MySQL 9.1 Release Notes
Here we take an example from the test suite.
#
# Tracing of ORDER BY & GROUP BY simplification.
#
SET optimizer_trace="enabled=on",end_markers_in_json=on; # make readable
SET optimizer_trace_max_mem_size=1000000; # avoid small default
CREATE TABLE t1 (
pk INT, col_int_key INT,
col_varchar_key VARCHAR(1),
col_varchar_nokey VARCHAR(1)
);
INSERT INTO t1 VALUES
(10,7,'v','v'),(11,0,'s','s'),(12,9,'l','l'),(13,3,'y','y'),(14,4,'c','c'),
(15,2,'i','i'),(16,5,'h','h'),(17,3,'q','q'),(18,1,'a','a'),(19,3,'v','v'),
(20,6,'u','u'),(21,7,'s','s'),(22,5,'y','y'),(23,1,'z','z'),(24,204,'h','h'),
(25,224,'p','p'),(26,9,'e','e'),(27,5,'i','i'),(28,0,'y','y'),(29,3,'w','w');
CREATE TABLE t2 (
pk INT, col_int_key INT,
col_varchar_key VARCHAR(1),
col_varchar_nokey VARCHAR(1),
PRIMARY KEY (pk)
);
INSERT INTO t2 VALUES
(1,4,'b','b'),(2,8,'y','y'),(3,0,'p','p'),(4,0,'f','f'),(5,0,'p','p'),
(6,7,'d','d'),(7,7,'f','f'),(8,5,'j','j'),(9,3,'e','e'),(10,188,'u','u'),
(11,4,'v','v'),(12,9,'u','u'),(13,6,'i','i'),(14,1,'x','x'),(15,5,'l','l'),
(16,6,'q','q'),(17,2,'n','n'),(18,4,'r','r'),(19,231,'c','c'),(20,4,'h','h'),
(21,3,'k','k'),(22,3,'t','t'),(23,7,'t','t'),(24,6,'k','k'),(25,7,'g','g'),
(26,9,'z','z'),(27,4,'n','n'),(28,4,'j','j'),(29,2,'l','l'),(30,1,'d','d'),
(31,2,'t','t'),(32,194,'y','y'),(33,2,'i','i'),(34,3,'j','j'),(35,8,'r','r'),
(36,4,'b','b'),(37,9,'o','o'),(38,4,'k','k'),(39,5,'a','a'),(40,5,'f','f'),
(41,9,'t','t'),(42,3,'c','c'),(43,8,'c','c'),(44,0,'r','r'),(45,98,'k','k'),
(46,3,'l','l'),(47,1,'o','o'),(48,0,'t','t'),(49,189,'v','v'),(50,8,'x','x'),
(51,3,'j','j'),(52,3,'x','x'),(53,9,'k','k'),(54,6,'o','o'),(55,8,'z','z'),
(56,3,'n','n'),(57,9,'c','c'),(58,5,'d','d'),(59,9,'s','s'),(60,2,'j','j'),
(61,2,'w','w'),(62,5,'f','f'),(63,8,'p','p'),(64,6,'o','o'),(65,9,'f','f'),
(66,0,'x','x'),(67,3,'q','q'),(68,6,'g','g'),(69,5,'x','x'),(70,8,'p','p'),
(71,2,'q','q'),(72,120,'q','q'),(73,25,'v','v'),(74,1,'g','g'),(75,3,'l','l'),
(76,1,'w','w'),(77,3,'h','h'),(78,153,'c','c'),(79,5,'o','o'),(80,9,'o','o'),
(81,1,'v','v'),(82,8,'y','y'),(83,7,'d','d'),(84,6,'p','p'),(85,2,'z','z'),
(86,4,'t','t'),(87,7,'b','b'),(88,3,'y','y'),(89,8,'k','k'),(90,4,'c','c'),
(91,6,'z','z'),(92,1,'t','t'),(93,7,'o','o'),(94,1,'u','u'),(95,0,'t','t'),
(96,2,'k','k'),(97,7,'u','u'),(98,2,'b','b'),(99,1,'m','m'),(100,5,'o','o');
SELECT SUM(alias2.col_varchar_nokey) AS c1, alias2.pk AS c2
FROM t1 AS alias1
STRAIGHT_JOIN t2 AS alias2
ON alias2.pk = alias1.col_int_key
WHERE alias1.pk
GROUP BY c2
ORDER BY alias1.col_int_key, alias2.pk;
+------+----+
| c1 | c2 |
+------+----+
| 0 | 1 |
| 0 | 2 |
| 0 | 3 |
| 0 | 4 |
| 0 | 5 |
| 0 | 6 |
| 0 | 7 |
| 0 | 9 |
+------+----+
Note
For reference, the complete trace is shown uninterrupted at the end of this section.
Now we can examine the trace, whose first column
(QUERY), containing the original statement to
be traced, is shown here:
SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE\G
*************************** 1. row ***************************
QUERY: SELECT SUM(alias2.col_varchar_nokey) AS c1, alias2.pk AS c2
FROM t1 AS alias1
STRAIGHT_JOIN t2 AS alias2
ON alias2.pk = alias1.col_int_key
WHERE alias1.pk
GROUP BY c2
ORDER BY alias1.col_int_key, alias2.pkThis can be useful mark when several traces are stored.
The TRACE column begins by showing that
execution of the statement is made up of discrete steps, like
this:
"steps": [
{
This is followed by the preparation of the join for the first (and
only) SELECT in the statement being traced, as
shown here:
"steps": [
{
"expanded_query": "/* select#1 */ select \
sum(`test`.`alias2`.`col_varchar_nokey`) AS \
`SUM(alias2.col_varchar_nokey)`,`test`.`alias2`.`pk` AS `field2` \
from (`test`.`t1` `alias1` straight_join `test`.`t2` `alias2` \
on((`test`.`alias2`.`pk` = `test`.`alias1`.`col_int_key`))) \
where `test`.`alias1`.`pk` \
group by `test`.`alias2`.`pk` \
order by `test`.`alias1`.`col_int_key`,`test`.`alias2`.`pk`"
}
] /* steps */
} /* join_preparation */
},
The output just shown displays the query as it is used for
preparing the join; all columns (fields) have been resolved to
their databases and tables, and each SELECT is
annotated with a sequence number, which can be useful when
studying subqueries.
The next portion of the trace shows how the join is optimized, starting with condition processing:
{
"join_optimization": {
"select#": 1,
"steps": [
{
"condition_processing": {
"condition": "WHERE",
"original_condition": "(`test`.`alias1`.`pk` and \
(`test`.`alias2`.`pk` = `test`.`alias1`.`col_int_key`))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "(`test`.`alias1`.`pk` and \
multiple equal(`test`.`alias2`.`pk`, \
`test`.`alias1`.`col_int_key`))"
},
{
"transformation": "constant_propagation",
"resulting_condition": "(`test`.`alias1`.`pk` and \
multiple equal(`test`.`alias2`.`pk`, \
`test`.`alias1`.`col_int_key`))"
},
{
"transformation": "trivial_condition_removal",
"resulting_condition": "(`test`.`alias1`.`pk` and \
multiple equal(`test`.`alias2`.`pk`, \
`test`.`alias1`.`col_int_key`))"
}
] /* steps */
} /* condition_processing */
},
Next, the optimizer checks for possible ref
accesses, and identifies one:
{
"ref_optimizer_key_uses": [
{
"database": "test",
"table": "alias2",
"field": "pk",
"equals": "`test`.`alias1`.`col_int_key`",
"null_rejecting": true
}
] /* ref_optimizer_key_uses */
},
A ref access which rejects
NULL has been identified: no
NULL in
test.alias1.col_int_key can have a match.
(Observe that it could have a match, were the operator a null-safe
equals
<=>).
Next, for every table in the query, we estimate the cost of, and number of records returned by, a table scan or a range access.
We need to find an optimal order for the tables. Normally, greedy search is used, but since the statement uses a straight join, only the requested order is explored, and one or more access methods are selected. As shown in this portion of the trace, we can choose a table scan:
{
"records_estimation": [
{
"database": "test",
"table": "alias1",
"const_keys_added": {
"keys": [
] /* keys */,
"cause": "group_by"
} /* const_keys_added */,
"range_analysis": {
"table_scan": {
"records": 20,
"cost": 8.1977
} /* table_scan */
} /* range_analysis */
},
{
"database": "test",
"table": "alias2",
"const_keys_added": {
"keys": [
"PRIMARY"
] /* keys */,
"cause": "group_by"
} /* const_keys_added */,
"range_analysis": {
"table_scan": {
"records": 100,
"cost": 24.588
} /* table_scan */,
"potential_range_indices": [
{
"index": "PRIMARY",
"usable": true,
"key_parts": [
"pk"
] /* key_parts */
}
] /* potential_range_indices */,
"setup_range_conditions": [
] /* setup_range_conditions */,
"group_index_range": {
"chosen": false,
"cause": "not_single_table"
} /* group_index_range */
} /* range_analysis */
}
] /* records_estimation */
},
As just shown in the second portion of the range analysis, it is
not possible to use GROUP_MIN_MAX because it
accepts only one table, and we have two in the join. This means
that no range access is possible.
The optimizer estimates that reading the first table, and applying any required conditions to it, yields 20 rows:
{
"considered_execution_plans": [
{
"database": "test",
"table": "alias1",
"best_access_path": {
"considered_access_paths": [
{
"access_type": "scan",
"records": 20,
"cost": 2.0977,
"chosen": true
}
] /* considered_access_paths */
} /* best_access_path */,
"cost_for_plan": 6.0977,
"records_for_plan": 20,
For alias2, we choose ref
access on the primary key rather than a table scan, because the
number of records returned by the latter (75) is far greater than
that returned by ref access (1), as shown here:
"rest_of_plan": [
{
"database": "test",
"table": "alias2",
"best_access_path": {
"considered_access_paths": [
{
"access_type": "ref",
"index": "PRIMARY",
"records": 1,
"cost": 20.2,
"chosen": true
},
{
"access_type": "scan",
"using_join_cache": true,
"records": 75,
"cost": 7.4917,
"chosen": false
}
] /* considered_access_paths */
} /* best_access_path */,
"cost_for_plan": 30.098,
"records_for_plan": 20,
"chosen": true
}
] /* rest_of_plan */
}
] /* considered_execution_plans */
},
Now that the order of tables is fixed, we can split the
WHERE condition into chunks which can be tested
early (pushdown of conditions down the join tree):
{
"attaching_conditions_to_tables": {
"original_condition": "((`test`.`alias2`.`pk` = \
`test`.`alias1`.`col_int_key`) and `test`.`alias1`.`pk`)",
"attached_conditions_computation": [
] /* attached_conditions_computation */,
"attached_conditions_summary": [
{
"database": "test",
"table": "alias1",
"attached": "(`test`.`alias1`.`pk` and \
(`test`.`alias1`.`col_int_key` is not null))"
},
This condition can be tested on rows of alias1
without reading rows from alias2.
{
"database": "test",
"table": "alias2",
"attached": null
}
] /* attached_conditions_summary */
} /* attaching_conditions_to_tables */
},
{
Now we try to simplify the ORDER BY:
"clause_processing": {
"clause": "ORDER BY",
"original_clause": "`test`.`alias1`.`col_int_key`,`test`.`alias2`.`pk`",
"items": [
{
"item": "`test`.`alias1`.`col_int_key`"
},
{
"item": "`test`.`alias2`.`pk`",
"eq_ref_to_preceding_items": true
}
] /* items */,
Because the WHERE clause contains
alias2.pk=alias1.col_int_key, ordering by both
columns is unnecessary; we can order by the first column alone,
since the second column is always equal to it.
"resulting_clause_is_simple": true,
"resulting_clause": "`test`.`alias1`.`col_int_key`"
} /* clause_processing */
},
The shorter ORDER BY clause (which is not
visible in in the output of
EXPLAIN) can be implemented as an
index scan, since it uses only a single column of one table.
{
"clause_processing": {
"clause": "GROUP BY",
"original_clause": "`test`.`alias2`.`pk`",
"items": [
{
"item": "`test`.`alias2`.`pk`"
}
] /* items */,
"resulting_clause_is_simple": false,
"resulting_clause": "`test`.`alias2`.`pk`"
} /* clause_processing */
},
{
"refine_plan": [
{
"database": "test",
"table": "alias1",
"scan_type": "table"
},
{
"database": "test",
"table": "alias2"
}
] /* refine_plan */
}
] /* steps */
} /* join_optimization */
},
{Now the join is executed:
"join_execution": {
"select#": 1,
"steps": [
] /* steps */
} /* join_execution */
}
] /* steps */
} 0 0All traces have the same basic structure. If a statement uses subqueries, there can be mutliple preparations, optimizations, and executions, as well as subquery-specific transformations.
The complete trace is shown here:
mysql> SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE\G
*************************** 1. row ***************************
QUERY: SELECT SUM(alias2.col_varchar_nokey) AS c1, alias2.pk AS c2
FROM t1 AS alias1
STRAIGHT_JOIN t2 AS alias2
ON alias2.pk = alias1.col_int_key
WHERE alias1.pk
GROUP BY c2
ORDER BY alias1.col_int_key, alias2.pk
TRACE: {
"steps": [
{
"join_preparation": {
"select#": 1,
"steps": [
{
"expanded_query": "/* select#1 */ select sum(`alias2`.`col_varchar_nokey`) AS `c1`,`alias2`.`pk` AS `c2` from (`t1` `alias1` straight_join `t2` `alias2` on((`alias2`.`pk` = `alias1`.`col_int_key`))) where (0 <> `alias1`.`pk`) group by `c2` order by `alias1`.`col_int_key`,`alias2`.`pk`"
},
{
"transformations_to_nested_joins": {
"transformations": [
"JOIN_condition_to_WHERE",
"parenthesis_removal"
] /* transformations */,
"expanded_query": "/* select#1 */ select sum(`alias2`.`col_varchar_nokey`) AS `c1`,`alias2`.`pk` AS `c2` from `t1` `alias1` straight_join `t2` `alias2` where ((0 <> `alias1`.`pk`) and (`alias2`.`pk` = `alias1`.`col_int_key`)) group by `c2` order by `alias1`.`col_int_key`,`alias2`.`pk`"
} /* transformations_to_nested_joins */
},
{
"functional_dependencies_of_GROUP_columns": {
"all_columns_of_table_map_bits": [
1
] /* all_columns_of_table_map_bits */,
"columns": [
"test.alias2.pk",
"test.alias1.col_int_key"
] /* columns */
} /* functional_dependencies_of_GROUP_columns */
}
] /* steps */
} /* join_preparation */
},
{
"join_optimization": {
"select#": 1,
"steps": [
{
"condition_processing": {
"condition": "WHERE",
"original_condition": "((0 <> `alias1`.`pk`) and (`alias2`.`pk` = `alias1`.`col_int_key`))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "((0 <> `alias1`.`pk`) and multiple equal(`alias2`.`pk`, `alias1`.`col_int_key`))"
},
{
"transformation": "constant_propagation",
"resulting_condition": "((0 <> `alias1`.`pk`) and multiple equal(`alias2`.`pk`, `alias1`.`col_int_key`))"
},
{
"transformation": "trivial_condition_removal",
"resulting_condition": "((0 <> `alias1`.`pk`) and multiple equal(`alias2`.`pk`, `alias1`.`col_int_key`))"
}
] /* steps */
} /* condition_processing */
},
{
"substitute_generated_columns": {
} /* substitute_generated_columns */
},
{
"table_dependencies": [
{
"table": "`t1` `alias1`",
"row_may_be_null": false,
"map_bit": 0,
"depends_on_map_bits": [
] /* depends_on_map_bits */
},
{
"table": "`t2` `alias2`",
"row_may_be_null": false,
"map_bit": 1,
"depends_on_map_bits": [
0
] /* depends_on_map_bits */
}
] /* table_dependencies */
},
{
"ref_optimizer_key_uses": [
{
"table": "`t2` `alias2`",
"field": "pk",
"equals": "`alias1`.`col_int_key`",
"null_rejecting": true
}
] /* ref_optimizer_key_uses */
},
{
"rows_estimation": [
{
"table": "`t1` `alias1`",
"table_scan": {
"rows": 20,
"cost": 0.25
} /* table_scan */
},
{
"table": "`t2` `alias2`",
"const_keys_added": {
"keys": [
"PRIMARY"
] /* keys */,
"cause": "group_by"
} /* const_keys_added */,
"range_analysis": {
"table_scan": {
"rows": 100,
"cost": 12.35
} /* table_scan */,
"potential_range_indexes": [
{
"index": "PRIMARY",
"usable": true,
"key_parts": [
"pk"
] /* key_parts */
}
] /* potential_range_indexes */,
"setup_range_conditions": [
] /* setup_range_conditions */,
"group_index_skip_scan": {
"chosen": false,
"cause": "not_single_table"
} /* group_index_skip_scan */,
"skip_scan_range": {
"chosen": false,
"cause": "not_single_table"
} /* skip_scan_range */
} /* range_analysis */
}
] /* rows_estimation */
},
{
"considered_execution_plans": [
{
"plan_prefix": [
] /* plan_prefix */,
"table": "`t1` `alias1`",
"best_access_path": {
"considered_access_paths": [
{
"rows_to_scan": 20,
"filtering_effect": [
] /* filtering_effect */,
"final_filtering_effect": 0.9,
"access_type": "scan",
"resulting_rows": 18,
"cost": 2.25,
"chosen": true
}
] /* considered_access_paths */
} /* best_access_path */,
"condition_filtering_pct": 100,
"rows_for_plan": 18,
"cost_for_plan": 2.25,
"rest_of_plan": [
{
"plan_prefix": [
"`t1` `alias1`"
] /* plan_prefix */,
"table": "`t2` `alias2`",
"best_access_path": {
"considered_access_paths": [
{
"access_type": "eq_ref",
"index": "PRIMARY",
"rows": 1,
"cost": 6.3,
"chosen": true,
"cause": "clustered_pk_chosen_by_heuristics"
},
{
"rows_to_scan": 100,
"filtering_effect": [
] /* filtering_effect */,
"final_filtering_effect": 1,
"access_type": "scan",
"using_join_cache": true,
"buffers_needed": 1,
"resulting_rows": 100,
"cost": 180.25,
"chosen": false
}
] /* considered_access_paths */
} /* best_access_path */,
"condition_filtering_pct": 100,
"rows_for_plan": 18,
"cost_for_plan": 8.55,
"chosen": true
}
] /* rest_of_plan */
}
] /* considered_execution_plans */
},
{
"attaching_conditions_to_tables": {
"original_condition": "((`alias2`.`pk` = `alias1`.`col_int_key`) and (0 <> `alias1`.`pk`))",
"attached_conditions_computation": [
] /* attached_conditions_computation */,
"attached_conditions_summary": [
{
"table": "`t1` `alias1`",
"attached": "((0 <> `alias1`.`pk`) and (`alias1`.`col_int_key` is not null))"
},
{
"table": "`t2` `alias2`",
"attached": "(`alias2`.`pk` = `alias1`.`col_int_key`)"
}
] /* attached_conditions_summary */
} /* attaching_conditions_to_tables */
},
{
"optimizing_distinct_group_by_order_by": {
"simplifying_order_by": {
"original_clause": "`alias1`.`col_int_key`,`alias2`.`pk`",
"items": [
{
"item": "`alias1`.`col_int_key`"
},
{
"item": "`alias2`.`pk`",
"eq_ref_to_preceding_items": true
}
] /* items */,
"resulting_clause_is_simple": true,
"resulting_clause": "`alias1`.`col_int_key`"
} /* simplifying_order_by */,
"simplifying_group_by": {
"original_clause": "`c2`",
"items": [
{
"item": "`alias2`.`pk`"
}
] /* items */,
"resulting_clause_is_simple": false,
"resulting_clause": "`c2`"
} /* simplifying_group_by */
} /* optimizing_distinct_group_by_order_by */
},
{
"finalizing_table_conditions": [
{
"table": "`t1` `alias1`",
"original_table_condition": "((0 <> `alias1`.`pk`) and (`alias1`.`col_int_key` is not null))",
"final_table_condition ": "((0 <> `alias1`.`pk`) and (`alias1`.`col_int_key` is not null))"
},
{
"table": "`t2` `alias2`",
"original_table_condition": "(`alias2`.`pk` = `alias1`.`col_int_key`)",
"final_table_condition ": null
}
] /* finalizing_table_conditions */
},
{
"refine_plan": [
{
"table": "`t1` `alias1`"
},
{
"table": "`t2` `alias2`"
}
] /* refine_plan */
},
{
"considering_tmp_tables": [
{
"adding_tmp_table_in_plan_at_position": 2,
"write_method": "continuously_update_group_row"
},
{
"adding_sort_to_table": ""
} /* filesort */
] /* considering_tmp_tables */
}
] /* steps */
} /* join_optimization */
},
{
"join_execution": {
"select#": 1,
"steps": [
{
"temp_table_aggregate": {
"select#": 1,
"steps": [
{
"creating_tmp_table": {
"tmp_table_info": {
"table": "<temporary>",
"in_plan_at_position": 2,
"columns": 3,
"row_length": 18,
"key_length": 4,
"unique_constraint": false,
"makes_grouped_rows": true,
"cannot_insert_duplicates": false,
"location": "TempTable"
} /* tmp_table_info */
} /* creating_tmp_table */
}
] /* steps */
} /* temp_table_aggregate */
},
{
"sorting_table": "<temporary>",
"filesort_information": [
{
"direction": "asc",
"expression": "`alias1`.`col_int_key`"
}
] /* filesort_information */,
"filesort_priority_queue_optimization": {
"usable": false,
"cause": "not applicable (no LIMIT)"
} /* filesort_priority_queue_optimization */,
"filesort_execution": [
] /* filesort_execution */,
"filesort_summary": {
"memory_available": 262144,
"key_size": 9,
"row_size": 26,
"max_rows_per_buffer": 7710,
"num_rows_estimate": 18446744073709551615,
"num_rows_found": 8,
"num_initial_chunks_spilled_to_disk": 0,
"peak_memory_used": 32832,
"sort_algorithm": "std::sort",
"unpacked_addon_fields": "skip_heuristic",
"sort_mode": "<fixed_sort_key, additional_fields>"
} /* filesort_summary */
}
] /* steps */
} /* join_execution */
}
] /* steps */
}
MISSING_BYTES_BEYOND_MAX_MEM_SIZE: 0
INSUFFICIENT_PRIVILEGES: 0