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：
CREATE TABLE t1 (id INT NOT NULL PRIMARY KEY auto_increment, pad1 BLOB, pad2 BLOB, pad3 BLOB);
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM dual;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
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;
SELECT SLEEP(1);
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：
SHOW TABLE t1 REGIONS;
+-----------+--------------+--------------+-----------+-----------------+-------+------------+---------------+------------+----------------------+------------------+
| REGION_ID | START_KEY | END_KEY | LEADER_ID | LEADER_STORE_ID | PEERS | SCATTERING | WRITTEN_BYTES | READ_BYTES | APPROXIMATE_SIZE(MB) | APPROXIMATE_KEYS |
+-----------+--------------+--------------+-----------+-----------------+-------+------------+---------------+------------+----------------------+------------------+
| 64 | t_64_ | t_64_r_31766 | 65 | 1 | 65 | 0 | 1325 | 102033520 | 98 | 52797 |
| 66 | t_64_r_31766 | t_64_r_63531 | 67 | 1 | 67 | 0 | 1325 | 72522521 | 104 | 78495 |
| 68 | t_64_r_63531 | t_64_r_95296 | 69 | 1 | 69 | 0 | 1325 | 0 | 104 | 95433 |
| 2 | t_64_r_95296 | | 3 | 1 | 3 | 0 | 1501 | 0 | 81 | 63211 |
+-----------+--------------+--------------+-----------+-----------------+-------+------------+---------------+------------+----------------------+------------------+
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：
EXPLAIN SELECT COUNT(*) FROM t1;
+----------------------------+-----------+-----------+---------------+---------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------+-----------+-----------+---------------+---------------------------------+
| StreamAgg_16 | 1.00 | root | | funcs:count(Column#7)->Column#5 |
| └─TableReader_17 | 1.00 | root | | data:StreamAgg_8 |
| └─StreamAgg_8 | 1.00 | cop[tikv] | | funcs:count(1)->Column#7 |
| └─TableFullScan_15 | 242020.00 | cop[tikv] | table:t1 | keep order:false |
+----------------------------+-----------+-----------+---------------+---------------------------------+
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：
EXPLAIN ANALYZE SELECT COUNT(*) FROM t1;
+----------------------------+-----------+---------+-----------+---------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-----------+------+
| id | estRows | actRows | task | access object | execution info | operator info | memory | disk |
+----------------------------+-----------+---------+-----------+---------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-----------+------+
| StreamAgg_16 | 1.00 | 1 | root | | time:12.609575ms, loops:2 | funcs:count(Column#7)->Column#5 | 372 Bytes | N/A |
| └─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 |
| └─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 |
| └─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 |
+----------------------------+-----------+---------+-----------+---------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+-----------+------+
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：
EXPLAIN SELECT /*+ HASH_AGG() */ count(*) FROM t1;
+---------------------------+-----------+-----------+---------------+---------------------------------+
| id | estRows | task | access object | operator info |
+---------------------------+-----------+-----------+---------------+---------------------------------+
| HashAgg_9 | 1.00 | root | | funcs:count(Column#6)->Column#5 |
| └─TableReader_10 | 1.00 | root | | data:HashAgg_5 |
| └─HashAgg_5 | 1.00 | cop[tikv] | | funcs:count(1)->Column#6 |
| └─TableFullScan_8 | 242020.00 | cop[tikv] | table:t1 | keep order:false |
+---------------------------+-----------+-----------+---------------+---------------------------------+
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：
CREATE TABLE t2 (id INT NOT NULL PRIMARY KEY, col1 INT NOT NULL);
INSERT INTO t2 VALUES (1, 9),(2, 3),(3,1),(4,8),(6,3);
EXPLAIN SELECT /*+ STREAM_AGG() */ col1, count(*) FROM t2 GROUP BY col1;
Query OK, 0 rows affected (0.11 sec)
Query OK, 5 rows affected (0.01 sec)
Records: 5 Duplicates: 0 Warnings: 0
+------------------------------+----------+-----------+---------------+---------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+------------------------------+----------+-----------+---------------+---------------------------------------------------------------------------------------------+
| Projection_4 | 8000.00 | root | | test.t2.col1, Column#3 |
| └─StreamAgg_8 | 8000.00 | root | | group by:test.t2.col1, funcs:count(1)->Column#3, funcs:firstrow(test.t2.col1)->test.t2.col1 |
| └─Sort_13 | 10000.00 | root | | test.t2.col1 |
| └─TableReader_12 | 10000.00 | root | | data:TableFullScan_11 |
| └─TableFullScan_11 | 10000.00 | cop[tikv] | table:t2 | keep order:false, stats:pseudo |
+------------------------------+----------+-----------+---------------+---------------------------------------------------------------------------------------------+
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。
ALTER TABLE t2 ADD INDEX (col1);
EXPLAIN SELECT /*+ STREAM_AGG() */ col1, count(*) FROM t2 GROUP BY col1;
Query OK, 0 rows affected (0.28 sec)
+------------------------------+---------+-----------+----------------------------+----------------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+------------------------------+---------+-----------+----------------------------+----------------------------------------------------------------------------------------------------+
| Projection_4 | 4.00 | root | | test.t2.col1, Column#3 |
| └─StreamAgg_14 | 4.00 | root | | group by:test.t2.col1, funcs:count(Column#4)->Column#3, funcs:firstrow(test.t2.col1)->test.t2.col1 |
| └─IndexReader_15 | 4.00 | root | | index:StreamAgg_8 |
| └─StreamAgg_8 | 4.00 | cop[tikv] | | group by:test.t2.col1, funcs:count(1)->Column#4 |
| └─IndexFullScan_13 | 5.00 | cop[tikv] | table:t2, index:col1(col1) | keep order:true, stats:pseudo |
+------------------------------+---------+-----------+----------------------------+----------------------------------------------------------------------------------------------------+
5 rows in set (0.00 sec)
多维度数据聚合 ROLLUP
自 v7.4.0 起，TiDB 的 GROUP BY 子句支持 WITH ROLLUP 修饰符。
你可以在 GROUP BY 子句中指定一个或多个列，形成一个分组列表，然后添加 WITH ROLLUP 修饰符。TiDB 将会按照分组列表中的列进行多维度的递减分组，并在输出中为你提供各个分组数据的汇总结果。
explain SELECT year, month, grouping(year), grouping(month), SUM(profit) AS profit FROM bank GROUP BY year, month WITH ROLLUP;
+----------------------------------------+---------+--------------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------------------+---------+--------------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| TableReader_44 | 2.40 | root | | MppVersion: 2, data:ExchangeSender_43 |
| └─ExchangeSender_43 | 2.40 | mpp[tiflash] | | ExchangeType: PassThrough |
| └─Projection_8 | 2.40 | mpp[tiflash] | | Column#6->Column#12, Column#7->Column#13, grouping(gid)->Column#14, grouping(gid)->Column#15, Column#9->Column#16 |
| └─Projection_38 | 2.40 | mpp[tiflash] | | Column#9, Column#6, Column#7, gid |
| └─HashAgg_36 | 2.40 | mpp[tiflash] | | group by:Column#6, Column#7, gid, funcs:sum(test.bank.profit)->Column#9, funcs:firstrow(Column#6)->Column#6, funcs:firstrow(Column#7)->Column#7, funcs:firstrow(gid)->gid, stream_count: 8 |
| └─ExchangeReceiver_22 | 3.00 | mpp[tiflash] | | stream_count: 8 |
| └─ExchangeSender_21 | 3.00 | mpp[tiflash] | | ExchangeType: HashPartition, Compression: FAST, Hash Cols: [name: Column#6, collate: binary], [name: Column#7, collate: utf8mb4_bin], [name: gid, collate: binary], stream_count: 8 |
| └─Expand_20 | 3.00 | mpp[tiflash] | | level-projection:[test.bank.profit, <nil>->Column#6, <nil>->Column#7, 0->gid],[test.bank.profit, Column#6, <nil>->Column#7, 1->gid],[test.bank.profit, Column#6, Column#7, 3->gid]; schema: [test.bank.profit,Column#6,Column#7,gid] |
| └─Projection_16 | 3.00 | mpp[tiflash] | | test.bank.profit, test.bank.year->Column#6, test.bank.month->Column#7 |
| └─TableFullScan_17 | 3.00 | mpp[tiflash] | table:bank | keep order:false, stats:pseudo |
+----------------------------------------+---------+--------------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
10 rows in set (0.05 sec)
该语句的 SQL 聚合可以按照 GROUP BY year, month WITH ROLLUP 语法在 {year, month}、{year}、{} 这 3 个分组中分别计算并连接结果。
更多信息，请参考 GROUP BY 修饰符。