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移除书签用 EXPLAIN 查看聚合查询执行计划
SQL 查询中可能会使用聚合计算，可以通过 EXPLAIN 语句来查看聚合查询的执行计划。本文提供多个示例，以帮助用户理解聚合查询是如何执行的。
SQL 优化器会选择以下任一算子实现数据聚合：
- Hash Aggregation
- Stream Aggregation
为了提高查询效率，数据聚合在 Coprocessor 层和 TiDB 层均会执行。现有示例如下：
1. CREATE TABLE t1 (id INT NOT NULL PRIMARY KEY auto_increment, pad1 BLOB, pad2 BLOB, pad3 BLOB);
2. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM dual;
3. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
4. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
5. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
6. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
7. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
8. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
9. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
10. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
11. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
12. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
13. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
14. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
15. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
16. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
17. INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
18. SELECT SLEEP(1);
19. ANALYZE TABLE t1;
以上示例创建表格 t1 并插入数据后，再执行 SHOW TABLE REGIONS 语句。从以下 SHOW TABLE REGIONS 的执行结果可知，表 t1 被切分为多个 Region：
1. SHOW TABLE t1 REGIONS;
1. +-----------+--------------+--------------+-----------+-----------------+-------+------------+---------------+------------+----------------------+------------------+
2. | REGION_ID | START_KEY | END_KEY | LEADER_ID | LEADER_STORE_ID | PEERS | SCATTERING | WRITTEN_BYTES | READ_BYTES | APPROXIMATE_SIZE(MB) | APPROXIMATE_KEYS |
3. +-----------+--------------+--------------+-----------+-----------------+-------+------------+---------------+------------+----------------------+------------------+
4. | 64 | t_64_ | t_64_r_31766 | 65 | 1 | 65 | 0 | 1325 | 102033520 | 98 | 52797 |
5. | 66 | t_64_r_31766 | t_64_r_63531 | 67 | 1 | 67 | 0 | 1325 | 72522521 | 104 | 78495 |
6. | 68 | t_64_r_63531 | t_64_r_95296 | 69 | 1 | 69 | 0 | 1325 | 0 | 104 | 95433 |
7. | 2 | t_64_r_95296 | | 3 | 1 | 3 | 0 | 1501 | 0 | 81 | 63211 |
8. +-----------+--------------+--------------+-----------+-----------------+-------+------------+---------------+------------+----------------------+------------------+
9. 4 rows in set (0.00 sec)
使用 EXPLAIN 查看以下聚合语句的执行计划。可以看到 └─StreamAgg_8 算子先执行在 TiKV 内每个 Region 上，然后 TiKV 的每个 Region 会返回一行数据给 TiDB，TiDB 在 StreamAgg_16 算子上对每个 Region 返回的数据进行聚合：
1. EXPLAIN SELECT COUNT(*) FROM t1;
1. +----------------------------+-----------+-----------+---------------+---------------------------------+
2. | id | estRows | task | access object | operator info |
3. +----------------------------+-----------+-----------+---------------+---------------------------------+
4. | StreamAgg_16 | 1.00 | root | | funcs:count(Column#7)->Column#5 |
5. | └─TableReader_17 | 1.00 | root | | data:StreamAgg_8 |
6. | └─StreamAgg_8 | 1.00 | cop[tikv] | | funcs:count(1)->Column#7 |
7. | └─TableFullScan_15 | 242020.00 | cop[tikv] | table:t1 | keep order:false |
8. +----------------------------+-----------+-----------+---------------+---------------------------------+
9. 4 rows in set (0.00 sec)
同样，通过执行 EXPLAIN ANALYZE 语句可知，actRows 与 SHOW TABLE REGIONS 返回结果中的 Region 数匹配，这是因为执行使用了 TableFullScan 全表扫并且没有二级索引：
1. EXPLAIN ANALYZE SELECT COUNT(*) FROM t1;
1. +----------------------------+-----------+---------+-----------+---------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-----------+------+
2. | id | estRows | actRows | task | access object | execution info | operator info | memory | disk |
3. +----------------------------+-----------+---------+-----------+---------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-----------+------+
4. | StreamAgg_16 | 1.00 | 1 | root | | time:12.609575ms, loops:2 | funcs:count(Column#7)->Column#5 | 372 Bytes | N/A |
5. | └─TableReader_17 | 1.00 | 4 | root | | time:12.605155ms, loops:2, cop_task: {num: 4, max: 12.538245ms, min: 9.256838ms, avg: 10.895114ms, p95: 12.538245ms, max_proc_keys: 31765, p95_proc_keys: 31765, tot_proc: 48ms, rpc_num: 4, rpc_time: 43.530707ms, copr_cache_hit_ratio: 0.00} | data:StreamAgg_8 | 293 Bytes | N/A |
6. | └─StreamAgg_8 | 1.00 | 4 | cop[tikv] | | proc max:12ms, min:12ms, p80:12ms, p95:12ms, iters:122, tasks:4 | funcs:count(1)->Column#7 | N/A | N/A |
7. | └─TableFullScan_15 | 242020.00 | 121010 | cop[tikv] | table:t1 | proc max:12ms, min:12ms, p80:12ms, p95:12ms, iters:122, tasks:4 | keep order:false | N/A | N/A |
8. +----------------------------+-----------+---------+-----------+---------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-----------+------+
9. 4 rows in set (0.01 sec)
Hash Aggregation
Hash Aggregation 算法在执行聚合时使用 Hash 表存储中间结果。此算法采用多线程并发优化，执行速度快，但与 Stream Aggregation 算法相比会消耗较多内存。
下面是一个使用 Hash Aggregation（即 HashAgg 算子）的例子：
1. EXPLAIN SELECT /*+ HASH_AGG() */ count(*) FROM t1;
1. +---------------------------+-----------+-----------+---------------+---------------------------------+
2. | id | estRows | task | access object | operator info |
3. +---------------------------+-----------+-----------+---------------+---------------------------------+
4. | HashAgg_9 | 1.00 | root | | funcs:count(Column#6)->Column#5 |
5. | └─TableReader_10 | 1.00 | root | | data:HashAgg_5 |
6. | └─HashAgg_5 | 1.00 | cop[tikv] | | funcs:count(1)->Column#6 |
7. | └─TableFullScan_8 | 242020.00 | cop[tikv] | table:t1 | keep order:false |
8. +---------------------------+-----------+-----------+---------------+---------------------------------+
9. 4 rows in set (0.00 sec)
operator info 列显示，用于聚合数据的 Hash 函数为 funcs:count(1)->Column#6。
Stream Aggregation
Stream Aggregation 算法通常会比 Hash Aggregation 算法占用更少的内存。但是此算法要求数据按顺序发送，以便对依次到达的值实现流式数据聚合。
下面是一个使用 Stream Aggregation 的例子：
1. CREATE TABLE t2 (id INT NOT NULL PRIMARY KEY, col1 INT NOT NULL);
2. INSERT INTO t2 VALUES (1, 9),(2, 3),(3,1),(4,8),(6,3);
3. EXPLAIN SELECT /*+ STREAM_AGG() */ col1, count(*) FROM t2 GROUP BY col1;
1. Query OK, 0 rows affected (0.11 sec)
3. Query OK, 5 rows affected (0.01 sec)
4. Records: 5 Duplicates: 0 Warnings: 0
6. +------------------------------+----------+-----------+---------------+---------------------------------------------------------------------------------------------+
7. | id | estRows | task | access object | operator info |
8. +------------------------------+----------+-----------+---------------+---------------------------------------------------------------------------------------------+
9. | Projection_4 | 8000.00 | root | | test.t2.col1, Column#3 |
10. | └─StreamAgg_8 | 8000.00 | root | | group by:test.t2.col1, funcs:count(1)->Column#3, funcs:firstrow(test.t2.col1)->test.t2.col1 |
11. | └─Sort_13 | 10000.00 | root | | test.t2.col1 |
12. | └─TableReader_12 | 10000.00 | root | | data:TableFullScan_11 |
13. | └─TableFullScan_11 | 10000.00 | cop[tikv] | table:t2 | keep order:false, stats:pseudo |
14. +------------------------------+----------+-----------+---------------+---------------------------------------------------------------------------------------------+
15. 5 rows in set (0.00 sec)
以上示例中，可以在 col1 上添加索引来消除 └─Sort_13 算子。添加索引后，TiDB 就可以按顺序读取数据并消除 └─Sort_13 算子。
1. ALTER TABLE t2 ADD INDEX (col1);
2. EXPLAIN SELECT /*+ STREAM_AGG() */ col1, count(*) FROM t2 GROUP BY col1;
1. Query OK, 0 rows affected (0.28 sec)
3. +------------------------------+---------+-----------+----------------------------+----------------------------------------------------------------------------------------------------+
4. | id | estRows | task | access object | operator info |
5. +------------------------------+---------+-----------+----------------------------+----------------------------------------------------------------------------------------------------+
6. | Projection_4 | 4.00 | root | | test.t2.col1, Column#3 |
7. | └─StreamAgg_14 | 4.00 | root | | group by:test.t2.col1, funcs:count(Column#4)->Column#3, funcs:firstrow(test.t2.col1)->test.t2.col1 |
8. | └─IndexReader_15 | 4.00 | root | | index:StreamAgg_8 |
9. | └─StreamAgg_8 | 4.00 | cop[tikv] | | group by:test.t2.col1, funcs:count(1)->Column#4 |
10. | └─IndexFullScan_13 | 5.00 | cop[tikv] | table:t2, index:col1(col1) | keep order:true, stats:pseudo |
11. +------------------------------+---------+-----------+----------------------------+----------------------------------------------------------------------------------------------------+
12. 5 rows in set (0.00 sec)
