在使用 explain 方法运行查询时,MongoDB Search 查询返回有关 $search 查询计划和执行统计信息的信息。在使用 explain 运行查询时,MongoDB Search 返回 BSON 文档,其中包含统计信息和元数据,描述查询在 Lucene 内部的运行方式。
语法
db.<myCollection>.explain("<verbosity>").aggregate([ { $search: { "<operator>": { "<operator-options>" } } } ])
详细程度
详细模式控制 explain 的行为和返回的信息量。值可以是以下之一,按详细程度降序排列:
有关查询计划的信息,包括 | |
有关查询计划的信息,包括 | |
queryPlanner(默认) | 有关查询计划的信息。不包括 |
输出
使用 explain 方法的查询返回 stages.$_internalSearchMongotRemote 对象中的以下字段:
选项 | 类型 | 用途 |
|---|---|---|
| 文档 | 包含您运行的查询。 |
| 文档 | Contains the |
| 整数数组 | 包含 |
解释结果
The explain method returns a BSON document with the following fields in the explain document.
选项 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 文档 | Optional | 描述收集器的执行统计信息。如果 |
| 文档 | Optional | |
| 文档数组 | Optional | 包含每个索引分区的详细信息。仅当您配置了两个或多个索引分区时,才会返回此信息。 |
| 文档 | Optional | 包含有用的元数据。 |
| 文档 | Optional | 描述查询的执行统计信息。该值不予退回。如果 |
| 文档 | Optional | 有关在查询执行后从 Lucene 检索每个文档数据的详细信息。对于 |
| 文档 | Optional | 详细说明执行查询时的资源使用情况。对于 |
collectors
collectors BSON 文档包含以下字段:
字段 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 文档 | 必需 | Statistics of all collectors of the query. Statistics reported represent either the maximum value across all collectors used in the query or a sum of the statistic across all the sub-collectors. The timing statistics are summed to reflect the total time spent across all collectors for the entire query. To learn more, see allCollectorStats. |
| 文档 | Optional | |
| 文档 | Optional |
allCollectorStats
The allCollectorStats BSON document describes collector statistics across all collectors specified in the query, including facet and sort. It contains the following keys:
字段 | 说明 |
|---|---|
| 追踪收集器收集的结果数量和持续时间。 |
| 统计信息跟踪从收集器请求 |
| 统计信息跟踪在收集器上设置记分器的总持续时间和次数。 |
facet
facet 是一个 BSON 文档,当您在查询中指定分面时,该文档会详细说明查询和执行统计信息。它包含以下字段:
选项 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 文档 | Optional | 仅显示 |
| 文档 | Optional | 显示与创建内部 Lucene 对象相关的统计信息,该对象包含所有分面分组。它包含 |
| 文档 | 必需 | 对于与查询匹配的文档和整个 Lucene 索引,将分面字段映射到其关联基数。它为每个字段提供以下关联基数信息:
|
sort
sort 是一个 BSON 文档,详细说明了在查询中指定 排序 时的查询和执行统计信息。它包含以下字段:
选项 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 文档 | Optional | 追踪与
|
| 文档 | 必需 | 将正在排序的字段映射到该字段索引中的数据类型列表。 |
| 布尔 | Optional | 指示查询是否可以从索引排序中受益。当查询的排序字段与索引排序匹配或形成其前缀时,查询受益。如果值为:
如果查询未指定排序,或索引没有排序规范,则输出中不存在此内容。 |
highlight
highlight是一个BSON文档,当您在查询中指定突出显示时,它详细说明了查询和执行统计信息。它包含以下字段:
选项 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 列表<String> | 必需 | 所有高亮字段的列表。如果您在查询的 |
| QueryExecutionArea | Optional | 与设置和执行突出显示相关的调用和计时统计信息。它包含以下字段:
|
indexPartitionExplain
the indexPartitionExplain contains Explain Results for each index partition. The top-level collectors and query are inside the explain information of each index partition and absent at the top-level.
metadata
metadata 包含有用的元数据,例如:
字段 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 字符串 | Optional |
|
| 字符串 | Optional | 用于标识 |
| 字符串 | Optional | 查询中使用的MongoDB Search索引。 |
| 文档 | Optional | 为 |
| 整型 | Optional | 索引中包括已删除文档的索引对象总数。 |
query
query BSON 文档描述了查询的执行统计信息。它包含以下字段:
字段 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| 字符串 | Optional | 操作符的路径,前提是它不是根目录。 |
| 字符串 | 必需 | MongoDB Search操作符创建的Lucene查询的名称。有关更多信息,请参阅 |
| 字符串 | Optional | |
| 文档 | 必需 | Lucene 查询信息。有关更多信息,请参阅 |
| 文档 | Optional |
|
args
The explain response of a search command contains information about the query executed with that command. The response in the args field includes structured details of what Lucene queries MongoDB Search executed to satisfy a $search query.
本节包含:
MongoDB Search 操作符创建的一些Lucene查询
结构化摘要中包含的 Lucene 查询选项
每个 Lucene 查询类型的 Lucene 查询结构化摘要示例
注意
关于示例
The examples in this section are based on queries run against the sample datasets with the queryPlanner verbosity mode. In the example response, the:
mongotQuery字段显示MongoDB Search操作符和运行的查询。explain.type字段显示操作符创建的 Lucene 查询。
有关完整示例,请参阅示例。
BooleanQuery对于 Lucene
BooleanQuery,结构化摘要包含有关以下选项的详细信息:字段类型必要性说明mustOptional
必须匹配的条款。
mustNotOptional
不得匹配的子句。
shouldOptional
应匹配的子句。
filterOptional
必须全部匹配的子句。
minimumShouldMatch整型
Optional
必须匹配的最小
should子句数量。以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "compound" : { 7 "must" : [ { 8 "compound" : { 9 "should" : [ { 10 "text" : { 11 "query" : "historic", 12 "path" : "summary" 13 } 14 }, 15 { 16 "text" : { 17 "query" : "Portugal", 18 "path" : "address.country" 19 } 20 }, 21 { 22 "text" : { 23 "query" : "railway", 24 "path" : "transit" 25 } 26 } ] 27 } 28 } ], 29 "mustNot" : [ { 30 "text" : { 31 "query" : "Apartment", 32 "path" : "property_type" 33 } 34 } ] 35 } 36 }, 37 "explain" : { 38 "type" : "BooleanQuery", 39 "args" : { 40 "must" : [ { 41 "path" : "compound.must", 42 "type" : "BooleanQuery", 43 "args" : { 44 "must" : [ ], 45 "mustNot" : [ ], 46 "should" : [ 47 { 48 "path" : "compound.must.compound.should[0]", 49 "type" : "TermQuery", 50 "args" : { 51 "path" : "summary", 52 "value" : "historic" 53 } 54 }, 55 { 56 "path" : "compound.must.compound.should[1]", 57 "type" : "TermQuery", 58 "args" : { 59 "path" : "address.country", 60 "value" : "portugal" 61 } 62 }, 63 { 64 "path" : "compound.must.compound.should[2]", 65 "type" : "TermQuery", 66 "args" : { 67 "path" : "transit", 68 "value" : "railway" 69 } 70 } 71 ], 72 "filter" : [ ], 73 "minimumShouldMatch" : 0 74 } 75 } ], 76 "mustNot" : [ { 77 "path" : "compound.mustNot", 78 "type" : "TermQuery", 79 "args" : { 80 "path" : "property_type", 81 "value" : "apartment" 82 } 83 } ], 84 "should" : [ ], 85 "filter" : [ ], 86 "minimumShouldMatch" : 0 87 } 88 } 89 }, 90 ... 91 }, 92 ... 93 ], 94 ... 95 }
ConstantScoreQuery对于恒定得分查询,结构化摘要包括以下选项的详细信息:
字段类型必要性说明query必需
ConstantScoreQuery的子项。以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "equals" : { 7 "path" : "host.host_identity_verified", 8 "value" : true 9 } 10 }, 11 "explain" : { 12 "type" : "ConstantScoreQuery", 13 "args" : { 14 "query" : { 15 "type" : "TermQuery", 16 "args" : { 17 "path" : "host.host_identity_verified", 18 "value" : "T" 19 } 20 } 21 } 22 } 23 } 24 }, 25 { 26 "$_internalSearchIdLookup" : { } 27 } 28 ], 29 ... 30 }
FunctionScoreQuery对于 Lucene
FunctionScoreQuery查询,结构化摘要包含有关以下选项的详细信息:字段类型必要性说明scoreFunction字符串
必需
查询中使用的评分表达式。
query必需
查询。
以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "near" : { 7 "path" : "accomodates", 8 "origin" : 8, 9 "pivot" : 2 10 } 11 }, 12 "explain" : { 13 "type" : "BooleanQuery", 14 "args" : { 15 "must" : [ ], 16 "mustNot" : [ ], 17 "should" : [ 18 { 19 "type" : "BooleanQuery", 20 "args" : { 21 "must" : [ ], 22 "mustNot" : [ ], 23 "should" : [ 24 { 25 "type" : "FunctionScoreQuery", 26 "args" : { 27 "scoreFunction" : "expr(pivot / (pivot + abs(origin - value)))", 28 "query" : { 29 "type" : "LongDistanceFeatureQuery", 30 "args" : { }, 31 "stats" : { } 32 } 33 } 34 } 35 ], 36 "filter" : [ 37 { 38 "type" : "PointRangeQuery", 39 "args" : { 40 "path" : "accomodates", 41 "representation" : "double", 42 "gte" : 8.000000000000002, 43 "lte" : NaN 44 } 45 } 46 ], 47 "minimumShouldMatch" : 0 48 } 49 }, 50 { 51 "type" : "LongDistanceFeatureQuery", 52 "args" : { }, 53 "stats" : { } 54 } 55 ], 56 "filter" : [ ], 57 "minimumShouldMatch" : 0 58 } 59 } 60 }, 61 ... 62 }, 63 ... 64 ], 65 ... 66 }
LatLonPointDistanceQueryFor Lucene
LatLonPointDistanceQueryqueries, the response contains anstatsonly.以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "geoWithin" : { 7 "path" : "address.location", 8 "circle" : { 9 "radius" : 4800, 10 "center" : { 11 "type" : "Point", 12 "coordinates" : [ 13 -122.419472, 14 37.765302 15 ] 16 } 17 } 18 } 19 }, 20 "explain" : { 21 "type" : "LatLonPointDistanceQuery", 22 "args" : { } 23 } 24 } 25 }, 26 ... 27 ], 28 ... 29 }
LatLonShapeQueryFor Lucene
LatLonShapeQueryqueries, the response contains anstatsonly.以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "geoShape" : { 7 "path" : "address.location", 8 "relation" : "within", 9 "geometry" : { 10 "type" : "Polygon", 11 "coordinates" : [ 12 [ 13 [ -74.3994140625, 40.5305017757 ], 14 [ -74.7290039063, 40.5805846641 ], 15 [ -74.7729492188, 40.9467136651 ], 16 [ -74.0698242188, 41.1290213475 ], 17 [ -73.65234375, 40.9964840144 ], 18 [ -72.6416015625, 40.9467136651 ], 19 [ -72.3559570313, 40.7971774152 ], 20 [ -74.3994140625, 40.5305017757 ] 21 ] 22 ] 23 } 24 } 25 }, 26 "explain" : { 27 "type" : "LatLonShapeQuery", 28 "args" : { } 29 } 30 }, 31 ... 32 }, 33 ... 34 ], 35 ... 36 }
LongDistanceFeatureQueryFor Lucene
LongDistanceFeatureQuery, the response contains anstatsonly.以下示例显示了针对
sample_mflix.movies集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "near" : { 7 "path" : "released", 8 "origin" : ISODate("1915-09-13T00:00:00Z"), 9 "pivot" : 7776000000 10 } 11 }, 12 "explain" : { 13 "type" : "LongDistanceFeatureQuery", 14 "args" : { } 15 } 16 }, 17 ... 18 }, 19 ... 20 ], 21 ... 22 }
MultiTermQueryConstantScoreWrapper对于 Lucene
MultiTermQueryConstantScoreWrapper查询,结构化摘要包含有关以下参数的详细信息:字段类型必要性说明queriesList<Explain Results>
必需
查询列表。
以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "regex" : { 7 "path" : "access", 8 "query" : "full(.{0,5})", 9 "allowAnalyzedField" : true 10 } 11 }, 12 "explain" : { 13 "type" : "MultiTermQueryConstantScoreWrapper", 14 "args" : { 15 "queries" : [ 16 { 17 "type" : "DefaultQuery", 18 "args" : { 19 "queryType" : "RegexpQuery" 20 } 21 } 22 ] 23 } 24 } 25 }, 26 ... 27 }, 28 ... 29 ], 30 ... 31 }
PhraseQuery对于 Lucene
PhraseQuery查询,结构化摘要包含有关以下参数的详细信息:字段类型必要性说明path字符串
必需
要搜索的索引字段。
query字符串
必需
要搜索的一个或多个字符串。
slop数值
必需
query短语中单词之间允许的距离。以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "phrase" : { 7 "path" : "description", 8 "query" : "comfortable apartment", 9 "slop" : 2 10 } 11 }, 12 "explain" : { 13 "type" : "PhraseQuery", 14 "args" : { 15 "path" : "description", 16 "query" : "[comfortable, apartment]", 17 "slop" : 2 18 } 19 } 20 }, 21 ... 22 }, 23 ... 24 ], 25 ... 26 }
PointRangeQuery对于 Lucene
PointRangeQuery查询,结构化摘要包含有关以下参数的详细信息:字段类型必要性说明path字符串
必需
要搜索的索引字段。
representation字符串
Optional
数字表示形式。对日期类型数据的查询不包括表示形式。
gte数值
Optional
查询的下限。
lte数值
Optional
查询的上限。
以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "range" : { 7 "path" : "number_of_reviews", 8 "gt" : 5 9 } 10 }, 11 "explain" : { 12 "type" : "BooleanQuery", 13 "args" : { 14 "must" : [ ], 15 "mustNot" : [ ], 16 "should" : [ 17 { 18 "type" : "PointRangeQuery", 19 "args" : { 20 "path" : "number_of_reviews", 21 "representation" : "double", 22 "gte" : 5.000000000000001 23 } 24 }, 25 { 26 "type" : "PointRangeQuery", 27 "args" : { 28 "path" : "number_of_reviews", 29 "representation" : "int64", 30 "gte" : NumberLong(6) 31 } 32 } 33 ], 34 "filter" : [ ], 35 "minimumShouldMatch" : 0 36 } 37 } 38 }, 39 ... 40 }, 41 ... 42 ], 43 ... 44 }
TermQuery对于术语查询,结构化摘要包括有关以下参数的详细信息:
字段类型必要性说明path字符串
必需
要搜索的索引字段。
value字符串
必需
要搜索的字符串。
以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "queryString" : { 7 "defaultPath" : "summary", 8 "query" : "quiet" 9 } 10 }, 11 "explain" : { 12 "type" : "TermQuery", 13 "args" : { 14 "path" : "summary", 15 "value" : "quiet" 16 } 17 } 18 }, 19 ... 20 }, 21 ... 22 ], 23 ... 24 }
Default未由其他 Lucene 查询显式定义的 Lucene 查询将使用默认查询进行序列化。结构化摘要包含有关以下选项的详细信息:
字段类型必要性说明queryType字符串
必需。
Lucene 查询的类型。
以下示例显示了针对
sample_airbnb.listingsAndReviews集合运行的查询的explain响应。1 { 2 "stages" : [ 3 { 4 "$_internalSearchMongotRemote" : { 5 "mongotQuery" : { 6 "near" : { 7 "origin" : { 8 "type" : "Point", 9 "coordinates" : [ 10 -8.61308, 11 41.1413 12 ] 13 }, 14 "pivot" : 1000, 15 "path" : "address.location" 16 } 17 }, 18 "explain" : { 19 "type" : "DefaultQuery", 20 "args" : { 21 "queryType" : "LatLonPointDistanceFeatureQuery" 22 } 23 } 24 }, 25 ... 26 }, 27 ... 28 ], 29 ... 30 }
stats
The explain response for executionStats and allPlansExecution verbosity modes includes a stats field that contains information on how much time a query spends in various stages of query execution.
时序细分
时间细分描述了与查询执行区域相关的执行统计信息。以下字段显示时间细分:
查询区域
统计信息可用于以下查询领域:
选项 | 说明 | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 与 Lucene 查询执行相关的统计信息。 此区域中枚举了两个任务的调用计数:
The time spent in this area is related to the structure of the query, and is not based on the number of results that are iterated through and scored. 例如: | |||||||||||
| 与遍历和匹配结果文档相关的统计信息。 此统计信息显示确定下一个匹配的文档所需的时间。 根据查询的性质,匹配结果所花费的时间可能会有很大差异。 此区域中枚举了两个任务的调用计数:
例如: | |||||||||||
| 与结果集中的文档评分相关的统计信息。 此区域中枚举了两个任务的调用计数:
例如: |
resultMaterialization
resultMaterialization 文档显示 mongot 完成以下任务所需的时间:
检索以
_id或storedSource形式存储在 Lucene 中的结果数据。Serialize the data into BSON format before sending it to
mongod.
To learn more, see stats.
resourceUsage
resourceUsage 文档显示了用于运行查询的资源。它包含以下字段:
字段 | 类型 | 必要性 | 用途 |
|---|---|---|---|
| Long | 必需 | 主要页面错误的数量,当系统在查询执行期间无法在内存中找到所需数据,导致从磁盘等后备存储中读取数据时,就会发生这种错误。 |
| Long | 必需 | 次要页面错误的数量,当数据在页面缓存中,但尚未映射到进程的页表时发生。 |
| Long | 必需 | 在用户空间中花费的 CPU 时间,以毫秒为单位。 |
| Long | 必需 | 在系统空间中花费的 CPU 时间,以毫秒为单位。 |
| 整型 | 必需 | 在所有批处理中执行查询期间, |
| 整型 | 必需 | 处理查询时请求 |
示例
以下示例使用 sample_mflix 数据库中的 movies 集合。
提示
If you've already loaded the sample dataset, refer to the MongoDB Search Quick Start tutorial to create an index definition and run MongoDB Search queries.
allPlansExecution
以下示例使用不同的操作符以 allPlansExecution 详细模式查询 title 字段。
db.movies.explain("allPlansExecution").aggregate([ { $search: { "text": { "path": "title", "query": "yark", "fuzzy": { "maxEdits": 1, "maxExpansions": 100, } } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 text: { 8 path: 'title', 9 query: 'yark', 10 fuzzy: { maxEdits: 1, maxExpansions: 100 } 11 } 12 }, 13 explain: { 14 query: { 15 type: 'BooleanQuery', 16 args: { 17 must: [], 18 mustNot: [], 19 should: [ 20 { 21 type: 'BoostQuery', 22 args: { 23 query: { 24 type: 'TermQuery', 25 args: { path: 'title', value: 'mark' }, 26 stats: { 27 context: { millisElapsed: 0 }, 28 match: { millisElapsed: 0 }, 29 score: { millisElapsed: 0 } 30 } 31 }, 32 boost: 0.75 33 }, 34 stats: { 35 context: { 36 millisElapsed: 0.209279, 37 invocationCounts: { 38 createWeight: Long('2'), 39 createScorer: Long('18') 40 } 41 }, 42 match: { 43 millisElapsed: 0.028079, 44 invocationCounts: { nextDoc: Long('22') } 45 }, 46 score: { 47 millisElapsed: 0.01706, 48 invocationCounts: { score: Long('16') } 49 } 50 } 51 }, 52 { 53 type: 'BoostQuery', 54 args: { 55 query: { 56 type: 'TermQuery', 57 args: { path: 'title', value: 'yard' }, 58 stats: { 59 context: { millisElapsed: 0 }, 60 match: { millisElapsed: 0 }, 61 score: { millisElapsed: 0 } 62 } 63 }, 64 boost: 0.75 65 }, 66 stats: { 67 context: { 68 millisElapsed: 0.136254, 69 invocationCounts: { 70 createWeight: Long('2'), 71 createScorer: Long('14') 72 } 73 }, 74 match: { 75 millisElapsed: 0.008556, 76 invocationCounts: { nextDoc: Long('10') } 77 }, 78 score: { 79 millisElapsed: 0.006096, 80 invocationCounts: { score: Long('6') } 81 } 82 } 83 }, 84 { 85 type: 'BoostQuery', 86 args: { 87 query: { 88 type: 'TermQuery', 89 args: { path: 'title', value: 'york' }, 90 stats: { 91 context: { millisElapsed: 0 }, 92 match: { millisElapsed: 0 }, 93 score: { millisElapsed: 0 } 94 } 95 }, 96 boost: 0.75 97 }, 98 stats: { 99 context: { 100 millisElapsed: 0.303568, 101 invocationCounts: { 102 createWeight: Long('2'), 103 createScorer: Long('18') 104 } 105 }, 106 match: { 107 millisElapsed: 0.374856, 108 invocationCounts: { nextDoc: Long('62') } 109 }, 110 score: { 111 millisElapsed: 0.892383, 112 invocationCounts: { score: Long('56') } 113 } 114 } 115 }, 116 { 117 type: 'BoostQuery', 118 args: { 119 query: { 120 type: 'TermQuery', 121 args: { path: 'title', value: 'ark' }, 122 stats: { 123 context: { millisElapsed: 0 }, 124 match: { millisElapsed: 0 }, 125 score: { millisElapsed: 0 } 126 } 127 }, 128 boost: 0.6666666269302368 129 }, 130 stats: { 131 context: { 132 millisElapsed: 8.379562, 133 invocationCounts: { 134 createWeight: Long('2'), 135 createScorer: Long('10') 136 } 137 }, 138 match: { 139 millisElapsed: 2.073272, 140 invocationCounts: { nextDoc: Long('6') } 141 }, 142 score: { 143 millisElapsed: 0.004063, 144 invocationCounts: { score: Long('4') } 145 } 146 } 147 }, 148 { 149 type: 'BoostQuery', 150 args: { 151 query: { 152 type: 'TermQuery', 153 args: { path: 'title', value: 'dark' }, 154 stats: { 155 context: { millisElapsed: 0 }, 156 match: { millisElapsed: 0 }, 157 score: { millisElapsed: 0 } 158 } 159 }, 160 boost: 0.75 161 }, 162 stats: { 163 context: { 164 millisElapsed: 0.679029, 165 invocationCounts: { 166 createWeight: Long('2'), 167 createScorer: Long('18') 168 } 169 }, 170 match: { 171 millisElapsed: 5.500198, 172 invocationCounts: { nextDoc: Long('172') } 173 }, 174 score: { 175 millisElapsed: 2.465502, 176 invocationCounts: { score: Long('166') } 177 } 178 } 179 }, 180 { 181 type: 'BoostQuery', 182 args: { 183 query: { 184 type: 'TermQuery', 185 args: { path: 'title', value: 'park' }, 186 stats: { 187 context: { millisElapsed: 0 }, 188 match: { millisElapsed: 0 }, 189 score: { millisElapsed: 0 } 190 } 191 }, 192 boost: 0.75 193 }, 194 stats: { 195 context: { 196 millisElapsed: 0.221919, 197 invocationCounts: { 198 createWeight: Long('2'), 199 createScorer: Long('18') 200 } 201 }, 202 match: { 203 millisElapsed: 0.116139, 204 invocationCounts: { nextDoc: Long('60') } 205 }, 206 score: { 207 millisElapsed: 0.056817, 208 invocationCounts: { score: Long('54') } 209 } 210 } 211 } 212 ], 213 filter: [], 214 minimumShouldMatch: 0 215 }, 216 stats: { 217 context: { 218 millisElapsed: 25.303419, 219 invocationCounts: { createWeight: Long('2'), createScorer: Long('12') } 220 }, 221 match: { 222 millisElapsed: 10.533183, 223 invocationCounts: { nextDoc: Long('308') } 224 }, 225 score: { 226 millisElapsed: 5.501189, 227 invocationCounts: { score: Long('302') } 228 } 229 } 230 }, 231 collectStats: { 232 allCollectorStats: { 233 millisElapsed: 6.735626, 234 invocationCounts: { 235 collect: Long('302'), 236 competitiveIterator: Long('6'), 237 setScorer: Long('6') 238 } 239 }, 240 facet: { collectorStats: { millisElapsed: 0 } } 241 }, 242 resultMaterialization: { 243 stats: { 244 millisElapsed: 176.613905, 245 invocationCounts: { retrieveAndSerialize: Long('2') } 246 } 247 }, 248 metadata: { 249 <hostname>.mongodb.netmongotVersion: '1.42.0', 250 mongotHostName: '<hostname>.mongodb.net', 251 indexName: 'default', 252 cursorOptions: { batchSize: 108, requiresSearchSequenceToken: false }, 253 totalLuceneDocs: 21349 254 }, 255 resourceUsage: { 256 majorFaults: Long('99'), 257 minorFaults: Long('192'), 258 userTimeMs: Long('80'), 259 systemTimeMs: Long('10'), 260 maxReportingThreads: 1, 261 numBatches: 2 262 } 263 }, 264 requiresSearchMetaCursor: false, 265 internalMongotBatchSizeHistory: [ Long('108'), Long('162') ] 266 }, 267 nReturned: Long('151'), 268 executionTimeMillisEstimate: Long('83') 269 }, 270 { 271 '$_internalSearchIdLookup': { 272 subPipeline: [ 273 { '$match': { _id: { '$eq': '_id placeholder' } } } 274 ], 275 totalDocsExamined: Long('151'), 276 totalKeysExamined: Long('151'), 277 numDocsFilteredByIdLookup: Long('0') 278 }, 279 nReturned: Long('151'), 280 executionTimeMillisEstimate: Long('88') 281 } 282 ], 283 queryShapeHash: '6FD3791F785FA329D4ECD1171E0E5AF6772C18F5F0A7A50FC416D080A93C8CB7', 284 serverInfo: { 285 host: '<hostname>.mongodb.net', 286 port: 27017, 287 version: '8.2.0', 288 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 289 }, 290 serverParameters: { 291 ... 292 }, 293 command: { 294 aggregate: 'movies', 295 pipeline: [ 296 { 297 '$search': { 298 text: { 299 path: 'title', 300 query: 'yark', 301 fuzzy: { maxEdits: 1, maxExpansions: 100 } 302 } 303 } 304 } 305 ], 306 cursor: {}, 307 '$db': 'sample_mflix' 308 }, 309 ok: 1, 310 '$clusterTime': { 311 clusterTime: Timestamp({ t: 1758295936, i: 19 }), 312 signature: { 313 hash: Binary.createFromBase64('+CanjrL9jdXPTLa2sUaNPtImkBc=', 0), 314 keyId: Long('7551379485140975621') 315 } 316 }, 317 operationTime: Timestamp({ t: 1758295936, i: 19 }) 318 }
db.movies.explain("allPlansExecution").aggregate([ { $search: { "text": { "path": "title", "query": "prince" }, "highlight": { "path": "title", "maxNumPassages": 1, "maxCharsToExamine": 40 } } }, { $project: { "description": 1, "_id": 0, "highlights": { "$meta": "searchHighlights" } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 text: { path: 'title', query: 'prince' }, 8 highlight: { path: 'title', maxNumPassages: 1, maxCharsToExamine: 40 } 9 }, 10 explain: { 11 query: { 12 type: 'TermQuery', 13 args: { path: 'title', value: 'prince' }, 14 stats: { 15 context: { 16 millisElapsed: 9.880819, 17 invocationCounts: { createWeight: Long('1'), createScorer: Long('6') } 18 }, 19 match: { 20 millisElapsed: 3.566358, 21 invocationCounts: { nextDoc: Long('28') } 22 }, 23 score: { 24 millisElapsed: 2.762687, 25 invocationCounts: { score: Long('25') } 26 } 27 } 28 }, 29 collectStats: { 30 allCollectorStats: { 31 millisElapsed: 3.238152, 32 invocationCounts: { 33 collect: Long('25'), 34 competitiveIterator: Long('3'), 35 setScorer: Long('3') 36 } 37 }, 38 facet: { collectorStats: { millisElapsed: 0 } } 39 }, 40 highlight: { 41 resolvedHighlightPaths: [ '$type:string/title' ], 42 stats: { 43 millisElapsed: 157.543967, 44 invocationCounts: { 45 executeHighlight: Long('1'), 46 setupHighlight: Long('1') 47 } 48 } 49 }, 50 resultMaterialization: { 51 stats: { 52 millisElapsed: 3.781115, 53 invocationCounts: { retrieveAndSerialize: Long('1') } 54 } 55 }, 56 metadata: { 57 <hostname>.mongodb.netmongotVersion: '1.42.0', 58 mongotHostName: '<hostname>.mongodb.net', 59 indexName: 'default', 60 cursorOptions: { batchSize: 108, requiresSearchSequenceToken: false }, 61 totalLuceneDocs: 21349 62 }, 63 resourceUsage: { 64 majorFaults: Long('42'), 65 minorFaults: Long('167'), 66 userTimeMs: Long('50'), 67 systemTimeMs: Long('0'), 68 maxReportingThreads: 1, 69 numBatches: 1 70 } 71 }, 72 requiresSearchMetaCursor: false, 73 internalMongotBatchSizeHistory: [ Long('108') ] 74 }, 75 nReturned: Long('25'), 76 executionTimeMillisEstimate: Long('0') 77 }, 78 { 79 '$_internalSearchIdLookup': { 80 subPipeline: [ 81 { '$match': { _id: { '$eq': '_id placeholder' } } } 82 ], 83 totalDocsExamined: Long('25'), 84 totalKeysExamined: Long('25'), 85 numDocsFilteredByIdLookup: Long('0') 86 }, 87 nReturned: Long('25'), 88 executionTimeMillisEstimate: Long('1') 89 }, 90 { 91 '$project': { 92 description: true, 93 highlights: { '$meta': 'searchHighlights' }, 94 _id: false 95 }, 96 nReturned: Long('25'), 97 executionTimeMillisEstimate: Long('1') 98 } 99 ], 100 queryShapeHash: 'D08444272924C1E04A6E99D0CD4BF82FD929893862B3356F79EC18BBD1F0EF0C', 101 serverInfo: { 102 host: '<hostname>.mongodb.net', 103 port: 27017, 104 version: '8.2.0', 105 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 106 }, 107 serverParameters: { 108 internalQueryFacetBufferSizeBytes: 104857600, 109 internalQueryFacetMaxOutputDocSizeBytes: 104857600, 110 internalLookupStageIntermediateDocumentMaxSizeBytes: 104857600, 111 internalDocumentSourceGroupMaxMemoryBytes: 104857600, 112 internalQueryMaxBlockingSortMemoryUsageBytes: 104857600, 113 internalQueryProhibitBlockingMergeOnMongoS: 0, 114 internalQueryMaxAddToSetBytes: 104857600, 115 internalDocumentSourceSetWindowFieldsMaxMemoryBytes: 104857600, 116 internalQueryFrameworkControl: 'trySbeRestricted', 117 internalQueryPlannerIgnoreIndexWithCollationForRegex: 1 118 }, 119 command: { 120 aggregate: 'movies', 121 pipeline: [ 122 { 123 '$search': { 124 text: { path: 'title', query: 'prince' }, 125 highlight: { path: 'title', maxNumPassages: 1, maxCharsToExamine: 40 } 126 } 127 }, 128 { 129 '$project': { 130 description: 1, 131 _id: 0, 132 highlights: { '$meta': 'searchHighlights' } 133 } 134 } 135 ], 136 cursor: {}, 137 '$db': 'sample_mflix' 138 }, 139 ok: 1, 140 '$clusterTime': { 141 clusterTime: Timestamp({ t: 1758302099, i: 1 }), 142 signature: { 143 hash: Binary.createFromBase64('pUGxwCVnDOBIObmhURJQ1a1UwC8=', 0), 144 keyId: Long('7551379485140975621') 145 } 146 }, 147 operationTime: Timestamp({ t: 1758302099, i: 1 }) 148 }
db.movies.explain("allPlansExecution").aggregate([ { "$searchMeta": { "facet": { "operator": { "near": { "path": "released", "origin": ISODate("1921-11-01T00:00:00.000+00:00"), "pivot": 7776000000 } }, "facets": { "genresFacet": { "type": "string", "path": "genres" }, "yearFacet" : { "type" : "number", "path" : "year", "boundaries" : [1910,1920,1930,1940] } } } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$searchMeta': { 6 mongotQuery: { 7 facet: { 8 operator: { 9 near: { 10 path: 'released', 11 origin: ISODate('1921-11-01T00:00:00.000Z'), 12 pivot: 7776000000 13 } 14 }, 15 facets: { 16 genresFacet: { type: 'string', path: 'genres' }, 17 yearFacet: { 18 type: 'number', 19 path: 'year', 20 boundaries: [ 1910, 1920, 1930, 1940 ] 21 } 22 } 23 } 24 }, 25 explain: { 26 query: { 27 type: 'LongDistanceFeatureQuery', 28 args: {}, 29 stats: { 30 context: { 31 millisElapsed: 4.141763, 32 invocationCounts: { createWeight: Long('1'), createScorer: Long('6') } 33 }, 34 match: { 35 millisElapsed: 24.986327, 36 invocationCounts: { nextDoc: Long('20881') } 37 }, 38 score: { 39 millisElapsed: 33.324657, 40 invocationCounts: { score: Long('20878') } 41 } 42 } 43 }, 44 collectStats: { 45 allCollectorStats: { 46 millisElapsed: 72.243101, 47 invocationCounts: { 48 collect: Long('20878'), 49 competitiveIterator: Long('3'), 50 setScorer: Long('3') 51 } 52 }, 53 facet: { 54 collectorStats: { 55 millisElapsed: 10.424621, 56 invocationCounts: { collect: Long('20878'), setScorer: Long('3') } 57 }, 58 createCountsStats: { 59 millisElapsed: 60.095261, 60 invocationCounts: { generateFacetCounts: Long('2') } 61 }, 62 stringFacetCardinalities: { genresFacet: { queried: 10, total: 25 } } 63 } 64 }, 65 resultMaterialization: { 66 stats: { 67 millisElapsed: 13.764287, 68 invocationCounts: { retrieveAndSerialize: Long('1') } 69 } 70 }, 71 metadata: { 72 <hostname>.mongodb.netmongotVersion: '1.42.0', 73 mongotHostName: '<hostname>.mongodb.net', 74 indexName: 'default', 75 totalLuceneDocs: 21349 76 }, 77 resourceUsage: { 78 majorFaults: Long('10'), 79 minorFaults: Long('13'), 80 userTimeMs: Long('20'), 81 systemTimeMs: Long('0'), 82 maxReportingThreads: 1, 83 numBatches: 1 84 } 85 }, 86 requiresSearchMetaCursor: true 87 }, 88 nReturned: Long('1'), 89 executionTimeMillisEstimate: Long('336') 90 } 91 ], 92 queryShapeHash: '582DB864C9BCFB96896CF1A3079CF70FAC10A9A1E19E8D66DF20A2BB40424FB5', 93 serverInfo: { 94 host: '<hostname>.mongodb.net', 95 port: 27017, 96 version: '8.2.0', 97 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 98 }, 99 serverParameters: { 100 ... 101 }, 102 command: { 103 aggregate: 'movies', 104 pipeline: [ 105 { 106 '$searchMeta': { 107 facet: { 108 operator: { 109 near: { 110 path: 'released', 111 origin: ISODate('1921-11-01T00:00:00.000Z'), 112 pivot: 7776000000 113 } 114 }, 115 facets: { 116 genresFacet: { type: 'string', path: 'genres' }, 117 yearFacet: { 118 type: 'number', 119 path: 'year', 120 boundaries: [ 1910, 1920, 1930, 1940 ] 121 } 122 } 123 } 124 } 125 } 126 ], 127 cursor: {}, 128 '$db': 'sample_mflix' 129 }, 130 ok: 1, 131 '$clusterTime': { 132 clusterTime: Timestamp({ t: 1758304279, i: 1 }), 133 signature: { 134 hash: Binary.createFromBase64('DI9+ZTogU1QxHCWId6QLcA4R4tQ=', 0), 135 keyId: Long('7551379485140975621') 136 } 137 }, 138 operationTime: Timestamp({ t: 1758304279, i: 1 }) 139 }
db.movies.explain("allPlansExecution").aggregate([ { $search: { "text": { "path": "title", "query": "yark", "fuzzy": { "maxEdits": 1, "maxExpansions": 100, } } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 text: { 8 path: 'title', 9 query: 'yark', 10 fuzzy: { maxEdits: 1, maxExpansions: 100 } 11 } 12 }, 13 explain: { 14 query: { 15 type: 'BooleanQuery', 16 args: { 17 must: [], 18 mustNot: [], 19 should: [ 20 { 21 type: 'BoostQuery', 22 args: { 23 query: { 24 type: 'TermQuery', 25 args: { path: 'title', value: 'mark' }, 26 stats: { 27 context: { millisElapsed: 0 }, 28 match: { millisElapsed: 0 }, 29 score: { millisElapsed: 0 } 30 } 31 }, 32 boost: 0.75 33 }, 34 stats: { 35 context: { 36 millisElapsed: 0.164466, 37 invocationCounts: { 38 createWeight: Long('2'), 39 createScorer: Long('18') 40 } 41 }, 42 match: { 43 millisElapsed: 0.055889, 44 invocationCounts: { nextDoc: Long('22') } 45 }, 46 score: { 47 millisElapsed: 0.01638, 48 invocationCounts: { score: Long('16') } 49 } 50 } 51 }, 52 { 53 type: 'BoostQuery', 54 args: { 55 query: { 56 type: 'TermQuery', 57 args: { path: 'title', value: 'yard' }, 58 stats: { 59 context: { millisElapsed: 0 }, 60 match: { millisElapsed: 0 }, 61 score: { millisElapsed: 0 } 62 } 63 }, 64 boost: 0.75 65 }, 66 stats: { 67 context: { 68 millisElapsed: 0.109841, 69 invocationCounts: { 70 createWeight: Long('2'), 71 createScorer: Long('14') 72 } 73 }, 74 match: { 75 millisElapsed: 0.009747, 76 invocationCounts: { nextDoc: Long('10') } 77 }, 78 score: { 79 millisElapsed: 0.005449, 80 invocationCounts: { score: Long('6') } 81 } 82 } 83 }, 84 { 85 type: 'BoostQuery', 86 args: { 87 query: { 88 type: 'TermQuery', 89 args: { path: 'title', value: 'york' }, 90 stats: { 91 context: { millisElapsed: 0 }, 92 match: { millisElapsed: 0 }, 93 score: { millisElapsed: 0 } 94 } 95 }, 96 boost: 0.75 97 }, 98 stats: { 99 context: { 100 millisElapsed: 0.140144, 101 invocationCounts: { 102 createWeight: Long('2'), 103 createScorer: Long('18') 104 } 105 }, 106 match: { 107 millisElapsed: 0.058885, 108 invocationCounts: { nextDoc: Long('62') } 109 }, 110 score: { 111 millisElapsed: 0.877508, 112 invocationCounts: { score: Long('56') } 113 } 114 } 115 }, 116 { 117 type: 'BoostQuery', 118 args: { 119 query: { 120 type: 'TermQuery', 121 args: { path: 'title', value: 'ark' }, 122 stats: { 123 context: { millisElapsed: 0 }, 124 match: { millisElapsed: 0 }, 125 score: { millisElapsed: 0 } 126 } 127 }, 128 boost: 0.6666666269302368 129 }, 130 stats: { 131 context: { 132 millisElapsed: 0.26056, 133 invocationCounts: { 134 createWeight: Long('2'), 135 createScorer: Long('10') 136 } 137 }, 138 match: { 139 millisElapsed: 1.028141, 140 invocationCounts: { nextDoc: Long('6') } 141 }, 142 score: { 143 millisElapsed: 0.004226, 144 invocationCounts: { score: Long('4') } 145 } 146 } 147 }, 148 { 149 type: 'BoostQuery', 150 args: { 151 query: { 152 type: 'TermQuery', 153 args: { path: 'title', value: 'dark' }, 154 stats: { 155 context: { millisElapsed: 0 }, 156 match: { millisElapsed: 0 }, 157 score: { millisElapsed: 0 } 158 } 159 }, 160 boost: 0.75 161 }, 162 stats: { 163 context: { 164 millisElapsed: 0.3029, 165 invocationCounts: { 166 createWeight: Long('2'), 167 createScorer: Long('18') 168 } 169 }, 170 match: { 171 millisElapsed: 2.294511, 172 invocationCounts: { nextDoc: Long('172') } 173 }, 174 score: { 175 millisElapsed: 1.806661, 176 invocationCounts: { score: Long('166') } 177 } 178 } 179 }, 180 { 181 type: 'BoostQuery', 182 args: { 183 query: { 184 type: 'TermQuery', 185 args: { path: 'title', value: 'park' }, 186 stats: { 187 context: { millisElapsed: 0 }, 188 match: { millisElapsed: 0 }, 189 score: { millisElapsed: 0 } 190 } 191 }, 192 boost: 0.75 193 }, 194 stats: { 195 context: { 196 millisElapsed: 0.154143, 197 invocationCounts: { 198 createWeight: Long('2'), 199 createScorer: Long('18') 200 } 201 }, 202 match: { 203 millisElapsed: 0.052283, 204 invocationCounts: { nextDoc: Long('60') } 205 }, 206 score: { 207 millisElapsed: 0.050278, 208 invocationCounts: { score: Long('54') } 209 } 210 } 211 } 212 ], 213 filter: [], 214 minimumShouldMatch: 0 215 }, 216 stats: { 217 context: { 218 millisElapsed: 2.024454, 219 invocationCounts: { createWeight: Long('2'), createScorer: Long('12') } 220 }, 221 match: { 222 millisElapsed: 4.020593, 223 invocationCounts: { nextDoc: Long('308') } 224 }, 225 score: { 226 millisElapsed: 3.181962, 227 invocationCounts: { score: Long('302') } 228 } 229 } 230 }, 231 collectStats: { 232 allCollectorStats: { 233 millisElapsed: 4.062801, 234 invocationCounts: { 235 collect: Long('302'), 236 competitiveIterator: Long('6'), 237 setScorer: Long('6') 238 } 239 }, 240 facet: { collectorStats: { millisElapsed: 0 } } 241 }, 242 resultMaterialization: { 243 stats: { 244 millisElapsed: 127.205476, 245 invocationCounts: { retrieveAndSerialize: Long('2') } 246 } 247 }, 248 metadata: { 249 <hostname>.mongodb.netmongotVersion: '1.42.0', 250 mongotHostName: '<hostname>.mongodb.net', 251 indexName: 'default', 252 cursorOptions: { batchSize: 108, requiresSearchSequenceToken: false }, 253 totalLuceneDocs: 21349 254 }, 255 resourceUsage: { 256 majorFaults: Long('100'), 257 minorFaults: Long('31'), 258 userTimeMs: Long('20'), 259 systemTimeMs: Long('10'), 260 maxReportingThreads: 1, 261 numBatches: 2 262 } 263 }, 264 requiresSearchMetaCursor: false, 265 internalMongotBatchSizeHistory: [ Long('108'), Long('162') ] 266 }, 267 nReturned: Long('151'), 268 executionTimeMillisEstimate: Long('57') 269 }, 270 { 271 '$_internalSearchIdLookup': { 272 subPipeline: [ 273 { '$match': { _id: { '$eq': '_id placeholder' } } } 274 ], 275 totalDocsExamined: Long('151'), 276 totalKeysExamined: Long('151'), 277 numDocsFilteredByIdLookup: Long('0') 278 }, 279 nReturned: Long('151'), 280 executionTimeMillisEstimate: Long('64') 281 } 282 ], 283 queryShapeHash: '6FD3791F785FA329D4ECD1171E0E5AF6772C18F5F0A7A50FC416D080A93C8CB7', 284 serverInfo: { 285 host: '<hostname>.mongodb.net', 286 port: 27017, 287 version: '8.2.0', 288 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 289 }, 290 serverParameters: { 291 ... 292 }, 293 command: { 294 aggregate: 'movies', 295 pipeline: [ 296 { 297 '$search': { 298 text: { 299 path: 'title', 300 query: 'yark', 301 fuzzy: { maxEdits: 1, maxExpansions: 100 } 302 } 303 } 304 } 305 ], 306 cursor: {}, 307 '$db': 'sample_mflix' 308 }, 309 ok: 1, 310 '$clusterTime': { 311 clusterTime: Timestamp({ t: 1758302299, i: 1 }), 312 signature: { 313 hash: Binary.createFromBase64('pCKOPlBY/K4IObOkqDlOSnbRqw0=', 0), 314 keyId: Long('7551379485140975621') 315 } 316 }, 317 operationTime: Timestamp({ t: 1758302299, i: 1 }) 318 }
queryPlanner
以下示例使用不同的操作符以 queryPlanner 详细模式查询 title 字段。
db.movies.explain("queryPlanner").aggregate([ { $search: { "text": { "path": "title", "query": "yark", "fuzzy": { "maxEdits": 1, "maxExpansions": 100, } } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 text: { 8 path: 'title', 9 query: 'yark', 10 fuzzy: { maxEdits: 1, maxExpansions: 100 } 11 } 12 }, 13 explain: { 14 query: { 15 type: 'BooleanQuery', 16 args: { 17 must: [], 18 mustNot: [], 19 should: [ 20 { 21 type: 'BoostQuery', 22 args: { 23 query: { 24 type: 'TermQuery', 25 args: { path: 'title', value: 'park' } 26 }, 27 boost: 0.75 28 } 29 }, 30 { 31 type: 'BoostQuery', 32 args: { 33 query: { 34 type: 'TermQuery', 35 args: { path: 'title', value: 'york' } 36 }, 37 boost: 0.75 38 } 39 }, 40 { 41 type: 'BoostQuery', 42 args: { 43 query: { 44 type: 'TermQuery', 45 args: { path: 'title', value: 'dark' } 46 }, 47 boost: 0.75 48 } 49 }, 50 { 51 type: 'BoostQuery', 52 args: { 53 query: { 54 type: 'TermQuery', 55 args: { path: 'title', value: 'mark' } 56 }, 57 boost: 0.75 58 } 59 }, 60 { 61 type: 'BoostQuery', 62 args: { 63 query: { 64 type: 'TermQuery', 65 args: { path: 'title', value: 'yard' } 66 }, 67 boost: 0.75 68 } 69 }, 70 { 71 type: 'BoostQuery', 72 args: { 73 query: { 74 type: 'TermQuery', 75 args: { path: 'title', value: 'ark' } 76 }, 77 boost: 0.6666666269302368 78 } 79 } 80 ], 81 filter: [], 82 minimumShouldMatch: 0 83 } 84 }, 85 metadata: { 86 <hostname>.mongodb.netmongotVersion: '1.42.0', 87 mongotHostName: '<hostname>.mongodb.net', 88 indexName: 'default', 89 totalLuceneDocs: 21349 90 } 91 }, 92 requiresSearchMetaCursor: false 93 } 94 }, 95 { 96 '$_internalSearchIdLookup': { 97 subPipeline: [ 98 { '$match': { _id: { '$eq': '_id placeholder' } } } 99 ] 100 } 101 } 102 ], 103 queryShapeHash: '6FD3791F785FA329D4ECD1171E0E5AF6772C18F5F0A7A50FC416D080A93C8CB7', 104 serverInfo: { 105 host: '<hostname>.mongodb.net', 106 port: 27017, 107 version: '8.2.0', 108 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 109 }, 110 serverParameters: { 111 ... 112 }, 113 command: { 114 aggregate: 'movies', 115 pipeline: [ 116 { 117 '$search': { 118 text: { 119 path: 'title', 120 query: 'yark', 121 fuzzy: { maxEdits: 1, maxExpansions: 100 } 122 } 123 } 124 } 125 ], 126 cursor: {}, 127 '$db': 'sample_mflix' 128 }, 129 ok: 1, 130 '$clusterTime': { 131 clusterTime: Timestamp({ t: 1758305729, i: 1 }), 132 signature: { 133 hash: Binary.createFromBase64('IUnIrXR/VeUrj1cGgyEFlkoQKAM=', 0), 134 keyId: Long('7551379485140975621') 135 } 136 }, 137 operationTime: Timestamp({ t: 1758305729, i: 1 }) 138 }
db.movies.explain("queryPlanner").aggregate([ { $search: { "text": { "path": "title", "query": "prince" }, "highlight": { "path": "title", "maxNumPassages": 1, "maxCharsToExamine": 40 } } }, { $project: { "description": 1, "_id": 0, "highlights": { "$meta": "searchHighlights" } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 text: { path: 'title', query: 'prince' }, 8 highlight: { path: 'title', maxNumPassages: 1, maxCharsToExamine: 40 } 9 }, 10 explain: { 11 query: { 12 type: 'TermQuery', 13 args: { path: 'title', value: 'prince' } 14 }, 15 highlight: { resolvedHighlightPaths: [ '$type:string/title' ] }, 16 metadata: { 17 <hostname>.mongodb.netmongotVersion: '1.42.0', 18 mongotHostName: '<hostname>.mongodb.net', 19 indexName: 'default', 20 totalLuceneDocs: 21349 21 } 22 }, 23 requiresSearchMetaCursor: false 24 } 25 }, 26 { 27 '$_internalSearchIdLookup': { 28 subPipeline: [ 29 { '$match': { _id: { '$eq': '_id placeholder' } } } 30 ] 31 } 32 }, 33 { 34 '$project': { 35 description: true, 36 highlights: { '$meta': 'searchHighlights' }, 37 _id: false 38 } 39 } 40 ], 41 queryShapeHash: 'D08444272924C1E04A6E99D0CD4BF82FD929893862B3356F79EC18BBD1F0EF0C', 42 serverInfo: { 43 host: '<hostname>.mongodb.net', 44 port: 27017, 45 version: '8.2.0', 46 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 47 }, 48 serverParameters: { 49 ... 50 }, 51 command: { 52 aggregate: 'movies', 53 pipeline: [ 54 { 55 '$search': { 56 text: { path: 'title', query: 'prince' }, 57 highlight: { path: 'title', maxNumPassages: 1, maxCharsToExamine: 40 } 58 } 59 }, 60 { 61 '$project': { 62 description: 1, 63 _id: 0, 64 highlights: { '$meta': 'searchHighlights' } 65 } 66 } 67 ], 68 cursor: {}, 69 '$db': 'sample_mflix' 70 }, 71 ok: 1, 72 '$clusterTime': { 73 clusterTime: Timestamp({ t: 1758305809, i: 1 }), 74 signature: { 75 hash: Binary.createFromBase64('R7wN4/xS0eg0XFd23xeo/+hMPBY=', 0), 76 keyId: Long('7551379485140975621') 77 } 78 }, 79 operationTime: Timestamp({ t: 1758305809, i: 1 }) 80 }
db.movies.explain("queryPlanner").aggregate([ { "$searchMeta": { "facet": { "operator": { "near": { "path": "released", "origin": ISODate("1921-11-01T00:00:00.000+00:00"), "pivot": 7776000000 } }, "facets": { "genresFacet": { "type": "string", "path": "genres" }, "yearFacet" : { "type" : "number", "path" : "year", "boundaries" : [1910,1920,1930,1940] } } } } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$searchMeta': { 6 mongotQuery: { 7 facet: { 8 operator: { 9 near: { 10 path: 'released', 11 origin: ISODate('1921-11-01T00:00:00.000Z'), 12 pivot: 7776000000 13 } 14 }, 15 facets: { 16 genresFacet: { type: 'string', path: 'genres' }, 17 yearFacet: { 18 type: 'number', 19 path: 'year', 20 boundaries: [ 1910, 1920, 1930, 1940 ] 21 } 22 } 23 } 24 }, 25 explain: { 26 query: { type: 'LongDistanceFeatureQuery', args: {} }, 27 collectStats: { 28 facet: { 29 stringFacetCardinalities: { genresFacet: { queried: 10, total: 25 } } 30 } 31 }, 32 metadata: { 33 <hostname>.mongodb.netmongotVersion: '1.42.0', 34 mongotHostName: '<hostname>.mongodb.net', 35 indexName: 'default', 36 totalLuceneDocs: 21349 37 } 38 }, 39 requiresSearchMetaCursor: true 40 } 41 } 42 ], 43 queryShapeHash: '582DB864C9BCFB96896CF1A3079CF70FAC10A9A1E19E8D66DF20A2BB40424FB5', 44 serverInfo: { 45 host: '<hostname>.mongodb.net', 46 port: 27017, 47 version: '8.2.0', 48 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 49 }, 50 serverParameters: { 51 ... 52 }, 53 command: { 54 aggregate: 'movies', 55 pipeline: [ 56 { 57 '$searchMeta': { 58 facet: { 59 operator: { 60 near: { 61 path: 'released', 62 origin: ISODate('1921-11-01T00:00:00.000Z'), 63 pivot: 7776000000 64 } 65 }, 66 facets: { 67 genresFacet: { type: 'string', path: 'genres' }, 68 yearFacet: { 69 type: 'number', 70 path: 'year', 71 boundaries: [ 1910, 1920, 1930, 1940 ] 72 } 73 } 74 } 75 } 76 } 77 ], 78 cursor: {}, 79 '$db': 'sample_mflix' 80 }, 81 ok: 1, 82 '$clusterTime': { 83 clusterTime: Timestamp({ t: 1758305859, i: 1 }), 84 signature: { 85 hash: Binary.createFromBase64('8Zm16MEkzHnPpP9uLJK1YlT7a3o=', 0), 86 keyId: Long('7551379485140975621') 87 } 88 }, 89 operationTime: Timestamp({ t: 1758305859, i: 1 }) 90 }
db.movies.explain("queryPlanner").aggregate([ { $search: { index: "default", range: { path: "released", gt: ISODate("2015-01-01T00:00:00.000Z"), lt: ISODate("2015-12-31T00:00:00.000Z") }, sort: { released: -1 } } }, { $limit: 5 }, { $project: { _id: 0, title: 1, released: 1 } } ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 index: 'default', 8 range: { 9 path: 'released', 10 gt: ISODate('2015-01-01T00:00:00.000Z'), 11 lt: ISODate('2015-12-31T00:00:00.000Z') 12 }, 13 sort: { released: -1 } 14 }, 15 explain: { 16 query: { 17 type: 'ConstantScoreQuery', 18 args: { 19 query: { 20 type: 'IndexOrDocValuesQuery', 21 args: { 22 query: [ 23 { 24 type: 'PointRangeQuery', 25 args: { 26 path: 'released', 27 gte: ISODate('2015-01-01T00:00:00.001Z'), 28 lte: ISODate('2015-12-30T23:59:59.999Z') 29 } 30 }, 31 { 32 type: 'SortedNumericDocValuesRangeQuery', 33 args: {} 34 } 35 ] 36 } 37 } 38 } 39 }, 40 collectors: { 41 sort: { 42 fieldInfos: { released: [ 'dateV2' ] }, 43 usesIndexSort: true 44 } 45 }, 46 metadata: { 47 mongotVersion: '1.67.0', 48 mongotHostName: '<hostname>.mongodb.net', 49 indexName: 'default', 50 lucene: { totalSegments: 1, totalDocs: 21349 } 51 }, 52 featureFlags: [ 53 { 54 featureFlag: 'mongot.featureFlag.numericV2Semantics', 55 evaluationResult: true, 56 decisiveField: 'Phase' 57 } 58 ] 59 }, 60 mongotDocsRequested: Long('5'), 61 requiresSearchMetaCursor: false 62 } 63 }, 64 { 65 '$_internalSearchIdLookup': { 66 limit: Long('5'), 67 subPipeline: [ 68 { '$match': { _id: { '$eq': '_id placeholder' } } } 69 ] 70 } 71 }, 72 { '$limit': Long('5') }, 73 { '$project': { title: true, released: true, _id: false } } 74 ], 75 queryShapeHash: 'CA4950A32339C9853C4A295D89E9523DE568624672C87C231135012D27896CAE', 76 serverInfo: { 77 host: '<hostname>.mongodb.net', 78 port: 27017, 79 version: '8.3.2', 80 gitVersion: '89f54eb32e217d2f7bfcad50e5919f3033bb9611' 81 }, 82 serverParameters: { 83 internalQueryFacetBufferSizeBytes: 104857600, 84 internalDocumentSourceGroupMaxMemoryBytes: 104857600, 85 internalQueryMaxBlockingSortMemoryUsageBytes: 104857600, 86 internalDocumentSourceSetWindowFieldsMaxMemoryBytes: 104857600, 87 internalQueryFacetMaxOutputDocSizeBytes: 104857600, 88 internalLookupStageIntermediateDocumentMaxSizeBytes: 104857600, 89 internalQueryProhibitBlockingMergeOnMongoS: 0, 90 internalQueryMaxAddToSetBytes: 104857600, 91 internalQueryFrameworkControl: 'trySbeRestricted', 92 internalQueryPlannerIgnoreIndexWithCollationForRegex: 1 93 }, 94 command: { 95 aggregate: 'movies', 96 pipeline: [ 97 { 98 '$search': { 99 index: 'default', 100 range: { 101 path: 'released', 102 gt: ISODate('2015-01-01T00:00:00.000Z'), 103 lt: ISODate('2015-12-31T00:00:00.000Z') 104 }, 105 sort: { released: -1 } 106 } 107 }, 108 { '$limit': 5 }, 109 { '$project': { _id: 0, title: 1, released: 1 } } 110 ], 111 cursor: {}, 112 '$db': 'sample_mflix' 113 }, 114 ok: 1, 115 '$clusterTime': { 116 clusterTime: Timestamp({ t: 1778545038, i: 1 }), 117 signature: { 118 hash: Binary.createFromBase64('mExdEPAwssnzlpKaA1Rt+Ypv+cA=', 0), 119 keyId: Long('7634583163557117960') 120 } 121 }, 122 operationTime: Timestamp({ t: 1778545038, i: 1 }) 123 }
For queries that specify a $limit stage in the pipeline, the explain results include the mongotDocsRequested metric, which shows the number of documents that mongod requested from mongot.
例子
{ "mongotQuery": {}, "explain": {}, "limit": <int>, "sortSpec": {}, "mongotDocsRequested": <int>, }
executionStats
以下示例使用 autocomplete 以 executionStats 详细程度模式查询 title 字段。
1 db.movies.explain("executionStats").aggregate([ 2 { 3 "$search": { 4 "autocomplete": { 5 "path": "title", 6 "query": "pre", 7 "fuzzy": { 8 "maxEdits": 1, 9 "prefixLength": 1, 10 "maxExpansions": 256 11 } 12 } 13 } 14 } 15 ])
1 { 2 explainVersion: '1', 3 stages: [ 4 { 5 '$_internalSearchMongotRemote': { 6 mongotQuery: { 7 autocomplete: { 8 path: 'title', 9 query: 'pre', 10 fuzzy: { maxEdits: 1, prefixLength: 1, maxExpansions: 256 } 11 } 12 }, 13 explain: { 14 query: { 15 type: 'BooleanQuery', 16 args: { 17 must: [ 18 { 19 type: 'MultiTermQueryConstantScoreBlendedWrapper', 20 args: { 21 queries: [ 22 { 23 type: 'DefaultQuery', 24 args: { queryType: 'AutomatonQuery' }, 25 stats: { 26 context: { millisElapsed: 0 }, 27 match: { millisElapsed: 0 }, 28 score: { millisElapsed: 0 } 29 } 30 } 31 ] 32 }, 33 stats: { 34 context: { 35 millisElapsed: 12.517877, 36 invocationCounts: { 37 createWeight: Long('4'), 38 createScorer: Long('48') 39 } 40 }, 41 match: { 42 millisElapsed: 0.970794, 43 invocationCounts: { nextDoc: Long('2436') } 44 }, 45 score: { 46 millisElapsed: 0.638731, 47 invocationCounts: { score: Long('2420') } 48 } 49 } 50 } 51 ], 52 mustNot: [], 53 should: [ 54 { 55 type: 'TermQuery', 56 args: { path: 'title', value: 'pre' }, 57 stats: { 58 context: { 59 millisElapsed: 1.481341, 60 invocationCounts: { 61 createWeight: Long('4'), 62 createScorer: Long('16') 63 } 64 }, 65 match: { millisElapsed: 0 }, 66 score: { millisElapsed: 0 } 67 } 68 } 69 ], 70 filter: [], 71 minimumShouldMatch: 0 72 }, 73 stats: { 74 context: { 75 millisElapsed: 15.118651, 76 invocationCounts: { createWeight: Long('4'), createScorer: Long('32') } 77 }, 78 match: { 79 millisElapsed: 1.923822, 80 invocationCounts: { nextDoc: Long('2436') } 81 }, 82 score: { 83 millisElapsed: 1.954216, 84 invocationCounts: { score: Long('2420') } 85 } 86 } 87 }, 88 collectStats: { 89 allCollectorStats: { 90 millisElapsed: 4.189904, 91 invocationCounts: { 92 collect: Long('2420'), 93 competitiveIterator: Long('16'), 94 setScorer: Long('16') 95 } 96 }, 97 facet: { collectorStats: { millisElapsed: 0 } } 98 }, 99 resultMaterialization: { 100 stats: { 101 millisElapsed: 21.876621, 102 invocationCounts: { retrieveAndSerialize: Long('4') } 103 } 104 }, 105 metadata: { 106 <hostname>.mongodb.netmongotVersion: '1.42.0', 107 mongotHostName: '<hostname>.mongodb.net', 108 indexName: 'default', 109 cursorOptions: { batchSize: 108, requiresSearchSequenceToken: false }, 110 totalLuceneDocs: 21349 111 }, 112 resourceUsage: { 113 majorFaults: Long('2'), 114 minorFaults: Long('242'), 115 userTimeMs: Long('40'), 116 systemTimeMs: Long('0'), 117 maxReportingThreads: 1, 118 numBatches: 4 119 } 120 }, 121 requiresSearchMetaCursor: false, 122 internalMongotBatchSizeHistory: [ Long('108'), Long('162'), Long('243'), Long('365') ] 123 }, 124 nReturned: Long('605'), 125 executionTimeMillisEstimate: Long('44') 126 }, 127 { 128 '$_internalSearchIdLookup': { 129 subPipeline: [ 130 { '$match': { _id: { '$eq': '_id placeholder' } } } 131 ], 132 totalDocsExamined: Long('605'), 133 totalKeysExamined: Long('605'), 134 numDocsFilteredByIdLookup: Long('0') 135 }, 136 nReturned: Long('605'), 137 executionTimeMillisEstimate: Long('91') 138 } 139 ], 140 queryShapeHash: '6FD3791F785FA329D4ECD1171E0E5AF6772C18F5F0A7A50FC416D080A93C8CB7', 141 serverInfo: { 142 host: '<hostname>.mongodb.net', 143 port: 27017, 144 version: '8.2.0', 145 gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54' 146 }, 147 serverParameters: { 148 ... 149 }, 150 command: { 151 aggregate: 'movies', 152 pipeline: [ 153 { 154 '$search': { 155 autocomplete: { 156 path: 'title', 157 query: 'pre', 158 fuzzy: { maxEdits: 1, prefixLength: 1, maxExpansions: 256 } 159 } 160 } 161 } 162 ], 163 cursor: {}, 164 '$db': 'sample_mflix' 165 }, 166 ok: 1, 167 '$clusterTime': { 168 clusterTime: Timestamp({ t: 1758306209, i: 1 }), 169 signature: { 170 hash: Binary.createFromBase64('MIipFR5NAfl728L6h4ueQeZBLGM=', 0), 171 keyId: Long('7551379485140975621') 172 } 173 }, 174 operationTime: Timestamp({ t: 1758306209, i: 1 }) 175 }
要了解有关 explain 响应元素的更多信息,请参阅解释结果。