MongoDB index not helping query with multikey index

Question

I have a collection of documents with a multikey index defined. However, the performance of the query is pretty poor for just 43K documents. Is ~215ms for this query considered poor? Did I define the index correctly if nscanned is 43902 (which equals the total documents in the collection)?

Document:

{
    "_id": {
        "$oid": "50f7c95b31e4920008dc75dc"
    },
    "bank_accounts": [
        {
            "bank_id": {
                "$oid": "50f7c95a31e4920009b5fc5d"
            },
            "account_id": [
                "ff39089358c1e7bcb880d093e70eafdd",
                "adaec507c755d6e6cf2984a5a897f1e2"
            ]
        }
    ],
    "created_date": "2013,01,17,09,50,19,274089",
}

Index:

{ "bank_accounts.bank_id" : 1 , "bank_accounts.account_id" : 1}

Query:

db.visitor.find({ "bank_accounts.account_id" : "ff39089358c1e7bcb880d093e70eafdd" , "bank_accounts.bank_id" : ObjectId("50f7c95a31e4920009b5fc5d")}).explain()

Explain:

{
    "cursor" : "BtreeCursor bank_accounts.bank_id_1_bank_accounts.account_id_1",
    "isMultiKey" : true,
    "n" : 1,
    "nscannedObjects" : 43902,
    "nscanned" : 43902,
    "nscannedObjectsAllPlans" : 43902,
    "nscannedAllPlans" : 43902,
    "scanAndOrder" : false,
    "indexOnly" : false,
    "nYields" : 0,
    "nChunkSkips" : 0,
    "millis" : 213,
    "indexBounds" : {
        "bank_accounts.bank_id" : [
            [
                ObjectId("50f7c95a31e4920009b5fc5d"),
                ObjectId("50f7c95a31e4920009b5fc5d")
            ]
        ],
        "bank_accounts.account_id" : [
            [
                {
                    "$minElement" : 1
                },
                {
                    "$maxElement" : 1
                }
            ]
        ]
    },
    "server" : "Not_Important"
}

Answer 1

I see three factors in play.

First, for application purposes, make sure that $elemMatch isn't a more appropriate query for this use-case. http://docs.mongodb.org/manual/reference/operator/elemMatch/. It seems like it would be bad if the wrong results came back due to multiple subdocuments satisfying the query.

Second, I imagine the high nscanned value can be accounted for by querying on each of the field values independently. .find({ bank_accounts.bank_id: X }) vs. .find({"bank_accounts.account_id": Y}). You may see that nscanned for the full query is about equal to nscanned of the largest subquery. If the index key were being evaluated fully as a range, this would not be expected, but...

Third, the { "bank_accounts.account_id" : [[{"$minElement" : 1},{"$maxElement" : 1}]] } clause of the explain plan shows that no range is being applied to this portion of the key.

Not really sure why, but I suspect it has something to do with account_id's nature (an array within a subdocument within an array). 200ms seems about right for an nscanned that high.

A more performant document organization might be to denormalize the account_id -> bank_id relationship within the subdocument, and store:

{"bank_accounts": [
{
 "bank_id": X,
 "account_id: Y,
},
{
 "bank_id": X,
 "account_id: Z,
}
]}

instead of: {"bank_accounts": [{ "bank_id": X, "account_id: [Y, Z], }]}

My tests below show that with this organization, the query optimizer gets back to work and exerts a range on both keys:

> db.accounts.insert({"something": true, "blah": [{ a: "1", b: "2"} ] })
> db.accounts.ensureIndex({"blah.a": 1, "blah.b": 1})
> db.accounts.find({"blah.a": 1, "blah.b": "A RANGE"}).explain()
{
    "cursor" : "BtreeCursor blah.a_1_blah.b_1",
    "isMultiKey" : false,
    "n" : 0,
    "nscannedObjects" : 0,
    "nscanned" : 0,
    "nscannedObjectsAllPlans" : 0,
    "nscannedAllPlans" : 0,
    "scanAndOrder" : false,
    "indexOnly" : false,
    "nYields" : 0,
    "nChunkSkips" : 0,
    "millis" : 0,
    "indexBounds" : {
        "blah.a" : [
            [
                1,
                1
            ]
        ],
        "blah.b" : [
            [
                "A RANGE",
                "A RANGE"
        ]
    ]
    }
}