Partition Pruning

Partition pruning 是一种适用于分区表的性能优化技术。它会分析查询语句中的过滤条件，在不包含任何所需数据的分区上进行(裁剪)，从而排除掉这些分区。通过排除不必要的分区，TiDB 能够减少需要访问的数据量，从而可能显著提升查询执行时间。

以下是一个示例：

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)

使用场景：分区裁剪

分区裁剪的使用场景对于 Range 分区表和 Hash 分区表有所不同。

在 Hash 分区表中使用分区裁剪

本节描述 Hash 分区表中分区裁剪的适用和不适用场景。

适用场景

在 Hash 分区表中，分区裁剪仅适用于等值比较的查询条件。

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 |
+-------------------------+----------+-----------+-----------------------+--------------------------------+

从上述 SQL 语句可以看出，根据条件 x = 1，所有结果都落在一个分区中。经过哈希分区后，可以确认值 1 在 p1 分区内。因此，只需要扫描 p1 分区，无需访问不会匹配的 p2、p3 和 p4 分区。从执行计划可以看到，只有一个 TableFullScan 操作，并且在 access object 中指定了 p1 分区，因此可以确认分区裁剪已生效。

不适用场景

本节描述两个 Hash 分区表中分区裁剪不适用的场景。

场景一

如果不能确认查询结果只落在一个分区（例如 in、between、>、<、>=、<=），则不能使用分区裁剪优化。例如：

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 |
+------------------------------+----------+-----------+-----------------------+--------------------------------+

在此情况下，分区裁剪不适用，因为无法确认对应的 Hash 分区满足 x > 2 条件。

场景二

由于分区裁剪的规则优化是在查询计划生成阶段进行的，因此不适用于过滤条件只能在执行阶段获得的场景。例如：

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               |
+--------------------------------------+----------+-----------+------------------------+----------------------------------------------+

每次读取 t2 的一行数据时，都需要对 t1 的分区表进行查询。理论上，t1.x > val 的过滤条件在此时已满足，但实际上，分区裁剪只在查询计划的生成阶段生效，而非在执行阶段。

在 Range 分区表中使用分区裁剪

本节描述 Range 分区表中分区裁剪的适用和不适用场景。

适用场景

Range 分区表中，分区裁剪适用于以下三种场景。

场景一

分区裁剪适用于 Range 分区表中的等值比较查询条件。例如：

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 |
+-------------------------+----------+-----------+-----------------------+--------------------------------+

分区裁剪也适用于使用 in 查询条件的等值比较。例如：

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 |
+-----------------------------+----------+-----------+-----------------------+--------------------------------+

在上述 SQL 中，从 x in(1,13) 条件可以看出，所有结果只落在少数几个分区内。经过分析，发现 x=1 的所有记录都在 p0 分区，x=13 的所有记录都在 p2 分区，因此只需访问 p0 和 p2 分区。

场景二

分区裁剪适用于区间比较的查询条件，例如 between、>、<、=、>=、<=。例如：

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    |
+-----------------------------+----------+-----------+-----------------------+--------------------------------+

场景三

分区裁剪适用于分区表达式为 fn(col) 的简单形式，查询条件为 >、<、=、>=、<=，且 fn 函数为单调函数。

如果 fn 为单调函数，则对于任何 x 和 y，如果 x > y，则 fn(x) > fn(y)。在这种情况下，fn 可以被视为严格单调的。对于任何 x 和 y，如果 x > y，则 fn(x) >= fn(y)。此时，fn 也可以称为“单调函数”。理论上，所有单调函数（严格或非严格）都支持分区裁剪。目前，TiDB 仅支持以下单调函数：

UNIX_TIMESTAMP()
TO_DAYS()
EXTRACT(<time unit> FROM <DATETIME/DATE/TIME column>)。对于 DATE 和 DATETIME 列，YEAR 和 YEAR_MONTH 时间单位被视为单调函数。对于 TIME 列，HOUR、HOUR_MINUTE、HOUR_SECOND 和 HOUR_MICROSECOND 被视为单调函数。注意，WEEK 不支持作为 EXTRACT 的时间单位用于分区裁剪。

例如，当分区表达式为 fn(col)，且 fn 为单调函数 to_days 时，分区裁剪会生效：

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            |
+-------------------------+----------+-----------+-----------------------+-------------------------------------------+

不适用场景：Range 分区表

由于分区裁剪的规则优化是在查询计划生成阶段进行的，因此不适用于过滤条件只能在执行阶段获得的场景。例如：

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.t1.x, test.t2.x)                |
|             └─TableFullScan_25       | 2.50     | cop[tikv] | table:t1, partition:p1 | keep order:false, stats:pseudo                            |
+--------------------------------------+----------+-----------+------------------------+-----------------------------------------------------------+
14 rows in set (0.00 sec)