| title | summary |
|---|---|
Explain Statements That Use Subqueries |
Learn about the execution plan information returned by the EXPLAIN statement in TiDB. |
TiDB performs several optimizations to improve the performance of subqueries. This document describes some of these optimizations for common subqueries and how to interpret the output of EXPLAIN.
The examples in this document are based on the following sample data:
CREATE TABLE t1 (id BIGINT NOT NULL PRIMARY KEY auto_increment, pad1 BLOB, pad2 BLOB, pad3 BLOB, int_col INT NOT NULL DEFAULT 0);
CREATE TABLE t2 (id BIGINT NOT NULL PRIMARY KEY auto_increment, t1_id BIGINT NOT NULL, pad1 BLOB, pad2 BLOB, pad3 BLOB, INDEX(t1_id));
CREATE TABLE t3 (
id INT NOT NULL PRIMARY KEY auto_increment,
t1_id INT NOT NULL,
UNIQUE (t1_id)
);
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM dual;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000;
UPDATE t1 SET int_col = 1 WHERE pad1 = (SELECT pad1 FROM t1 ORDER BY RAND() LIMIT 1);
INSERT INTO t3 SELECT NULL, id FROM t1 WHERE id < 1000;
SELECT SLEEP(1);
ANALYZE TABLE t1, t2, t3;In the following example, the IN subquery searches for a list of IDs from the table t2. For semantic correctness, TiDB needs to guarantee that the column t1_id is unique. Using EXPLAIN, you can see the execution plan used to remove duplicates and perform an INNER JOIN operation:
EXPLAIN SELECT * FROM t1 WHERE id IN (SELECT t1_id FROM t2);+----------------------------------+----------+-----------+------------------------------+---------------------------------------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------------+----------+-----------+------------------------------+---------------------------------------------------------------------------------------------------------------------------+
| IndexJoin_14 | 5.00 | root | | inner join, inner:IndexLookUp_13, outer key:test.t2.t1_id, inner key:test.t1.id, equal cond:eq(test.t2.t1_id, test.t1.id) |
| ├─StreamAgg_49(Build) | 5.00 | root | | group by:test.t2.t1_id, funcs:firstrow(test.t2.t1_id)->test.t2.t1_id |
| │ └─IndexReader_50 | 5.00 | root | | index:StreamAgg_39 |
| │ └─StreamAgg_39 | 5.00 | cop[tikv] | | group by:test.t2.t1_id, |
| │ └─IndexFullScan_31 | 50000.00 | cop[tikv] | table:t2, index:t1_id(t1_id) | keep order:true |
| └─IndexLookUp_13(Probe) | 1.00 | root | | |
| ├─IndexRangeScan_11(Build) | 1.00 | cop[tikv] | table:t1, index:PRIMARY(id) | range: decided by [eq(test.t1.id, test.t2.t1_id)], keep order:false |
| └─TableRowIDScan_12(Probe) | 1.00 | cop[tikv] | table:t1 | keep order:false |
+----------------------------------+----------+-----------+------------------------------+---------------------------------------------------------------------------------------------------------------------------+
8 rows in set (0.00 sec)The result above shows that TiDB performs an index join operation that starts by reading the index on t2.t1_id. The values of t1_id are deduplicated inside TiKV first as a part of the └─HashAgg_31 operator task, and then deduplicated again in TiDB as a part of the ├─HashAgg_38(Build) operator task. The deduplication is performed by the aggregation function firstrow(test.t2.t1_id). The result is then joined against the t1 table's PRIMARY KEY.
In the previous example, aggregation is required to ensure that the values of t1_id are unique before joining against the table t1. But in the following example, t3.t1_id is already guaranteed unique because of a UNIQUE constraint:
EXPLAIN SELECT * FROM t1 WHERE id IN (SELECT t1_id FROM t3);+----------------------------------+---------+-----------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------------+---------+-----------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------+
| IndexJoin_17 | 1978.13 | root | | inner join, inner:IndexLookUp_16, outer key:test.t3.t1_id, inner key:test.t1.id, equal cond:eq(test.t3.t1_id, test.t1.id) |
| ├─TableReader_44(Build) | 1978.00 | root | | data:TableFullScan_43 |
| │ └─TableFullScan_43 | 1978.00 | cop[tikv] | table:t3 | keep order:false |
| └─IndexLookUp_16(Probe) | 1.00 | root | | |
| ├─IndexRangeScan_14(Build) | 1.00 | cop[tikv] | table:t1, index:PRIMARY(id) | range: decided by [eq(test.t1.id, test.t3.t1_id)], keep order:false |
| └─TableRowIDScan_15(Probe) | 1.00 | cop[tikv] | table:t1 | keep order:false |
+----------------------------------+---------+-----------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------+
6 rows in set (0.01 sec)Semantically because t3.t1_id is guaranteed unique, it can be executed directly as an INNER JOIN.
In the previous two examples, TiDB is able to perform an INNER JOIN operation after the data inside the subquery is made unique (via HashAgg) or guaranteed unique. Both joins are performed using an Index Join.
In this example, TiDB chooses a different execution plan:
EXPLAIN SELECT * FROM t1 WHERE id IN (SELECT t1_id FROM t2 WHERE t1_id != t1.int_col);+----------------------------------+---------+-----------+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+----------------------------------+---------+-----------+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------+
| IndexJoin_14 | 1582.40 | root | | anti semi join, inner:IndexLookUp_13, outer key:test.t3.t1_id, inner key:test.t1.id, equal cond:eq(test.t3.t1_id, test.t1.id) |
| ├─TableReader_35(Build) | 1978.00 | root | | data:TableFullScan_34 |
| │ └─TableFullScan_34 | 1978.00 | cop[tikv] | table:t3 | keep order:false |
| └─IndexLookUp_13(Probe) | 1.00 | root | | |
| ├─IndexRangeScan_10(Build) | 1.00 | cop[tikv] | table:t1, index:PRIMARY(id) | range: decided by [eq(test.t1.id, test.t3.t1_id)], keep order:false |
| └─Selection_12(Probe) | 1.00 | cop[tikv] | | lt(test.t1.int_col, 100) |
| └─TableRowIDScan_11 | 1.00 | cop[tikv] | table:t1 | keep order:false |
+----------------------------------+---------+-----------+-----------------------------+-------------------------------------------------------------------------------------------------------------------------------+
7 rows in set (0.00 sec)From the result above, you can see that TiDB uses a Semi Join algorithm. Semi-join differs from inner join: semi-join only permits the first value on the right key (t2.t1_id), which means that the duplicates are eliminated as a part of the join operator task. The join algorithm is also Merge Join, which is like an efficient zipper-merge as the operator reads data from both the left and the right side in sorted order.
The original statement is considered a correlated subquery, because the subquery refers to a column (t1.int_col) that exists outside of the subquery. However, the output of EXPLAIN shows the execution plan after the subquery decorrelation optimization has been applied. The condition t1_id != t1.int_col is rewritten to t1.id != t1.int_col. TiDB can perform this in └─Selection_21 as it is reading data from the table t1, so this decorrelation and rewriting make the execution a lot more efficient.
In the following example, the query semantically returns all rows from the table t3 unless t3.t1_id is in the subquery:
EXPLAIN SELECT * FROM t3 WHERE t1_id NOT IN (SELECT id FROM t1 WHERE int_col < 100);+-----------------------------+---------+-----------+---------------+-------------------------------------------------------------------------------------+
| id | estRows | task | access object | operator info |
+-----------------------------+---------+-----------+---------------+-------------------------------------------------------------------------------------+
| IndexMergeJoin_20 | 1598.40 | root | | anti semi join, inner:TableReader_15, outer key:test.t3.t1_id, inner key:test.t1.id |
| ├─TableReader_28(Build) | 1998.00 | root | | data:TableFullScan_27 |
| │ └─TableFullScan_27 | 1998.00 | cop[tikv] | table:t3 | keep order:false |
| └─TableReader_15(Probe) | 1.00 | root | | data:Selection_14 |
| └─Selection_14 | 1.00 | cop[tikv] | | lt(test.t1.int_col, 100) |
| └─TableRangeScan_13 | 1.00 | cop[tikv] | table:t1 | range: decided by [test.t3.t1_id], keep order:true |
+-----------------------------+---------+-----------+---------------+-------------------------------------------------------------------------------------+
6 rows in set (0.00 sec)This query starts by reading the table t3 and then probes the table t1 based on the PRIMARY KEY. The join type is an anti semi join; anti because this example is for the non-existence of the value (NOT IN) and semi-join because only the first row needs to match before the join is rejected.