| title | summary | aliases | |
|---|---|---|---|
分区裁剪 |
了解 TiDB 分区裁剪的使用场景。 |
|
分区裁剪是只有当目标表为分区表时，才可以进行的一种优化方式。分区裁剪通过分析查询语句中的过滤条件，只选择可能满足条件的分区，不扫描匹配不上的分区，进而显著地减少计算的数据量。
例如：
{{< copyable "sql" >}}
CREATE TABLE t1 (
id INT NOT NULL PRIMARY KEY,
pad VARCHAR(100)
)
PARTITION BY RANGE COLUMNS(id) (
PARTITION p0 VALUES LESS THAN (100),
PARTITION p1 VALUES LESS THAN (200),
PARTITION p2 VALUES LESS THAN (MAXVALUE)
);
INSERT INTO t1 VALUES (1, 'test1'),(101, 'test2'), (201, 'test3');
EXPLAIN SELECT * FROM t1 WHERE id BETWEEN 80 AND 120;+----------------------------+---------+-----------+------------------------+------------------------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------+---------+-----------+------------------------+------------------------------------------------+
| PartitionUnion_8 | 80.00 | root | | |
| ├─TableReader_10 | 40.00 | root | | data:TableRangeScan_9 |
| │ └─TableRangeScan_9 | 40.00 | cop[tikv] | table:t1, partition:p0 | range:[80,120], keep order:false, stats:pseudo |
| └─TableReader_12 | 40.00 | root | | data:TableRangeScan_11 |
| └─TableRangeScan_11 | 40.00 | cop[tikv] | table:t1, partition:p1 | range:[80,120], keep order:false, stats:pseudo |
+----------------------------+---------+-----------+------------------------+------------------------------------------------+
5 rows in set (0.00 sec)分区表有 Range 分区和 hash 分区两种形式，分区裁剪对两种分区表也有不同的使用场景。
只有等值比较的查询条件能够支持 Hash 分区表的裁剪。
{{< copyable "sql" >}}
create table t (x int) partition by hash(x) partitions 4;
explain select * from t where x = 1;+-------------------------+----------+-----------+-----------------------+--------------------------------+
| id | estRows | task | access object | operator info |
+-------------------------+----------+-----------+-----------------------+--------------------------------+
| TableReader_8 | 10.00 | root | | data:Selection_7 |
| └─Selection_7 | 10.00 | cop[tikv] | | eq(test.t.x, 1) |
| └─TableFullScan_6 | 10000.00 | cop[tikv] | table:t, partition:p1 | keep order:false, stats:pseudo |
+-------------------------+----------+-----------+-----------------------+--------------------------------+在这条 SQL 中，由条件 x = 1 可以知道所有结果均在一个分区上。数值 1 在经过 Hash 后，可以确定其在分区 p1 中。因此只需要扫描分区 p1 ,而无需访问一定不会出现相关结果的 p2 、p3 、p4 分区。从执行计划来看，其中只出现了一个 TableFullScan 算子，且在 access object 中指定了 p1 分区，确认 partition pruning 生效了。
不能确定查询结果只在一个分区上的条件：如 in, between, > < >= <= 等查询条件，不能使用分区裁剪的优化。
{{< copyable "sql" >}}
create table t (x int) partition by hash(x) partitions 4;
explain select * from t where x > 2;+------------------------------+----------+-----------+-----------------------+--------------------------------+
| id | estRows | task | access object | operator info |
+------------------------------+----------+-----------+-----------------------+--------------------------------+
| Union_10 | 13333.33 | root | | |
| ├─TableReader_13 | 3333.33 | root | | data:Selection_12 |
| │ └─Selection_12 | 3333.33 | cop[tikv] | | gt(test.t.x, 2) |
| │ └─TableFullScan_11 | 10000.00 | cop[tikv] | table:t, partition:p0 | keep order:false, stats:pseudo |
| ├─TableReader_16 | 3333.33 | root | | data:Selection_15 |
| │ └─Selection_15 | 3333.33 | cop[tikv] | | gt(test.t.x, 2) |
| │ └─TableFullScan_14 | 10000.00 | cop[tikv] | table:t, partition:p1 | keep order:false, stats:pseudo |
| ├─TableReader_19 | 3333.33 | root | | data:Selection_18 |
| │ └─Selection_18 | 3333.33 | cop[tikv] | | gt(test.t.x, 2) |
| │ └─TableFullScan_17 | 10000.00 | cop[tikv] | table:t, partition:p2 | keep order:false, stats:pseudo |
| └─TableReader_22 | 3333.33 | root | | data:Selection_21 |
| └─Selection_21 | 3333.33 | cop[tikv] | | gt(test.t.x, 2) |
| └─TableFullScan_20 | 10000.00 | cop[tikv] | table:t, partition:p3 | keep order:false, stats:pseudo |
+------------------------------+----------+-----------+-----------------------+--------------------------------+在这条 SQL 中，x > 2 条件无法确定对应的 Hash Partition，所以不能使用分区裁剪。
由于分区裁剪的规则优化是在查询计划的生成阶段，对于执行阶段才能获取到过滤条件的场景，无法利用分区裁剪的优化。
{{< copyable "sql" >}}
create table t (x int) partition by hash(x) partitions 4;
explain select * from t2 where x = (select * from t1 where t2.x = t1.x and t2.x < 2);+--------------------------------------+----------+-----------+------------------------+----------------------------------------------+
| id | estRows | task | access object | operator info |
+--------------------------------------+----------+-----------+------------------------+----------------------------------------------+
| Projection_13 | 9990.00 | root | | test.t2.x |
| └─Apply_15 | 9990.00 | root | | inner join, equal:[eq(test.t2.x, test.t1.x)] |
| ├─TableReader_18(Build) | 9990.00 | root | | data:Selection_17 |
| │ └─Selection_17 | 9990.00 | cop[tikv] | | not(isnull(test.t2.x)) |
| │ └─TableFullScan_16 | 10000.00 | cop[tikv] | table:t2 | keep order:false, stats:pseudo |
| └─Selection_19(Probe) | 0.80 | root | | not(isnull(test.t1.x)) |
| └─MaxOneRow_20 | 1.00 | root | | |
| └─Union_21 | 2.00 | root | | |
| ├─TableReader_24 | 2.00 | root | | data:Selection_23 |
| │ └─Selection_23 | 2.00 | cop[tikv] | | eq(test.t2.x, test.t1.x), lt(test.t2.x, 2) |
| │ └─TableFullScan_22 | 2500.00 | cop[tikv] | table:t1, partition:p0 | keep order:false, stats:pseudo |
| └─TableReader_27 | 2.00 | root | | data:Selection_26 |
| └─Selection_26 | 2.00 | cop[tikv] | | eq(test.t2.x, test.t1.x), lt(test.t2.x, 2) |
| └─TableFullScan_25 | 2500.00 | cop[tikv] | table:t1, partition:p1 | keep order:false, stats:pseudo |
+--------------------------------------+----------+-----------+------------------------+----------------------------------------------+这个查询每从 t2 读取一行，都会去分区表 t1 上进行查询，理论上这时会满足 t1.x = val 的过滤条件，但实际上由于分区裁剪只作用于查询计划生成阶段，而不是执行阶段，因而不会做裁剪。
等值比较的查询条件可以使用分区裁剪。
{{< copyable "sql" >}}
create table t (x int) partition by range (x) (
partition p0 values less than (5),
partition p1 values less than (10),
partition p2 values less than (15)
);
explain select * from t where x = 3;+-------------------------+----------+-----------+-----------------------+--------------------------------+
| id | estRows | task | access object | operator info |
+-------------------------+----------+-----------+-----------------------+--------------------------------+
| TableReader_8 | 10.00 | root | | data:Selection_7 |
| └─Selection_7 | 10.00 | cop[tikv] | | eq(test.t.x, 3) |
| └─TableFullScan_6 | 10000.00 | cop[tikv] | table:t, partition:p0 | keep order:false, stats:pseudo |
+-------------------------+----------+-----------+-----------------------+--------------------------------+使用 in 条件的等值比较查询条件也可以使用分区裁剪。
{{< copyable "sql" >}}
create table t (x int) partition by range (x) (
partition p0 values less than (5),
partition p1 values less than (10),
partition p2 values less than (15)
);
explain select * from t where x in(1,13);+-----------------------------+----------+-----------+-----------------------+--------------------------------+
| id | estRows | task | access object | operator info |
+-----------------------------+----------+-----------+-----------------------+--------------------------------+
| Union_8 | 40.00 | root | | |
| ├─TableReader_11 | 20.00 | root | | data:Selection_10 |
| │ └─Selection_10 | 20.00 | cop[tikv] | | in(test.t.x, 1, 13) |
| │ └─TableFullScan_9 | 10000.00 | cop[tikv] | table:t, partition:p0 | keep order:false, stats:pseudo |
| └─TableReader_14 | 20.00 | root | | data:Selection_13 |
| └─Selection_13 | 20.00 | cop[tikv] | | in(test.t.x, 1, 13) |
| └─TableFullScan_12 | 10000.00 | cop[tikv] | table:t, partition:p2 | keep order:false, stats:pseudo |
+-----------------------------+----------+-----------+-----------------------+--------------------------------+在这条 SQL 中，由条件 x in(1,13) 可以知道所有结果只会分布在几个分区上。经过分析，发现所有 x = 1 的记录都在分区 p0 上, 所有 x = 13 的记录都在分区 p2 上，因此只需要访问 p0、p2 这两个分区，
区间比较的查询条件如 between, > < = >= <= 可以使用分区裁剪。
{{< copyable "sql" >}}
create table t (x int) partition by range (x) (
partition p0 values less than (5),
partition p1 values less than (10),
partition p2 values less than (15)
);
explain select * from t where x between 7 and 14;+-----------------------------+----------+-----------+-----------------------+-----------------------------------+
| id | estRows | task | access object | operator info |
+-----------------------------+----------+-----------+-----------------------+-----------------------------------+
| Union_8 | 500.00 | root | | |
| ├─TableReader_11 | 250.00 | root | | data:Selection_10 |
| │ └─Selection_10 | 250.00 | cop[tikv] | | ge(test.t.x, 7), le(test.t.x, 14) |
| │ └─TableFullScan_9 | 10000.00 | cop[tikv] | table:t, partition:p1 | keep order:false, stats:pseudo |
| └─TableReader_14 | 250.00 | root | | data:Selection_13 |
| └─Selection_13 | 250.00 | cop[tikv] | | ge(test.t.x, 7), le(test.t.x, 14) |
| └─TableFullScan_12 | 10000.00 | cop[tikv] | table:t, partition:p2 | keep order:false, stats:pseudo |
+-----------------------------+----------+-----------+-----------------------+-----------------------------------+分区表达式为 fn(col) 的简单形式，查询条件是 > < = >= <= 之一，且 fn 是单调函数，可以使用分区裁剪。
关于 fn 函数，对于任意 x y，如果 x > y，则 fn(x) > fn(y)，那么这种是严格递增的单调函数。非严格递增的单调函数也可以符合分区裁剪要求，只要函数 fn 满足：对于任意 x y，如果 x > y，则 fn(x) >= fn(y)。理论上，所有满足单调条件（严格或者非严格）的函数都支持分区裁剪。目前，TiDB 支持的单调函数如下：
unix_timestamp
to_days例如，分区表达式是 fn(col) 形式，fn 为我们支持的单调函数 to_days，就可以使用分区裁剪：
{{< copyable "sql" >}}
create table t (id datetime) partition by range (to_days(id)) (
partition p0 values less than (to_days('2020-04-01')),
partition p1 values less than (to_days('2020-05-01')));
explain select * from t where id > '2020-04-18';+-------------------------+----------+-----------+-----------------------+-------------------------------------------+
| id | estRows | task | access object | operator info |
+-------------------------+----------+-----------+-----------------------+-------------------------------------------+
| TableReader_8 | 3333.33 | root | | data:Selection_7 |
| └─Selection_7 | 3333.33 | cop[tikv] | | gt(test.t.id, 2020-04-18 00:00:00.000000) |
| └─TableFullScan_6 | 10000.00 | cop[tikv] | table:t, partition:p1 | keep order:false, stats:pseudo |
+-------------------------+----------+-----------+-----------------------+-------------------------------------------+由于分区裁剪的规则优化是在查询计划的生成阶段，对于执行阶段才能获取到过滤条件的场景，无法利用分区裁剪的优化。
{{< copyable "sql" >}}
create table t1 (x int) partition by range (x) (
partition p0 values less than (5),
partition p1 values less than (10));
create table t2 (x int);
explain select * from t2 where x < (select * from t1 where t2.x < t1.x and t2.x < 2);+--------------------------------------+----------+-----------+------------------------+-----------------------------------------------------------+
| id | estRows | task | access object | operator info |
+--------------------------------------+----------+-----------+------------------------+-----------------------------------------------------------+
| Projection_13 | 9990.00 | root | | test.t2.x |
| └─Apply_15 | 9990.00 | root | | CARTESIAN inner join, other cond:lt(test.t2.x, test.t1.x) |
| ├─TableReader_18(Build) | 9990.00 | root | | data:Selection_17 |
| │ └─Selection_17 | 9990.00 | cop[tikv] | | not(isnull(test.t2.x)) |
| │ └─TableFullScan_16 | 10000.00 | cop[tikv] | table:t2 | keep order:false, stats:pseudo |
| └─Selection_19(Probe) | 0.80 | root | | not(isnull(test.t1.x)) |
| └─MaxOneRow_20 | 1.00 | root | | |
| └─Union_21 | 2.00 | root | | |
| ├─TableReader_24 | 2.00 | root | | data:Selection_23 |
| │ └─Selection_23 | 2.00 | cop[tikv] | | lt(test.t2.x, 2), lt(test.t2.x, test.t1.x) |
| │ └─TableFullScan_22 | 2.50 | cop[tikv] | table:t1, partition:p0 | keep order:false, stats:pseudo |
| └─TableReader_27 | 2.00 | root | | data:Selection_26 |
| └─Selection_26 | 2.00 | cop[tikv] | | lt(test.t2.x, 2), lt(test.t2.x, test.t1.x) |
| └─TableFullScan_25 | 2.50 | cop[tikv] | table:t1, partition:p1 | keep order:false, stats:pseudo |
+--------------------------------------+----------+-----------+------------------------+-----------------------------------------------------------+
14 rows in set (0.00 sec)这个查询每从 t2 读取一行，都会去分区表 t1 上进行查询，理论上这时会满足 t1.x > val 的过滤条件，但实际上由于分区裁剪只作用于查询计划生成阶段，而不是执行阶段，因而不会做裁剪。