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用 EXPLAIN 查看带聚合计算的 SQL 执行计划

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)