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分区裁剪
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:
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)
分区裁剪的使用场景
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。
分区裁剪在 Hash 分区表上的应用
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
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。
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。
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。
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。
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 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,
场景二
区间比较的查询条件如 between
, > < = >= <=
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 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:
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));
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。