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用于EagleEye3.0 规则集漏报和误报测试的示例项目，项目收集于github和gitee
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test-example-projects/c++_projects/280w-mysql-8.0.18/mysql-test/r/hash_join.result

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#
# Start out by testing some simple in-memory inner hash joins.
#
# Join on two integer columns.
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (col1 INTEGER);
INSERT INTO t1 VALUES (1), (3), (5), (7);
INSERT INTO t2 VALUES (1), (2), (5), (6);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
EXPLAIN
-> Sort: <temporary>.col1
-> Stream results
-> Inner hash join (t2.col1 = t1.col1) (cost=2.50 rows=4)
-> Table scan on t2 (cost=0.09 rows=4)
-> Hash
-> Table scan on t1 (cost=0.65 rows=4)
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
col1 col1
1 1
5 5
DROP TABLE t1, t2;
# Join on a integer column and a string column.
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (col1 VARCHAR(255));
INSERT INTO t1 VALUES (1), (3), (5), (7);
INSERT INTO t2 VALUES (1), (2), (5), (6);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
EXPLAIN
-> Sort: <temporary>.col1
-> Stream results
-> Inner hash join (t1.col1 = t2.col1) (cost=2.50 rows=4)
-> Table scan on t2 (cost=0.09 rows=4)
-> Hash
-> Table scan on t1 (cost=0.65 rows=4)
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
col1 col1
1 1
5 5
DROP TABLE t1, t2;
# Join on two datetime columns.
CREATE TABLE t1 (col1 DATETIME(6));
CREATE TABLE t2 (col1 DATETIME(6));
INSERT INTO t1 VALUES ('2018-01-01 00:00:00.000000'),
('2018-01-01 00:00:00.000001'),
('2018-01-02 00:00:00.000000'),
('2018-01-02 00:00:00.000001');
INSERT INTO t2 VALUES ('2018-01-01 00:00:00.000000'),
('2018-01-01 00:00:00.000002'),
('2018-01-02 00:00:00.000001'),
('2019-01-02 00:00:00.000001');
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t2.col1 = t1.col1) (cost=2.50 rows=4)
-> Table scan on t2 (cost=0.09 rows=4)
-> Hash
-> Table scan on t1 (cost=0.65 rows=4)
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
col1 col1
2018-01-01 00:00:00.000000 2018-01-01 00:00:00.000000
2018-01-02 00:00:00.000001 2018-01-02 00:00:00.000001
DROP TABLE t1, t2;
# Join on a string and datetime column, where datetime comparison is
# picked.
CREATE TABLE t1 (a DATETIME);
INSERT INTO t1 VALUES ('2001-01-01 00:00:00');
CREATE TABLE t2 (b VARCHAR(64));
INSERT INTO t2 VALUES ('2001#01#01');
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1, t2 WHERE a=b;
EXPLAIN
-> Inner hash join (t1.a = t2.b) (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SELECT * FROM t1, t2 WHERE a=b;
a b
2001-01-01 00:00:00 2001#01#01
DROP TABLE t1, t2;
# Join on two double columns.
CREATE TABLE t1 (col1 DOUBLE);
CREATE TABLE t2 (col1 DOUBLE);
INSERT INTO t1 VALUES (1.1), (3.3), (5.5), (7.7);
INSERT INTO t2 VALUES (1.1), (1.11), (5.5), (6.6);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t2.col1 = t1.col1) (cost=2.50 rows=4)
-> Table scan on t2 (cost=0.09 rows=4)
-> Hash
-> Table scan on t1 (cost=0.65 rows=4)
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
col1 col1
1.1 1.1
5.5 5.5
DROP TABLE t1, t2;
# Join on two decimal columns.
CREATE TABLE t1 (col1 DECIMAL(6, 2));
CREATE TABLE t2 (col1 DECIMAL(6, 2));
INSERT INTO t1 VALUES (1.1), (3.3), (5.5), (7.7);
INSERT INTO t2 VALUES (1.1), (1.10), (5.5), (6.6);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t2.col1 = t1.col1) (cost=2.50 rows=4)
-> Table scan on t2 (cost=0.09 rows=4)
-> Hash
-> Table scan on t1 (cost=0.65 rows=4)
SELECT t1.col1, t2.col1 FROM t1 JOIN t2 ON t1.col1 = t2.col1 ORDER BY t1.col1;
col1 col1
1.10 1.10
1.10 1.10
5.50 5.50
DROP TABLE t1, t2;
# See that comparison between decimal and bigint works well. The main
# challenge is that decimals with different amount of leading/trailing
# zeroes should compare equally.
CREATE TABLE t1 (col1 BIGINT);
CREATE TABLE t2 (col1 DECIMAL(64,30));
INSERT INTO t1 VALUES (5);
INSERT INTO t2 VALUES (5.000000000000000000000000000000);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1,t2 WHERE t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t1.col1 = t2.col1) (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SELECT * FROM t1,t2 WHERE t1.col1 = t2.col1;
col1 col1
5 5.000000000000000000000000000000
DROP TABLE t1, t2;
CREATE TABLE t1 (col1 DECIMAL(5));
CREATE TABLE t2 (col1 BIGINT);
INSERT INTO t1 VALUES (1);
INSERT INTO t2 VALUES (1);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1,t2 where t1.col1=t2.col1;
EXPLAIN
-> Inner hash join (t1.col1 = t2.col1) (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SELECT * FROM t1,t2 where t1.col1=t2.col1;
col1 col1
1 1
DROP TABLE t1, t2;
# Bit fields, which is a bit different depending on the storage engine.
create table t1 (id1 int, b1 bit(1)) engine = myisam;
create table t2 (id2 int, b2 bit(1)) engine = myisam;
insert into t1 values (2, 0), (3, 1);
insert into t2 values (2, 1), (3, 0);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1, t2 WHERE id1 = id2;
EXPLAIN
-> Inner hash join (t2.id2 = t1.id1) (cost=1.60 rows=2)
-> Table scan on t2 (cost=0.30 rows=2)
-> Hash
-> Table scan on t1 (cost=0.70 rows=2)
SELECT id1, HEX(b1), id2, HEX(b2) FROM t1, t2 WHERE id1 = id2;
id1 HEX(b1) id2 HEX(b2)
2 0 2 1
3 1 3 0
DROP TABLE t1, t2;
create table t1 (id1 int, b1 bit(64)) engine = innodb;
create table t2 (id2 int, b2 bit(64)) engine = innodb;
insert into t1 values (2, 0), (3, 2);
insert into t2 values (2, 2), (3, 0);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1, t2 WHERE id1 = id2;
EXPLAIN
-> Inner hash join (t2.id2 = t1.id1) (cost=1.10 rows=2)
-> Table scan on t2 (cost=0.18 rows=2)
-> Hash
-> Table scan on t1 (cost=0.45 rows=2)
SELECT id1, HEX(b1), id2, HEX(b2) FROM t1, t2 WHERE id1 = id2;
id1 HEX(b1) id2 HEX(b2)
2 0 2 2
3 2 3 0
DROP TABLE t1, t2;
# See that we handle NULL values properly.
CREATE TABLE t1 (col1 VARCHAR(255));
CREATE TABLE t2 (col1 VARCHAR(255));
INSERT INTO t1 VALUES (NULL);
INSERT INTO t2 VALUES ("");
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1, t2 WHERE t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t2.col1 = t1.col1) (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SELECT * FROM t1, t2 WHERE t1.col1 = t2.col1;
col1 col1
DROP TABLE t1,t2;
#
# Now, do some queries where we end up with a GRACE hash join. That is,
# the right table of the join is bigger than the join_buffer_size.
#
CREATE TABLE t1 (col1 BIGINT);
CREATE TABLE t2 (col1 BIGINT);
INSERT INTO t1 SELECT 1;
INSERT INTO t1 SELECT col1 + 1 FROM t1;
INSERT INTO t1 SELECT col1 + 2 FROM t1;
INSERT INTO t1 SELECT col1 + 4 FROM t1;
INSERT INTO t1 SELECT col1 + 8 FROM t1;
INSERT INTO t1 SELECT col1 + 16 FROM t1;
INSERT INTO t1 SELECT col1 + 32 FROM t1;
INSERT INTO t1 SELECT col1 + 64 FROM t1;
INSERT INTO t1 SELECT col1 + 128 FROM t1;
INSERT INTO t1 SELECT col1 + 256 FROM t1;
INSERT INTO t2 SELECT col1 FROM t1;
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
SET join_buffer_size = 2048;
EXPLAIN FORMAT=tree
SELECT SUM(t1.col1), SUM(t2.col1) FROM t1, t2 WHERE t1.col1 = t2.col1;
EXPLAIN
-> Aggregate: sum(t1.col1), sum(t2.col1)
-> Inner hash join (t2.col1 = t1.col1) (cost=*** rows=26214)
-> Table scan on t2 (cost=*** rows=512)
-> Hash
-> Table scan on t1 (cost=*** rows=512)
TRUNCATE performance_schema.file_summary_by_event_name;
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
0
SELECT SUM(t1.col1), SUM(t2.col1) FROM t1, t2 WHERE t1.col1 = t2.col1;
SUM(t1.col1) SUM(t2.col1)
131328 131328
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
1
SET join_buffer_size = DEFAULT;
DROP TABLE t1,t2;
# See that spill to disk (GRACE hash join) works with all kind of
# data types.
CREATE TABLE t1 (
str_col VARCHAR(255),
blob_col LONGBLOB,
text_col LONGTEXT,
bit_col BIT(64),
tinyint_col TINYINT,
smallint_col SMALLINT,
mediumint_col MEDIUMINT,
int_col INTEGER,
bigint_col BIGINT,
float_col FLOAT,
double_col DOUBLE,
decimal_col DECIMAL(65, 30),
year_col YEAR,
date_col DATE,
time_col TIME(6),
datetime_col DATETIME(6),
timestamp_col TIMESTAMP(6),
json_col JSON,
geometry_col GEOMETRY
);
SET time_zone = '+00:00';
INSERT INTO t1 VALUES (
'',
'',
'',
b'0000000000000000000000000000000000000000000000000000000000000000',
-128,
-32768,
-8388608,
-2147483648,
-9223372036854775808,
-3.402823466E+38,
-1.7976931348623157E+308,
'-99999999999999999999999999999999999.999999999999999999999999999999',
1901,
'1000-01-01',
'-838:59:59.000000',
'1000-01-01 00:00:00.000000',
'1970-01-01 00:00:01.000000',
'{}',
ST_GeomFromText('GEOMETRYCOLLECTION()')
);
INSERT INTO t1 VALUES (
'a very long and interesting string',
'a very long and interesting blob',
'a very long and interesting text',
b'1111111111111111111111111111111111111111111111111111111111111111',
127,
32767,
8388607,
2147483647,
9223372036854775807,
3.402823466E+38,
1.7976931348623157E+308,
'99999999999999999999999999999999999.999999999999999999999999999999',
2155,
'9999-12-31',
'838:59:59.000000',
'9999-12-31 23:59:59.999999',
'2038-01-19 03:14:07.999999',
'{"key": [1, 2, 3]}',
ST_GeomFromText('GEOMETRYCOLLECTION(POINT(1 2), POINT(3 4))')
);
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t1 SELECT * FROM t1;
SET join_buffer_size = 99968;
# Just do a few aggregations for sanity checking. We don't want to
# pollute the result log with thousands of lines with binary data.
ANALYZE TABLE t1;
Table Op Msg_type Msg_text
test.t1 analyze status OK
EXPLAIN FORMAT=tree SELECT
COUNT(*),
SUM(LENGTH(t1.text_col)),
SUM(t2.bigint_col)
FROM
t1,
t1 AS t2
WHERE
t1.int_col = t2.int_col
ORDER BY
t1.int_col;
EXPLAIN
-> Aggregate: count(0), sum(length(t1.text_col)), sum(t2.bigint_col)
-> Inner hash join (t2.int_col = t1.int_col) (cost=*** rows=1638)
-> Table scan on t2 (cost=*** rows=128)
-> Hash
-> Table scan on t1 (cost=*** rows=128)
SELECT
COUNT(*),
SUM(LENGTH(t1.text_col)),
SUM(t2.bigint_col)
FROM
t1,
t1 AS t2
WHERE
t1.int_col = t2.int_col
ORDER BY
t1.int_col;
COUNT(*) SUM(LENGTH(t1.text_col)) SUM(t2.bigint_col)
8192 131072 -4096
DROP TABLE t1;
SET join_buffer_size = DEFAULT;
SET time_zone = DEFAULT;
#
# A query where we end up with a weedout + hash join. This forces hash
# join to keep the row ID for each row, so that the duplicate removal
# works.
#
SET optimizer_switch="materialization=off,firstmatch=off";
CREATE TABLE t1 (i BIGINT);
CREATE TABLE t2 (i BIGINT);
INSERT INTO t1 VALUES (1), (2), (3);
INSERT INTO t2 VALUES (2), (3);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t2 WHERE (t2.i) IN (SELECT t1.i FROM t1);
EXPLAIN
-> Remove duplicate t2 rows using temporary table (weedout) (cost=1.30 rows=2)
-> Inner hash join (t1.i = t2.i) (cost=1.30 rows=2)
-> Table scan on t1 (cost=0.18 rows=3)
-> Hash
-> Table scan on t2 (cost=0.45 rows=2)
SELECT * FROM t2 WHERE (t2.i) IN (SELECT t1.i FROM t1);
i
2
3
# Increase the data volume, and reduce the join_buffer_size, in order to
# test that we can keep the row ID in case of GRACE hash join as well.
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
INSERT INTO t1 SELECT * FROM t1;
INSERT INTO t2 SELECT * FROM t2;
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
SET join_buffer_size = 2048;
EXPLAIN FORMAT=tree
SELECT COUNT(*) FROM t2 WHERE (t2.i) IN (SELECT t1.i FROM t1);
EXPLAIN
-> Aggregate: count(0)
-> Remove duplicate t2 rows using temporary table (weedout) (cost=*** rows=39322)
-> Inner hash join (t1.i = t2.i) (cost=*** rows=39322)
-> Table scan on t1 (cost=*** rows=768)
-> Hash
-> Table scan on t2 (cost=*** rows=512)
TRUNCATE performance_schema.file_summary_by_event_name;
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
0
SELECT COUNT(*) FROM t2 WHERE (t2.i) IN (SELECT t1.i FROM t1);
COUNT(*)
512
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
1
DROP TABLE t1, t2;
SET join_buffer_size = DEFAULT;
SET optimizer_switch = DEFAULT;
# Test a case where the RAND() function is pushed as late as possible in
# the join. The optimizer ends up rewriting t1.col1 = FLOOR(...) to
# t2.col1 = FLOOR(...), so this test case ensures that the executor is
# able to put the condition after the join. FLOOR and division/addition
# make this query deterministic.
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (col1 INTEGER);
INSERT INTO t1 VALUES (1), (2);
INSERT INTO t2 VALUES (1), (2);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT
t1.col1,
t2.col1
FROM
t1,
t2
WHERE
t1.col1 = t2.col1
AND t1.col1 = FLOOR(RAND() / 2 + 2);
EXPLAIN
-> Filter: (t1.col1 = floor(((rand() / 2) + 2))) (cost=1.00 rows=2)
-> Inner hash join (t2.col1 = t1.col1) (cost=1.00 rows=2)
-> Table scan on t2 (cost=0.35 rows=2)
-> Hash
-> Table scan on t1 (cost=0.35 rows=2)
SELECT
t1.col1,
t2.col1
FROM
t1,
t2
WHERE
t1.col1 = t2.col1
AND t1.col1 = FLOOR(RAND() / 2 + 2);
col1 col1
2 2
DROP TABLE t1, t2;
# Ensure that the hash join picks the correct fields and tables when both
# sides of the join condition are from the same source table.
CREATE TABLE c (
col1 varchar(1)
) ENGINE = myisam;
INSERT INTO c VALUES ('w');
INSERT INTO c VALUES ('d');
ANALYZE TABLE c;
Table Op Msg_type Msg_text
test.c analyze status OK
EXPLAIN format=tree SELECT *
FROM
(SELECT * FROM c) AS table1
JOIN (SELECT * FROM c) AS table2
ON table2.col1 = table1.col1;
EXPLAIN
-> Inner hash join (c.col1 = c.col1) (cost=1.60 rows=2)
-> Table scan on c (cost=0.30 rows=2)
-> Hash
-> Table scan on c (cost=0.70 rows=2)
SELECT *
FROM
(SELECT * FROM c) AS table1
JOIN (SELECT * FROM c) AS table2
ON table2.col1 = table1.col1;
col1 col1
w w
d d
DROP TABLE c;
# This query ends up with a BNL between t3 and t2. Ensure that we don't
# end up with a hash join like:
#
# -> Constant row from <subquery2>
# -> Materialize with deduplication
# -> HashJoin inner join (t3.i = '2')
# -> Table scan on t2
# -> Table scan on t3
#
# We don't want a join condition on a constant, so it should be pushed as
# a filter.
SET optimizer_switch='firstmatch=off';
CREATE TABLE t1 (i INTEGER) ENGINE = MyISAM;
CREATE TABLE t2 (i INTEGER) ENGINE = MyISAM;
CREATE TABLE t3 (i INTEGER) ENGINE = MyISAM;
INSERT INTO t1 VALUES (2);
INSERT INTO t2 VALUES (2);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT * FROM t1 WHERE (t1.i) IN (SELECT t3.i FROM t2 STRAIGHT_JOIN t3);
EXPLAIN
-> Constant row from <subquery2>
-> Materialize with deduplication
-> Filter: (t3.i is not null) (cost=1.10 rows=0)
-> Inner hash join (cost=1.10 rows=0)
-> Filter: (t3.i = '2') (cost=0.50 rows=0)
-> Table scan on t3 (cost=0.50 rows=0)
-> Hash
-> Table scan on t2 (cost=0.60 rows=1)
SELECT * FROM t1 WHERE (t1.i) IN (SELECT t3.i FROM t2 STRAIGHT_JOIN t3);
i
DROP TABLE t1,t2,t3;
SET optimizer_switch=DEFAULT;
# A bit more complicated join condition where we have multiple join
# conditions, and one of them is an expression.
CREATE TABLE t1 (a INTEGER, b INTEGER);
INSERT INTO t1 (a) VALUES (1),(2);
CREATE TABLE t3 (a INTEGER, b INTEGER);
INSERT INTO t3 VALUES (1, 10), (1, 11), (2, 10), (2, 11);
ANALYZE TABLE t1, t3;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t3 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1, t3 WHERE t3.b = t1.a + 9 AND t3.a = t1.a;
EXPLAIN
-> Inner hash join (t3.a = t1.a), (t3.b = (t1.a + 9)) (cost=1.50 rows=2)
-> Table scan on t3 (cost=0.18 rows=4)
-> Hash
-> Table scan on t1 (cost=0.45 rows=2)
SELECT * FROM t1, t3 WHERE t3.b = t1.a + 9 AND t3.a = t1.a;
a b a b
1 NULL 1 10
2 NULL 2 11
DROP TABLE t1,t3;
# Ensure that outer joins doesn't degrade into a nested loop,
# but still uses join buffering.
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (col1 INTEGER);
INSERT INTO t1 VALUES (1), (2);
INSERT INTO t2 VALUES (2);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT * FROM t1 LEFT JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
<not executable by iterator executor>
EXPLAIN SELECT * FROM t1 LEFT JOIN t2 ON t1.col1 = t2.col1;
id select_type table partitions type possible_keys key key_len ref rows filtered Extra
1 SIMPLE t1 NULL ALL NULL NULL NULL NULL 2 100.00 NULL
1 SIMPLE t2 NULL ALL NULL NULL NULL NULL 1 100.00 Using where; Using join buffer (Block Nested Loop)
Warnings:
Note 1003 /* select#1 */ select `test`.`t1`.`col1` AS `col1`,`test`.`t2`.`col1` AS `col1` from `test`.`t1` left join `test`.`t2` on((`test`.`t2`.`col1` = `test`.`t1`.`col1`)) where true
DROP TABLE t1, t2;
# See that we can replace a BNL with hash join, even if we have extra
# join conditions that are not equi-join conditions. The result should be
# that the non-equi-join conditions should be attached as a filter after
# the join.
CREATE TABLE t1 (col1 INTEGER, col2 INTEGER);
CREATE TABLE t2 (col1 INTEGER, col2 INTEGER);
INSERT INTO t1 VALUES (1, 1), (2, 2), (3, 3);
INSERT INTO t2 VALUES (1, 1), (2, 4), (3, 6);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree
SELECT * FROM t1 JOIN t2 ON t1.col1 = t2.col1 AND t1.col2 < t2.col2;
EXPLAIN
-> Filter: (t1.col2 < t2.col2) (cost=1.70 rows=3)
-> Inner hash join (t2.col1 = t1.col1) (cost=1.70 rows=3)
-> Table scan on t2 (cost=0.12 rows=3)
-> Hash
-> Table scan on t1 (cost=0.55 rows=3)
SELECT * FROM t1 JOIN t2 ON t1.col1 = t2.col1 AND t1.col2 < t2.col2;
col1 col2 col1 col2
2 2 2 4
3 3 3 6
DROP TABLE t1, t2;
CREATE TABLE t1 (col1 BIGINT);
INSERT INTO t1 VALUES (1), (1), (1), (1), (1), (1), (1), (1), (1), (1);
ANALYZE TABLE t1;
Table Op Msg_type Msg_text
test.t1 analyze status OK
EXPLAIN FORMAT=tree SELECT SUM(t1.col1)
FROM t1, t1 t2, t1 t3, t1 t4, t1 t5, t1 t6;
EXPLAIN
-> Aggregate: sum(t1.col1)
-> Inner hash join (cost=111113.45 rows=1000000)
-> Table scan on t6 (cost=0.00 rows=10)
-> Hash
-> Inner hash join (cost=11112.35 rows=100000)
-> Table scan on t5 (cost=0.00 rows=10)
-> Hash
-> Inner hash join (cost=1112.01 rows=10000)
-> Table scan on t4 (cost=0.00 rows=10)
-> Hash
-> Inner hash join (cost=111.75 rows=1000)
-> Table scan on t3 (cost=0.01 rows=10)
-> Hash
-> Inner hash join (cost=11.50 rows=100)
-> Table scan on t2 (cost=0.13 rows=10)
-> Hash
-> Table scan on t1 (cost=1.25 rows=10)
SELECT SUM(t1.col1) FROM t1, t1 t2, t1 t3, t1 t4, t1 t5, t1 t6;
SUM(t1.col1)
1000000
DROP TABLE t1;
# Test that comparison between FLOAT and DOUBLE works as expected if
# given an explicit number of decimals.
CREATE TABLE t1 (col1 FLOAT(5,2), col2 DOUBLE(15,2));
Warnings:
Warning 1681 Specifying number of digits for floating point data types is deprecated and will be removed in a future release.
Warning 1681 Specifying number of digits for floating point data types is deprecated and will be removed in a future release.
INSERT INTO t1 VALUES (1.01, 1.01);
SELECT * FROM t1 a, t1 b WHERE a.col1 = b.col2;
col1 col2 col1 col2
1.01 1.01 1.01 1.01
DROP TABLE t1;
# The point of the following test is to see that if the innermost hash
# join returns zero rows, the outermost hash join should not scan the
# probe table.
CREATE TABLE t1 (col1 INT);
CREATE TABLE t2 (col1 INT);
CREATE TABLE t3 (col1 INT);
INSERT INTO t1 VALUES (1), (2), (3);
INSERT INTO t2 VALUES (1), (2), (3);
INSERT INTO t3 VALUES (1), (2), (3);
ANALYZE TABLE t1, t2, t3;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
test.t3 analyze status OK
EXPLAIN FORMAT=tree SELECT STRAIGHT_JOIN * FROM t1
JOIN t2 ON t1.col1 + 10 = t2.col1
JOIN t3 ON t2.col1 = t3.col1;
EXPLAIN
-> Inner hash join (t3.col1 = t2.col1) (cost=2.85 rows=3)
-> Table scan on t3 (cost=0.12 rows=3)
-> Hash
-> Inner hash join ((t1.col1 + 10) = t2.col1) (cost=1.70 rows=3)
-> Table scan on t2 (cost=0.12 rows=3)
-> Hash
-> Table scan on t1 (cost=0.55 rows=3)
SELECT SUM(variable_value) AS Total_handler_reads
FROM performance_schema.session_status
WHERE variable_name LIKE 'Handler_read%';
Total_handler_reads
12
DROP TABLE t1, t2, t3;
#
# Bug#29898802 WL#2241: SIG6 IN HASH_JOIN_BUFFER::LOADINTOTABLEBUFFERS()
# AT HASH_JOIN_BUFFER.CC
#
CREATE TABLE t1 (
pk int NOT NULL AUTO_INCREMENT,
col_varchar varchar(1),
col_varchar_key varchar(1),
PRIMARY KEY (pk),
KEY idx_CC_col_varchar_key (col_varchar_key)
);
INSERT INTO t1 VALUES (1,'n','X'),(2,'Y','8'),(3,'R','l');
ANALYZE TABLE t1;
Table Op Msg_type Msg_text
test.t1 analyze status OK
EXPLAIN FORMAT=tree SELECT
t1.col_varchar_key AS field1
FROM
(t1, t1 as alias1)
WHERE
NOT
EXISTS(
SELECT
alias2.col_varchar_key
FROM
t1 AS alias2
WHERE
alias2.col_varchar_key >= t1.col_varchar
)
GROUP BY
field1;
EXPLAIN
-> Table scan on <temporary>
-> Temporary table with deduplication
-> Inner hash join (cost=4.65 rows=27)
-> Index scan on alias1 using idx_CC_col_varchar_key (cost=0.18 rows=3)
-> Hash
-> Nested loop anti-join (cost=1.70 rows=9)
-> Table scan on t1 (cost=0.55 rows=3)
-> Filter: (alias2.col_varchar_key >= t1.col_varchar) (cost=0.55 rows=3)
-> Index range scan on alias2 (re-planned for each iteration) (cost=0.55 rows=3)
Warnings:
Note 1276 Field or reference 'test.t1.col_varchar' of SELECT #2 was resolved in SELECT #1
SELECT
t1.col_varchar_key AS field1
FROM
(t1, t1 as alias1)
WHERE
NOT
EXISTS(
SELECT
alias2.col_varchar_key
FROM
t1 AS alias2
WHERE
alias2.col_varchar_key >= t1.col_varchar
)
GROUP BY
field1;
field1
8
DROP TABLE t1;
# See that typed arrays are handled as blobs. That is, we do not try to
# allocate 4GB of memory during the hash join.
CREATE TABLE t1 (
col_int_key INTEGER,
col_json JSON,
KEY mv_idx ((CAST(col_json->'$[*]' AS CHAR(40) ARRAY)))
);
INSERT INTO t1 VALUES (NULL, '[1]'), (4, '[1]'), (1, '[2]');
CREATE TABLE t2(col_int INTEGER);
INSERT INTO t2 VALUES (1), (2), (3), (11), (12);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT t1.col_int_key AS field1, t2.col_int AS field2 FROM t2
JOIN t1 ON 1 WHERE (CAST("1" AS JSON) MEMBER OF( t1.col_json->'$[*]'));
EXPLAIN
-> Inner hash join (cost=*** rows=5)
-> Table scan on t2 (cost=*** rows=5)
-> Hash
-> Filter: json'"1"' member of (cast(json_extract(t1.col_json,_utf8mb4'$[*]') as char(40) array)) (cost=*** rows=1)
-> Index lookup on t1 using mv_idx (cast(json_extract(t1.col_json,_utf8mb4'$[*]') as char(40) array)=json'"1"') (cost=*** rows=1)
SELECT t1.col_int_key AS field1, t2.col_int AS field2 FROM t2
JOIN t1 ON 1 WHERE (CAST("1" AS JSON) MEMBER OF( t1.col_json->'$[*]'));
DROP TABLE t1,t2;
#
# Bug#29906372 WL#2241: SIG6 IN HASH_JOIN_BUFFER::STOREFROMTABLEBUFFERS
# AT HASH_JOIN_BUFFER.CC
#
CREATE TABLE a (
pk INTEGER NOT NULL AUTO_INCREMENT,
col_varchar VARCHAR(1),
col_varchar_key VARCHAR(1),
PRIMARY KEY (pk),
KEY varchar_key (col_varchar_key)
);
CREATE TABLE b (
pk INTEGER NOT NULL AUTO_INCREMENT,
col_varchar VARCHAR(1),
col_varchar_key VARCHAR(1),
PRIMARY KEY (pk),
KEY varchar_key (col_varchar_key)
);
INSERT INTO a VALUES (1, 'N', '0');
INSERT INTO b VALUES (1, '8', 'r'), (2, 'v', 'C'), (3, 'b', 'p'), (4, '7', 'W');
ANALYZE TABLE a, b;
Table Op Msg_type Msg_text
test.a analyze status OK
test.b analyze status OK
EXPLAIN FORMAT=tree SELECT 1 FROM (b AS table1
INNER JOIN a AS table2 ON table2.pk = table1.pk OR table1.col_varchar < 'D')
WHERE (NOT EXISTS
(SELECT 1 FROM (b AS alias3 STRAIGHT_JOIN a AS alias4
ON alias4.col_varchar = alias3.col_varchar_key)
WHERE alias3.pk >= table1.pk));
EXPLAIN
-> Nested loop anti-join (cost=2.30 rows=8)
-> Filter: ((table1.pk = table2.pk) or (table1.col_varchar < 'D')) (cost=1.00 rows=2)
-> Inner hash join (cost=1.00 rows=2)
-> Table scan on table1 (cost=0.45 rows=4)
-> Hash
-> Index scan on table2 using varchar_key (cost=0.35 rows=1)
-> Nested loop inner join (cost=2.85 rows=4)
-> Filter: (alias3.pk >= table1.pk) (cost=0.45 rows=4)
-> Index range scan on alias3 (re-planned for each iteration) (cost=0.45 rows=4)
-> Filter: (alias4.col_varchar = alias3.col_varchar_key) (cost=1.05 rows=1)
-> Table scan on alias4 (cost=1.05 rows=1)
Warnings:
Note 1276 Field or reference 'test.table1.pk' of SELECT #2 was resolved in SELECT #1
SELECT 1 FROM (b AS table1
INNER JOIN a AS table2 ON table2.pk = table1.pk OR table1.col_varchar < 'D')
WHERE (NOT EXISTS
(SELECT 1 FROM (b AS alias3 STRAIGHT_JOIN a AS alias4
ON alias4.col_varchar = alias3.col_varchar_key)
WHERE alias3.pk >= table1.pk));
DROP TABLE a, b;
#
# Bug#29947439 WL#2241: FLOATING POINT EXCEPTION: INITIALIZECHUNKFILES AT
# HASH_JOIN_ITERATOR.CC
#
CREATE TABLE t1 (col1 TEXT);
INSERT INTO t1 VALUES (REPEAT('A', 50000)), (REPEAT('A', 50000));
EXPLAIN FORMAT=tree SELECT a.col1 FROM t1 AS a, t1 AS b;
EXPLAIN
-> Inner hash join (cost=*** rows=4)
-> Table scan on b (cost=*** rows=2)
-> Hash
-> Table scan on a (cost=*** rows=2)
SET join_buffer_size = 128;
SELECT a.col1 FROM t1 AS a, t1 AS b;
DROP TABLE t1;
# Set up a case where we have very skewed data in the probe input, and we
# degrade into an on-disk hash join. We want to trigger a code path where
# we have empty chunk files from the probe input.
CREATE TABLE t1 (col1 VARCHAR(255));
CREATE TABLE t2 (col1 VARCHAR(255));
INSERT INTO t1 VALUES (SHA2(UUID(), 512));
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t1 SELECT SHA2(UUID(), 512) FROM t1;
INSERT INTO t2 SELECT REPEAT("a", 255) FROM t1;
SET GLOBAL innodb_stats_persistent_sample_pages = 2000;
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
SET GLOBAL innodb_stats_persistent_sample_pages = DEFAULT;
EXPLAIN FORMAT=tree SELECT STRAIGHT_JOIN COUNT(*) FROM t1 JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
-> Aggregate: count(0)
-> Inner hash join (t2.col1 = t1.col1) (cost=*** rows=26214)
-> Table scan on t2 (cost=*** rows=512)
-> Hash
-> Table scan on t1 (cost=*** rows=512)
SET join_buffer_size = 1024;
SELECT STRAIGHT_JOIN COUNT(*) FROM t1 JOIN t2 ON t1.col1 = t2.col1;
DROP TABLE t1, t2;
SET join_buffer_size = DEFAULT;
# See that the hints for hash join works as expected.
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (col1 INTEGER);
# By default, hash join should be used.
EXPLAIN FORMAT=tree SELECT t1.col1 FROM t1, t2;
EXPLAIN
-> Inner hash join (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
# Try disabling hash join using the hint.
EXPLAIN FORMAT=tree SELECT /*+ NO_HASH_JOIN(t1, t2) */ t1.col1 FROM t1, t2;
EXPLAIN
<not executable by iterator executor>
# Turn off hash join using the optimizer switch, and then enable it again
# using the hint.
SET optimizer_switch="hash_join=off";
EXPLAIN FORMAT=tree SELECT t1.col1 FROM t1, t2;
EXPLAIN
<not executable by iterator executor>
EXPLAIN FORMAT=tree SELECT /*+ HASH_JOIN(t1, t2) */ t1.col1 FROM t1, t2;
EXPLAIN
-> Inner hash join (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SET optimizer_switch=DEFAULT;
DROP TABLE t1, t2;
#
# Bug#29964536 WL#2241: ASSERTION FAILURE IN
# TEMPTABLE::HANDLER::POSITION() AT SRC/HANDLER.CC
#
CREATE TABLE tc (
col_int INTEGER,
col_varchar VARCHAR(1)
);
INSERT INTO tc VALUES (0,'x');
CREATE TABLE tcc (
col_varchar VARCHAR(1)
);
INSERT INTO tcc VALUES ('r'), ('f'), ('y'), ('u'), ('m'), (NULL);
CREATE TABLE t1 (field1 INTEGER);
INSERT INTO t1 VALUES (0);
SET optimizer_switch="firstmatch=off";
UPDATE t1 SET field1 = 9999 WHERE field1 NOT IN ( SELECT alias1.col_int AS
field1 FROM ( tcc, ( SELECT * FROM tc WHERE col_int < 1 ) AS alias1 ) WHERE (
alias1.col_varchar IN ( SELECT col_varchar FROM tcc ) ) GROUP BY field1
HAVING field1 <> 1 );
SET optimizer_switch="firstmatch=on";
DROP TABLE tc,tcc,t1;
# Do a join between DECIMAL and INTEGER to verify that we get a match
# between these two types.
CREATE TABLE t1 (col1 DECIMAL(4, 2));
INSERT INTO t1 VALUES (0);
CREATE TABLE t2 (col1 INTEGER);
INSERT INTO t2 VALUES (0);
EXPLAIN FORMAT=tree SELECT * FROM t1 JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t1.col1 = t2.col1) (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SELECT * FROM t1 JOIN t2 ON t1.col1 = t2.col1;
col1 col1
0.00 0
DROP TABLE t1, t2;
# See that we get the correct results with a PAD SPACE collation and
# PAD_CHAR_TO_FULL_LENGTH. Note that the latter is deprecated, so this
# test should go away once the SQL mode is removed.
CREATE TABLE t1 (
col1 CHAR(4)
) DEFAULT CHARSET=latin1 COLLATE=latin1_general_cs;
INSERT INTO t1 VALUES ("foo");
CREATE TABLE t2 (
col1 CHAR(40)
) DEFAULT CHARSET=latin1 COLLATE=latin1_general_cs;
INSERT INTO t2 VALUES ("foo");
SET sql_mode="PAD_CHAR_TO_FULL_LENGTH";
Warnings:
Warning 3090 Changing sql mode 'PAD_CHAR_TO_FULL_LENGTH' is deprecated. It will be removed in a future release.
EXPLAIN FORMAT=tree SELECT * FROM t1 JOIN t2 ON t1.col1 = t2.col1;
EXPLAIN
-> Inner hash join (t1.col1 = t2.col1) (cost=0.70 rows=1)
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Table scan on t1 (cost=0.35 rows=1)
SELECT * FROM t1 JOIN t2 ON t1.col1 = t2.col1;
col1 col1
foo foo
SET sql_mode=DEFAULT;
DROP TABLE t1, t2;
# Set up a case where the join planner will set up a BNL with linked
# join buffers, and where the row ID should be kept due to duplicate
# removal. rowid_status will be set on several QEP_TABs to indicate that
# a row ID is needed, even though we should not request the row ID on all
# of them.
CREATE TABLE b1 (col_int INTEGER);
INSERT INTO b1 VALUES (1);
CREATE TABLE c1 (
col_int INTEGER,
col_timestamp TIMESTAMP NULL,
col_decimal DECIMAL(10, 4)
);
INSERT INTO c1 VALUES
(1741569678,'2004-01-07 20:47:51',-4.7563),
(-1533615975,'2037-10-27 16:40:24',7.7785);
CREATE TABLE cc1 (
col_int INTEGER,
col_decimal DECIMAL(10, 4),
col_timestamp TIMESTAMP NULL
);
INSERT INTO cc1 VALUES
(-190646953,6.4052,'2007-11-21 09:45:29'),
(-423321712,6.9636,'1988-01-04 13:34:47');
SELECT
1
FROM
b1
LEFT JOIN (
c1
RIGHT JOIN (SELECT DISTINCT * FROM cc1) AS alias3 ON
alias3.col_timestamp = c1.col_timestamp
) ON b1.col_int = c1.col_int AND 1
WHERE
EXISTS(
SELECT
1
FROM
cc1 JOIN c1 ON c1.col_decimal = cc1.col_decimal AND 1
WHERE
cc1.col_int <= b1.col_int OR cc1.col_int = c1.col_int
);
1
DROP TABLE b1, c1, cc1;
# Yet another problematic case involing duplicate weedout.
CREATE TABLE t1 (
col_int_key int(11) DEFAULT NULL,
col_varchar_key varchar(1) DEFAULT NULL,
col_varchar_nokey varchar(1) DEFAULT NULL,
KEY col_int_key (col_int_key),
KEY col_varchar_key (col_varchar_key,col_int_key)
) charset utf8mb4;
Warnings:
Warning 1681 Integer display width is deprecated and will be removed in a future release.
INSERT INTO t1 VALUES (4,'v','v');
INSERT INTO t1 VALUES (62,'v','v');
INSERT INTO t1 VALUES (7,'c','c');
INSERT INTO t1 VALUES (1,NULL,NULL);
set optimizer_switch='firstmatch=off';
set optimizer_switch='materialization=off';
SELECT
alias1.col_varchar_nokey AS a1_nokey,
alias1.col_varchar_key AS a1_key,
alias2.col_varchar_nokey AS a2_nokey
FROM
t1 AS alias1, t1 AS alias2
WHERE
(alias1.col_varchar_nokey,alias2.col_varchar_nokey)
IN
(
SELECT
sq2_alias2.col_varchar_nokey, sq2_alias1.col_varchar_key
FROM
t1 AS sq2_alias1, t1 AS sq2_alias2
)
;
a1_nokey a1_key a2_nokey
c c c
c c v
c c v
v v c
v v c
v v v
v v v
v v v
v v v
set optimizer_switch=DEFAULT;
DROP TABLE t1;
# A case where we have a hash join iterator both above and below a
# WeedoutIterator.
CREATE TABLE t1(f1 INT(11) NOT NULL);
Warnings:
Warning 1681 Integer display width is deprecated and will be removed in a future release.
INSERT INTO t1 VALUES (10);
CREATE TABLE t2
(
f1 INT(11) NOT NULL AUTO_INCREMENT,
f2 INT(11) DEFAULT NULL,
PRIMARY KEY (f1),
KEY (f2)
);
Warnings:
Warning 1681 Integer display width is deprecated and will be removed in a future release.
Warning 1681 Integer display width is deprecated and will be removed in a future release.
INSERT INTO t2 VALUES (1, 7), (2, 1), (4, 7);
CREATE TABLE t4(f1 INT DEFAULT NULL);
INSERT INTO t4 VALUES (2);
ANALYZE TABLE t1, t2, t4;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
test.t4 analyze status OK
EXPLAIN FORMAT=tree SELECT /*+ JOIN_PREFIX(t2@qb2, t4@qb1, ta3, ta4) */
COUNT(*) FROM t1 JOIN t2 AS ta3 JOIN t2 AS ta4
WHERE ta4.f1 IN (SELECT /*+ QB_NAME(qb1) */ f1 FROM t4) AND
ta3.f2 IN (SELECT /*+ QB_NAME(qb2) */ f2 FROM t2);
EXPLAIN
-> Aggregate: count(0)
-> Inner hash join (cost=3.70 rows=4)
-> Table scan on t1 (cost=0.08 rows=1)
-> Hash
-> Remove duplicate (ta3, ta4) rows using temporary table (weedout) (cost=3.00 rows=4)
-> Nested loop inner join (cost=3.00 rows=4)
-> Nested loop inner join (cost=2.30 rows=4)
-> Inner hash join (cost=1.10 rows=3)
-> Filter: (t4.f1 is not null) (cost=0.12 rows=1)
-> Table scan on t4 (cost=0.12 rows=1)
-> Hash
-> Filter: (t2.f2 is not null) (cost=0.55 rows=3)
-> Index scan on t2 using f2 (cost=0.55 rows=3)
-> Index lookup on ta3 using f2 (f2=t2.f2) (cost=0.30 rows=2)
-> Single-row index lookup on ta4 using PRIMARY (f1=t4.f1) (cost=0.08 rows=1)
SELECT /*+ JOIN_PREFIX(t2@qb2, t4@qb1, ta3, ta4) */
COUNT(*) FROM t1 JOIN t2 AS ta3 JOIN t2 AS ta4
WHERE ta4.f1 IN (SELECT /*+ QB_NAME(qb1) */ f1 FROM t4) AND
ta3.f2 IN (SELECT /*+ QB_NAME(qb2) */ f2 FROM t2);
COUNT(*)
3
SELECT /*+ JOIN_PREFIX(t2@qb2, t4@qb1, ta3, ta4) */
COUNT(*) FROM t1 JOIN t2 AS ta3 JOIN t2 AS ta4
WHERE ta4.f1 IN (SELECT /*+ QB_NAME(qb1) */ f1 FROM t4) AND
ta3.f2 IN (SELECT /*+ QB_NAME(qb2) */ f2 FROM t2);
COUNT(*)
3
DROP TABLE t1, t2, t4;
#
# Bug#30035890 SIG 11 IN HASHJOINITERATOR::READJOINEDROW AT
# SQL/HASH_JOIN_ITERATOR.CC
#
# Note that this test case needs ASAN to reproduce.
CREATE TABLE t1 (a INT);
INSERT INTO t1 VALUES (7), (7);
CREATE TABLE t2 (b INT, c DATETIME);
INSERT IGNORE INTO t2 VALUES (7, NULL), (7, '2006'), (7, '2002');
Warnings:
Warning 1265 Data truncated for column 'c' at row 2
Warning 1265 Data truncated for column 'c' at row 3
# Set up a case where the hash join row buffer will be re-inited.
UPDATE t1
SET a = 42
WHERE a NOT IN (
SELECT alias2.b FROM t2 AS alias2 JOIN t2 AS alias1 ON (alias2.c = alias1.c)
);
DROP TABLE t1, t2;
#
# Bug#30060691 ASSERTION `M_INDEX_CURSOR.IS_POSITIONED()' IN
# TEMPTABLE::HANDLER::POSITION()
#
CREATE TABLE c (
col_int INTEGER,
col_varchar VARCHAR(1) ,
col_varchar_key VARCHAR(1));
CREATE TABLE bb (
pk INTEGER auto_increment,
col_int_key INTEGER,
col_varchar VARCHAR(1),
col_varchar_key VARCHAR(1),
PRIMARY KEY (pk));
CREATE TABLE cc (
col_varchar_key VARCHAR(1),
INDEX idx (col_varchar_key));
INSERT INTO bb VALUES (DEFAULT, 41509313, 'S', 'W');
INSERT INTO c VALUES
(-792274908, 'P', 'r'),
(281391051, 'w', 'x'),
(-1381986093, 'l', '2'),
(-78303180, 'f', 'Q'),
(1027797776, 'w', 'G'),
(-1361294690, 'm', 'L'),
(65604698, '7', 'Y'),
(-964881813, 'j', 'F'),
(1831120981, 'q', 'q'),
(-573388832, 'F', 'M'),
(571640392, '1', 'R'),
(857813414, 'y', 'l'),
(555892383, 'x', 'P'),
(601556555, 'z', 'k'),
(-578249624, 'N', 'e'),
(-843749952, '4', 'J'),
(2058477272, '4', 'R'),
(-1732353317, 'C', 'Z'),
(-1639317818, '9', 'f'),
(19700948, 'K', 'V');
INSERT INTO cc VALUES
('b'), ('E'), ('v'), ('4'), ('L'), ('g'), ('i'), ('D'), ('S'), ('s'), ('4'),
('5'), ('4'), ('y'), ('v'), ('Z'), ('O'), ('2'), ('v'), ('5');
ANALYZE TABLE c, bb, cc;
Table Op Msg_type Msg_text
test.c analyze status OK
test.bb analyze status OK
test.cc analyze status OK
EXPLAIN FORMAT=tree SELECT *
FROM
cc AS alias1
LEFT JOIN (
(
bb AS alias2
INNER JOIN (SELECT DISTINCT sq1_alias1.* FROM bb AS sq1_alias1)
AS alias3 ON alias3.col_int_key = alias2.col_int_key
)
) ON alias3.col_varchar_key = alias2.col_varchar_key
WHERE
alias1.col_varchar_key
IN (
SELECT
sq2_alias1.col_varchar AS sq2_field1
FROM
c AS sq2_alias1
WHERE
sq2_alias1.col_varchar_key != alias2.col_varchar
AND sq2_alias1.col_int > alias2.pk
);
EXPLAIN
-> Remove duplicate (alias2, alias3, alias1) rows using temporary table (weedout)
-> Nested loop inner join
-> Filter: ((sq2_alias1.col_int > alias2.pk) and (sq2_alias1.col_varchar_key <> alias2.col_varchar))
-> Inner hash join
-> Filter: (sq2_alias1.col_varchar is not null) (cost=0.43 rows=6)
-> Table scan on sq2_alias1 (cost=0.43 rows=20)
-> Hash
-> Nested loop inner join
-> Table scan on alias2 (cost=0.35 rows=1)
-> Index lookup on alias3 using <auto_key0> (col_int_key=alias2.col_int_key, col_varchar_key=alias2.col_varchar_key)
-> Materialize
-> Table scan on sq1_alias1 (cost=0.35 rows=1)
-> Index lookup on alias1 using idx (col_varchar_key=sq2_alias1.col_varchar) (cost=0.26 rows=1)
Warnings:
Note 1276 Field or reference 'test.alias2.col_varchar' of SELECT #3 was resolved in SELECT #1
Note 1276 Field or reference 'test.alias2.pk' of SELECT #3 was resolved in SELECT #1
# We only want to see that the query does not hit an assertion, so ignore
# the results.
SELECT *
FROM
cc AS alias1
LEFT JOIN (
(
bb AS alias2
INNER JOIN (SELECT DISTINCT sq1_alias1.* FROM bb AS sq1_alias1)
AS alias3 ON alias3.col_int_key = alias2.col_int_key
)
) ON alias3.col_varchar_key = alias2.col_varchar_key
WHERE
alias1.col_varchar_key
IN (
SELECT
sq2_alias1.col_varchar AS sq2_field1
FROM
c AS sq2_alias1
WHERE
sq2_alias1.col_varchar_key != alias2.col_varchar
AND sq2_alias1.col_int > alias2.pk
);
DROP TABLE bb, c, cc;
#
# Bug#30049217 ASSERTION FAILURE AT
# TEMPTABLE::HANDLER::POSITION|SRC/HANDLER.CC
#
CREATE TABLE t1 (c1 INT);
INSERT INTO t1 VALUES
(6),(7),(8),(9),(10),(11),(12),(13),(14),(15),(16),(17),(18),(19),(20);
CREATE TABLE t2 (c2 INT, c3 INT, KEY (c3));
INSERT INTO t2 VALUES
(1,-823867270),
(19,1130654803),
(20,1299270309);
CREATE TABLE t3 (c4 INT);
INSERT INTO t3 VALUES (1);
ANALYZE TABLE t1, t2, t3;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
test.t3 analyze status OK
SELECT *
FROM ((SELECT DISTINCT * FROM t2) AS alias2 JOIN t3 ON (t3.c4 = alias2.c2))
WHERE (EXISTS (SELECT * FROM (t1 LEFT JOIN (t3 JOIN t2 ON (t2.c3 = t3.c4)) ON
(1))))
AND alias2.c3 < 19;
c2 c3 c4
1 -823867270 1
DROP TABLE t1, t2, t3;
#
# Bug#30153695 ASSERTION SIG6 TEMPTABLE::HANDLER::POSITION
# SRC/HANDLER.CC:715
#
CREATE TABLE c (
col_date date,
col_datetime_key datetime,
col_varchar_key varchar (1),
col_varchar varchar (1),
col_date_key date,
col_int_key int,
col_time time,
col_time_key time,
col_int int,
pk integer auto_increment,
col_datetime datetime,
key (col_datetime_key ),
key (col_varchar_key ),
key (col_date_key ),
key (col_int_key ),
key (col_time_key ),
primary key (pk)) ENGINE=innodb;
INSERT IGNORE INTO c VALUES
('2001-07-23', '2004-12-11', 'k', 's', NULL, 7, '2004-11-12', '2000-03-18', 3,
NULL, NULL),
(NULL, NULL, 's', 'j', NULL, 6, NULL, '2005', 1, NULL, NULL),
('2006-07-02', NULL, 'w', 'y', NULL, 2, '04:35:59.017853', '2002', 7, NULL,
'2004-09-04 21:23:05.023144'),
(NULL, '2009-02-16 21:37:23.010045', 'w', 'o', '2005-05-25', NULL, NULL,
'04:32:06.000870', 9, NULL, '2004'),
(NULL, NULL, 'y', 'k', '2002-12-15', 81, NULL, '2009-03-14', 3, NULL, NULL),
(NULL, '2005', 'x', 's', '2004-07-12', 9, NULL, NULL, 7, NULL, '2009'),
('2003', '2000-11-08', 'd', 'h', '2002-09-25', 8, NULL, '2002', NULL, NULL,
'2004'),
('2000', '2008-01-08 20:49:13.011386', 't', 'w', '2000-12-11', 6,
'18:31:35.007025', '19:28:20.040544', 4, NULL, '2005-03-13'),
('2006-10-04', '2000-12-16', 'i', 'f', NULL, 3, '2008', NULL, 5, NULL,
'2003-12-03 13:55:06.040156'),
('2009-07-26', '2009-11-22 07:59:12.037926', 'o', 'n', '2004-07-23', 4,
'2005', '12:00:51.020344', 5, NULL, '2006'),
('2009-02-25', NULL, 'm', NULL, '2003', NULL, '2000', '2002-07-28', 1, NULL,
'2004-06-26'),
('2008-01-11', '2001-05-27', 'c', 'w', '2001-11-21', 4, '2004-07-23',
'2005-07-19', 3, NULL, '2001'),
('2009', NULL, 'x', NULL, NULL, 6, '2006-10-03', NULL, 1, NULL, '2009-12-03'),
('2008-09-22', '2008-08-09 11:16:52.037869', 'r', 'c', '2008-01-23', 3, NULL,
NULL, 6, NULL, '2008'),
('2007-01-21', NULL, 'u', 'u', '2008', 5, '2003-07-15', '07:04:43.054922',
NULL, NULL, NULL),
('2009-06-15', '2004-01-25', 'x', NULL, NULL, 189, '2008', '2000-06-14', 1,
NULL, NULL),
('2005', '2008-03-22', NULL, 'g', '2008', 1, '20:53:08.022885', '2006', 3,
NULL, '2009-04-06 15:24:52.051014'),
('2002', '2003-07-10 12:29:23.023649', 'g', 'u', '2000-10-16', 9, '2003',
'2006', 9, NULL, NULL),
('2005-10-23', NULL, 's', 'x', '2005', 9, '2008-07-09', '2001-08-12', 8, NULL,
NULL),
('2005', NULL, 'g', 'm', '2000-01-03', 9, '2008', NULL, 1, NULL,
'2001-01-21');
Warnings:
Warning 1265 Data truncated for column 'col_time' at row 1
Warning 1265 Data truncated for column 'col_time_key' at row 1
Warning 1265 Data truncated for column 'col_datetime' at row 4
Warning 1265 Data truncated for column 'col_time_key' at row 5
Warning 1265 Data truncated for column 'col_datetime_key' at row 6
Warning 1265 Data truncated for column 'col_datetime' at row 6
Warning 1265 Data truncated for column 'col_date' at row 7
Warning 1265 Data truncated for column 'col_datetime' at row 7
Warning 1265 Data truncated for column 'col_date' at row 8
Warning 1265 Data truncated for column 'col_datetime' at row 10
Warning 1265 Data truncated for column 'col_date_key' at row 11
Warning 1265 Data truncated for column 'col_time_key' at row 11
Warning 1265 Data truncated for column 'col_time' at row 12
Warning 1265 Data truncated for column 'col_time_key' at row 12
Warning 1265 Data truncated for column 'col_datetime' at row 12
Warning 1265 Data truncated for column 'col_date' at row 13
Warning 1265 Data truncated for column 'col_time' at row 13
Warning 1265 Data truncated for column 'col_datetime' at row 14
Warning 1265 Data truncated for column 'col_date_key' at row 15
Warning 1265 Data truncated for column 'col_time' at row 15
Warning 1265 Data truncated for column 'col_time_key' at row 16
Warning 1265 Data truncated for column 'col_date' at row 17
Warning 1265 Data truncated for column 'col_date_key' at row 17
Warning 1265 Data truncated for column 'col_date' at row 18
Warning 1265 Data truncated for column 'col_date_key' at row 19
Warning 1265 Data truncated for column 'col_time' at row 19
Warning 1265 Data truncated for column 'col_time_key' at row 19
Warning 1265 Data truncated for column 'col_date' at row 20
CREATE TABLE cc (
col_date date,
col_int int,
col_int_key int,
col_varchar_key varchar (1),
col_datetime_key datetime,
col_datetime datetime,
pk integer auto_increment,
col_varchar varchar (1),
col_time_key time,
col_time time,
col_date_key date,
key (col_int_key ),
key (col_varchar_key ),
key (col_datetime_key ),
primary key (pk),
key (col_time_key ),
key (col_date_key )) ENGINE=innodb;
ALTER TABLE cc DISABLE KEYS;
Warnings:
Note 1031 Table storage engine for 'cc' doesn't have this option
INSERT IGNORE INTO cc VALUES
('2006-06-04', 3, 0, 'y', '2006-04-12 00:44:48.055959', NULL, NULL, 'l',
'2005-01-10', '2004', '2004-07-14'),
('2008', 6, 8, NULL, '2006-10-23', NULL, NULL, 'a', NULL, NULL, '2000-04-26'),
('2009-06-11', NULL, 9, 'w', '2008', '2005', NULL, 'q', '04:42:05.061538',
'2004-08-18', NULL),
('2007-03-01', 4, 7, 'f', NULL, '2000-10-06 15:26:40.040137', NULL, 'd',
'2008', '2006-11-17', '2006'),
('2001-02-08', 4, 210, 'j', '2003-11-14 04:26:34.047333', NULL, NULL, 'h',
'06:13:13.012974', '02:20:21.050151', '2006-08-20'),
('2000', 9, 5, 'b', '2006-12-16', NULL, NULL, 'z', '2000-09-09', '2007-06-15',
'2008'),
(NULL, 1, 6, 'z', '2007-12-10 00:57:04.007939', NULL, NULL, 'i', '2002-02-11',
'2004', '2006-08-08'),
('2007', NULL, 1, 'w', '2007-09-03 21:11:14.028959', '2009', NULL, 'n',
'2009-05-03', '2005-06-23', NULL),
(NULL, 4, NULL, 'f', '2007-04-12', NULL, NULL, 'f', '2007-12-01', '2006',
'2000-05-11'),
('2008', 7, 1, 's', NULL, NULL, NULL, 'o', '2002', '2003', '2009-12-03'),
(NULL, 5, 62, 'i', '2009-10-06 12:22:10.055548', '2003', NULL, 'p', NULL,
NULL, '2006-02-03'),
('2006-02-10', 4, 9, 'g', NULL, '2000-07-26 23:20:24.031805', NULL, 'c',
'2007-12-12', '2002', '2003'),
('2000', 5, 0, 'j', '2000-02-23', '2000', NULL, 'a', '2005', '2000-04-15',
'2000-09-19'),
(NULL, 2, 9, 'q', '2003-12-24', NULL, NULL, NULL, NULL, '2000', '2008-05-23'),
(NULL, 9, NULL, 'i', '2003-10-22 02:03:47.003490', '2006-01-03', NULL, 'b',
NULL, '2003', '2008-01-21'),
('2008-06-09', 9, 0, 'a', '2000', NULL, NULL, 'c', '21:15:46.049912', '2001',
NULL),
('2000', 2, 8, NULL, '2009-11-27', NULL, NULL, NULL, '2004-05-08',
'12:30:30.041709', '2005-12-01'),
('2009-03-27', 3, 0, 'l', '2009', '2009', NULL, 'a', NULL, '04:16:53.049190',
NULL),
('2008-08-26', 114, 3, 'o', '2008-03-06', NULL, NULL, 'k', '07:26:47.018798',
'2002-08-17', '2004-09-07'),
(NULL, 8, 7, 'm', '2007-12-28 23:49:04.022501', '2005-04-08', NULL, 't',
'2000-11-12', '22:19:29.060590', '2005-09-20');
Warnings:
Warning 1265 Data truncated for column 'col_time_key' at row 1
Warning 1265 Data truncated for column 'col_date' at row 2
Warning 1265 Data truncated for column 'col_datetime_key' at row 3
Warning 1265 Data truncated for column 'col_datetime' at row 3
Warning 1265 Data truncated for column 'col_time' at row 3
Warning 1265 Data truncated for column 'col_time' at row 4
Warning 1265 Data truncated for column 'col_date_key' at row 4
Warning 1265 Data truncated for column 'col_date' at row 6
Warning 1265 Data truncated for column 'col_time_key' at row 6
Warning 1265 Data truncated for column 'col_time' at row 6
Warning 1265 Data truncated for column 'col_date_key' at row 6
Warning 1265 Data truncated for column 'col_time_key' at row 7
Warning 1265 Data truncated for column 'col_date' at row 8
Warning 1265 Data truncated for column 'col_datetime' at row 8
Warning 1265 Data truncated for column 'col_time_key' at row 8
Warning 1265 Data truncated for column 'col_time' at row 8
Warning 1265 Data truncated for column 'col_time_key' at row 9
Warning 1265 Data truncated for column 'col_date' at row 10
Warning 1265 Data truncated for column 'col_datetime' at row 11
Warning 1265 Data truncated for column 'col_time_key' at row 12
Warning 1265 Data truncated for column 'col_date_key' at row 12
Warning 1265 Data truncated for column 'col_date' at row 13
Warning 1265 Data truncated for column 'col_datetime' at row 13
Warning 1265 Data truncated for column 'col_time' at row 13
Warning 1265 Data truncated for column 'col_datetime_key' at row 16
Warning 1265 Data truncated for column 'col_date' at row 17
Warning 1265 Data truncated for column 'col_time_key' at row 17
Warning 1265 Data truncated for column 'col_datetime_key' at row 18
Warning 1265 Data truncated for column 'col_datetime' at row 18
Warning 1265 Data truncated for column 'col_time' at row 19
Warning 1265 Data truncated for column 'col_time_key' at row 20
ALTER TABLE cc ENABLE KEYS;
Warnings:
Note 1031 Table storage engine for 'cc' doesn't have this option
ANALYZE TABLE c, cc;
Table Op Msg_type Msg_text
test.c analyze status OK
test.cc analyze status OK
EXPLAIN FORMAT=tree SELECT
alias1.pk AS field1
FROM
(
SELECT
sq1_alias2.*
FROM
cc AS sq1_alias1
RIGHT JOIN cc AS sq1_alias2 ON
sq1_alias2.col_varchar_key = sq1_alias1.col_varchar_key
LIMIT
100
)
AS alias1
WHERE
alias1.col_varchar_key
IN (
SELECT
sq2_alias1.col_varchar_key AS sq2_field1
FROM
(cc AS sq2_alias1, c AS sq2_alias2)
WHERE
sq2_alias1.col_varchar_key != alias1.col_varchar
)
GROUP BY
field1
HAVING
field1 != 'pg'
ORDER BY
alias1.col_int_key DESC, field1
LIMIT 2 OFFSET 2;
EXPLAIN
-> Limit/Offset: 2/2 row(s)
-> Sort: <temporary>.col_int_key DESC, <temporary>.pk
-> Filter: (field1 <> 0)
-> Table scan on <temporary>
-> Temporary table with deduplication
-> Remove duplicate alias1 rows using temporary table (weedout)
-> Inner hash join
-> Index scan on sq2_alias2 using col_date_key (cost=1.93 rows=20)
-> Hash
-> Nested loop inner join
-> Filter: (sq2_alias1.col_varchar_key is not null) (cost=2.25 rows=20)
-> Index scan on sq2_alias1 using col_varchar_key (cost=2.25 rows=20)
-> Filter: (sq2_alias1.col_varchar_key <> alias1.col_varchar)
-> Index lookup on alias1 using <auto_key0> (col_varchar_key=sq2_alias1.col_varchar_key)
-> Materialize
-> Limit: 100 row(s)
-> Nested loop left join (cost=9.92 rows=27)
-> Table scan on sq1_alias2 (cost=2.25 rows=20)
-> Index lookup on sq1_alias1 using col_varchar_key (col_varchar_key=sq1_alias2.col_varchar_key) (cost=0.26 rows=1)
Warnings:
Note 1276 Field or reference 'alias1.col_varchar' of SELECT #3 was resolved in SELECT #1
Warning 1292 Truncated incorrect DOUBLE value: 'pg'
SELECT
alias1.pk AS field1
FROM
(
SELECT
sq1_alias2.*
FROM
cc AS sq1_alias1
RIGHT JOIN cc AS sq1_alias2 ON
sq1_alias2.col_varchar_key = sq1_alias1.col_varchar_key
LIMIT
100
)
AS alias1
WHERE
alias1.col_varchar_key
IN (
SELECT
sq2_alias1.col_varchar_key AS sq2_field1
FROM
(cc AS sq2_alias1, c AS sq2_alias2)
WHERE
sq2_alias1.col_varchar_key != alias1.col_varchar
)
GROUP BY
field1
HAVING
field1 != 'pg'
ORDER BY
alias1.col_int_key DESC, field1
LIMIT 2 OFFSET 2;
field1
3
12
Warnings:
Warning 1292 Truncated incorrect DOUBLE value: 'pg'
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime_key' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime_key' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date_key' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date_key' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date_key' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date_key' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime_key' at row 1
Warning 1292 Incorrect date value: '0000-00-00' for column 'col_date' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime_key' at row 1
Warning 1292 Incorrect datetime value: '0000-00-00 00:00:00' for column 'col_datetime' at row 1
DROP TABLE c, cc;
#
# Bug#30119783 SIG11 IN
# HASH_JOIN_BUFFER::STOREFROMTABLEBUFFERS|SQL/HASH_JOIN_BUFFER.CC
#
CREATE TABLE b(pk INT PRIMARY KEY, col_varchar VARCHAR(1));
CREATE TABLE cc(pk INT PRIMARY KEY, col_varchar VARCHAR(1));
INSERT INTO b VALUES (1, '4');
INSERT INTO cc VALUES (1, 'c'), (2, 'c');
EXPLAIN FORMAT=tree SELECT
table1.col_varchar
FROM
(
SELECT
subquery1_t1.*
FROM
b AS subquery1_t1
INNER JOIN cc AS subquery1_t2 ON
subquery1_t1.col_varchar = subquery1_t2.col_varchar
)
AS table1
LEFT JOIN (
SELECT
col_varchar
FROM
cc AS subquery2_t1
GROUP BY
subquery2_t1.col_varchar
)
AS table2 ON
table2.col_varchar = table1.col_varchar
AND table1.col_varchar
IN (
SELECT
lower(subquery3_t1.pk) AS subquery3_field1
FROM
b AS subquery3_t1
);
EXPLAIN
-> Remove duplicate (subquery1_t1, table2, subquery1_t2) rows using temporary table (weedout)
-> Inner hash join (subquery1_t2.col_varchar = subquery1_t1.col_varchar)
-> Table scan on subquery1_t2 (cost=0.18 rows=2)
-> Hash
-> Nested loop left join
-> Table scan on subquery1_t1 (cost=0.35 rows=1)
-> Nested loop inner join
-> Filter: (subquery1_t1.col_varchar = lower(subquery3_t1.pk)) (cost=0.35 rows=1)
-> Index scan on subquery3_t1 using PRIMARY (cost=0.35 rows=1)
-> Index lookup on table2 using <auto_key0> (col_varchar=subquery1_t1.col_varchar)
-> Materialize
-> Table scan on <temporary>
-> Temporary table with deduplication
-> Table scan on subquery2_t1 (cost=0.45 rows=2)
SELECT
table1.col_varchar
FROM
(
SELECT
subquery1_t1.*
FROM
b AS subquery1_t1
INNER JOIN cc AS subquery1_t2 ON
subquery1_t1.col_varchar = subquery1_t2.col_varchar
)
AS table1
LEFT JOIN (
SELECT
col_varchar
FROM
cc AS subquery2_t1
GROUP BY
subquery2_t1.col_varchar
)
AS table2 ON
table2.col_varchar = table1.col_varchar
AND table1.col_varchar
IN (
SELECT
lower(subquery3_t1.pk) AS subquery3_field1
FROM
b AS subquery3_t1
);
col_varchar
DROP TABLE b, cc;
#
# Bug#30049083 [REGRESSION]REPLACE/INSERT WITH LIMIT TAKING MORE TIME AND
# SPACE
#
# If the query has a LIMIT, the hash join should not spill to disk. Note
# that if the query contains either grouping or sorting, we allow spill
# to disk even if the query contains a LIMIT.
CREATE TABLE t1 (col1 BIGINT);
INSERT INTO t1 SELECT 1;
INSERT INTO t1 SELECT col1 + 1 FROM t1;
INSERT INTO t1 SELECT col1 + 2 FROM t1;
INSERT INTO t1 SELECT col1 + 4 FROM t1;
INSERT INTO t1 SELECT col1 + 8 FROM t1;
INSERT INTO t1 SELECT col1 + 16 FROM t1;
INSERT INTO t1 SELECT col1 + 32 FROM t1;
INSERT INTO t1 SELECT col1 + 64 FROM t1;
INSERT INTO t1 SELECT col1 + 128 FROM t1;
INSERT INTO t1 SELECT col1 + 256 FROM t1;
CREATE TABLE t2 SELECT col1 FROM t1;
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
SET join_buffer_size = 2048;
# This should spill to disk since we do not have any LIMIT.
TRUNCATE performance_schema.file_summary_by_event_name;
SELECT * FROM t1, t2 WHERE t1.col1 = t2.col1;
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
1
# This should NOT spill to disk since we have a LIMIT.
TRUNCATE performance_schema.file_summary_by_event_name;
SELECT * FROM t1, t2 WHERE t1.col1 = t2.col1 LIMIT 1;
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
0
# This should spill to disk since we have sorting.
TRUNCATE performance_schema.file_summary_by_event_name;
SELECT * FROM t1, t2 WHERE t1.col1 = t2.col1 ORDER BY t1.col1 LIMIT 1;
col1 col1
1 1
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
1
# This should spill to disk since we have (implicit) grouping.
TRUNCATE performance_schema.file_summary_by_event_name;
SELECT SUM(t1.col1) FROM t1, t2 WHERE t1.col1 = t2.col1 LIMIT 10;
SUM(t1.col1)
131328
SELECT COUNT_STAR > 0 FROM performance_schema.file_summary_by_event_name
WHERE event_name LIKE '%hash_join%';
COUNT_STAR > 0
1
SET join_buffer_size = DEFAULT;
DROP TABLE t1,t2;
#
# Bug#30214767 SIG11 AT QUICK_INDEX_MERGE_SELECT::GET_NEXT |
# SQL/OPT_RANGE.CC
#
# Set up a query with hash join, where the build input uses an index
# range scan with index merge sort-union. Also, a LIMIT greater than
# the number of rows satisfying the join condition is needed to
# reproduce the bug. What we want to achieve is to get the hash join
# to call Read() on the build input after it has returned EOF. This can
# be triggered by using LIMIT, as this causes the hash join to go back
# and read from the build input after the probe iterator has returned
# EOF (see comment on HashJoinIterator regarding spill to disk and LIMIT
# for more details around this).
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (
col1 INTEGER,
col2 INTEGER,
col3 INTEGER,
INDEX idx_a (col2),
INDEX idx_b (col3));
INSERT INTO t1 VALUES (1);
INSERT INTO t2 VALUES (1, 1, 1);
ANALYZE TABLE t1, t2;
Table Op Msg_type Msg_text
test.t1 analyze status OK
test.t2 analyze status OK
EXPLAIN FORMAT=tree SELECT /*+ JOIN_ORDER(t2, t1) INDEX_MERGE(t2) */ t1.col1
FROM t1
JOIN t2 ON t1.col1 = t2.col1
WHERE t2.col2 > 0 OR t2.col3 > 0 LIMIT 10;
EXPLAIN
-> Limit: 10 row(s)
-> Inner hash join (t1.col1 = t2.col1) (cost=1.86 rows=1)
-> Table scan on t1 (cost=0.35 rows=1)
-> Hash
-> Filter: ((t2.col2 > 0) or (t2.col3 > 0)) (cost=1.51 rows=1)
-> Index range scan on t2 using sort_union(idx_a,idx_b) (cost=1.51 rows=1)
SELECT /*+ JOIN_ORDER(t2, t1) INDEX_MERGE(t2) */ t1.col1
FROM t1
JOIN t2 ON t1.col1 = t2.col1
WHERE t2.col2 > 0 OR t2.col3 > 0 LIMIT 10;
col1
1
DROP TABLE t1, t2;
#
# Bug#30224582 ASSERTION `M_INDEX_CURSOR.IS_POSITIONED()' FAILED
#
# Set up a query where the hash join build input consists of a
# materialized table, where we do an index lookup on the materialized
# table. The LIMIT is also needed in order to trigger a second build
# phase in the hash join.
CREATE TABLE t1 (col1 INTEGER);
CREATE TABLE t2 (col1 INTEGER);
INSERT INTO t1 VALUES (1);
INSERT INTO t2 VALUES (1);
EXPLAIN FORMAT=tree SELECT /*+ JOIN_ORDER(table1, t2) */
*
FROM
(
SELECT
DISTINCT t1.*
FROM
t1
) AS table1 JOIN t2
WHERE table1.col1 = 1
LIMIT 50;
EXPLAIN
-> Limit: 50 row(s)
-> Inner hash join
-> Table scan on t2 (cost=0.35 rows=1)
-> Hash
-> Index lookup on table1 using <auto_key0> (col1=1)
-> Materialize
-> Table scan on <temporary>
-> Temporary table with deduplication
-> Table scan on t1 (cost=0.35 rows=1)
SELECT /*+ JOIN_ORDER(table1, t2) */
*
FROM
(
SELECT
DISTINCT t1.*
FROM
t1
) AS table1 JOIN t2
WHERE table1.col1 = 1
LIMIT 50;
col1 col1
1 1
DROP TABLE t1, t2;