查看新的Trace内容
-- 再看下优化器执行过程
{
"steps": [
{
"join_preparation": {
"select#": 1,
"steps": [
{
"expanded_query": "/* select#1 */ select `staffs`.`id` AS `id`,`staffs`.`name` AS `name`,`staffs`.`age` AS `age`,`staffs`.`pos` AS `pos`,`staffs`.`add_time` AS `add_time` from `staffs` where ((`staffs`.`name` = 'July') and (`staffs`.`age` = 23))"
}
]
}
},
{
"join_optimization": {
"select#": 1,
"steps": [
{
"condition_processing": {
"condition": "WHERE",
"original_condition": "((`staffs`.`name` = 'July') and (`staffs`.`age` = 23))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "((`staffs`.`name` = 'July') and multiple equal(23, `staffs`.`age`))"
},
{
"transformation": "constant_propagation",
"resulting_condition": "((`staffs`.`name` = 'July') and multiple equal(23, `staffs`.`age`))"
},
{
"transformation": "trivial_condition_removal",
"resulting_condition": "((`staffs`.`name` = 'July') and multiple equal(23, `staffs`.`age`))"
}
]
}
},
{
"substitute_generated_columns": {
}
},
{
"table_dependencies": [
{
"table": "`staffs`",
"row_may_be_null": false,
"map_bit": 0,
"depends_on_map_bits": [
]
}
]
},
{
"ref_optimizer_key_uses": [
{
"table": "`staffs`",
"field": "name",
"equals": "'July'",
"null_rejecting": false
},
{
"table": "`staffs`",
"field": "age",
"equals": "23",
"null_rejecting": false
}
]
},
{
"rows_estimation": [
{
"table": "`staffs`",
"range_analysis": {
"table_scan": {
"rows": 27,
"cost": 8.5
},
"potential_range_indexes": [
{
"index": "PRIMARY",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_nap",
"usable": true,
"key_parts": [
"name",
"age",
"pos",
"id"
]
}
],
"setup_range_conditions": [
],
"group_index_range": {
"chosen": false,
"cause": "not_group_by_or_distinct"
},
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "idx_nap",
"ranges": [
"July <= name <= July AND 23 <= age <= 23"
],
"index_dives_for_eq_ranges": true,
"rowid_ordered": false,
"using_mrr": false,
"index_only": false,
"rows": 13,
"cost": 16.61,
"chosen": false,
"cause": "cost"
}
],
"analyzing_roworder_intersect": {
"usable": false,
"cause": "too_few_roworder_scans"
}
}
}
}
]
},
{
"considered_execution_plans": [
{
"plan_prefix": [
],
"table": "`staffs`",
"best_access_path": {
"considered_access_paths": [
{
//使用索引的成本变为了5.3
"access_type": "ref",
"index": "idx_nap",
"rows": 13,
"cost": 5.3,
"chosen": true
},
{
//scan的成本变为了6.4
"rows_to_scan": 27,
"access_type": "scan",
"resulting_rows": 27,
"cost": 6.4,
"chosen": false
}
]
},
//使用索引查询的成本更低，因此选择了走索引
"condition_filtering_pct": 100,
"rows_for_plan": 13,
"cost_for_plan": 5.3,
"chosen": true
}
]
},
{
"attaching_conditions_to_tables": {
"original_condition": "((`staffs`.`age` = 23) and (`staffs`.`name` = 'July'))",
"attached_conditions_computation": [
],
"attached_conditions_summary": [
{
"table": "`staffs`",
"attached": null
}
]
}
},
{
"refine_plan": [
{
"table": "`staffs`"
}
]
}
]
}
},
{
"join_execution": {
"select#": 1,
"steps": [
]
}
}
]
}
结论
MySQL表数据量的大小，会影响索引的选择，具体的情况还是通过Explain和Optimizer Trace来查看与分析。
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