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MongoDB 2.6 is $out

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MongoDB 2.6 is $out

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Introduction

MongoDB is evolving rapidly. The 2.2 version introduced the aggregation framework as an alternative to the Map-Reduce query model. Generating aggregated reports is a recurrent requirement for enterprise systems and MongoDB shines in this regard. If you’re new to it you might want to check this aggregation framework introduction or the performance tuning and the data modelling guides.

Let’s reuse the data model I first introduced while demonstrating the blazing fast MongoDB insert capabilities:

{
        "_id" : ObjectId("5298a5a03b3f4220588fe57c"),
        "created_on" : ISODate("2012-04-22T01:09:53Z"),
        "value" : 0.1647851116706831
}

MongoDB 2.6 Aggregation enhancements

In the 2.4 version, if I run the following aggregation query:

db.randomData.aggregate( [
{
    $match: {
        "created_on" : {
            $gte : new Date(Date.UTC(2012, 0, 1)),
            $lte : new Date(Date.UTC(2012, 0, 10))
        }
    }
}, 
{
    $group: {
        _id : {
            "minute" : {
                $minute : "$created_on"
            }
        }, 
        "values": {
            $addToSet: "$value"
        }
    }
}]);

I hit the 16MB aggregation result limitation:

{
    "errmsg" : "exception: aggregation result exceeds maximum document size (16MB)",
    "code" : 16389,
    "ok" : 0
}

MongoDB documents are limited to 16MB, and prior to the 2.6 version, the aggregation result was a BSON document. The 2.6 version replaced it with a cursor instead.

Running the same query on 2.6 yields the following result:

db.randomData.aggregate( [
{
    $match: {
        "created_on" : {
            $gte : new Date(Date.UTC(2012, 0, 1)),
            $lte : new Date(Date.UTC(2012, 0, 10))
        }
    }
}, 
{
    $group: {
        _id : {
            "minute" : {
                $minute : "$created_on"
            }
        }, 
        "values": {
            $addToSet: "$value"
        }
    }
}])
.objsLeftInBatch();
14

I used the cursor-based objsLeftInBatch method to test the aggregation result type and the 16MB limitation no longer applies to the overall result. The cursor inner results are regular BSON documents, hence they are still limited to 16MB, but this is way more manageable than the previous overall result limit.

The 2.6 version also addresses the aggregation memory restrictions. A full collection scan such as:

db.randomData.aggregate( [  
{
    $group: {
        _id : {
            "minute" : {
                $minute : "$created_on"
            }
        }, 
        "values": {
            $addToSet: "$value"
        }
    }
}])
.objsLeftInBatch();

can end up with the following error:

{
    "errmsg" : "exception: Exceeded memory limit for $group, but didn't allow external sort. Pass allowDiskUse:true to opt in.",
    "code" : 16945,
    "ok" : 0
}

So, we can now perform large sort operations using the allowDiskUse parameter:

db.randomData.aggregate( [  
{
    $group: {
        _id : {
            "minute" : {
                $minute : "$created_on"
            }
        }, 
        "values": {
            $addToSet: "$value"
        }
    }
}]
,
{
    allowDiskUse : true
})
.objsLeftInBatch();

The 2.6 version allows us to save the aggregation result to a different collection using the newly added $out stage.

db.randomData.aggregate( [
{
    $match: {
        "created_on" : {
            $gte : new Date(Date.UTC(2012, 0, 1)),
            $lte : new Date(Date.UTC(2012, 0, 10))
        }
    }
}, 
{
    $group: {
        _id : {
            "minute" : {
                $minute : "$created_on"
            }
        }, 
        "values": {
            $addToSet: "$value"
        }
    }
},
{
    $out : "randomAggregates"
}
]);
db.randomAggregates.count();
60

New operators have been added such as let, map, cond, to name a few.

The next example will append AM or PM to the time info of each specific event entry.

var dataSet = db.randomData.aggregate( [
{
    $match: {
        "created_on" : {
            $gte : new Date(Date.UTC(2012, 0, 1)),
            $lte : new Date(Date.UTC(2012, 0, 2))
        }
    }
}, 
{
    $project: {
        "clock" : {
            $let: {
                vars: {
                    "hour": {
                        $substr: ["$created_on", 11, -1]
                    },             
                    "am_pm": { $cond: { if: { $lt: [ {$hour : "$created_on" }, 12 ] } , then: 'AM',else: 'PM'} }
                },
                in: { $concat: [ "$$hour", " ", "$$am_pm"] }               
            }          
        }  
    }
},
{
    $limit : 10
}
]);
dataSet.forEach(function(document)  {
    printjson(document);
});

Resulting in:

"clock" : "16:07:14 PM"
"clock" : "22:14:42 PM"
"clock" : "21:46:12 PM"
"clock" : "03:35:00 AM"
"clock" : "04:14:20 AM"
"clock" : "03:41:39 AM"
"clock" : "17:08:35 PM"
"clock" : "18:44:02 PM"
"clock" : "19:36:07 PM"
"clock" : "07:37:55 AM"

If you enjoyed this article, I bet you are going to love my book as well.






Conclusion

MongoDB 2.6 version comes with a lot of other enhancements such as bulk operations or index intersection. MongoDB is constantly evolving, offering a viable alternative for document-based storage. At such a development rate, there’s no wonder it was named 2013 database of the year.

If you have enjoyed reading my article and you’re looking forward to getting instant email notifications of my latest posts, you just need to follow my blog.


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Topics:
java ,nosql ,$out

Published at DZone with permission of Vlad Mihalcea. See the original article here.

Opinions expressed by DZone contributors are their own.

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