MongoDB exact match on multiple document fields - mongodb

Question

I am trying to build a Python script using PyMongo that will be able to hit a Mongo DB that can get exact matches of n number of objects that may exist in the database. Currently, I have this setup:
db.entries.find({'$or': [<list-of-objects]})
Where the list of objects looks something like this:
[{'email': 'some#email.com', 'zip': '11111'}, {'email': 'another#email.com', 'zip': '11112'}, ...]
Using $or works okay when I have 10 or so items in the list. I am testing now with 100, and it is taking a very long time to return. I have considered using multiple $in filters, but I don't know if that is the best option.
I'm sure there is a better way to handle this, but I am fairly new to Mongo.
EDIT: Output of .explain() below:
{
"executionStats": {
"executionTimeMillis": 228734,
"nReturned": 2,
"totalKeysExamined": 0,
"allPlansExecution": [],
"executionSuccess": true,
"executionStages": {
"needYield": 0,
"saveState": 43556,
"restoreState": 43556,
"isEOF": 1,
"inputStage": {
"needYield": 0,
"saveState": 43556,
"restoreState": 43556,
"isEOF": 1,
"inputStage": {
"needYield": 0,
"direction": "forward",
"saveState": 43556,
"restoreState": 43556,
"isEOF": 1,
"docsExamined": 5453000,
"nReturned": 2,
"needTime": 5452999,
"filter": {
"$or": [{
"$and": [{
"email": {
"$eq": "some#email.com"
}
}, {
"zipcode": {
"$eq": "11111"
}
}]
}, {
"$and": [{
"email": {
"$eq": "another#email.com"
}
}, {
"zipcode": {
"$eq": "11112"
}
}]
}]
},
"executionTimeMillisEstimate": 208083,
"invalidates": 0,
"works": 5453002,
"advanced": 2,
"stage": "COLLSCAN"
},
"nReturned": 2,
"needTime": 5452999,
"executionTimeMillisEstimate": 211503,
"transformBy": {
"_id": false
},
"invalidates": 0,
"works": 5453002,
"advanced": 2,
"stage": "PROJECTION"
},
"nReturned": 2,
"needTime": 5452999,
"executionTimeMillisEstimate": 213671,
"invalidates": 0,
"works": 5453002,
"advanced": 2,
"stage": "SUBPLAN"
},
"totalDocsExamined": 5453000
},
"queryPlanner": {
"parsedQuery": {
"$or": [{
"$and": [{
"email": {
"$eq": "some#email.com"
}
}, {
"zipcode": {
"$eq": "11111"
}
}]
}, {
"$and": [{
"email": {
"$eq": "another#email.com"
}
}, {
"zipcode": {
"$eq": "11112"
}
}]
}]
},
"rejectedPlans": [],
"namespace": "db.entries",
"winningPlan": {
"inputStage": {
"transformBy": {
"_id": false
},
"inputStage": {
"filter": {
"$or": [{
"$and": [{
"email": {
"$eq": "some#email.com"
}
}, {
"zipcode": {
"$eq": "11111"
}
}]
}, {
"$and": [{
"email": {
"$eq": "another#email.com"
}
}, {
"zipcode": {
"$eq": "11112"
}
}]
}]
},
"direction": "forward",
"stage": "COLLSCAN"
},
"stage": "PROJECTION"
},
"stage": "SUBPLAN"
},
"indexFilterSet": false,
"plannerVersion": 1
},
"ok": 1.0,
"serverInfo": {
"host": "somehost",
"version": "3.4.6",
"port": 27017,
"gitVersion": "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
}
}

Answer 1

I suggest to create a new index (a compound index) as in your case you are using two fields for your search:
db.entries.createIndex( {"email": 1, "zip": 1} )
Now run your query appending the explain() command at your query, you should see that instead of COLLSCAN it has started to use IXSCAN.

Answer 2

To avoid indexing and re-indexing (this query will not just pertain to email/zip, would be dynamic), I build the lists of data with each header and use them as $in arguments, then pass those into an $and. It seems to be working well enough and it hasn't queried longer than 3 minutes.
Example:
{'$and': [{'email': {'$in': ['some#example.com', 'fake#example.com', 'email#example.com']}, 'zipcode': {'$in': ['12345', '11111', '11112']}}]}