MongoDB performs slow query on sum() based on monthly groups - mongodb

Question

This is What I tried so far on aggregated query:
db.getCollection('storage').aggregate([
{
"$match": {
"user_id": 2
}
},
{
"$project": {
"formattedDate": {
"$dateToString": { "format": "%Y-%m", "date": "$created_on" }
},
"size": "$size"
}
},
{ "$group": {
"_id" : "$formattedDate",
"size" : { "$sum": "$size" }
} }
])
This is the result:
/* 1 */
{
"_id" : "2018-02",
"size" : NumberLong(10860595386)
}
/* 2 */
{
"_id" : "2017-12",
"size" : NumberLong(524288)
}
/* 3 */
{
"_id" : "2018-01",
"size" : NumberLong(21587971)
}
And this is the document structure:
{
"_id" : ObjectId("5a59efedd006b9036159e708"),
"user_id" : NumberLong(2),
"is_transferred" : false,
"is_active" : false,
"process_id" : NumberLong(0),
"ratio" : 0.000125759169459343,
"type_id" : 201,
"size" : NumberLong(1687911),
"is_processed" : false,
"created_on" : ISODate("2018-01-13T11:39:25.000Z"),
"processed_on" : ISODate("1970-01-01T00:00:00.000Z")
}
And last, the explain result:
/* 1 */
{
"stages" : [
{
"$cursor" : {
"query" : {
"user_id" : 2.0
},
"fields" : {
"created_on" : 1,
"size" : 1,
"_id" : 1
},
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "data.storage",
"indexFilterSet" : false,
"parsedQuery" : {
"user_id" : {
"$eq" : 2.0
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"user_id" : 1
},
"indexName" : "user_id",
"isMultiKey" : false,
"multiKeyPaths" : {
"user_id" : []
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"user_id" : [
"[2.0, 2.0]"
]
}
}
},
"rejectedPlans" : []
}
}
},
{
"$project" : {
"_id" : true,
"formattedDate" : {
"$dateToString" : {
"format" : "%Y-%m",
"date" : "$created_on"
}
},
"size" : "$size"
}
},
{
"$group" : {
"_id" : "$formattedDate",
"size" : {
"$sum" : "$size"
}
}
}
],
"ok" : 1.0
}
The problem:
I can navigate and get all results in almost instantly like in 0,002sec. However, when I specify user_id and sum them by grouping on each month, My result came in between 0,300s to 0,560s. I do similar tasks in one request and it becaomes more than a second to finish.
What I tried so far:
I've added an index for user_id
I've added an index for created_on
I used more $match conditions. However, This makes even worse.
This collection have almost 200,000 documents in it currently and approximately 150,000 of them are belongs to user_id = 2
How can I minimize the response time for this query?
Note: MongoDB 3.4.10 used.

Answer 1

Pratha,
try to add sort on "created_on" and "size" fields as the first stage in aggregation pipeline.
db.getCollection('storage').aggregate([
{
"$sort": {
"created_on": 1, "size": 1
}
}, ....
Before that, add compound key index:
db.getCollection('storage').createIndex({created_on:1,size:1})
If you sort data before the $group stage, it will improve the efficiency of accumulation of the totals.
Note about sort aggregation stage:
The $sort stage has a limit of 100 megabytes of RAM. By default, if the stage exceeds this limit, $sort will produce an error. To allow for the handling of large datasets, set the allowDiskUse option to true to enable $sort operations to write to temporary files.
P.S
get rid of match stage by userID to test performance, or add userID to compound key also.