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,actRowsSHOW 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)

其他类型查询的执行计划

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