- GreatSQL社區原創內容未經授權不得隨意使用，轉載請聯系小編并注明來源，
- GreatSQL是MySQL的國產分支版本，使用上與MySQL一致，
- 作者： xryz
- 文章來源：GreatSQL社區原創
前言：
我們在日常維護資料庫的時候，經常會遇到查詢慢的陳述句，這時候一般會通過執行EXPLAIN去查看它的執行計劃，但是執行計劃往往只給我們帶來了最基礎的分析資訊，比如是否有使用索引，還有一些其他供我們分析的資訊，比如使用了臨時表、排序等等，卻無法展示為什么一些其他的執行計劃未被選擇，比如說明明有索引，或者好幾個索引，但是為什么查詢時未使用到期望的索引等
explain select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
| 1 | SIMPLE | t2 | NULL | range | id_num_unique,idx_age,idx_age_id_num | idx_age | 1 | NULL | 9594 | 100.00 | Using index condition |
| 1 | SIMPLE | t1 | NULL | eq_ref | id_num_unique,idx_age | id_num_unique | 60 | test.t2.id_num | 1 | 50.00 | Using where |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
2 rows in set, 1 warning (0.01 sec)
如上面這個例子，為什么t2表上列出了多個可能使用的索引，卻選擇了idx_age，優化器為什么選擇了指定的索引，這時候并不能直觀的看出問題，這時候我們就可以開啟optimizer_trace跟蹤分析MySQL具體是怎么選擇出最優的執行計劃的，
OPTIMIZER_TRACE：
optimizer_trace是什么：
optimizer_trace是一個具有跟蹤功能的工具，可以跟蹤執行的陳述句的決議優化執行程序，并將跟蹤到的資訊記錄到INFORMATION_SCHEMA.OPTIMIZER_TRACE表中，但是每個會話都只能跟蹤它自己執行的陳述句，并且表中默認只記錄最后一個查詢的跟蹤結果
使用方法：
# 打開optimizer trace功能 (默認情況下它是關閉的):
set optimizer_trace="enabled=on";
select ...; # 這里輸入你自己的查詢陳述句
SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE;
# 當你停止查看陳述句的優化程序時，把optimizer trace功能關閉
set optimizer_trace="enabled=off";
相關引數：
mysql> show variables like '%optimizer_trace%';
+------------------------------+----------------------------------------------------------------------------+
| Variable_name | Value |
+------------------------------+----------------------------------------------------------------------------+
| optimizer_trace | enabled=on,one_line=off |
| optimizer_trace_features | greedy_search=on,range_optimizer=on,dynamic_range=on,repeated_subselect=on |
| optimizer_trace_limit | 1 |
| optimizer_trace_max_mem_size | 1048576 |
| optimizer_trace_offset | -1 |
+------------------------------+----------------------------------------------------------------------------+
- optimizer_trace： enabled 開啟/關閉optimizer_trace，one_line 是否單行顯示，關閉為json模式，一般不開啟
- optimizer_trace_features：跟蹤資訊中可列印的項，一般不調整默認列印所有項
- optimizer_trace_limit：存盤的跟蹤sql條數
- optimizer_trace_offset：開始記錄的sql陳述句的偏移量，負數表示從最近執行倒數第幾條開始記錄
- optimizer_trace_max_mem_size：optimizer_trace記憶體的大小，如果跟蹤資訊超過這個大小，資訊將會被截斷
optimizer_trace表資訊：
該表總共有4個欄位
- QUERY 表示我們的查詢陳述句，
- TRACE 表示優化程序的JSON格式文本，(重點關注)
- MISSING_BYTES_BEYOND_MAX_MEM_SIZE 由于優化程序可能會輸出很多，如果超過某個限制時，多余的文本將不會被顯示，這個欄位展示了被忽略的文本位元組數，
- INSUFFICIENT_PRIVILEGES 表示是否沒有權限查看優化程序，默認值是0，只有某些特殊情況下才會是 1，我們暫時不關心這個欄位的值，
資訊解讀：
通過 optimizer_trace表的query欄位可以看到，一條陳述句的執行程序主要分為三個步驟：
"join_preparation": {},（準備階段）
"join_optimization": {},（優化階段）
"join_execution": {},（執行階段）
各個步驟的詳細內容解讀：
- preparation：
expanded_query ：將陳述句進行格式化，補充隱藏的列名和表名等
transformations_to_nested_joins ：查詢重寫，比如join的on改為where陳述句
- optimization：
condition_processing{ ：條件句處理，
transformation{：轉換型別句，這三次的轉換分別是
equality_propagation（等值條件句轉換），如：a = b and b = c and c = 5
constant_propagation（常量條件句轉換），如：a = 1 AND b > a
trivial_condition_removal（無效條件移除的轉換），如：1 = 1
}
}
substitute_generated_columns ：替換虛擬生成列，測驗了很多sql，這一列都沒有看到有用的資訊
table_dependencies ：梳理表之間的依賴關系，
ref_optimizer_key_uses ：如果優化器認為查詢可以使用ref的話，在這里列出可以使用的索引，
rows_estimation{ ：估算表行數和掃描的代價，如果查詢中存在range掃描的話，對range掃描進行計劃分析及代價估算，
table_scan：全表掃描的行數(rows)以及所需要的代價(cost)，
potential_range_indexes：該階段會列出表中所有的索引并分析其是否可用，并且還會列出索引中可用的列欄位，
analyzing_range_alternatives ：分析可選方案的代價，
}
considered_execution_plans{ ：對比各可行計劃的代價，選擇相對最優的執行計劃，
plan_prefix：前置的執行計劃，
best_access_path：當前最優的執行順序資訊結果集，
access_type表示使用索引的方式，可參照為explain中的type欄位，
condition_filtering_pct：類似于explain中的filtered列，這是一個估算值，
rows_for_plan：該執行計劃最終的掃描行數，這里的行數其實也是估算值，是由considered_access_paths的resulting_rows相乘之后再乘以condition_filtering_pct獲得，
cost_for_plan：該執行計劃的執行代價，由considered_access_paths的cost相加而得，
chosen：是否選擇了該執行計劃，
}
attaching_conditions_to_tables ：添加附加條件，使得條件盡可能篩選單表資料，
refine_plan ：優化后的執行計劃，
trace資訊中的json資訊很長，因為我們關心的是不同執行計劃的cost區別，所以只需要重點關注兩個部分rows_estimation 和considered_execution_plans
代價模型計算：
統計資訊和cost計算引數：
計算cost會涉及到表的主鍵索引資料頁(聚簇索引)數量和表中的記錄數，兩個資訊都可以通過innodb的表統計資訊mysql.innodb_table_stats查到，n_rows是記錄數，clustered_index_size是聚簇索引頁數，
mysql> select * from mysql.innodb_table_stats where table_name='basic_person_info';
+---------------+-------------------+---------------------+--------+----------------------+--------------------------+
| database_name | table_name | last_update | n_rows | clustered_index_size | sum_of_other_index_sizes |
+---------------+-------------------+---------------------+--------+----------------------+--------------------------+
| test | basic_person_info | 2022-12-23 18:27:24 | 86632 | 737 | 1401 |
+---------------+-------------------+---------------------+--------+----------------------+--------------------------+
1 row in set (0.01 sec)
代價模型將操作分為Server層和Engine（存盤引擎）層兩類，Server層主要是CPU代價，Engine層主要是IO代價，比如MySQL從磁盤讀取一個資料頁的代價io_block_read_cost為1，從buffer pool讀取的代價memory_block_read_cost為0.25，計算符合條件的行代價為row_evaluate_cost為0.1，除此之外還有：
- memory_temptable_create_cost (default 1.0) 記憶體臨時表的創建代價，
- memory_temptable_row_cost (default 0.1) 記憶體臨時表的行代價，
- key_compare_cost (default 0.1) 鍵比較的代價，例如排序，
- disk_temptable_create_cost (default 20.0) 內部myisam或innodb臨時表的創建代價，
- disk_temptable_row_cost (default 0.5) 內部myisam或innodb臨時表的行代價，
這些都可以通過mysql.server_cost、mysql.engine_cost查看defalt值和設定值
mysql> select * from mysql.server_cost;
+------------------------------+------------+---------------------+---------+---------------+
| cost_name | cost_value | last_update | comment | default_value |
+------------------------------+------------+---------------------+---------+---------------+
| disk_temptable_create_cost | NULL | 2022-05-11 16:09:37 | NULL | 20 |
| disk_temptable_row_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.5 |
| key_compare_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.05 |
| memory_temptable_create_cost | NULL | 2022-05-11 16:09:37 | NULL | 1 |
| memory_temptable_row_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.1 |
| row_evaluate_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.1 |
+------------------------------+------------+---------------------+---------+---------------+
mysql> select * from mysql.engine_cost;
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| engine_name | device_type | cost_name | cost_value | last_update | comment | default_value |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| default | 0 | io_block_read_cost | NULL | 2022-05-11 16:09:37 | NULL | 1 |
| default | 0 | memory_block_read_cost | NULL | 2023-01-09 11:17:39 | NULL | 0.25 |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
計算公式：
如上面介紹的一樣，代價模型將操作分為兩類io_cost和cpu_cost，io_cost+cpu_cost就是總的cost，下面是具體的計算方法：
全表掃描：
全表掃描成本 = io_cost + 1.1 + cpu_cost + 1 (io_cost +1.1和cpu_cost +1在代碼里是直接硬加上的，不知道為什么，計算的時候直接加上)
io_cost = clustered_index_size (統計資訊中的主鍵頁數) * avg_single_page_cost(讀取一個頁的平均成本)
avg_single_page_cost = pages_in_memory_percent * 0.25(memory_block_read_cost) + pages_on_disk_percent * 1.0(io_block_read_cost)
pages_in_memory_percent 表示已經加載到 Buffer Pool 中的葉結點占所有葉結點的比例 pages_on_disk_percent 表示沒有加載到 Buffer Pool 中的葉結點占所有葉結點的比例
所以當資料已經全部讀取到buffer pool中的時候：
io_cost=clustered_index_size * 0.25
都沒有讀取到buffer pool中的時候：
io_cost=clustered_index_size * 1.0
當部分資料在buffer pool中，部分資料需要從磁盤讀取時，這時的系數介于0.25到1之間
cpu_cost = n_rows(統計資訊中記錄數) * 0.1(row_evaluate_cost)
走索引的成本：
和全表掃描的計算方法類似，其中io_cost與搜索的區間數有關，比如掃描三個區間where a between 1 and 10 or a between 20 and 30 or a between 40 and 50，此時：
io_cost=3 * avg_single_page_cost
cpu_cost=記錄數 * 0.1(row_evaluate_cost)+0.01(代碼中的微調引數)
針對二級索引還會有回表的操作：
MySQL認為每次回表都相當于是訪問一個頁面，所以每次回表都會進行一次IO，這部分成本：
io_cost=rows(記錄數)*avg_single_page_cost
對回表查詢的資料還需要進行一次計算：
cpu_cost=rows(記錄數) * 0.1(row_evaluate_cost)(需要注意的是當索引需要回表掃描時，在rows_estimation階段并不會計算這個值，在considered_execution_plans階段會重新加上這部分成本)
所以針對需要回表的查詢：
io_cost=查詢區間 * avg_single_page_cost + rows(記錄數) * avg_single_page_cost
cpu_cost=記錄數 * 0.1(row_evaluate_cost) + 0.01(代碼中的微調引數) + rows(記錄數) * 0.1(row_evaluate_cost)
例子：
mysql> set optimizer_trace='enabled=on';
Query OK, 0 rows affected (0.00 sec)
mysql>explain select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
| 1 | SIMPLE | t2 | NULL | range | id_num_unique,idx_age,idx_age_id_num | idx_age | 1 | NULL | 9594 | 100.00 | Using index condition |
| 1 | SIMPLE | t1 | NULL | eq_ref | id_num_unique,idx_age | id_num_unique | 60 | test.t2.id_num | 1 | 50.00 | Using where |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
2 rows in set, 1 warning (0.04 sec)
查看optimizer_trace的內容
select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20 | {
"steps": [
{
"join_preparation": {
"select#": 1,
"steps": [
{
"expanded_query": "/* select#1 */ select `t1`.`id` AS `id`,`t1`.`id_num` AS `id_num`,`t1`.`lastname` AS `lastname`,`t1`.`firstname` AS `firstname`,`t1`.`mobile` AS `mobile`,`t1`.`sex` AS `sex`,`t1`.`birthday` AS `birthday`,`t1`.`age` AS `age`,`t1`.`top_education` AS `top_education`,`t1`.`address` AS `address`,`t1`.`income_by_year` AS `income_by_year`,`t1`.`create_time` AS `create_time`,`t1`.`update_time` AS `update_time`,`t2`.`id` AS `id`,`t2`.`id_num` AS `id_num`,`t2`.`lastname` AS `lastname`,`t2`.`firstname` AS `firstname`,`t2`.`mobile` AS `mobile`,`t2`.`sex` AS `sex`,`t2`.`birthday` AS `birthday`,`t2`.`age` AS `age`,`t2`.`top_education` AS `top_education`,`t2`.`address` AS `address`,`t2`.`income_by_year` AS `income_by_year`,`t2`.`create_time` AS `create_time`,`t2`.`update_time` AS `update_time` from (`basic_person_info` `t1` join `basic_person_info2` `t2` on((`t1`.`id_num` = `t2`.`id_num`))) where ((`t1`.`age` > 10) and (`t2`.`age` < 20))"
},
{
"transformations_to_nested_joins": {
"transformations": [
"JOIN_condition_to_WHERE",
"parenthesis_removal"
],
"expanded_query": "/* select#1 */ select `t1`.`id` AS `id`,`t1`.`id_num` AS `id_num`,`t1`.`lastname` AS `lastname`,`t1`.`firstname` AS `firstname`,`t1`.`mobile` AS `mobile`,`t1`.`sex` AS `sex`,`t1`.`birthday` AS `birthday`,`t1`.`age` AS `age`,`t1`.`top_education` AS `top_education`,`t1`.`address` AS `address`,`t1`.`income_by_year` AS `income_by_year`,`t1`.`create_time` AS `create_time`,`t1`.`update_time` AS `update_time`,`t2`.`id` AS `id`,`t2`.`id_num` AS `id_num`,`t2`.`lastname` AS `lastname`,`t2`.`firstname` AS `firstname`,`t2`.`mobile` AS `mobile`,`t2`.`sex` AS `sex`,`t2`.`birthday` AS `birthday`,`t2`.`age` AS `age`,`t2`.`top_education` AS `top_education`,`t2`.`address` AS `address`,`t2`.`income_by_year` AS `income_by_year`,`t2`.`create_time` AS `create_time`,`t2`.`update_time` AS `update_time` from `basic_person_info` `t1` join `basic_person_info2` `t2` where ((`t1`.`age` > 10) and (`t2`.`age` < 20) and (`t1`.`id_num` = `t2`.`id_num`))"
}
}
]
}
},
{
"join_optimization": {
"select#": 1,
"steps": [
{
"condition_processing": {
"condition": "WHERE",
"original_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and (`t1`.`id_num` = `t2`.`id_num`))",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and multiple equal(`t1`.`id_num`, `t2`.`id_num`))"
},
{
"transformation": "constant_propagation",
"resulting_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and multiple equal(`t1`.`id_num`, `t2`.`id_num`))"
},
{
"transformation": "trivial_condition_removal",
"resulting_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and multiple equal(`t1`.`id_num`, `t2`.`id_num`))"
}
]
}
},
{
"substitute_generated_columns": {
}
},
{
"table_dependencies": [
{
"table": "`basic_person_info` `t1`",
"row_may_be_null": false,
"map_bit": 0,
"depends_on_map_bits": [
]
},
{
"table": "`basic_person_info2` `t2`",
"row_may_be_null": false,
"map_bit": 1,
"depends_on_map_bits": [
]
}
]
},
{
"ref_optimizer_key_uses": [
{
"table": "`basic_person_info` `t1`",
"field": "id_num",
"equals": "`t2`.`id_num`",
"null_rejecting": true
},
{
"table": "`basic_person_info2` `t2`",
"field": "id_num",
"equals": "`t1`.`id_num`",
"null_rejecting": true
}
]
},
{
"rows_estimation": [
{
"table": "`basic_person_info` `t1`",
"range_analysis": {
"table_scan": {
"rows": 86734,
"cost": 8859.75
t1表的scan成本=聚簇索引頁數*0.25 + 行數 * 0.1 +1.1+1
737*0.25+1.1+86734*0.1+1=8859.75
},
"potential_range_indexes": [
{
"index": "PRIMARY",
"usable": false,
"cause": "not_applicable"
},
{
"index": "id_num_unique",
"usable": false,
"cause": "not_applicable"
},
{
"index": "mobile_unique",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_name",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_top_education",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_create_time",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_mobile",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_age",
"usable": true,
"key_parts": [
"age",
"id"
]
}
],
"setup_range_conditions": [
],
"group_index_range": {
"chosen": false,
"cause": "not_single_table"
},
"skip_scan_range": {
"chosen": false,
"cause": "not_single_table"
},
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "idx_age",
"ranges": [
"10 < age"
],
"index_dives_for_eq_ranges": true,
"rowid_ordered": false,
"using_mrr": false,
"index_only": false,
"rows": 43367,
"cost": 15178.7,
通過索引idx_age讀取資料：
io_cost=區間數* 0.25 +記錄數* 0.25
io_cost=1*0.25+43367*0.25=10,842
cpu_cost=記錄數* 0.1 (沒有回表的cost)
cpu_cost=43367*0.1=4,336.7
cost=10842+4,336.7=15178.7
"chosen": false,
"cause": "cost"
}
],
"analyzing_roworder_intersect": {
"usable": false,
"cause": "too_few_roworder_scans"
}
}
}
},
{
"table": "`basic_person_info2` `t2`",
"range_analysis": {
"table_scan": {
"rows": 73845,
"cost": 7538.85
t2表的scan成本=聚簇索引頁數*0.25 + 行數 * 0.1 +1.1+1
609*0.25+1+73845*0.1+1.1=7538.85
},
"potential_range_indexes": [
{
"index": "PRIMARY",
"usable": false,
"cause": "not_applicable"
},
{
"index": "id_num_unique",
"usable": false,
"cause": "not_applicable"
},
{
"index": "mobile_unique",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_name",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_top_education",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_create_time",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_basic_person_info_mobile",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_age",
"usable": true,
"key_parts": [
"age",
"id"
]
},
{
"index": "idx_age_id_num",
"usable": true,
"key_parts": [
"age",
"id_num",
"id"
]
}
],
"setup_range_conditions": [
],
"group_index_range": {
"chosen": false,
"cause": "not_single_table"
},
"skip_scan_range": {
"chosen": false,
"cause": "not_single_table"
},
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "idx_age",
"ranges": [
"age < 20"
],
"index_dives_for_eq_ranges": true,
"rowid_ordered": false,
"using_mrr": false,
"index_only": false,
"rows": 9594,
"cost": 3358.16,
通過索引idx_age讀取資料：
io_cost=區間數* 0.25 +記錄數* 0.25
io_cost=1*0.25+9594*0.25=2,398.75
cpu_cost=記錄數* 0.1 (沒有回表的cost)
cpu_cost=9594*0.1959.4
cost=2,398.75+959.4=3,358.15
"chosen": true
},
{
"index": "idx_age_id_num",
"ranges": [
"age < 20"
],
"index_dives_for_eq_ranges": true,
"rowid_ordered": false,
"using_mrr": false,
"index_only": false,
"rows": 19086,
"cost": 6680.36,
通過索引idx_age_id_num讀取資料：
io_cost=區間數* 0.25 +記錄數* 0.25
io_cost=1*0.25+19086*0.25=4,771.75
cpu_cost=記錄數* 0.1 (沒有回表的cost)
cpu_cost=19086*0.1=1908.6
cost=4,771.75+1908.6=6,680.35
"chosen": false,
"cause": "cost"
}
],
"analyzing_roworder_intersect": {
"usable": false,
"cause": "too_few_roworder_scans"
}
},
"chosen_range_access_summary": {
"range_access_plan": {
"type": "range_scan",
"index": "idx_age",
"rows": 9594,
"ranges": [
"age < 20"
]
},
"rows_for_plan": 9594,
"cost_for_plan": 3358.16,
"chosen": true
}
}
}
]
},
{
"considered_execution_plans": [
{
"plan_prefix": [
],
"table": "`basic_person_info2` `t2`",
"best_access_path": {
"considered_access_paths": [
{
"access_type": "ref",
"index": "id_num_unique",
"usable": false,
"chosen": false
},
{
"rows_to_scan": 9594,
"filtering_effect": [
],
"final_filtering_effect": 1,
"access_type": "range",
"range_details": {
"used_index": "idx_age"
},
"resulting_rows": 9594,
"cost": 4317.56,
通過索引idx_age讀取資料：
io_cost=區間數* 0.25 +記錄數* 0.25
io_cost=1*0.25+9594*0.25=2,398.75
cpu_cost=記錄數* 0.1 + 記錄數* 0.1
cpu_cost=9594*0.1*2=1,918.8
cost=2,398.75+1,918.8=4317.56
"chosen": true
}
]
},
"condition_filtering_pct": 100,
"rows_for_plan": 9594,
"cost_for_plan": 4317.56,
"rest_of_plan": [
{
"plan_prefix": [
"`basic_person_info2` `t2`"
],
"table": "`basic_person_info` `t1`",
"best_access_path": {
"considered_access_paths": [
{
"access_type": "eq_ref",
"index": "id_num_unique",
"rows": 1,
"cost": 3357.9,
io_cost=t2表記錄數*0.25=9594*0.25=2398.5
cpu_cost=記錄數*0.1=9594*0.1=959.4
cost=2398.5+959.4=3357.9
"chosen": true
},
{
"rows_to_scan": 86734,
"filtering_effect": [
],
"final_filtering_effect": 0.5,
"access_type": "scan",
"using_join_cache": true,
"buffers_needed": 14,
"resulting_rows": 43367,
"cost": 4.16701e+07,
"chosen": false
}
]
},
"condition_filtering_pct": 100,
"rows_for_plan": 9594,
"cost_for_plan": 7675.46,
總cost=4,317.56+3,357.9=7,675.46
"chosen": true
}
]
},
{
"plan_prefix": [
],
"table": "`basic_person_info` `t1`",
"best_access_path": {
"considered_access_paths": [
{
"access_type": "ref",
"index": "id_num_unique",
"usable": false,
"chosen": false
},
{
"rows_to_scan": 86734,
"filtering_effect": [
],
"final_filtering_effect": 0.5,
"access_type": "scan",
"resulting_rows": 43367,
"cost": 8857.65,
t1的scan成本
"chosen": true
}
]
},
"condition_filtering_pct": 100,
"rows_for_plan": 43367,
"cost_for_plan": 8857.65,
"pruned_by_cost": true
放棄后續的計算
}
]
},
{
"attaching_conditions_to_tables": {
"original_condition": "((`t1`.`id_num` = `t2`.`id_num`) and (`t1`.`age` > 10) and (`t2`.`age` < 20))",
"attached_conditions_computation": [
],
"attached_conditions_summary": [
{
"table": "`basic_person_info2` `t2`",
"attached": "(`t2`.`age` < 20)"
},
{
"table": "`basic_person_info` `t1`",
"attached": "((`t1`.`id_num` = `t2`.`id_num`) and (`t1`.`age` > 10))"
}
]
}
},
{
"finalizing_table_conditions": [
{
"table": "`basic_person_info2` `t2`",
"original_table_condition": "(`t2`.`age` < 20)",
"final_table_condition ": "(`t2`.`age` < 20)"
},
{
"table": "`basic_person_info` `t1`",
"original_table_condition": "((`t1`.`id_num` = `t2`.`id_num`) and (`t1`.`age` > 10))",
"final_table_condition ": "(`t1`.`age` > 10)"
}
]
},
{
"refine_plan": [
{
"table": "`basic_person_info2` `t2`",
"pushed_index_condition": "(`t2`.`age` < 20)",
"table_condition_attached": null
},
{
"table": "`basic_person_info` `t1`"
}
]
}
]
}
},
{
"join_execution": {
"select#": 1,
"steps": [
]
}
}
]
}
成本常數修改：
前面已經介紹了成本常量值實際上存放在MySQL自帶的系統庫MySQL中的server_cost和engine_cost表中，其中server_cost表存放server層的成本常量，engine_cost表存放engine層成本常量
mysql> select * from mysql.server_cost;
+------------------------------+------------+---------------------+---------+---------------+
| cost_name | cost_value | last_update | comment | default_value |
+------------------------------+------------+---------------------+---------+---------------+
| disk_temptable_create_cost | NULL | 2022-05-11 16:09:37 | NULL | 20 |
| disk_temptable_row_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.5 |
| key_compare_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.05 |
| memory_temptable_create_cost | NULL | 2022-05-11 16:09:37 | NULL | 1 |
| memory_temptable_row_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.1 |
| row_evaluate_cost | NULL | 2022-05-11 16:09:37 | NULL | 0.1 |
+------------------------------+------------+---------------------+---------+---------------+
mysql> select * from mysql.engine_cost;
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| engine_name | device_type | cost_name | cost_value | last_update | comment | default_value |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| default | 0 | io_block_read_cost | NULL | 2022-05-11 16:09:37 | NULL | 1 |
| default | 0 | memory_block_read_cost | NULL | 2023-01-09 11:17:39 | NULL | 0.25 |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
其中 default_value的值是系統默認的，不能修改，cost_value列的值我們可以修改，如果cost_value列的值不為空系統將用該值覆寫默認值，我們可以通過update陳述句來修改
mysql> update mysql.engine_cost set cost_value=https://www.cnblogs.com/greatsql/archive/2023/03/10/10 where cost_name='memory_block_read_cost';
Query OK, 0 rows affected (0.00 sec)
mysql> update mysql.engine_cost set cost_value=https://www.cnblogs.com/greatsql/archive/2023/03/10/10 where cost_name='io_block_read_cost';
Query OK, 0 rows affected (0.00 sec)
很多資料都說執行flush optimizer_costs就可以生效，不過我在修改完后并執行flush optimizer_costs并不能馬上生效，最后是通過重啟資料庫實體才生效，這個可能是資料庫版本的差異，大家可以自行驗證，
mysql> explain select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+-------+----------+-------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+-------+----------+-------------+
| 1 | SIMPLE | t2 | NULL | ALL | id_num_unique,idx_age,idx_age_id_num | NULL | NULL | NULL | 73990 | 12.97 | Using where |
| 1 | SIMPLE | t1 | NULL | eq_ref | id_num_unique,idx_age | id_num_unique | 60 | test.t2.id_num | 1 | 50.00 | Using where |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+-------+----------+-------------+
"table": "`basic_person_info2` `t2`",
"range_analysis": {
"table_scan": {
"rows": 73990,
"cost": 13491.1
全表掃描cost=609*10+73990*0.1+1.1+1= 13491.1
},
"index": "idx_age",
"ranges": [
"age < 20"
],
"index_dives_for_eq_ranges": true,
"rowid_ordered": false,
"using_mrr": false,
"index_only": false,
"rows": 9594,
"cost": 96909.4,
idx_age索引掃描cost=1*10+9594*10+9594*0.1=96,909.4
"chosen": false,
"cause": "cost"
},
修改后的執行計劃，發現t2表走了全表掃描了而沒有走idx_age索引，分別查看一下t2表走全表掃描和idx_age索引的cost發現全表掃描的cost為13491.1，而走索引的cost為96,909.4，因為全表掃描的cost比走索引低，所以優化器沒有選擇idx_age索引，
從這個例子可以看出，更改成本常量值會直接影響優化器的方案選擇，所以一定要慎重，沒有特殊原因建議不要修改，
explain format=json
雖然通過optimizer_trace可以看到很多詳細的優化器選擇程序，但是使用起來起來還是比較麻煩，需要過濾的資訊很多，這時explain format=json輸出json格式的分析資料也是一個不錯的選擇，它也包含陳述句將要執行的成本資訊，如下：
query_cost 總查詢成本
read_cost IO成本+除 eval_cost以外cpu成本
eval_cost 檢測rows * filter條記錄的成本
prefix_cost 單次查詢的成本,等于read_cost+eval_cost
mysql> explain format=json select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
{
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "7675.46"
},
"nested_loop": [
{
"table": {
"table_name": "t2",
"access_type": "range",
"possible_keys": [
"id_num_unique",
"idx_age",
"idx_age_id_num"
],
"key": "idx_age",
"used_key_parts": [
"age"
],
"key_length": "1",
"rows_examined_per_scan": 9594,
"rows_produced_per_join": 9594,
"filtered": "100.00",
"index_condition": "(`test`.`t2`.`age` < 20)",
"cost_info": {
"read_cost": "3358.16",
包含所有io成本+(cpu成本-eval_cost)
"eval_cost": "959.40",
計算扇出的cpu成本，優化器利用啟發式規則估算出滿足所有條件的的比例(filtered)
=rows_examined_per_scan*filtered*0.1
"prefix_cost": "4317.56",
單表查詢的總成本
"data_read_per_join": "3M"
},
"used_columns": [
"id",
"id_num",
"lastname",
"firstname",
"mobile",
"sex",
"birthday",
"age",
"top_education",
"address",
"income_by_year",
"create_time",
"update_time"
]
}
},
{
"table": {
"table_name": "t1",
"access_type": "eq_ref",
"possible_keys": [
"id_num_unique",
"idx_age"
],
"key": "id_num_unique",
"used_key_parts": [
"id_num"
],
"key_length": "60",
"ref": [
"test.t2.id_num"
],
"rows_examined_per_scan": 1,
"rows_produced_per_join": 4797,
"filtered": "50.00",
"cost_info": {
"read_cost": "2398.50",
包含所有io成本+(cpu成本-eval_cost)
"eval_cost": "479.70",
計算扇出的cpu成本，優化器利用啟發式規則估算出滿足所有條件的的比例(filtered)
=rows_examined_per_scan*filtered*0.1
"prefix_cost": "7675.46",
兩表查詢的總cost
"data_read_per_join": "1M"
},
"used_columns": [
"id",
"id_num",
"lastname",
"firstname",
"mobile",
"sex",
"birthday",
"age",
"top_education",
"address",
"income_by_year",
"create_time",
"update_time"
],
"attached_condition": "(`test`.`t1`.`age` > 10)"
}
}
]
}
}
另外，explain結合show warnings陳述句一起使用還可以得知優化器改寫后的陳述句
mysql> show warnings;
+-------+------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Level | Code | Message |
+-------+------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Note | 1003 | /* select#1 */ select `test`.`t1`.`id` AS `id`,`test`.`t1`.`id_num` AS `id_num`,`test`.`t1`.`lastname` AS `lastname`,`test`.`t1`.`firstname` AS `firstname`,`test`.`t1`.`mobile` AS `mobile`,`test`.`t1`.`sex` AS `sex`,`test`.`t1`.`birthday` AS `birthday`,`test`.`t1`.`age` AS `age`,`test`.`t1`.`top_education` AS `top_education`,`test`.`t1`.`address` AS `address`,`test`.`t1`.`income_by_year` AS `income_by_year`,`test`.`t1`.`create_time` AS `create_time`,`test`.`t1`.`update_time` AS `update_time`,`test`.`t2`.`id` AS `id`,`test`.`t2`.`id_num` AS `id_num`,`test`.`t2`.`lastname` AS `lastname`,`test`.`t2`.`firstname` AS `firstname`,`test`.`t2`.`mobile` AS `mobile`,`test`.`t2`.`sex` AS `sex`,`test`.`t2`.`birthday` AS `birthday`,`test`.`t2`.`age` AS `age`,`test`.`t2`.`top_education` AS `top_education`,`test`.`t2`.`address` AS `address`,`test`.`t2`.`income_by_year` AS `income_by_year`,`test`.`t2`.`create_time` AS `create_time`,`test`.`t2`.`update_time` AS `update_time` from `test`.`basic_person_info` `t1` join `test`.`basic_person_info2` `t2` where ((`test`.`t1`.`id_num` = `test`.`t2`.`id_num`) and (`test`.`t1`.`age` > 10) and (`test`.`t2`.`age` < 20)) |
+-------+------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec)
總結：
- MySQL的優化器是基于成本來選擇最優執行方案的，哪個成本最少就選哪個，所以重點在于計算出各個執行計劃的cost
- 成本由CPU成本和IO成本組成，每個成本常數值可以自己調整，非必要的情況下不要調整，以免影響整個資料庫的執行計劃選擇
- 通過開啟optimizer_trace可以跟蹤優化器的各個環節的分析步驟，可以判斷有時候為什么沒有走索引而走了全表掃描
- explain加上format=json選項后可以查看成本資訊分為read_cost和eval_cost，但只能看到當前已經選擇的執行計劃，另外通過show warnings可以看到優化器改寫后的陳述句
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