title | summary |
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用 EXPLAIN 查看 SQL 语句需要访问的分区 |
了解 TiDB 中 EXPLAIN 语句返回的执行计划信息。 |
使用 EXPLAIN
语句可以查看 TiDB 在执行查询时需要访问的分区。由于存在分区裁剪,所显示的分区通常只是所有分区的一个子集。本文档介绍了常见分区表的一些优化方式,以及如何解读 EXPLAIN
语句返回的执行计划信息。
本文档所使用的示例数据如下:
{{< copyable "sql" >}}
CREATE TABLE t1 (
id BIGINT NOT NULL auto_increment,
d date NOT NULL,
pad1 BLOB,
pad2 BLOB,
pad3 BLOB,
PRIMARY KEY (id,d)
) PARTITION BY RANGE (YEAR(d)) (
PARTITION p2016 VALUES LESS THAN (2017),
PARTITION p2017 VALUES LESS THAN (2018),
PARTITION p2018 VALUES LESS THAN (2019),
PARTITION p2019 VALUES LESS THAN (2020),
PARTITION pmax VALUES LESS THAN MAXVALUE
);
INSERT INTO t1 (d, pad1, pad2, pad3) VALUES
('2016-01-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2016-06-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2016-09-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2017-01-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2017-06-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2017-09-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2018-01-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2018-06-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2018-09-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2019-01-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2019-06-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2019-09-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2020-01-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2020-06-01', RANDOM_BYTES(102), RANDOM_BYTES(1024), RANDOM_BYTES(1024)),
('2020-09-01', RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024));
INSERT INTO t1 SELECT NULL, a.d, 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, a.d, 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, a.d, 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, a.d, 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;
以下示例解释了基于新建分区表 t1
的一条语句:
{{< copyable "sql" >}}
EXPLAIN SELECT COUNT(*) FROM t1 WHERE d = '2017-06-01';
+------------------------------+---------+-----------+---------------------------+-------------------------------------------+
| id | estRows | task | access object | operator info |
+------------------------------+---------+-----------+---------------------------+-------------------------------------------+
| StreamAgg_21 | 1.00 | root | | funcs:count(Column#8)->Column#6 |
| └─TableReader_22 | 1.00 | root | | data:StreamAgg_10 |
| └─StreamAgg_10 | 1.00 | cop[tikv] | | funcs:count(1)->Column#8 |
| └─Selection_20 | 8.87 | cop[tikv] | | eq(test.t1.d, 2017-06-01 00:00:00.000000) |
| └─TableFullScan_19 | 8870.00 | cop[tikv] | table:t1, partition:p2017 | keep order:false |
+------------------------------+---------+-----------+---------------------------+-------------------------------------------+
5 rows in set (0.01 sec)
由上述 EXPLAIN
结果可知,从最末尾的 —TableFullScan_19
算子开始,再返回到根部的 StreamAgg_21
算子的执行过程如下:
- TiDB 成功地识别出只需要访问一个分区 (
p2017
),并将该信息在access object
列中注明。 └─TableFullScan_19
算子先对整个分区进行扫描,然后执行└─Selection_20
算子筛选起始日期为2017-06-01 00:00:00.000000
的行。- 之后,
└─Selection_20
算子匹配的行在 Coprocessor 中进行流式聚合,Coprocessor 本身就可以理解聚合函数count
。 - 每个 Coprocessor 请求会发送一行数据给 TiDB 的
└─TableReader_22
算子,然后将数据在StreamAgg_21
算子下进行流式聚合,再将一行数据返回给客户端。
以下示例中,分区裁剪不会消除任何分区:
{{< copyable "sql" >}}
EXPLAIN SELECT COUNT(*) FROM t1 WHERE YEAR(d) = 2017;
+------------------------------------+----------+-----------+---------------------------+----------------------------------+
| id | estRows | task | access object | operator info |
+------------------------------------+----------+-----------+---------------------------+----------------------------------+
| HashAgg_20 | 1.00 | root | | funcs:count(Column#7)->Column#6 |
| └─PartitionUnion_21 | 5.00 | root | | |
| ├─StreamAgg_36 | 1.00 | root | | funcs:count(Column#9)->Column#7 |
| │ └─TableReader_37 | 1.00 | root | | data:StreamAgg_25 |
| │ └─StreamAgg_25 | 1.00 | cop[tikv] | | funcs:count(1)->Column#9 |
| │ └─Selection_35 | 6000.00 | cop[tikv] | | eq(year(test.t1.d), 2017) |
| │ └─TableFullScan_34 | 7500.00 | cop[tikv] | table:t1, partition:p2016 | keep order:false |
| ├─StreamAgg_55 | 1.00 | root | | funcs:count(Column#11)->Column#7 |
| │ └─TableReader_56 | 1.00 | root | | data:StreamAgg_44 |
| │ └─StreamAgg_44 | 1.00 | cop[tikv] | | funcs:count(1)->Column#11 |
| │ └─Selection_54 | 14192.00 | cop[tikv] | | eq(year(test.t1.d), 2017) |
| │ └─TableFullScan_53 | 17740.00 | cop[tikv] | table:t1, partition:p2017 | keep order:false |
| ├─StreamAgg_74 | 1.00 | root | | funcs:count(Column#13)->Column#7 |
| │ └─TableReader_75 | 1.00 | root | | data:StreamAgg_63 |
| │ └─StreamAgg_63 | 1.00 | cop[tikv] | | funcs:count(1)->Column#13 |
| │ └─Selection_73 | 3977.60 | cop[tikv] | | eq(year(test.t1.d), 2017) |
| │ └─TableFullScan_72 | 4972.00 | cop[tikv] | table:t1, partition:p2018 | keep order:false |
| ├─StreamAgg_93 | 1.00 | root | | funcs:count(Column#15)->Column#7 |
| │ └─TableReader_94 | 1.00 | root | | data:StreamAgg_82 |
| │ └─StreamAgg_82 | 1.00 | cop[tikv] | | funcs:count(1)->Column#15 |
| │ └─Selection_92 | 20361.60 | cop[tikv] | | eq(year(test.t1.d), 2017) |
| │ └─TableFullScan_91 | 25452.00 | cop[tikv] | table:t1, partition:p2019 | keep order:false |
| └─StreamAgg_112 | 1.00 | root | | funcs:count(Column#17)->Column#7 |
| └─TableReader_113 | 1.00 | root | | data:StreamAgg_101 |
| └─StreamAgg_101 | 1.00 | cop[tikv] | | funcs:count(1)->Column#17 |
| └─Selection_111 | 8892.80 | cop[tikv] | | eq(year(test.t1.d), 2017) |
| └─TableFullScan_110 | 11116.00 | cop[tikv] | table:t1, partition:pmax | keep order:false |
+------------------------------------+----------+-----------+---------------------------+----------------------------------+
27 rows in set (0.00 sec)
由上述 EXPLAIN
结果可知:
- TiDB 认为需要访问所有分区
(p2016..pMax)
。这是因为 TiDB 将谓词YEAR(d)= 2017
视为 non-sargable。这个问题并非是 TiDB 特有的。 - 在扫描每个分区时,
Selection
算子将筛选出年份不为 2017 的行。 - 在每个分区上会执行流式聚合,以计算匹配的行数。
└─PartitionUnion_21
算子会合并访问每个分区后的结果。