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How to Retrieve Query Plan and Execution Statistics

The MongoDB Search query returns information about the $search query plan and execution statistics when the query is run with the explain method enabled. When you run a query with explain, MongoDB Search returns a BSON document containing statistics and metadata that describes how the query was run internally in Lucene.

Tip

explain command

Syntax

db.<myCollection>.explain("<verbosity>").aggregate([
 {
 $search: {
 "<operator>": {
 "<operator-options>"
 }
 }
 }
])

Verbosity

Verbosity mode controls the behavior of explain and the amount of information returned. Value can be one of the following, in order of decreasing verbosity:

allPlansExecution	Information about the query plan including the `stats` fields, which contain execution statistics for the query. Includes partial execution data captured during plan selection.
executionStats	Information about the query plan including the `stats` fields, which contain execution statistics for the query.
queryPlanner (default)	Information about the query plan. Does not include the `stats` fields, which contain execution statistics for the query.

Tip

Verbosity Modes

Output

The query with the explain method returns the following fields in the stages.$_internalSearchMongotRemote object:

Option	Type	Purpose
`mongotQuery`	document	Contains the query that you ran.
`explain`	document	Contains the `explain` results. To learn more, see Explain Results.
`internalMongotBatchSizeHistory`	array of integers	Contains the size of each batch of documents that `mongot` returned to `mongod`.

Explain Results

The explain method returns a BSON document with the following fields in the explain document.

Option	Type	Necessity	Purpose
`collectors`	document	Optional	Describes the execution statistics for the collector. If `indexPartitionExplain` is present, this is nested within each `indexPartitionExplain` instead of at the top-level.
`highlight`	document	Optional	Details about the highlight execution for the query. This is returned only if you specified highlighting in the query.
`indexPartitionExplain`	array of documents	Optional	Contains per index partition details. This is only returned if you configured two or more index partitions.
`metadata`	document	Optional	Contains useful metadata.
`query`	document	Optional	Describes the execution statistics for the query. This is not returned. If `indexPartitionExplain` is present, this is nested within each `indexPartitionExplain`, instead of at the top-level.
`resultMaterialization`	document	Optional	Details related to retrieving per-document data from Lucene after query execution. This is not returned for `queryPlanner` verbosity mode.
`resourceUsage`	document	Optional	Details the resource usage of the query when it was executed. This is not returned for `queryPlanner` verbosity mode.

`collectors`

The collectors BSON document contains the following fields:

Field	Type	Necessity	Purpose
`allCollectorStats`	document	Required	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`.
`facet`	document	Optional	Breakdown of the query that specifies facet. To learn more, see `facet`.
`sort`	document	Optional	Breakdown of the query that specifies sort. To learn more, see `sort`.

`allCollectorStats`

The allCollectorStats BSON document describes collector statistics across all collectors specified in the query, including facet and sort. It contains the following keys:

Field	Description
`collect`	Tracks the duration and number of results collected by the collector.
`competitiveIterator`	Statistics tracking the total duration and the number of times a `competitiveIterator` was requested from the collector.
`setScorer`	Statistics tracking the total duration and number of times a scorer was set on the collector.

`facet`

The facet is a BSON document that details query and execution statistics when you specify a facet in the query. It contains the following fields:

Option	Type	Necessity	Purpose
`collectorStats`	document	Optional	Shows statistics only for the `facet` collector. The keys in here are identical to the `allCollectorStats`. You can evaluate the difference between this and the `allCollectorStats` to determine the `sort` collector execution statistics if you used `sort` in the query.
`createCountsStats`	document	Optional	Shows statistics related to creating the internal Lucene object that holds all of the facet groupings. It contains `generateFacetCounts` field, which returns the statistics tracking the total duration and number of times `mongot` generates the Lucene object that holds all of the facets and their counts.
`stringFacetFieldCardinalities`	document	Required	Maps the field being faceted on to their cardinalities for both the documents that matched the query and across the entire Lucene index. It provides the following cardinality information for each field: `queried` - Represents the facet cardinality of the field across all queried documents. `total` - Represents the facet cardinality of the field across all documents in the index.

`sort`

The sort is a BSON document that details query and execution statistics when you specify a sort in the query. It contains the following fields:

Option	Type	Necessity	Purpose
`stats`	document	Optional	Tracks statistics related to `sort` execution across all sort fields. It contains the following fields: `comparator` - Shows statistics and metadata around how the Lucene sorts the results. `setBottom` - Returns how often the current least competitive sort value is updated with a new and more competitive value. When sorting, MongoDB Search keeps track of the bottom value and if a new value surpasses the current bottom value in relevance or competitiveness, this metric is incremented. `compareBottom` - Returns the timing and invocation statistics related to comparing the current `bottom` value with a candidate `bottom` value. `compareTop` - Returns the timing and invocation statistics related to comparing the current `top` value with a candidate `top` value. `setHitsThresholdReached` - Returns the timing and invocation statistics related to when the maximum number of results (`limit`) was reached. `competitiveIterator` - Returns the timing and invocation statistics related to iterating (only) over documents that are likely to compete for top spots in the sorted results. `setScorer` - Returns the timing and invocation statistics related to computing the score and relevance of documents. `prunedResultIterator` - Shows the invocations and timing statistics of `nextDoc` and `advance` method calls on Lucene's `competitiveIterator`. This is not present in the output if it performs a mixed data type sort. It contains only the `nextDoc` field.
`fieldInfos`	document	Required	Maps the field being sorted on to the list of data types present in the index for the field.

`highlight`

The highlight is a BSON document that details query and execution statistics when you specify highlighting in the query. It contains the following fields:

Option	Type	Necessity	Purpose
`resolvedHighlightPaths`	List<String>	Required	List of all the highlighted fields. If you specified a wildcard path in the `highlight` section of your query, contains a list of fully resolved document paths.
`stats`	QueryExecutionArea	Optional	Invocation and timing statistics related to setting up and executing highlights. It contains the following fields: `setupHighlight` - Time it took for setting up the internal Lucene objects used during the `executeHighlight` phase. `executeHighlight` - Time it took to run highlighting over the matching documents.

`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`

The metadata contains helpful metadata, such as the following:

Field	Type	Necessity	Purpose
`mongotVersion`	String	Optional	Current version of `mongot`.
`mongotHostName`	String	Optional	Human readable label that identifies the `mongot` host.
`indexName`	String	Optional	MongoDB Search index used in the query.
`cursorOptions`	Document	Optional	Cursor options given to `mongot`.
`totalLuceneDocs`	Integer	Optional	Total number of index objects in the index including deleted documents.

`query`

The query BSON document describes the execution statistics for the query. It contains the following fields:

Field	Type	Necessity	Purpose
`path`	string	Optional	Path to the operator, only if it isn't the root.
`type`	string	Required	Name of the Lucene Query that the MongoDB Search operator created. See `query` for more information.
`analyzer`	string	Optional	MongoDB Search analyzer used with the query.
`args`	document	Required	Lucene query information. See `query` for more information.
`stats`	document	Optional	`stats` for the query if `explain` ran with `executionStats` or `allPlansExecution` verbosity.

`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.

This section contains:

Some of the Lucene queries that MongoDB Search operators create
Lucene query options that are included in the structured summary
Lucene query structured summary example for each Lucene query type

Note

About the Examples

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 field shows the MongoDB Search operator and the query that was run.
explain.type field shows the Lucene Query that the operator created.

For complete examples, see Examples.

BooleanQuery

For Lucene BooleanQuery, the structured summary includes details on the following options:

Field	Type	Necessity	Description
`must`	<Explain Results>	Optional	Clauses which must match.
`mustNot`	<Explain Results>	Optional	Clauses which must not match.
`should`	<Explain Results>	Optional	Clauses which should match.
`filter`	<Explain Results>	Optional	Clauses which must all match.
`minimumShouldMatch`	Integer	Optional	The minimum number of `should` clauses which must match.

ConstantScoreQuery

For constant score queries, the structured summary includes details on the following options:

Field	Type	Necessity	Description
`query`	Explain Results	Required	Child of the `ConstantScoreQuery`.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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

For Lucene FunctionScoreQuery queries, the structured summary includes details on the following options:

Field	Type	Necessity	Description
`scoreFunction`	string	Required	Scoring expression used in the query.
`query`	Explain Results	Required	The query.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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 }

LatLonPointDistanceQuery

For Lucene LatLonPointDistanceQuery queries, the response contains an stats only.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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 }

LatLonShapeQuery

For Lucene LatLonShapeQuery queries, the response contains an stats only.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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 }

LongDistanceFeatureQuery

For Lucene LongDistanceFeatureQuery, the response contains an stats only.

The following example shows the explain response for a query run against the sample_mflix.movies collection.

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

For Lucene MultiTermQueryConstantScoreWrapper queries, the structured summary includes details on the following arguments:

Field	Type	Necessity	Description
`queries`	List<Explain Results>	Required	List of queries.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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

For Lucene PhraseQuery queries, the structured summary includes details on the following arguments:

Field	Type	Necessity	Description
`path`	String	Required	Indexed field to search.
`query`	String	Required	String or strings to search for.
`slop`	Number	Required	Allowable distance between words in the `query` phrase.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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

For Lucene PointRangeQuery queries, the structured summary includes details on the following arguments:

Field	Type	Necessity	Description
`path`	String	Required	Indexed field to search.
`representation`	String	Optional	Numeric representation. Queries over date-typed data do not include representation.
`gte`	Number	Optional	Lower bound of the query.
`lte`	Number	Optional	Upper bound of the query.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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

For term queries, the structured summary includes details on the following arguments:

Field	Type	Necessity	Description
`path`	String	Required	Indexed field to search.
`value`	String	Required	String to search for.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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

Lucene queries that are not explicitly defined by another Lucene query are serialized using the default query. The structured summary includes details on the following option:

Field	Type	Necessity	Description
`queryType`	String	Required.	Type of Lucene query.

The following example shows the explain response for a query run against the sample_airbnb.listingsAndReviews collection.

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.

Timing Breakdown

The timing breakdown describes execution statistics pertinent to an areas of query execution. The following fields show the timing breakdown:

Field	Type	Description
`millisElapsed`	Long	Approximate wall-clock time elapsed performing tasks in this area including the amount of time the children of the query spent in this area. The value is approximate number of milliseconds elapsed while performing tasks in this area.
`invocationCounts`	Document	Number of invocations of tasks included in this area. The value is a map of task names to their invocation count.

Areas of Query

Statistics are available for the following areas of query:

Option

Description

context

Statistics related to the execution of the Lucene query. There are two tasks whose invocation counts are enumerated in this area:

`createScorer`	Scorer iterates over documents and generates a score for each document. Invocations of `createScorer` create the object responsible for scoring. Note that time associated with this task is not time spent actually scoring documents. Count includes the number of `scorerSupplier` invocations.
`createWeight`	Weight stores state associated with a query and `IndexSearcher`. Count includes the number of `createWeight` invocations.

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.

For example:

"context" : {
 "millisElapsed" : NumberDouble(4.934751),
 "invocationCounts" : {
 "createWeight" : NumberLong(1),
 "createScorer" : NumberLong(10)
 }
}

match

Statistics related to iterating over and matching result documents. This statistic shows the time it takes to determine which document is the next match. Time spent matching results can vary significantly depending on the nature of the query. There are two tasks whose invocation counts are enumerated in this area:

`nextDoc`	Requests to advance to the next document of the result set. This involves identifying and moving past skips, or other tasks necessary to find the next match. Count includes the number of `nextDoc` and `advance` invocations.
`refineRoughMatch`	Performs a more thorough match. Some queries execute in a two-phase process where a document is first "roughly" matched, and is checked with a second, more thorough phase only after satisfying the first rough match. The `refineRoughMatch` task is the second phase of the two-phase process. Count includes the number of `refineRoughMatch` invocations.

For example:

"match" : {
 "millisElapsed" : NumberDouble(4.901597),
 "invocationCounts" : {
 "nextDoc" : NumberLong(541),
 "refineRoughMatch" : NumberLong(0)
 }
}

score

Statistics related to scoring documents in the result set. There are two tasks whose invocation counts are enumerated in this area:

`score`	Scores each document in the result set. Count includes the number of `score` invocations.
`setMinCompetitiveScore`	Ignores documents whose score is less than the given value. Indicates that a query may have been able to reduce the number of scoring operations performed by ignoring documents with scores below some uncompetitive threshold. Count includes the number of `setMinCompetitiveScore` invocations.

For example:

"score" : {
 "millisElapsed" : NumberDouble(3.931312),
 "invocationCounts" : {
 "score" : NumberLong(536),
 "setMinCompetitiveScore" : NumberLong(0)
 }
}

`resultMaterialization`

The resultMaterialization document shows the amount of time mongot takes to accomplish the following:

Retrieve result data stored in Lucene in the form of either _id or storedSource.
Serialize the data into BSON format before sending it to mongod.

To learn more, see stats.

`resourceUsage`

The resourceUsage document shows the resource used for running the query. It contains the following fields:

Field	Type	Necessity	Purpose
`majorFaults`	Long	Required	Number of major page faults, which occur when the system can't find the required data in memory resulting in reading from the backing store such as disk, during query execution.
`minorFaults`	Long	Required	Number of minor page faults, which occur when the data is in the page cache, but hasn't yet been mapped to the process' page table.
`userTimeMs`	Long	Required	Amount of CPU time, in milliseconds, spent in user space.
`systemTimeMs`	Long	Required	Amount of CPU time, in milliseconds, spent in system space.
`maxReportingThreads`	Integer	Required	Maximum number of threads that `mongot` used during query execution across all batches. For non-concurrent explain queries, the value is `1`.
`numBatches`	Integer	Required	Total number of batches that `mongot` was requested when processing the query.

Examples

The following examples use the movies collection in the sample_mflix database.

Tip

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`

The following example uses different operators to query the title field with the allPlansExecution verbosity mode.

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: {
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69  invocationCounts: {
70  createWeight: Long('2'),
71  createScorer: Long('14')
72  }
73  },
74  match: {
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76  invocationCounts: { nextDoc: Long('10') }
77  },
78  score: {
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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: {
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92  match: { millisElapsed: 0 },
93  score: { millisElapsed: 0 }
94  }
95  },
96  boost: 0.75
97  },
98  stats: {
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101  invocationCounts: {
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103  createScorer: Long('18')
104  }
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106  match: {
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108  invocationCounts: { nextDoc: Long('62') }
109  },
110  score: {
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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: {
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133  invocationCounts: {
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136  }
137  },
138  match: {
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140  invocationCounts: { nextDoc: Long('6') }
141  },
142  score: {
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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: {
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172  invocationCounts: { nextDoc: Long('172') }
173  },
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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: {
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188  match: { millisElapsed: 0 },
189  score: { millisElapsed: 0 }
190  }
191  },
192  boost: 0.75
193  },
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200  }
201  },
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204  invocationCounts: { nextDoc: Long('60') }
205  },
206  score: {
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208  invocationCounts: { score: Long('54') }
209  }
210  }
211  }
212  ],
213  filter: [],
214  minimumShouldMatch: 0
215  },
216  stats: {
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219  invocationCounts: { createWeight: Long('2'), createScorer: Long('12') }
220  },
221  match: {
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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: {
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286  port: 27017,
287  version: '8.2.0',
288  gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54'
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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" }
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} ])

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17  invocationCounts: { createWeight: Long('1'), createScorer: Long('6') }
18  },
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22  },
23  score: {
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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: {
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42  stats: {
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44  invocationCounts: {
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46  setupHighlight: Long('1')
47  }
48  }
49  },
50  resultMaterialization: {
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53  invocationCounts: { retrieveAndSerialize: Long('1') }
54  }
55  },
56  metadata: {
57  <hostname>.mongodb.netmongotVersion: '1.42.0',
58  mongotHostName: '<hostname>.mongodb.net',
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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  {
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81  { '$match': { _id: { '$eq': '_id placeholder' } } }
82  ],
83  totalDocsExamined: Long('25'),
84  totalKeysExamined: Long('25'),
85  numDocsFilteredByIdLookup: Long('0')
86  },
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88  executionTimeMillisEstimate: Long('1')
89  },
90  {
91  '$project': {
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93  highlights: { '$meta': 'searchHighlights' },
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95  },
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97  executionTimeMillisEstimate: Long('1')
98  }
99  ],
100  queryShapeHash: 'D08444272924C1E04A6E99D0CD4BF82FD929893862B3356F79EC18BBD1F0EF0C',
101  serverInfo: {
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104  version: '8.2.0',
105  gitVersion: '13e629eeccd63f00d17568fc4c12b7530fa34b54'
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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
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119  command: {
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121  pipeline: [
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125  highlight: { path: 'title', maxNumPassages: 1, maxCharsToExamine: 40 }
126  }
127  },
128  {
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131  _id: 0,
132  highlights: { '$meta': 'searchHighlights' }
133  }
134  }
135  ],
136  cursor: {},
137  '$db': 'sample_mflix'
138  },
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140  '$clusterTime': {
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142  signature: {
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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]
 }
 }
 }
 }
 }
])

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2  explainVersion: '1',
3  stages: [
4  {
5  '$searchMeta': {
6  mongotQuery: {
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11  origin: ISODate('1921-11-01T00:00:00.000Z'),
12  pivot: 7776000000
13  }
14  },
15  facets: {
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17  yearFacet: {
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19  path: 'year',
20  boundaries: [ 1910, 1920, 1930, 1940 ]
21  }
22  }
23  }
24  },
25  explain: {
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28  args: {},
29  stats: {
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32  invocationCounts: { createWeight: Long('1'), createScorer: Long('6') }
33  },
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36  invocationCounts: { nextDoc: Long('20881') }
37  },
38  score: {
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40  invocationCounts: { score: Long('20878') }
41  }
42  }
43  },
44  collectStats: {
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47  invocationCounts: {
48  collect: Long('20878'),
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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: {
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60  invocationCounts: { generateFacetCounts: Long('2') }
61  },
62  stringFacetCardinalities: { genresFacet: { queried: 10, total: 25 } }
63  }
64  },
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68  invocationCounts: { retrieveAndSerialize: Long('1') }
69  }
70  },
71  metadata: {
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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  },
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90  }
91  ],
92  queryShapeHash: '582DB864C9BCFB96896CF1A3079CF70FAC10A9A1E19E8D66DF20A2BB40424FB5',
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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: {
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111  origin: ISODate('1921-11-01T00:00:00.000Z'),
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114  },
115  facets: {
116  genresFacet: { type: 'string', path: 'genres' },
117  yearFacet: {
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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: {
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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,
 }
 }
 }
 }
])

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2  explainVersion: '1',
3  stages: [
4  {
5  '$_internalSearchMongotRemote': {
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9  query: 'yark',
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11  }
12  },
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18  mustNot: [],
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24  type: 'TermQuery',
25  args: { path: 'title', value: 'mark' },
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28  match: { millisElapsed: 0 },
29  score: { millisElapsed: 0 }
30  }
31  },
32  boost: 0.75
33  },
34  stats: {
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37  invocationCounts: {
38  createWeight: Long('2'),
39  createScorer: Long('18')
40  }
41  },
42  match: {
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44  invocationCounts: { nextDoc: Long('22') }
45  },
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48  invocationCounts: { score: Long('16') }
49  }
50  }
51  },
52  {
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54  args: {
55  query: {
56  type: 'TermQuery',
57  args: { path: 'title', value: 'yard' },
58  stats: {
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60  match: { millisElapsed: 0 },
61  score: { millisElapsed: 0 }
62  }
63  },
64  boost: 0.75
65  },
66  stats: {
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69  invocationCounts: {
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84  {
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92  match: { millisElapsed: 0 },
93  score: { millisElapsed: 0 }
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109  },
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115  },
116  {
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120  type: 'TermQuery',
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147  },
148  {
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158  }
159  },
160  boost: 0.75
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173  },
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176  invocationCounts: { score: Long('166') }
177  }
178  }
179  },
180  {
181  type: 'BoostQuery',
182  args: {
183  query: {
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185  args: { path: 'title', value: 'park' },
186  stats: {
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188  match: { millisElapsed: 0 },
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190  }
191  },
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193  },
194  stats: {
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197  invocationCounts: {
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200  }
201  },
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204  invocationCounts: { nextDoc: Long('60') }
205  },
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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`

The following example uses different operators to query the title field with the queryPlanner verbosity mode.

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 }

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.

Example

{
 "mongotQuery": {},
 "explain": {},
 "limit": <int>,
 "sortSpec": {},
 "mongotDocsRequested": <int>,
}

`executionStats`

The following example uses the autocomplete to query the title field with the executionStats verbosity mode.

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 }

To learn more about the explain response elements, see Explain Results.

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	}

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	}

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	}

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	])