Question

If we have a collection photos and each entry is a large document which contains all the information about photo including views details and detailed upvotes/downvotes.

{
_id:ObjectId('...'),
title:'...',
location:'...',
views:[
    {...},
    {...},
    ...
    ],
upvotes:[
    {...},
    {...},
    ...
    ],
downvotes:[
    {...},
    {...},
    ...
    ],
}

Which query would work faster and be more effective (memory, CPU usage):

db.photos.find().limit(100)

OR

db.photos.find({}, {views:0,upvotes:0,downvotes:0}).limit(100)

?

Answer 1

you could it do yourself. just add explain() at the end of query.

for example:

db.photos.find().limit(100).explain()


{
  "cursor" : "<Cursor Type and Index>",
  "isMultiKey" : <boolean>,
  "n" : <num>,
  "nscannedObjects" : <num>,
  "nscanned" : <num>,
  "nscannedObjectsAllPlans" : <num>,
  "nscannedAllPlans" : <num>,
  "scanAndOrder" : <boolean>,
  "indexOnly" : <boolean>,
  "nYields" : <num>,
  "nChunkSkips" : <num>,
  "millis" : <num>,
  "indexBounds" : { <index bounds> },
  "allPlans" : [
                 { "cursor" : "<Cursor Type and Index>",
                   "n" : <num>,
                   "nscannedObjects" : <num>,
                   "nscanned" : <num>,
                   "indexBounds" : { <index bounds> }
                 },
                  ...
               ],
  "oldPlan" : {
                "cursor" : "<Cursor Type and Index>",
                "indexBounds" : { <index bounds> }
              }
  "server" : "<host:port>",
}

mills param is what you want

If you want to see cpu usage just add --cpu key in launch mongod script.

--cpu
Forces mongod to report the percentage of CPU time in write lock. mongod generates output every four seconds. MongoDB writes this data to standard output or the logfile if using the logpath option.

http://docs.mongodb.org/manual/reference/explain/

You could provide hint() to mongo for projection() smth like this:

we have simple collection:

> db.performance.findOne()
{
        "_id" : ObjectId("50d2e4c08861fdb7e1c601ea"),
        "a" : 1,
        "b" : 1,
        "c" : 1,
        "d" : 1
}

Which consist of 23 elements:

> db.performance.count()
23

Now we can create compound index:

> db.performance.ensureIndex({'c':1, 'd':1})

And provide mongo a hint to use index for projection.

> db.performance.find({'a':1}, {'c':1, 'd':1}).hint({'c':1, 'd':1}).explain()
{
        "cursor" : "BtreeCursor c_1_d_1",
        "isMultiKey" : false,
        "n" : 1,
        "nscannedObjects" : 23,
        "nscanned" : 23,
        "nscannedObjectsAllPlans" : 23,
        "nscannedAllPlans" : 23,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "indexBounds" : {
                "c" : [
                        [
                                {
                                        "$minElement" : 1
                                },
                                {
                                        "$maxElement" : 1
                                }
                        ]
                ],
                "d" : [
                        [
                                {
                                        "$minElement" : 1
                                },
                                {
                                        "$maxElement" : 1
                                }
                        ]
                ]
        },
        "server" : ""
}
>