MongoDB aggregation query that experiences a significant increase in execution time when adding $count or $group stages

I am attempting to execute a query to count documents based on the ledgerName field, which is used to search for the required ledger name. Additionally, I need to obtain distinct counts of each status for the searched ledger name and store all data that matches each status into a list.

Below is the demo data for three collections involved in my query:

1. Settings: (Collection Size: 30K documents)
{

	_id: // ObjectId of doc
	company_id: // ObjectId of company
	ledgerName_id: // ID of ledger in String

}
2. History: (Collection Size: 600K documents)
{
	_id: // ObjectId of doc
	company_id: // ObjectId of company
	ledgerName_id: // ID of ledger in String
	createdAt: // Date of creation of history
	status: // Status 1 or 0
}
3. Ledger_Name: (Collection Size 50M documents)
{
	_id: // ObjectId of doc
	company_id: // ObjectId of company
	name_id: // ID of ledger in String
	ledgerName: // Name of ledger in string
}

Here is the query that utilizes indexed scans across the Settings collection and also leverages indices in the other two collections during the lookup stages:

Query:
db.getCollection("Settings").aggregate(
[{
	$match:{
	"company_id": // Some Company ID
	}
},
{
	$lookup:{
		as: "SettingsData",
		from: "Settings",
		"let": {
			"company_id": "$company_id",
			"ledgerName_id": "$ledgerName_id"
                },
		pipeline:[
                    {
                        "$match": {
                            "$expr": {
                                "$and": [
                                    { "$eq": [ "$company_id", "$company_id" ] },
                                    { "$eq": [ "$ledgerName_id", "$ledgerName_id" ] }
                                ]
                            },
                            "createdAt": {
                                "$gte": ISODate("2024-04-01T00:00:00.000+0530"),
                                "$lt": ISODate("2025-04-01T00:00:00.000+0530")
                            },

                        }
                    },
                    { "$sort": { "createdAt": -1 } }, // Sort by createdAt in descending order
                    { "$limit": 1 }, //
                    {
                        "$project": {
                            "_id": 1,
                            "createdAt": 1,
                            "status": 1,
                        }
                    }
                ]
	}
},
{
	"$unwind": {
		"path": "$SettingsData",
		"preserveNullAndEmptyArrays": true
            }
},
{
            "$lookup": {
                "as": "ledgersData",
                "from": "Ledger_Name",
                "let": {
                    "company_id": "$company_id",
                    "ledgerName_id": "$ledgerName_id"
                },
                "pipeline": [
                    {
                        "$match": {
                            "$expr": {
                                "$and": [
                                    { "$eq": [ "$company_id", "$company_id" ] },
                                    { "$eq": [ "$name_id", "$ledgerName_id" ] } 
				] 
				}
			}
		    },
                    {
                        "$project": {
                            "_id": 0,
                            "ledgerName": 1 // Search Term
                        }
                    }
                ]
            }
        },	
},
{
	"$unwind": {
                "path": "$ledgersData",
                "preserveNullAndEmptyArrays": true
        }
},
{
	"$count": "count"
}

When I run this query without any aggregation operations like $count or $group, it completes within 500 milliseconds. However, adding these stages extends the execution time to 15-20 seconds. Could anyone help me in optimizing this query?

@Peter_Hubbard Hello

Yes I am sure that without $count stage query is taking below 500ms and after adding it, goes to 15 to 20 seconds.

@steevej Appologies for late reply.

Answer to your 1st question: the query that I have provided, just remove $count stage from it. It is the same query.

Additional pipelines will include $group stage that will fetch summation of docmentations with different business logics. (I can’t share that thing with you that’s why i have created a simple sample query with count stage. If that query is executed in minimum amount of time then I could also get those thing too)

Explain output for 1st Lookup stage:

   "totalDocsExamined" : NumberLong(42526),
    "totalKeysExamined" : NumberLong(42526),
    "collectionScans" : NumberLong(0),
    "indexesUsed" : [
        "company_id_1_ledgerName_id_1_createdAt_1"
    ],
    "nReturned" : NumberLong(42544),
    "executionTimeMillisEstimate" : NumberLong(639)

Explain output for 2nd Lookup stage:

    "totalDocsExamined" : NumberLong(42534),
    "totalKeysExamined" : NumberLong(42534),
    "collectionScans" : NumberLong(0),
    "indexesUsed" : [
        "company_id_1_ledgerName_id_1_name_1"
    ],
    "nReturned" : NumberLong(42544),
    "executionTimeMillisEstimate" : NumberLong(9909)