MongoDB Aggregation Pipeline Optimization - mongodb

Question

I have an aggregation pipeline as mentioned below. Considering that there can be hundreds of thousands of documents on which I want to apply this pipeline, I have questions on how it can be optimized. Below are my questions:
1.) What is the most optimal index that be applied to the match query. Should I have an index = myArray.tags.tagKey, myArray.tags.tagValue, myArray.someRules.count, myArray.entryTime OR should I create separate indexes for each of these fields. What is the best way to do this?
2.) If you see my pipeline, I am applying match operator twice to filter the documents: Once in the beginning of pipeline to get the benefit of my index. Second, I am using it after unwinding operation to filter the specific array elements of the array "myArray". Is there any way I can combine them. In other words, can array elements be filtered using $match aggregation operator?
Here is an example of my document:
{
"_id" : "8001409457639",
"myArray" : [
{
"entryID" : ObjectId("5101ab1116b12614fb083022"),
"requestID" : "adb0d2asdfbe10",
"owner" : "ABC",
"version" : 1,
"requestType" : "1",
"someRules" : {
"count" : 0,
"somethingElse" : 11
},
"entryTime" : ISODate("2015-09-22T19:25:19.014Z"),
"tags" : [
{
"tagKey" : "Owner",
"tagValue" : "ABC"
},
{
"tagKey" : "Request Type",
"tagValue" : "1"
}
]
},
{
"entryID" : ObjectId("5101a111c6b12614fb083022"),
"requestID" : "fc057asdf16480",
"owner" : "ABC",
"version" : 1,
"requestType" : "1",
"someRules" : {
"count" : 10,
"somethingElse" : 0
},
"entryTime" : ISODate("2015-09-22T19:44:26.558Z"),
"tags" : [
{
"tagKey" : "Owner",
"tagValue" : "ABC"
},
{
"tagKey" : "Request Type",
"tagValue" : "1"
}
]
},
{
"entryID" : ObjectId("5101b111c6b12614fb083011"),
"requestID" : "40c7a0ads2dd8c2",
"owner" : "ABC",
"version" : 1,
"requestType" : "1",
"someRules" : {
"count" : 10,
"somethingElse" : 0
},
"entryTime" : ISODate("2015-09-22T20:24:15.347Z"),
"tags" : [
{
"tagKey" : "Owner",
"tagValue" : "ABC"
},
{
"tagKey" : "Request Type",
"tagValue" : "1"
}
]
}
],
"lockAcquiredBy" : "james",
"lockStartTime" : ISODate("2015-11-18T22:36:05.266Z")
}
This is my aggregation pipeline:
[{
"$match": {
"$and": [{
"$or": [{
"myArray.tags": {
"$elemMatch": {
"tagKey": "Owner",
"tagValue": "ABC"
}
}
}, {
"myArray.tags": {
"$elemMatch": {
"tagKey": "Owner",
"tagValue": "DEF"
}
}
}]
}, {
"$or": [{
"myArray.tags": {
"$elemMatch": {
"tagKey": "Request Type",
"tagValue": "4"
}
}
}, {
"myArray.tags": {
"$elemMatch": {
"tagKey": "Request Type",
"tagValue": "Retry"
}
}
}]
}],
"myArray.someRules.count": 0,
"myArray.entryTime": {
"$gte": {
"$date": "2016-01-05T01:59:07.763Z"
}
}
}
}, {
"$unwind": "$myArray"
}, {
"$match": {
"$and": [{
"$or": [{
"myArray.tags": {
"$elemMatch": {
"tagKey": "Owner",
"tagValue": "ABC"
}
}
}, {
"myArray.tags": {
"$elemMatch": {
"tagKey": "Owner",
"tagValue": "DEF"
}
}
}]
}, {
"$or": [{
"myArray.tags": {
"$elemMatch": {
"tagKey": "Request Type",
"tagValue": "4"
}
}
}, {
"myArray.tags": {
"$elemMatch": {
"tagKey": "Request Type",
"tagValue": "Retry"
}
}
}]
}],
"myArray.someRules.count": 0,
"myArray.entryTime": {
"$gte": {
"$date": "2016-01-05T01:59:07.763Z"
}
}
}
}
// More steps in the pipeline.
]