EXPLAIN Walkthrough
由于 SQL 是一种声明式语言，你无法自动判断一个查询是否高效执行。你必须先使用 EXPLAIN 语句来了解当前的执行计划。
以下语句来自 bikeshare example database，统计 2017 年 7 月 1 日的出行次数：
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)
从子操作符 └─TableFullScan_18 返回，你可以看到其执行过程如下，目前还不够理想：
- 协程（TiKV）以
TableFullScan操作读取整个trips表，然后将读取到的行传递给Selection_19操作符，仍在 TiKV 内部。 WHERE start_date BETWEEN ..条件在Selection_19操作符中进行过滤。估算符合条件的行数大约为250行。注意，这个数字是根据统计信息和操作符的逻辑估算得出。└─TableFullScan_18操作符显示stats:pseudo，意味着该表没有实际的统计信息。运行ANALYZE TABLE trips收集统计信息后，统计数据会更准确。- 满足筛选条件的行随后会应用
count函数。这也在StreamAgg_9操作符中完成，仍在 TiKV（cop[tikv]）内部。TiKV 的协程可以执行许多 MySQL 内置函数，count就是其中之一。 StreamAgg_9的结果随后传递给TableReader_21操作符，该操作符现在在 TiDB 服务器（root）内部。此操作符的estRows值为1，意味着它会从每个 TiKV Region 接收一行数据。关于这些请求的更多信息，请参见EXPLAIN ANALYZE。StreamAgg_20操作符对来自└─TableReader_21操作符的每一行应用count函数，从SHOW TABLE REGIONS可以看到大约有 56 行。由于这是根操作符，它会将结果返回给客户端。
评估当前性能
EXPLAIN 只返回查询的执行计划，不会执行查询。若要获得实际的执行时间，可以直接执行查询或使用 EXPLAIN ANALYZE：
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)
上述示例查询耗时 1.03 秒，性能尚不理想。
从 EXPLAIN ANALYZE 的结果可以看出，actRows 表示部分估算（estRows）不准确（预期 1 万行，实际找到 1900 万行），这在 └─TableFullScan_18 的 operator info（stats:pseudo）中已提示。如果先运行 ANALYZE TABLE，再执行 EXPLAIN ANALYZE，可以看到估算值更接近实际：
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 的估算行数变得准确，└─Selection_19 的估算也更接近实际。在上述两种情况下，虽然执行计划（TiDB 用于执行此查询的操作符集合）没有变化，但过时的统计信息常常会导致次优的执行计划。
除了 ANALYZE TABLE，TiDB 还会在达到 tidb_auto_analyze_ratio 阈值后，自动在后台重新生成统计信息。你可以通过执行 SHOW STATS_HEALTHY 来查看 TiDB 对统计信息的健康程度：
SHOW STATS_HEALTHY;
+-----------+------------+----------------+---------+
| Db_name | Table_name | Partition_name | Healthy |
+-----------+------------+----------------+---------+
| bikeshare | trips | | 100 |
+-----------+------------+----------------+---------+
1 row in set (0.00 sec)
识别优化点
当前执行计划在以下方面是高效的：
- 大部分工作由 TiKV 协程内部处理。只需 56 行数据通过网络传回 TiDB 进行处理。每一行都很短，只包含符合条件的计数。
- 在 TiDB（
StreamAgg_20）和 TiKV（└─StreamAgg_9）中对行数进行聚合，采用流式聚合，内存使用非常高效。
当前执行计划的最大问题在于条件 start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59' 并未立即应用。所有行首先通过 TableFullScan 读取，然后再进行筛选。你可以通过 SHOW CREATE TABLE trips 的输出找到原因：
SHOW CREATE TABLE trips\G
*************************** 1. row ***************************
Table: trips
Create Table: CREATE TABLE `trips` (
`trip_id` bigint NOT NULL AUTO_INCREMENT,
`duration` int NOT NULL,
`start_date` datetime DEFAULT NULL,
`end_date` datetime DEFAULT NULL,
`start_station_number` int DEFAULT NULL,
`start_station` varchar(255) DEFAULT NULL,
`end_station_number` int 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)
在 start_date 上没有索引。你需要添加索引，将此条件推入索引读取操作符。可以如下添加索引：
ALTER TABLE trips ADD INDEX (start_date);
Query OK, 0 rows affected (2 min 10.23 sec)
添加索引后，可以再次在 EXPLAIN 中执行该查询。以下输出显示，已选择新的执行计划，TableFullScan 和 Selection 操作符已被消除：
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)
为了比较实际的执行时间，你可以再次使用 EXPLAIN ANALYZE：
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)
从上面的结果可以看出，查询耗时已从 1.03 秒缩短到 0.0 秒。
禁用子查询的提前执行
在查询优化过程中，TiDB 会预先执行可以直接计算的子查询。例如：
CREATE TABLE t1(a int);
INSERT INTO t1 VALUES(1);
CREATE TABLE t2(a int);
EXPLAIN SELECT * FROM t2 WHERE a = (SELECT a FROM t1);
+--------------------------+----------+-----------+---------------+--------------------------------+
| id | estRows | task | access object | operator info |
+--------------------------+----------+-----------+---------------+--------------------------------+
| TableReader_14 | 10.00 | root | | data:Selection_13 |
| └─Selection_13 | 10.00 | cop[tikv] | | eq(test.t2.a, 1) |
| └─TableFullScan_12 | 10000.00 | cop[tikv] | table:t2 | keep order:false, stats:pseudo |
+--------------------------+----------+-----------+---------------+--------------------------------+
3 rows in set (0.00 sec)
在上述示例中，子查询 a = (SELECT a FROM t1) 在优化阶段被计算，并重写为 t2.a=1。这允许在优化过程中进行常量传播和折叠等优化。然而，这会影响 EXPLAIN 语句的执行时间。当子查询本身执行时间较长时，EXPLAIN 可能无法完成，从而影响线上排查。
从 v7.3.0 版本开始，TiDB 引入了 tidb_opt_enable_non_eval_scalar_subquery 系统变量，用于控制是否禁用 EXPLAIN 中此类子查询的预执行。该变量的默认值为 OFF，表示会预先计算子查询。你可以将其设置为 ON 来禁用子查询的预执行：
SET @@tidb_opt_enable_non_eval_scalar_subquery = ON;
EXPLAIN SELECT * FROM t2 WHERE a = (SELECT a FROM t1);
+---------------------------+----------+-----------+---------------+---------------------------------+
| id | estRows | task | access object | operator info |
+---------------------------+----------+-----------+---------------+---------------------------------+
| Selection_13 | 8000.00 | root | | eq(test.t2.a, ScalarQueryCol#5) |
| └─TableReader_15 | 10000.00 | root | | data:TableFullScan_14 |
| └─TableFullScan_14 | 10000.00 | cop[tikv] | table:t2 | keep order:false, stats:pseudo |
| ScalarSubQuery_10 | N/A | root | | Output: ScalarQueryCol#5 |
| └─MaxOneRow_6 | 1.00 | root | | |
| └─TableReader_9 | 1.00 | root | | data:TableFullScan_8 |
| └─TableFullScan_8 | 1.00 | cop[tikv] | table:t1 | keep order:false, stats:pseudo |
+---------------------------+----------+-----------+---------------+---------------------------------+
7 rows in set (0.00 sec)
可以看到，标量子查询在执行过程中没有展开，这样更容易理解此类 SQL 的具体执行流程。