MongoDB Aggregation Query Optimization : match -> unwind -> match vs unwind->match - mongodb

Question

Input Data
{
"_id" : ObjectId("5dc7ac6e720a2772c7b76671"),
"idList" : [
{
"queueUpdateTimeStamp" : "2019-12-12T07:16:47.577Z",
"displayId" : "H14",
"currentQueue" : "10",
"isRejected" : true,
"isDispacthed" : true
},
{
"queueUpdateTimeStamp" : "2019-12-12T07:16:47.577Z",
"displayId" : "H14",
"currentQueue" : "10",
"isRejected" : true,
"isDispacthed" : false
}
],
"poDetailsId" : ObjectId("5dc7ac15720a2772c7b7666f"),
"processtype" : 1
}
Output Data
{
"_id" : ObjectId("5dc7ac6e720a2772c7b76671"),
"idList":
{
"queueUpdateTimeStamp" : "2019-12-12T07:16:47.577Z",
"displayId" : "H14",
"currentQueue" : "10",
"isRejected" : true,
"isDispacthed" : true
},
"poDetailsId" : ObjectId("5dc7ac15720a2772c7b7666f"),
"processtype" : 1
}
Query 1 ( unwind then match)
aggregate([
{
$unwind: { path: "$idList" }
},
{
$match: { 'idList.isDispacthed': isDispatched }
}
])
Query 2 ( match then unwind then match)
aggregate([
{
$match: { 'idList.isDispacthed': isDispatched }
},
{
$unwind: { path: "$idList" }
},
{
$match: { 'idList.isDispacthed': isDispatched }
}
])
My Question / My Concern
(suppose i have large number of documents(50k +) in this collection and assuming i have other lookups and projections after this query in same pipeline)
match -> unwind -> match VS unwind ->match
is there any performance difference between these two queries ?
is there any other ( better ) way to write this query?

Answer 1

It all depends on the MongoDB query planner optimizer:
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.
https://docs.mongodb.com/manual/core/aggregation-pipeline-optimization/
Create index for poDetailsId and run this query:
db.getCollection('collection').explain().aggregate([
{
$unwind: "$idList"
},
{
$match: {
'idList.isDispacthed': true,
"poDetailsId" : ObjectId("5dc7ac15720a2772c7b7666f")
}
}
])
{
"stages" : [
{
"$cursor" : {
"query" : {
"poDetailsId" : {
"$eq" : ObjectId("5dc7ac15720a2772c7b7666f")
}
},
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.collection",
"indexFilterSet" : false,
"parsedQuery" : {
"poDetailsId" : {
"$eq" : ObjectId("5dc7ac15720a2772c7b7666f")
}
},
"queryHash" : "2CF7E390",
"planCacheKey" : "A8739F51",
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"poDetailsId" : 1.0
},
"indexName" : "poDetailsId_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"poDetailsId" : []
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"poDetailsId" : [
"[ObjectId('5dc7ac15720a2772c7b7666f'), ObjectId('5dc7ac15720a2772c7b7666f')]"
]
}
}
},
"rejectedPlans" : []
}
}
},
{
"$unwind" : {
"path" : "$idList"
}
},
{
"$match" : {
"idList.isDispacthed" : {
"$eq" : true
}
}
}
],
"ok" : 1.0
}
As you see MongoDB will change this aggregation to:
db.getCollection('collection').aggregate([
{
$match: { "poDetailsId" : ObjectId("5dc7ac15720a2772c7b7666f") }
}
{
$unwind: "$idList"
},
{
$match: { 'idList.isDispacthed': true }
}
])
Logically, $match -> $unwind -> $match is better since you filter (by index) a subset of records instead of full scan (working with 100 matched documents ≠ all documents).
If your aggregation operation requires only a subset of the data in a collection, use the $match, $limit, and $skip stages to restrict the documents that enter at the beginning of the pipeline. When placed at the beginning of a pipeline, $match operations use suitable indexes to scan only the matching documents in a collection.
https://docs.mongodb.com/manual/core/aggregation-pipeline/#early-filtering
Once you manipulate your documents, MongoDB cannot apply indexes.