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。

以 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 estRows 为 1，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。

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。

除 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 列没有索引。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)

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 TableFullScan 和 Selection 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。