文档目录

使用 EXPLAIN 解读执行计划

SQL 是一种声明性语言,因此用户无法根据 SQL 语句直接判断一条查询的执行是否有效率。用户首先要使用 EXPLAIN 语句查看当前的执行计划。

bikeshare 数据库示例(英文) 中的一个 SQL 语句为例,该语句统计了 2017 年 7 月 1 日的行程次数:

EXPLAIN SELECT count(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59';
+------------------------------+----------+-----------+---------------+------------------------------------------------------------------------------------------------------------------------+
| id                           | estRows  | task      | access object | operator info                                                                                                          |
+------------------------------+----------+-----------+---------------+------------------------------------------------------------------------------------------------------------------------+
| StreamAgg_20                 | 1.00     | root      |               | funcs:count(Column#13)->Column#11                                                                                      |
| └─TableReader_21             | 1.00     | root      |               | data:StreamAgg_9                                                                                                       |
|   └─StreamAgg_9              | 1.00     | cop[tikv] |               | funcs:count(1)->Column#13                                                                                              |
|     └─Selection_19           | 250.00   | cop[tikv] |               | ge(bikeshare.trips.start_date, 2017-07-01 00:00:00.000000), le(bikeshare.trips.start_date, 2017-07-01 23:59:59.000000) |
|       └─TableFullScan_18     | 10000.00 | cop[tikv] | table:trips   | keep order:false, stats:pseudo                                                                                         |
+------------------------------+----------+-----------+---------------+------------------------------------------------------------------------------------------------------------------------+
5 rows in set (0.00 sec)

以上是该查询的执行计划结果。从 └─TableFullScan_18 算子开始向上看,查询的执行过程如下(非最佳执行计划):

  1. Coprocessor (TiKV) 读取整张 trips 表的数据,作为一次 TableFullScan 操作,再将读取到的数据传递给 Selection_19 算子。Selection_19 算子仍在 TiKV 内。

  2. Selection_19 算子根据谓词 WHERE start_date BETWEEN .. 进行数据过滤。预计大约有 250 行数据满足该过滤条件(基于统计信息以及算子的执行逻辑估算而来)。└─TableFullScan_18 算子显示 stats:pseudo,表示该表没有实际统计信息,执行 ANALYZE TABLE trips 收集统计信息后,预计的估算的数字会更加准确。

  3. COUNT 函数随后应用于满足过滤条件的行,这一过程也是在 TiKV (cop[tikv]) 中的 StreamAgg_9 算子内完成的。TiKV coprocessor 能执行一些 MySQL 内置函数,COUNT 是其中之一。

  4. StreamAgg_9 算子执行的结果会被传递给 TableReader_21 算子(位于 TiDB 进程中,即 root 任务)。执行计划中,TableReader_21 算子的 estRows1,表示该算子将从每个访问的 TiKV Region 接收一行数据。这一请求过程的详情,可参阅 EXPLAIN ANALYZE

  5. StreamAgg_20 算子随后对 └─TableReader_21 算子传来的每行数据计算 COUNT 函数的结果。StreamAgg_20 是根算子,会将结果返回给客户端。

注意:

要查看 TiDB 中某张表的 Region 信息,可执行 SHOW TABLE REGIONS 语句。

评估当前的性能

EXPLAIN 语句只返回查询的执行计划,并不执行该查询。若要获取实际的执行时间,可执行该查询,或使用 EXPLAIN ANALYZE 语句:

EXPLAIN ANALYZE SELECT count(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59';
+------------------------------+----------+----------+-----------+---------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+-----------+------+
| id                           | estRows  | actRows  | task      | access object | execution info                                                                                                                                                                                                                                    | operator info                                                                                                          | memory    | disk |
+------------------------------+----------+----------+-----------+---------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+-----------+------+
| StreamAgg_20                 | 1.00     | 1        | root      |               | time:1.031417203s, loops:2                                                                                                                                                                                                                        | funcs:count(Column#13)->Column#11                                                                                      | 632 Bytes | N/A  |
| └─TableReader_21             | 1.00     | 56       | root      |               | time:1.031408123s, loops:2, cop_task: {num: 56, max: 782.147269ms, min: 5.759953ms, avg: 252.005927ms, p95: 609.294603ms, max_proc_keys: 910371, p95_proc_keys: 704775, tot_proc: 11.524s, tot_wait: 580ms, rpc_num: 56, rpc_time: 14.111932641s} | data:StreamAgg_9                                                                                                       | 328 Bytes | N/A  |
|   └─StreamAgg_9              | 1.00     | 56       | cop[tikv] |               | proc max:640ms, min:8ms, p80:276ms, p95:480ms, iters:18695, tasks:56                                                                                                                                                                              | funcs:count(1)->Column#13                                                                                              | N/A       | N/A  |
|     └─Selection_19           | 250.00   | 11409    | cop[tikv] |               | proc max:640ms, min:8ms, p80:276ms, p95:476ms, iters:18695, tasks:56                                                                                                                                                                              | ge(bikeshare.trips.start_date, 2017-07-01 00:00:00.000000), le(bikeshare.trips.start_date, 2017-07-01 23:59:59.000000) | N/A       | N/A  |
|       └─TableFullScan_18     | 10000.00 | 19117643 | cop[tikv] | table:trips   | proc max:612ms, min:8ms, p80:248ms, p95:460ms, iters:18695, tasks:56                                                                                                                                                                              | keep order:false, stats:pseudo                                                                                         | N/A       | N/A  |
+------------------------------+----------+----------+-----------+---------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+-----------+------+
5 rows in set (1.03 sec)

执行以上示例查询耗时 1.03 秒,说明执行性能较为理想。

以上 EXPLAIN ANALYZE 的结果中,actRows 表明一些 estRows 预估数不准确(预估返回 10000 行数据但实际返回 19117643 行)。└─TableFullScan_18 算子的 operator info 列 (stats:pseudo) 信息也表明该算子的预估数不准确。

如果先执行 ANALYZE TABLE 再执行 EXPLAIN ANALYZE,预估数与实际数会更接近:

ANALYZE TABLE trips;
EXPLAIN ANALYZE SELECT count(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59';
Query OK, 0 rows affected (10.22 sec)

+------------------------------+-------------+----------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+-----------+------+
| id                           | estRows     | actRows  | task      | access object | execution info                                                                                                                                                                                                                                   | operator info                                                                                                          | memory    | disk |
+------------------------------+-------------+----------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+-----------+------+
| StreamAgg_20                 | 1.00        | 1        | root      |               | time:926.393612ms, loops:2                                                                                                                                                                                                                       | funcs:count(Column#13)->Column#11                                                                                      | 632 Bytes | N/A  |
| └─TableReader_21             | 1.00        | 56       | root      |               | time:926.384792ms, loops:2, cop_task: {num: 56, max: 850.94424ms, min: 6.042079ms, avg: 234.987725ms, p95: 495.474806ms, max_proc_keys: 910371, p95_proc_keys: 704775, tot_proc: 10.656s, tot_wait: 904ms, rpc_num: 56, rpc_time: 13.158911952s} | data:StreamAgg_9                                                                                                       | 328 Bytes | N/A  |
|   └─StreamAgg_9              | 1.00        | 56       | cop[tikv] |               | proc max:592ms, min:4ms, p80:244ms, p95:480ms, iters:18695, tasks:56                                                                                                                                                                             | funcs:count(1)->Column#13                                                                                              | N/A       | N/A  |
|     └─Selection_19           | 432.89      | 11409    | cop[tikv] |               | proc max:592ms, min:4ms, p80:244ms, p95:480ms, iters:18695, tasks:56                                                                                                                                                                             | ge(bikeshare.trips.start_date, 2017-07-01 00:00:00.000000), le(bikeshare.trips.start_date, 2017-07-01 23:59:59.000000) | N/A       | N/A  |
|       └─TableFullScan_18     | 19117643.00 | 19117643 | cop[tikv] | table:trips   | proc max:564ms, min:4ms, p80:228ms, p95:456ms, iters:18695, tasks:56                                                                                                                                                                             | keep order:false                                                                                                       | N/A       | N/A  |
+------------------------------+-------------+----------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+-----------+------+
5 rows in set (0.93 sec)

执行 ANALYZE TABLE 后,可以看到 └─TableFullScan_18 算子的预估行数是准确的,└─Selection_19 算子的预估行数也更接近实际行数。以上两个示例中的执行计划(即 TiDB 执行查询所使用的一组算子)未改变,但过时的统计信息常常导致 TiDB 选择到非最优的执行计划。

ANALYZE TABLE 外,达到 tidb_auto_analyze_ratio 阈值后,TiDB 会自动在后台重新生成统计数据。若要查看 TiDB 有多接近该阈值(即 TiDB 判断统计数据有多健康),可执行 SHOW STATS_HEALTHY 语句。

SHOW STATS_HEALTHY;
+-----------+------------+----------------+---------+
| Db_name   | Table_name | Partition_name | Healthy |
+-----------+------------+----------------+---------+
| bikeshare | trips      |                |     100 |
+-----------+------------+----------------+---------+
1 row in set (0.00 sec)

确定优化方案

当前执行计划是有效率的:

  • 大部分任务是在 TiKV 内处理的,需要通过网络传输给 TiDB 处理的仅有 56 行数据,每行都满足过滤条件,而且都很短。

  • 在 TiDB (StreamAgg_20) 中和在 TiKV (└─StreamAgg_9) 中汇总行数都使用了 Stream Aggregate,该算法在内存使用方面很有效率。

当前执行计划存在的最大问题在于谓词 start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59' 并未立即生效,先是 TableFullScan 算子读取所有行数据,然后才进行过滤选择。可以在 SHOW CREATE TABLE trips 的返回结果中找出问题原因:

SHOW CREATE TABLE trips\G
*************************** 1. row ***************************
       Table: trips
Create Table: CREATE TABLE `trips` (
  `trip_id` bigint(20) NOT NULL AUTO_INCREMENT,
  `duration` int(11) NOT NULL,
  `start_date` datetime DEFAULT NULL,
  `end_date` datetime DEFAULT NULL,
  `start_station_number` int(11) DEFAULT NULL,
  `start_station` varchar(255) DEFAULT NULL,
  `end_station_number` int(11) DEFAULT NULL,
  `end_station` varchar(255) DEFAULT NULL,
  `bike_number` varchar(255) DEFAULT NULL,
  `member_type` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`trip_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin AUTO_INCREMENT=20477318
1 row in set (0.00 sec)

以上返回结果显示,start_date没有索引。要将该谓词下推到 index reader 算子,还需要一个索引。添加索引如下:

ALTER TABLE trips ADD INDEX (start_date);
Query OK, 0 rows affected (2 min 10.23 sec)

注意:

你可通过执行 ADMIN SHOW DDL JOBS 语句来查看 DDL 任务的进度。TiDB 中的默认值的设置较为保守,因此添加索引不会对生产环境下的负载造成太大影响。测试环境下,可以考虑调大 tidb_ddl_reorg_batch_sizetidb_ddl_reorg_worker_cnt 的值。在参照系统上,将批处理大小设为 10240,将 worker count 并发度设置为 32,该系统可获得 10 倍的性能提升(较之使用默认值)。

添加索引后,可以使用 EXPLAIN 重复该查询。在以下返回结果中,可见 TiDB 选择了新的执行计划,而且不再使用 TableFullScanSelection 算子。

EXPLAIN SELECT count(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59';
+-----------------------------+---------+-----------+-------------------------------------------+-------------------------------------------------------------------+
| id                          | estRows | task      | access object                             | operator info                                                     |
+-----------------------------+---------+-----------+-------------------------------------------+-------------------------------------------------------------------+
| StreamAgg_17                | 1.00    | root      |                                           | funcs:count(Column#13)->Column#11                                 |
| └─IndexReader_18            | 1.00    | root      |                                           | index:StreamAgg_9                                                 |
|   └─StreamAgg_9             | 1.00    | cop[tikv] |                                           | funcs:count(1)->Column#13                                         |
|     └─IndexRangeScan_16     | 8471.88 | cop[tikv] | table:trips, index:start_date(start_date) | range:[2017-07-01 00:00:00,2017-07-01 23:59:59], keep order:false |
+-----------------------------+---------+-----------+-------------------------------------------+-------------------------------------------------------------------+
4 rows in set (0.00 sec)

若要比较实际的执行时间,可再次使用 EXPLAIN ANALYZE 语句:

EXPLAIN ANALYZE SELECT count(*) FROM trips WHERE start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59';
+-----------------------------+---------+---------+-----------+-------------------------------------------+------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------+-----------+------+
| id                          | estRows | actRows | task      | access object                             | execution info                                                                                                   | operator info                                                     | memory    | disk |
+-----------------------------+---------+---------+-----------+-------------------------------------------+------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------+-----------+------+
| StreamAgg_17                | 1.00    | 1       | root      |                                           | time:4.516728ms, loops:2                                                                                         | funcs:count(Column#13)->Column#11                                 | 372 Bytes | N/A  |
| └─IndexReader_18            | 1.00    | 1       | root      |                                           | time:4.514278ms, loops:2, cop_task: {num: 1, max:4.462288ms, proc_keys: 11409, rpc_num: 1, rpc_time: 4.457148ms} | index:StreamAgg_9                                                 | 238 Bytes | N/A  |
|   └─StreamAgg_9             | 1.00    | 1       | cop[tikv] |                                           | time:4ms, loops:12                                                                                               | funcs:count(1)->Column#13                                         | N/A       | N/A  |
|     └─IndexRangeScan_16     | 8471.88 | 11409   | cop[tikv] | table:trips, index:start_date(start_date) | time:4ms, loops:12                                                                                               | range:[2017-07-01 00:00:00,2017-07-01 23:59:59], keep order:false | N/A       | N/A  |
+-----------------------------+---------+---------+-----------+-------------------------------------------+------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------+-----------+------+
4 rows in set (0.00 sec)

从以上结果可看出,查询时间已从 1.03 秒减少到 0.0 秒。

注意:

以上示例另一个可用的优化方案是 coprocessor cache。如果你无法添加索引,可考虑开启 coprocessor cache 功能。开启后,只要算子上次执行以来 Region 未作更改,TiKV 将从缓存中返回值。这也有助于减少 TableFullScanSelection 算子的大部分运算成本。