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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：
| 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。
Hash 分区表上可以使用分区裁剪的场景
只有等值比较的查询条件能够支持 Hash 分区表的裁剪。
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。
Hash 分区表上不能使用分区裁剪的场景
场景一
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 || │ └─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。
场景二
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。
+--------------------------------------+----------+-----------+------------------------+----------------------------------------------+| 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。
分区裁剪在 Range 分区表上的应用
Range 分区表上可以使用分区裁剪的场景
场景一
等值比较的查询条件可以使用分区裁剪。
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。
create table t (x int) partition by range (x) (partition p0 values less than (5),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 |+-----------------------------+----------+-----------+-----------------------+--------------------------------+
场景二
区间比较的查询条件如 between, > < = >= <= 可以使用分区裁剪。
+-----------------------------+----------+-----------+-----------------------+-----------------------------------+| 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 |+-----------------------------+----------+-----------+-----------------------+-----------------------------------+
场景三
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_timestampto_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：
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 |+-------------------------+----------+-----------+-----------------------+-------------------------------------------+
Range 分区表上不能使用分区裁剪的场景
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。
create table t1 (x int) partition by range (x) (partition p0 values less than (5),partition p1 values less than (10));explain select * from t2 where x < (select * from t1 where t2.x < t1.x and t2.x < 2);
