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Aggregation Pipeline Optimization

On this page

  • Projection Optimization
  • Pipeline Sequence Optimization
  • Pipeline Coalescence Optimization
  • Slot-Based Query Execution Engine Pipeline Optimizations
  • Improve Performance with Indexes and Document Filters
  • Example

Aggregation pipeline operations have an optimization phase which attempts to reshape the pipeline for improved performance.

To see how the optimizer transforms a particular aggregation pipeline, include the explain option in the db.collection.aggregate() method.

Optimizations are subject to change between releases.

In addition to learning about the aggregation pipeline optimizations performed during the optimization phase, you will also see how to improve aggregation pipeline performance using indexes and document filters. See Improve Performance with Indexes and Document Filters.

The aggregation pipeline can determine if it requires only a subset of the fields in the documents to obtain the results. If so, the pipeline will only use those required fields, reducing the amount of data passing through the pipeline.

For an aggregation pipeline that contains a projection stage ($project or $unset or $addFields or $set) followed by a $match stage, MongoDB moves any filters in the $match stage that do not require values computed in the projection stage to a new $match stage before the projection.

If an aggregation pipeline contains multiple projection and/or $match stages, MongoDB performs this optimization for each $match stage, moving each $match filter before all projection stages that the filter does not depend on.

Consider a pipeline of the following stages:

{ $addFields: {
maxTime: { $max: "$times" },
minTime: { $min: "$times" }
} },
{ $project: {
_id: 1, name: 1, times: 1, maxTime: 1, minTime: 1,
avgTime: { $avg: ["$maxTime", "$minTime"] }
} },
{ $match: {
name: "Joe Schmoe",
maxTime: { $lt: 20 },
minTime: { $gt: 5 },
avgTime: { $gt: 7 }
} }

The optimizer breaks up the $match stage into four individual filters, one for each key in the $match query document. The optimizer then moves each filter before as many projection stages as possible, creating new $match stages as needed. Given this example, the optimizer produces the following optimized pipeline:

{ $match: { name: "Joe Schmoe" } },
{ $addFields: {
maxTime: { $max: "$times" },
minTime: { $min: "$times" }
} },
{ $match: { maxTime: { $lt: 20 }, minTime: { $gt: 5 } } },
{ $project: {
_id: 1, name: 1, times: 1, maxTime: 1, minTime: 1,
avgTime: { $avg: ["$maxTime", "$minTime"] }
} },
{ $match: { avgTime: { $gt: 7 } } }

The $match filter { avgTime: { $gt: 7 } } depends on the $project stage to compute the avgTime field. The $project stage is the last projection stage in this pipeline, so the $match filter on avgTime could not be moved.

The maxTime and minTime fields are computed in the $addFields stage but have no dependency on the $project stage. The optimizer created a new $match stage for the filters on these fields and placed it before the $project stage.

The $match filter { name: "Joe Schmoe" } does not use any values computed in either the $project or $addFields stages so it was moved to a new $match stage before both of the projection stages.

Note

After optimization, the filter { name: "Joe Schmoe" } is in a $match stage at the beginning of the pipeline. This has the added benefit of allowing the aggregation to use an index on the name field when initially querying the collection. See Improve Performance with Indexes and Document Filters for more information.

When you have a sequence with $sort followed by a $match, the $match moves before the $sort to minimize the number of objects to sort. For example, if the pipeline consists of the following stages:

{ $sort: { age : -1 } },
{ $match: { status: 'A' } }

During the optimization phase, the optimizer transforms the sequence to the following:

{ $match: { status: 'A' } },
{ $sort: { age : -1 } }

When possible, when the pipeline has the $redact stage immediately followed by the $match stage, the aggregation can sometimes add a portion of the $match stage before the $redact stage. If the added $match stage is at the start of a pipeline, the aggregation can use an index as well as query the collection to limit the number of documents that enter the pipeline. See Improve Performance with Indexes and Document Filters for more information.

For example, if the pipeline consists of the following stages:

{ $redact: { $cond: { if: { $eq: [ "$level", 5 ] }, then: "$$PRUNE", else: "$$DESCEND" } } },
{ $match: { year: 2014, category: { $ne: "Z" } } }

The optimizer can add the same $match stage before the $redact stage:

{ $match: { year: 2014 } },
{ $redact: { $cond: { if: { $eq: [ "$level", 5 ] }, then: "$$PRUNE", else: "$$DESCEND" } } },
{ $match: { year: 2014, category: { $ne: "Z" } } }

When you have a sequence with $project or $unset followed by $skip, the $skip moves before $project. For example, if the pipeline consists of the following stages:

{ $sort: { age : -1 } },
{ $project: { status: 1, name: 1 } },
{ $skip: 5 }

During the optimization phase, the optimizer transforms the sequence to the following:

{ $sort: { age : -1 } },
{ $skip: 5 },
{ $project: { status: 1, name: 1 } }

When possible, the optimization phase coalesces a pipeline stage into its predecessor. Generally, coalescence occurs after any sequence reordering optimization.

Changed in version 4.0.

When a $sort precedes a $limit, the optimizer can coalesce the $limit into the $sort if no intervening stages modify the number of documents (e.g. $unwind, $group). MongoDB will not coalesce the $limit into the $sort if there are pipeline stages that change the number of documents between the $sort and $limit stages..

For example, if the pipeline consists of the following stages:

{ $sort : { age : -1 } },
{ $project : { age : 1, status : 1, name : 1 } },
{ $limit: 5 }

During the optimization phase, the optimizer coalesces the sequence to the following:

{
"$sort" : {
"sortKey" : {
"age" : -1
},
"limit" : NumberLong(5)
}
},
{ "$project" : {
"age" : 1,
"status" : 1,
"name" : 1
}
}

This allows the sort operation to only maintain the top n results as it progresses, where n is the specified limit, and MongoDB only needs to store n items in memory [1]. See $sort Operator and Memory for more information.

Note

Sequence Optimization with $skip

If there is a $skip stage between the $sort and $limit stages, MongoDB will coalesce the $limit into the $sort stage and increase the $limit value by the $skip amount. See $sort + $skip + $limit Sequence for an example.

[1] The optimization will still apply when allowDiskUse is true and the n items exceed the aggregation memory limit.

When a $limit immediately follows another $limit, the two stages can coalesce into a single $limit where the limit amount is the smaller of the two initial limit amounts. For example, a pipeline contains the following sequence:

{ $limit: 100 },
{ $limit: 10 }

Then the second $limit stage can coalesce into the first $limit stage and result in a single $limit stage where the limit amount 10 is the minimum of the two initial limits 100 and 10.

{ $limit: 10 }

When a $skip immediately follows another $skip, the two stages can coalesce into a single $skip where the skip amount is the sum of the two initial skip amounts. For example, a pipeline contains the following sequence:

{ $skip: 5 },
{ $skip: 2 }

Then the second $skip stage can coalesce into the first $skip stage and result in a single $skip stage where the skip amount 7 is the sum of the two initial limits 5 and 2.

{ $skip: 7 }

When a $match immediately follows another $match, the two stages can coalesce into a single $match combining the conditions with an $and. For example, a pipeline contains the following sequence:

{ $match: { year: 2014 } },
{ $match: { status: "A" } }

Then the second $match stage can coalesce into the first $match stage and result in a single $match stage

{ $match: { $and: [ { "year" : 2014 }, { "status" : "A" } ] } }

When a $unwind immediately follows another $lookup, and the $unwind operates on the as field of the $lookup, the optimizer can coalesce the $unwind into the $lookup stage. This avoids creating large intermediate documents.

For example, a pipeline contains the following sequence:

{
$lookup: {
from: "otherCollection",
as: "resultingArray",
localField: "x",
foreignField: "y"
}
},
{ $unwind: "$resultingArray"}

The optimizer can coalesce the $unwind stage into the $lookup stage. If you run the aggregation with explain option, the explain output shows the coalesced stage:

{
$lookup: {
from: "otherCollection",
as: "resultingArray",
localField: "x",
foreignField: "y",
unwinding: { preserveNullAndEmptyArrays: false }
}
}

MongoDB can use the slot-based query execution engine to execute certain pipeline stages when specific conditions are met. In most cases, the slot-based engine provides improved performance and lower CPU and memory costs compared to the classic query engine.

To verify that the slot-based engine is used, run the aggregation with the explain option. This option outputs information on the aggregation's query plan. For more information on using explain with aggregations, see Return Information on Aggregation Pipeline Operation.

The following sections describe:

  • The conditions when the slot-based engine is used for aggregation.

  • How to verify if the slot-based engine was used.

New in version 5.2.

Starting in version 5.2, MongoDB uses the slot-based execution query engine to execute $group stages if either:

  • $group is the first stage in the pipeline.

  • All preceding stages in the pipeline can also be executed by the slot-based engine.

When the slot-based query execution engine is used for $group, the explain results include:

  • explain.explainVersion: '2'

  • queryPlanner.winningPlan.queryPlan.stage: "GROUP"

    The location of the queryPlanner object depends on whether the pipeline contains stages after the $group stage which cannot be executed using the slot-based engine.

    • If $group is the last stage or all stages after $group can be executed using the slot-based engine, the queryPlanner object is in the top-level explain output object (explain.queryPlanner).

    • If the pipeline contains stages after $group which cannot be executed using the slot-based engine, the queryPlanner object is in explain.stages[0].$cursor.queryPlanner.

New in version 6.0.

Starting in version 6.0, MongoDB can use the slot-based execution query engine to execute $lookup stages if all preceding stages in the pipeline can also be executed by the slot-based engine and none of the following conditions are true:

  • The $lookup operation executes a pipeline on a joined collection. To see an example of this kind of operation, see Join Conditions and Subqueries on a Joined Collection.

  • The $lookup's localField or foreignField specify numeric components. For example: { localField: "restaurant.0.review" }.

  • The from field of any $lookup in the pipeline specifies a view or sharded collection.

When the slot-based query execution engine is used for $lookup, the explain results include:

  • explain.explainVersion: '2'

  • queryPlanner.winningPlan.queryPlan.stage: "EQ_LOOKUP". EQ_LOOKUP means "equality lookup".

    The location of the queryPlanner object depends on whether the pipeline contains stages after the $lookup stage which cannot be executed using the slot-based engine.

    • If $lookup is the last stage or all stages after $lookup can be executed using the slot-based engine, the queryPlanner object is in the top-level explain output object (explain.queryPlanner).

    • If the pipeline contains stages after $lookup which cannot be executed using the slot-based engine, the queryPlanner object is in explain.stages[0].$cursor.queryPlanner.

The following sections show how you can improve aggregation performance using indexes and document filters.

An aggregation pipeline can use indexes from the input collection to improve performance. Using an index limits the amount of documents a stage processes. Ideally, an index can cover the stage query. A covered query has especiallly high performance, since the index returns all matching documents.

For example, a pipeline that consists of $match, $sort, $group can benefit from indexes at every stage:

  • An index on the $match query field can efficiently identify the relevant data

  • An index on the sorting field can return data in sorted order for the $sort stage

  • An index on the grouping field that matches the $sort order can return all of the field values needed to execute the $group stage (a covered query)

To determine whether a pipeline uses indexes, review the query plan and look for IXSCAN or DISTINCT_SCAN plans.

Note

In some cases, the query planner uses a DISTINCT_SCAN index plan that returns one document per index key value. DISTINCT_SCAN executes faster than IXSCAN if there are multiple documents per key value. However, index scan parameters might affect the time comparison of DISTINCT_SCAN and IXSCAN.

For early stages in your aggregation pipeline, consider indexing the query fields. Stages that can benefit from indexes are:

$match stage
$match can use an index to filter documents if it is the first stage in the pipeline, after any optimizations from the query planner.
$sort stage
$sort can benefit from an index as long as it is not preceded by a $project, $unwind, or $group stage.
$group stage

$group can use an index to find the first document in each group if it meets all of the following conditions:

  • a $sort stage sorts the grouping field before $group

  • an index exists that matches the sort order on the grouped field

  • $first is the only accumulator in the $group stage

See $group Performance Optimizations for an example.

$geoNear stage
$geoNear always uses an index, since it must be the first stage in a pipeline and requires a geospatial index.

Additionally, stages later in the pipeline that retrieve data from other, unmodified collections can use indexes on those collections for optimization. These stages include:

If your aggregation operation requires only a subset of the documents in a collection, filter the documents first:

  • Use the $match, $limit, and $skip stages to restrict the documents that enter the pipeline.

  • When possible, put $match at the beginning of the pipeline to use indexes that scan the matching documents in a collection.

  • $match followed by $sort at the start of the pipeline is equivalent to a single query with a sort, and can use an index.

A pipeline contains a sequence of $sort followed by a $skip followed by a $limit

{ $sort: { age : -1 } },
{ $skip: 10 },
{ $limit: 5 }

The optimizer performs $sort + $limit Coalescence to transforms the sequence to the following:

{
"$sort" : {
"sortKey" : {
"age" : -1
},
"limit" : NumberLong(15)
}
},
{
"$skip" : NumberLong(10)
}

MongoDB increases the $limit amount with the reordering.

Tip

See also:

←  Aggregation PipelineAggregation Pipeline Limits →
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