SHOW PLAN SQL 语句和 "plan": N JSON 接口选项显示查询执行计划。计划在实际执行过程中生成并存储,因此在 SQL 的情况下,必须在当前会话中在运行该语句之前启用分析。可以通过 SET profiling=1 语句完成此操作。
在 SQL 模式下返回两个项目:
transformed_tree,显示全文查询的分解文本。enabled_indexes,提供有关有效二级索引的信息。
要在 JSON 查询中查看查询执行计划,请在查询中添加 "plan": N。结果将作为结果集中的 plan 属性出现。N 可以是以下之一:
- 1 - 仅显示根节点的文本计划,类似于
SHOW PLANSQL 查询返回的内容。这是最紧凑的形式。 - 2 - 仅显示 JSON 对象计划,便于处理。
- 3 - 显示带有每个节点文本描述的 JSON 对象。请注意,子节点的描述也存在,并且重复了父节点描述的一部分,这使得整个表示相当庞大。
- SQL
- JSON
set profiling=1;
select * from hn_small where match('dog|cat') limit 0;
show plan;POST /search
{
"table": "hn_small",
"query": {"query_string": "dog|cat"},
"_source": { "excludes":["*"] },
"limit": 0,
"plan": 3
}*************************** 1. row ***************************
Variable: transformed_tree
Value: OR(
AND(KEYWORD(dog, querypos=1)),
AND(KEYWORD(cat, querypos=2)))
*************************** 2. row ***************************
Variable: enabled_indexes
Value:
2 rows in set (0.00 sec){
"took": 0,
"timed_out": false,
"hits": {
"total": 4453,
"total_relation": "eq",
"hits": []
},
"plan": {
"query": {
"type": "OR",
"description": "OR( AND(KEYWORD(dog, querypos=1)), AND(KEYWORD(cat, querypos=2)))",
"children": [
{
"type": "AND",
"description": "AND(KEYWORD(dog, querypos=1))",
"children": [
{
"type": "KEYWORD",
"word": "dog",
"querypos": 1
}
]
},
{
"type": "AND",
"description": "AND(KEYWORD(cat, querypos=2))",
"children": [
{
"type": "KEYWORD",
"word": "cat",
"querypos": 2
}
]
}
]
}
}
}在某些情况下,由于扩展和其他转换,评估的查询树可能与原始查询树有很大不同。
- SQL
- JSON
SET profiling=1;
SELECT id FROM forum WHERE MATCH('@title way* @content hey') LIMIT 1;
SHOW PLAN;POST /search
{
"table": "forum",
"query": {"query_string": "@title way* @content hey"},
"_source": { "excludes":["*"] },
"limit": 1,
"plan": 1
}Query OK, 0 rows affected (0.00 sec)
+--------+
| id |
+--------+
| 711651 |
+--------+
1 row in set (0.04 sec)
+------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Variable | Value |
+------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| transformed_tree | AND(
OR(
OR(
AND(fields=(title), KEYWORD(wayne, querypos=1, expanded)),
OR(
AND(fields=(title), KEYWORD(ways, querypos=1, expanded)),
AND(fields=(title), KEYWORD(wayyy, querypos=1, expanded)))),
AND(fields=(title), KEYWORD(way, querypos=1, expanded)),
OR(fields=(title), KEYWORD(way*, querypos=1, expanded))),
AND(fields=(content), KEYWORD(hey, querypos=2))) |
+------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec){
"took":33,
"timed_out":false,
"hits":
{
"total":105,
"hits":
[
{
"_id": 711651,
"_score":2539,
"_source":{}
}
]
},
"plan":
{
"query":
{
"description":"AND( OR( OR( AND(fields=(title), KEYWORD(wayne, querypos=1, expanded)), OR( AND(fields=(title), KEYWORD(ways, querypos=1, expanded)), AND(fields=(title), KEYWORD(wayyy, querypos=1, expanded)))), AND(fields=(title), KEYWORD(way, querypos=1, expanded)), OR(fields=(title), KEYWORD(way*, querypos=1, expanded))), AND(fields=(content), KEYWORD(hey, querypos=2)))"
}
}
}另请参见 EXPLAIN QUERY。它显示全文查询的执行树,而不实际执行查询。请注意,当在实时表查询后使用 SHOW PLAN 时,结果将基于随机的磁盘/RAM 块。因此,如果您最近修改了表的分词设置,或者块在字典等方面差异较大,您可能无法获得预期的结果。请考虑这一点,并考虑同时使用 EXPLAIN QUERY。
query 属性包含转换后的全文查询树。每个节点包含:
type:节点类型。可以是AND、OR、PHRASE、KEYWORD等。description:该节点的查询子树,以字符串形式显示(SHOW PLAN格式)。children:子节点(如果有)。max_field_pos:字段内的最大位置。word:转换后的关键词。仅关键词节点。querypos:该关键词在查询中的位置。仅关键词节点。excluded:关键词是否被排除在查询之外。仅关键词节点。expanded:关键词是否由前缀扩展添加。仅关键词节点。field_start:关键词必须出现在字段的开头。仅关键词节点。field_end:关键词必须出现在字段的结尾。仅关键词节点。boost:关键词的 IDF 将乘以此值。仅关键词节点。
SHOW PLAN format=dot 允许以分层格式返回全文查询执行树,适合使用现有工具进行可视化,例如 https://dreampuf.github.io/GraphvizOnline:
MySQL [(none)]> show plan option format=dot\G
*************************** 1. row ***************************
Variable: transformed_tree
Value: digraph "transformed_tree"
{
0 [shape=record,style=filled,bgcolor="lightgrey" label="AND"]
0 -> 1
1 [shape=record,style=filled,bgcolor="lightgrey" label="AND"]
1 -> 2
2 [shape=record label="i | { querypos=1 }"]
0 -> 3
3 [shape=record,style=filled,bgcolor="lightgrey" label="AND"]
3 -> 4
4 [shape=record label="me | { querypos=2 }"]
}

≫ 表设置和状态
SHOW TABLE INDEXES SQL 语句显示指定表的可用二级索引及其属性。二级索引 通过创建额外的数据结构来加速特定列的搜索,从而提升查询性能。
语法如下:
SHOW TABLE table_name INDEXES
显示的属性包括以下列:
- Name:二级索引的名称。可用于查询优化器提示。
- Type:二级索引中存储的数据类型。对于普通属性,类型与原始属性类型相同。对于从 JSON 属性生成的二级索引,类型通过扫描所有文档并确定所有 JSON 属性的类型来推断。
- Enabled:指示索引当前是否启用,是否可用于提升搜索速度。当属性被更新时,该属性的二级索引会暂时禁用,直到索引重建。您可以使用ALTER TABLE ... REBUILD SECONDARY 命令重建被禁用的索引。
- Percent:在 RT 表中,不同的磁盘块可能包含不同的二级索引,尤其是在使用 JSON 属性时。此百分比显示有多少块包含具有相同名称、类型和启用状态的索引。
注意: 对于 RT 表,二级索引仅为磁盘块创建,不为 RAM 段中的数据创建。当您首次向 RT 表插入数据时,数据保存在 RAM 中,二级索引不会显示。索引仅在数据刷新到磁盘块后可见,默认情况下,当表变为活动状态(同时接收插入和搜索)时会自动刷新。
- SQL
SHOW TABLE test INDEXES;+------------------------------+--------+---------+---------+
| Name | Type | Enabled | Percent |
+------------------------------+--------+---------+---------+
| j['addresses'] | uint32 | 1 | 100 |
| j['addresses']['a1'] | uint32 | 1 | 100 |
| j['addresses']['a2'] | uint32 | 1 | 100 |
| j['addresses']['a3'] | uint32 | 1 | 100 |
| j['addresses']['a4'] | uint32 | 1 | 100 |
| j['addresses']['a5'] | uint32 | 1 | 100 |
| j['addresses']['a6'] | uint32 | 1 | 100 |
| j['factor'] | uint32 | 1 | 100 |
| j['int_arr'] | uint32 | 1 | 100 |
| j['tags'] | uint32 | 1 | 100 |
| id | int64 | 1 | 100 |
| j['price'] | float | 1 | 100 |
| j['addresses']['a1']['id'] | string | 1 | 100 |
| j['addresses']['a1']['name'] | string | 1 | 100 |
| j['addresses']['a2']['id'] | string | 1 | 100 |
| j['addresses']['a2']['name'] | string | 1 | 100 |
| j['addresses']['a3']['id'] | string | 1 | 100 |
| j['addresses']['a3']['name'] | string | 1 | 100 |
| j['addresses']['a4']['id'] | string | 1 | 100 |
| j['addresses']['a4']['name'] | string | 1 | 100 |
| j['addresses']['a5']['id'] | string | 1 | 100 |
| j['addresses']['a5']['name'] | string | 1 | 100 |
| j['addresses']['a6']['id'] | string | 1 | 100 |
| j['addresses']['a6']['name'] | string | 1 | 100 |
| j['arr'] | string | 1 | 100 |
| j['str'] | string | 1 | 100 |
| j['tags']['1'] | string | 1 | 100 |
| j['tags']['2'] | string | 1 | 100 |
| j['tags']['3'] | string | 1 | 100 |
+------------------------------+--------+---------+---------+
29 rows in set (0.00 sec)SHOW TABLE STATUS 是一个显示每个表各种统计信息的 SQL 语句。
语法为:
SHOW TABLE table_name STATUS
根据索引类型,显示的统计信息包含不同的行集:
- template:
index_type。 - distributed:
index_type,query_time_1min,query_time_5min,query_time_15min,query_time_total,exact_query_time_1min,exact_query_time_5min,exact_query_time_15min,exact_query_time_total,found_rows_1min,found_rows_5min,found_rows_15min,found_rows_total。 - percolate:
index_type,stored_queries,ram_bytes,disk_bytes,max_stack_need,average_stack_base,desired_thread_stack,tid,tid_saved,query_time_1min,query_time_5min,query_time_15min,query_time_total,exact_query_time_1min,exact_query_time_5min,exact_query_time_15min,exact_query_time_total,found_rows_1min,found_rows_5min,found_rows_15min,found_rows_total。 - plain:
index_type,indexed_documents,indexed_bytes,可能包含一组field_tokens_*和total_tokens,ram_bytes,disk_bytes,disk_mapped,disk_mapped_cached,disk_mapped_doclists,disk_mapped_cached_doclists,disk_mapped_hitlists,disk_mapped_cached_hitlists,killed_documents,killed_rate,query_time_1min,query_time_5min,query_time_15min,query_time_total,exact_query_time_1min,exact_query_time_5min,exact_query_time_15min,exact_query_time_total,found_rows_1min,found_rows_5min,found_rows_15min,found_rows_total。 - rt:
index_type,indexed_documents,indexed_bytes,可能包含一组field_tokens_*和total_tokens,ram_bytes,disk_bytes,disk_mapped,disk_mapped_cached,disk_mapped_doclists,disk_mapped_cached_doclists,disk_mapped_hitlists,disk_mapped_cached_hitlists,killed_documents,killed_rate,ram_chunk,ram_chunk_segments_count,disk_chunks,mem_limit,mem_limit_rate,ram_bytes_retired,optimizing,locked,tid,tid_saved,query_time_1min,query_time_5min,query_time_15min,query_time_total,exact_query_time_1min,exact_query_time_5min,exact_query_time_15min,exact_query_time_total,found_rows_1min,found_rows_5min,found_rows_15min,found_rows_total。
以下是这些值的含义:
index_type:当前为disk、rt、percolate、template和distributed之一。indexed_documents:已索引文档的数量。indexed_bytes:已索引文本的总体大小。注意,该值不是严格的,因为在全文索引中不可能严格还原存储的文本来测量它。stored_queries:表中存储的 percolate 查询数量。field_tokens_XXX:可选,整个表中每个字段的总长度(以令牌计)(内部用于BM25A和BM25F排名函数)。仅适用于使用index_field_lengths=1构建的表。total_tokens:可选,所有field_tokens_XXX的总和。ram_bytes:表占用的总 RAM。disk_bytes:表占用的总磁盘空间。disk_mapped:文件映射的总大小。disk_mapped_cached:实际缓存于 RAM 中的文件映射总大小。disk_mapped_doclists和disk_mapped_cached_doclists:属于文档列表的总映射和缓存映射部分。disk_mapped_hitlists和disk_mapped_cached_hitlists:属于命中列表的总映射和缓存映射部分。文档列表和命中列表的值分开显示,因为它们通常很大(例如,大约占整个表大小的 90%)。killed_documents和killed_rate:前者表示已删除文档的数量,后者表示删除文档与已索引文档的比例。从技术上讲,删除文档意味着在搜索结果中屏蔽它,但它仍然物理存在于表中,只有在合并/优化表后才会被清除。ram_chunk:实时或 percolate 表的 RAM 块大小。ram_chunk_segments_count:RAM 块内部由段组成,通常不超过 32。此行显示当前段数。disk_chunks:实时表中的磁盘块数量。mem_limit:表的rt_mem_limit实际值。mem_limit_rate:RAM 块被刷新为磁盘块的比例,例如,如果rt_mem_limit是 128M,比例是 50%,当 RAM 块超过 64M 时将保存一个新的磁盘块。ram_bytes_retired:表示 RAM 块中的垃圾大小(例如,已删除或替换但尚未永久移除的文档)。optimizing:值大于 0 表示表当前正在执行优化(即正在合并某些磁盘块)。locked:值大于 0 表示表当前被 FREEZE 锁定。数字表示表被冻结的次数。例如,表可能先被manticore-backup冻结,然后又被复制冻结。只有当没有其他进程需要冻结时,才应完全解冻。max_stack_need:计算存储的 percolate 查询中最复杂部分所需的栈空间。这是动态值,取决于构建细节如编译器、优化、硬件等。average_stack_base:通常在开始计算 percolate 查询时占用的栈空间。desired_thread_stack:上述值的总和,向上取整到 128 字节边界。如果此值大于thread_stack,则可能无法在此表上执行call pq,因为某些存储的查询会失败。默认thread_stack值为 1M(即 1048576);其他值应自行配置。tid和tid_saved:表示表的保存状态。tid随每次更改(事务)递增。tid_saved显示保存在<table>.ram文件中 RAM 块状态的最大tid。当数字不同时,某些更改仅存在于 RAM 中,并且也由 binlog 备份(如果启用)。执行FLUSH TABLE或安排定期刷新会保存这些更改。刷新后,binlog 被清除,tid_saved表示新的实际状态。query_time_*,exact_query_time_*:查询执行时间统计,针对最近 1 分钟、5 分钟、15 分钟和服务器启动以来的总时间;数据封装为 JSON 对象,包括查询次数以及最小、最大、平均、95 和 99 百分位值。found_rows_*:查询找到的行数统计;提供最近 1 分钟、5 分钟、15 分钟和服务器启动以来的总时间;数据封装为 JSON 对象,包括查询次数以及最小、最大、平均、95 和 99 百分位值。command_*:针对该表成功执行特定命令的总次数计数器。search_stats_ms_*:搜索查询执行时间(毫秒)统计。* 表示时间窗口(例如 1min、5min、15min、total)。这些统计基于 1、5 和 15 分钟的滑动窗口计算,显示查询时间的平均值、最小值、最大值以及 95 和 99 百分位值。insert_replace_stats_ms_*:插入和替换查询执行时间(毫秒)统计。* 表示时间窗口(例如 1min、5min、15min、total)。这些统计基于 1、5 和 15 分钟的滑动窗口计算,显示查询时间的平均值、最小值、最大值以及 95 和 99 百分位值。update_stats_ms_*:更新查询执行时间(毫秒)统计。* 表示时间窗口(例如 1min、5min、15min、total)。这些统计基于 1、5 和 15 分钟的滑动窗口计算,显示查询时间的平均值、最小值、最大值以及 95 和 99 百分位值。
- SQL
- PHP
- Python
- Python-asyncio
- Javascript
- Java
- C#
- Rust
- TypeScript
- Go
mysql> SHOW TABLE statistic STATUS;$index->status();utilsApi.sql('SHOW TABLE statistic STATUS')await utilsApi.sql('SHOW TABLE statistic STATUS')res = await utilsApi.sql('SHOW TABLE statistic STATUS');utilsApi.sql("SHOW TABLE statistic STATUS");utilsApi.Sql("SHOW TABLE statistic STATUS");utils_api.sql("SHOW TABLE statistic STATUS", Some(true)).await;res = await utilsApi.sql('SHOW TABLE statistic STATUS');apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW TABLE statistic STATUS").Execute()+-------------------------------+--------------------------------------------------------------------------+
| Variable_name | Value |
+-------------------------------+--------------------------------------------------------------------------+
| index_type | rt |
| indexed_documents | 146000 |
| indexed_bytes | 149504000 |
| ram_bytes | 87674788 |
| disk_bytes | 1762811 |
| disk_mapped | 794147 |
| disk_mapped_cached | 802816 |
| disk_mapped_doclists | 0 |
| disk_mapped_cached_doclists | 0 |
| disk_mapped_hitlists | 0 |
| disk_mapped_cached_hitlists | 0 |
| killed_documents | 0 |
| killed_rate | 0.00% |
| ram_chunk | 86865484 |
| ram_chunk_segments_count | 24 |
| disk_chunks | 1 |
| mem_limit | 134217728 |
| mem_limit_rate | 95.00% |
| ram_bytes_retired | 0 |
| optimizing | 1 |
| locked | 0 |
| tid | 0 |
| tid_saved | 0 |
| query_time_1min | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| query_time_5min | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| query_time_15min | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| query_time_total | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| found_rows_1min | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| found_rows_5min | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| found_rows_15min | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| found_rows_total | {"queries":0, "avg":"-", "min":"-", "max":"-", "pct95":"-", "pct99":"-"} |
| command_search | 2 |
| command_excerpt | 0 |
| command_update | 3 |
| command_keywords | 0 |
| command_status | 2 |
| command_delete | 0 |
| command_insert | 1 |
| command_replace | 0 |
| command_commit | 0 |
| command_suggest | 0 |
| command_callpq | 0 |
| command_getfield | 0 |
| insert_replace_stats_ms_avg | 0.284 0.284 0.284 |
| insert_replace_stats_ms_min | 0.284 0.284 0.284 |
| insert_replace_stats_ms_max | 0.284 0.284 0.284 |
| insert_replace_stats_ms_pct95 | 0.284 0.284 0.284 |
| insert_replace_stats_ms_pct99 | 0.284 0.284 0.284 |
| search_stats_ms_avg | 0.000 0.000 0.000 |
| search_stats_ms_min | 0.000 0.000 0.000 |
| search_stats_ms_max | 0.000 0.000 0.000 |
| search_stats_ms_pct95 | 0.000 0.000 0.000 |
| search_stats_ms_pct99 | 0.000 0.000 0.000 |
| update_stats_ms_avg | 0.479 0.479 0.479 |
| update_stats_ms_min | 0.431 0.431 0.431 |
| update_stats_ms_max | 0.530 0.530 0.530 |
| update_stats_ms_pct95 | 0.530 0.530 0.530 |
| update_stats_ms_pct99 | 0.530 0.530 0.530 |
+-------------------------------+--------------------------------------------------------------------------+
29 rows in set (0.00 sec)Array(
[index_type] => rt
[indexed_documents] => 3
[indexed_bytes] => 0
[ram_bytes] => 6678
[disk_bytes] => 611
[ram_chunk] => 990
[ram_chunk_segments_count] => 2
[mem_limit] => 134217728
[ram_bytes_retired] => 0
[optimizing] => 0
[locked] => 0
[tid] => 15
[query_time_1min] => {"queries":1, "avg_sec":0.001, "min_sec":0.001, "max_sec":0.001, "pct95_sec":0.001, "pct99_sec":0.001}
[query_time_5min] => {"queries":1, "avg_sec":0.001, "min_sec":0.001, "max_sec":0.001, "pct95_sec":0.001, "pct99_sec":0.001}
[query_time_15min] => {"queries":1, "avg_sec":0.001, "min_sec":0.001, "max_sec":0.001, "pct95_sec":0.001, "pct99_sec":0.001}
[query_time_total] => {"queries":1, "avg_sec":0.001, "min_sec":0.001, "max_sec":0.001, "pct95_sec":0.001, "pct99_sec":0.001}
[found_rows_1min] => {"queries":1, "avg":3, "min":3, "max":3, "pct95":3, "pct99":3}
[found_rows_5min] => {"queries":1, "avg":3, "min":3, "max":3, "pct95":3, "pct99":3}
[found_rows_15min] => {"queries":1, "avg":3, "min":3, "max":3, "pct95":3, "pct99":3}
[found_rows_total] => {"queries":1, "avg":3, "min":3, "max":3, "pct95":3, "pct99":3}
[command_search] => 2
[command_excerpt] => 0
[command_update] => 3
[command_keywords] => 0
[command_status] => 2
[command_delete] => 0
[command_insert] => 1
[command_replace] => 0
[command_commit] => 0
[command_suggest] => 0
[command_callpq] => 0
[command_getfield] => 0
[insert_replace_stats_ms_avg] => 0.284 0.284 0.284
[insert_replace_stats_ms_min] => 0.284 0.284 0.284
[insert_replace_stats_ms_max] => 0.284 0.284 0.284
[insert_replace_stats_ms_pct95] => 0.284 0.284 0.284
[insert_replace_stats_ms_pct99] => 0.284 0.284 0.284
[search_stats_ms_avg] => 0.000 0.000 0.000
[search_stats_ms_min] => 0.000 0.000 0.000
[search_stats_ms_max] => 0.000 0.000 0.000
[search_stats_ms_pct95] => 0.000 0.000 0.000
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