Aggregation Pipeline Optimization

$project Stage Placement

When you use a $project stage it should typically be the last stage in your pipeline, used to specify which fields to return to the client.

Using a $project stage at the beginning or middle of a pipeline to reduce the number of fields passed to subsequent pipeline stages is unlikely to improve performance, as the database performs this optimization automatically.

($project or $unset or $addFields or $set) + $match Sequence Optimization

For an aggregation pipeline that contains a projection stage ($addFields, $project, $set, or $unset) 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 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 with 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 automatically 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.

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.

$sort + $match Sequence Optimization

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 } }

$redact + $match Sequence Optimization

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" } } }

$project/$unset + $skip Sequence Optimization

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 } }

$sort + $limit Coalescence

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.

$limit + $limit Coalescence

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 }

$skip + $skip Coalescence

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 }

$match + $match Coalescence

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" } ] } }

$lookup, $unwind, and $match Coalescence

When $unwind immediately follows $lookup, and the $unwind operates on the as field of the $lookup, the optimizer coalesces the $unwind into the $lookup stage. This avoids creating large intermediate documents. Furthermore, if $unwind is followed by a $match on any as subfield of the $lookup, the optimizer also coalesces the $match.

For example, a pipeline contains the following sequence:

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

The optimizer coalesces the $unwind and $match stages into the $lookup stage. If you run the aggregation with explain option, the explain output shows the coalesced stages:

{
   $lookup: {
     from: "otherCollection",
     as: "resultingArray",
     localField: "x",
     foreignField: "y",
     let: {},
     pipeline: [
       {
         $match: {
           "foo": {
             "$eq": "bar"
           }
         }
       }
     ],
     unwinding: {
       "preserveNullAndEmptyArrays": false
     }
   }
}

You can see this optimized pipeline in the explain plan.

The unwinding field shown in the previous explain output differs from the $unwind stage. The unwinding field shows how the pipeline is internally optimized. The $unwind stage deconstructs an array field from the input documents and outputs a document for each element.

$group Optimization

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 execution engine.

When the slot-based query execution engine is used for $group, the explain results include queryPlanner.winningPlan.queryPlan.stage: "GROUP".

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

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

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

$lookup Optimization

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 execution engine and none of the following conditions are true:

• The $lookup operation executes a pipeline on a foreign collection. To see an example of this kind of operation, see Join Conditions and Subqueries on a Foreign 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 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 that cannot be executed using the slot-based execution engine.

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

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

Indexes

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 especially 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 efficiently identifies the relevant data

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

• An index on the grouping field that matches the $sort order returns all of the field values needed for the $group stage, making it a covered query.

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

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

$match stage

During the $match stage, the server can use an index if $match is the first stage in the pipeline, after any optimizations from the query planner.

$sort stage

During the $sort stage, the server can use an index if the stage is not preceded by a $project, $unwind, or $group stage.

$group stage

During the $group stage, the server can use an index to quickly find the $first or $last document in each group if the stage meets both of these conditions:

• The pipeline sorts and groups by the same field.

• The $group stage only uses the $first or $last accumulator operator.

See $group Performance Optimizations for an example.

$geoNear stage

The server always uses an index for the $geoNear stage, since it 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:

• $lookup

• $graphLookup

• $unionWith

Document Filters

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.

$sort + $skip + $limit Sequence

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.