NULL          | NULL | NULL    | NULL | 20481855 |     1.11 | Using where; Using temporary |
    +----+-------------+-----------------------+------------+------+---------------+------+---------+------+----------+----------+------------------------------+
    
    table i/o:
    select * from table_io_waits_summary_by_table where count_star>0 \G;
    *************************** 1. row ***************************
         OBJECT_TYPE: TABLE
       OBJECT_SCHEMA: test
         OBJECT_NAME: parcel_with_partition
          COUNT_STAR: 22123040
      SUM_TIMER_WAIT: 55709022384855
      MIN_TIMER_WAIT: 55709022384855
      AVG_TIMER_WAIT: 2517879
      MAX_TIMER_WAIT: 55709022384855
          COUNT_READ: 22123040
      SUM_TIMER_READ: 55709022384855
      MIN_TIMER_READ: 55709022384855
      AVG_TIMER_READ: 2517879
      MAX_TIMER_READ: 55709022384855
         COUNT_WRITE: 0
     SUM_TIMER_WRITE: 0
     MIN_TIMER_WRITE: 0
     AVG_TIMER_WRITE: 0
     MAX_TIMER_WRITE: 0
         COUNT_FETCH: 22123040
     SUM_TIMER_FETCH: 55709022384855
     MIN_TIMER_FETCH: 55709022384855
     AVG_TIMER_FETCH: 2517879
     MAX_TIMER_FETCH: 55709022384855
        COUNT_INSERT: 0
    SUM_TIMER_INSERT: 0
    MIN_TIMER_INSERT: 0
    AVG_TIMER_INSERT: 0
    MAX_TIMER_INSERT: 0
        COUNT_UPDATE: 0
    SUM_TIMER_UPDATE: 0
    MIN_TIMER_UPDATE: 0
    AVG_TIMER_UPDATE: 0
    MAX_TIMER_UPDATE: 0
        COUNT_DELETE: 0
    SUM_TIMER_DELETE: 0
    MIN_TIMER_DELETE: 0
    AVG_TIMER_DELETE: 0
    MAX_TIMER_DELETE: 0
    
    file i/o:
                    FILE_NAME: /home/wedo/mysql/data/mysql/test/parcel_with_partition#p#jun.ibd
                   EVENT_NAME: wait/io/file/innodb/innodb_data_file
        OBJECT_INSTANCE_BEGIN: 140081009323904
                   COUNT_STAR: 422002
               SUM_TIMER_WAIT: 3965120084812
               MIN_TIMER_WAIT: 5739112
               AVG_TIMER_WAIT: 9395831
               MAX_TIMER_WAIT: 498159493
                   COUNT_READ: 422002
               SUM_TIMER_READ: 3965120084812
               MIN_TIMER_READ: 5739112
               AVG_TIMER_READ: 9395831
               MAX_TIMER_READ: 498159493
     SUM_NUMBER_OF_BYTES_READ: 6914080768
                  COUNT_WRITE: 0
              SUM_TIMER_WRITE: 0
              MIN_TIMER_WRITE: 0
              AVG_TIMER_WRITE: 0
              MAX_TIMER_WRITE: 0
    SUM_NUMBER_OF_BYTES_WRITE: 0
                   COUNT_MISC: 0
               SUM_TIMER_MISC: 0
               MIN_TIMER_MISC: 0
               AVG_TIMER_MISC: 0
               MAX_TIMER_MISC: 0
    
    

    原始分表查询详情

    explain:
     +----+-------------+-----------------------------+------------+------+---------------+------+---------+------+----------+----------+------------------------------+
    | id | select_type | table                        | partitions | type | possible_keys | key  | key_len | ref  | rows     | filtered | Extra                        |
    +----+-------------+------------------------------+------------+------+---------------+------+---------+------+----------+----------+------------------------------+
    |  1 | SIMPLE      | parcel_with_no_partition_m06 | NULL       | ALL  | NULL          | NULL | NULL    | NULL | 22123040 |     1.11 | Using where; Using temporary |
    +----+-------------+------------------------------+------------+------+---------------+------+---------+------+----------+----------+------------------------------+
    
    table i/o:
         OBJECT_TYPE: TABLE
       OBJECT_SCHEMA: test
         OBJECT_NAME: parcel_with_no_partition_m06
          COUNT_STAR: 22123040
      SUM_TIMER_WAIT: 23182194259854
      MIN_TIMER_WAIT: 23182194259854
      AVG_TIMER_WAIT: 1047813
      MAX_TIMER_WAIT: 23182194259854
          COUNT_READ: 22123040
      SUM_TIMER_READ: 23182194259854
      MIN_TIMER_READ: 23182194259854
      AVG_TIMER_READ: 1047813
      MAX_TIMER_READ: 23182194259854
         COUNT_WRITE: 0
     SUM_TIMER_WRITE: 0
     MIN_TIMER_WRITE: 0
     AVG_TIMER_WRITE: 0
     MAX_TIMER_WRITE: 0
         COUNT_FETCH: 22123040
     SUM_TIMER_FETCH: 23182194259854
     MIN_TIMER_FETCH: 23182194259854
     AVG_TIMER_FETCH: 1047813
     MAX_TIMER_FETCH: 23182194259854
        COUNT_INSERT: 0
    SUM_TIMER_INSERT: 0
    MIN_TIMER_INSERT: 0
    AVG_TIMER_INSERT: 0
    MAX_TIMER_INSERT: 0
        COUNT_UPDATE: 0
    SUM_TIMER_UPDATE: 0
    MIN_TIMER_UPDATE: 0
    AVG_TIMER_UPDATE: 0
    MAX_TIMER_UPDATE: 0
        COUNT_DELETE: 0
    SUM_TIMER_DELETE: 0
    MIN_TIMER_DELETE: 0
    AVG_TIMER_DELETE: 0
    MAX_TIMER_DELETE: 0
    
    file i/o:
                    FILE_NAME: /home/wedo/mysql/data/mysql/test/parcel_with_no_partition_m06.ibd
                   EVENT_NAME: wait/io/file/innodb/innodb_data_file
        OBJECT_INSTANCE_BEGIN: 140081009372480
                   COUNT_STAR: 424687
               SUM_TIMER_WAIT: 4045883911263
               MIN_TIMER_WAIT: 5301220
               AVG_TIMER_WAIT: 9526557
               MAX_TIMER_WAIT: 861143891
                   COUNT_READ: 424687
               SUM_TIMER_READ: 4045883911263
               MIN_TIMER_READ: 5301220
               AVG_TIMER_READ: 9526557
               MAX_TIMER_READ: 861143891
     SUM_NUMBER_OF_BYTES_READ: 6958071808
                  COUNT_WRITE: 0
              SUM_TIMER_WRITE: 0
              MIN_TIMER_WRITE: 0
              AVG_TIMER_WRITE: 0
              MAX_TIMER_WRITE: 0
    SUM_NUMBER_OF_BYTES_WRITE: 0
                   COUNT_MISC: 0
               SUM_TIMER_MISC: 0
               MIN_TIMER_MISC: 0
               AVG_TIMER_MISC: 0
               MAX_TIMER_MISC: 0
    

    在 explain 和 file i/o 中都没有看出来异常情况,但是在 table i/o 时 内部分区 使用的时间远高于 原始的分表

    这是不是说明多浪费的时间的罪魁祸首是逻辑 i/o 部分?为什么分表后会出现这样的结果呢?希望大神们多多指教

    补充一下环境: 系统:centos7 存储引擎:mysql8 服务器配置:12 核 2.5Ghz 64G 内存 硬盘:机械硬盘 在执行查询 sql 时最大的内存占用 3G

    10 条回复    2020-07-01 17:20:42 +08:00
    xuanbg
        1
    xuanbg  
       2020-06-17 23:36:50 +08:00
    2000 万和 2.4 亿能一样? PARTITION BY RANGE 实际上还是 1 张表,只不过数据分开存罢了。
    ajsonx
        2
    ajsonx  
       2020-06-18 00:02:23 +08:00
    8 的窗口函数我试过几个,主要是为了编写方便吧。试过 1 千万的数据,性能上都不如写联表查询。
    Pendragon
        3
    Pendragon  
    OP
       2020-06-18 00:09:11 +08:00
    @xuanbg 我最开始也是这么想的,但是分析结果显示两种查询方法的硬盘 i/o 时间是一样的,占用的最大内存也是单张分表 /分区的大小,所以分区方式应该并没有把 2.4 亿的数据全部取出来
    mahone3297
        4
    mahone3297  
       2020-06-18 00:18:12 +08:00
    #从 mysql 自带的分区中取数据
    select sql_no_cache count(*) as num,`day_of_month` as day,`hour_of_day` as hour,`scan_id` as scanId from `parcel_with_partition` partition(`JUN`) where `equip_id` = 3 and `create_datetime` >'2020-06-01' and `create_datetime` <= '2020-06-29' group by `scan_id`,`day_of_month`,`hour_of_day`
    2018 rows in set (59.56 sec)

    #从原始分表中取数据
    select sql_no_cache count(*) as num,`day_of_month` as day,`hour_of_day` as hour,`scan_id` as scanId from `parcel_with_no_partition_m06` where `equip_id` = 3 and `create_datetime` >'2020-06-01' and `sort_datetime` <= '2020-06-29' group by `scan_id`,`day_of_month`,`hour_of_day`
    2018 rows in set, 1 warning (29.13 sec)


    * 2 个的条件不一样?
    partition 都是 `create_datetime` >'2020-06-01' and `create_datetime` <= '2020-06-29'
    分表 `create_datetime` >'2020-06-01' and `sort_datetime` <= '2020-06-29'
    * 还有,你是 from `parcel_with_partition` partition(`JUN`) 这样指定其中一张分区表的?能否 from parcel_with_partition_JUN 这样来对比?
    mahone3297
        5
    mahone3297  
       2020-06-18 00:19:28 +08:00
    分区表的用法,应该是你 where 条件中,有了分区条件,使得,你的 sql 可以直接查询某几张表,而不是所有表。
    flashrick
        6
    flashrick  
       2020-06-18 09:40:30 +08:00
    依我看,你分区的查询用错了啊,你用 month_of_year 分区的,查询的时候就是一条普通的 select 就行了,只是 where 条件要用到 month_of_year 。说错了请轻喷
    Pendragon
        7
    Pendragon  
    OP
       2020-06-18 09:54:05 +08:00
    @flashrick
    @mahone3297
    你说的没错 在 where 条件中指定 month_of_year 的值也能命中相应的分区,我在这里用 partition 强制限制分区是为了减少其他条件对查询的影响, 最终结果都是一样的
    Still4
        8
    Still4  
       2020-06-18 10:00:05 +08:00
    看了下分区表实现原理,每个分区一个文件,这样的话两个对照表文件大小一致,如果没有加索引,都是扫全表,执行时间不应该差很多啊

    再复制一个 parcel_with_no_partition_m06 表,确认下机械盘内道外道的影响?
    someonedeng
        9
    someonedeng  
       2020-06-24 17:13:16 +08:00 via Android
    话说本来是分表的,为什么又合成一个大表再分区而不是直接对小表分区?
    encro
        10
    encro  
       2020-07-01 17:20:42 +08:00
    既然你都是根据 create_datetime 查询的,那么就根据 create_datetime 分区即可

    http://mysql.taobao.org/monthly/2017/11/09/
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