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    <p>You can apply mongodb <a href="http://docs.mongodb.org/manual/core/map-reduce/" rel="nofollow"><strong>Map/Reduce</strong></a> technique for this task.</p> <p>1) <strong><code>map</code></strong> function will determine nested objects from <code>relation</code> object, which contain <code>status</code> field equal to <code>"friends"</code> value:</p> <pre><code>var map = function () { for (var key in this.relation) { var relation = this.relation[key]; if (relation.status == "friends") emit(key, relation); } } </code></pre> <p>2) <strong><code>reduce</code></strong> function will actually do nothing, just return single emitted relation:</p> <pre><code>var reduce = function (key, values) { return values[0]; } </code></pre> <p>As a result you'll have collection with such objects:</p> <pre><code>{ _id: "2b2b2b2b2b2b2b2b2b2b2b", value: { status: "friends", status_changed_date: ISODate("2013-10-28T00:00:00.0000Z"), // etc... } } </code></pre> <hr> <p>If you decide to replace <code>relation</code> object with array, you can use <a href="http://docs.mongodb.org/manual/reference/operator/aggregation/" rel="nofollow">aggregation framework</a> to construct similar query.</p> <p><strong>User Data:</strong></p> <pre><code>{ _id: ObjectId("1a1a1a1a111a1a11a1a1a1a"), name: "Tester", relations: [ { id: "2b2b2b2b2b2b2b2b2b2b2b", status: "friends", status_changed_date: ISODate("2013-10-28T00:00:00.0000Z"), // etc... }, { id: "3c3c3c3c3c3c3c3c3c3c3c", status: "engaged", status_changed_date: ISODate("2013-10-28T00:00:00.0000Z"), // etc... } ] } </code></pre> <p><strong>Query:</strong></p> <pre><code>db.collection.aggregate([ { $unwind: "$relations" }, { $match: { "relations.status": "friends" } }, { $project: _id: 0, id: "$relations.id", status: "$relations.status", status_changed_date: "$relations.status_chaged_date" // etc... } ]) </code></pre> <p><strong>Output:</strong></p> <pre><code>{ id: "2b2b2b2b2b2b2b2b2b2b2b", status: "friends", status_changed_date: ISODate("2013-10-28T00:00:00.0000Z"), // etc... } </code></pre>
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    1. COWould this be any faster than just obtaining all the data and using, say, Python to run the map section? The trade off seems to just be putting load on Mongo instead of some other system, right?
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    2. COIt depends on many factors: system architecture, network and machines speed, etc. You may to test these two approaches, but I believe, if the work could be done in Mongo (and DB in general), it should be done there.
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    3. COAlso, try to reconsider your db schema and save relations in array (as you mentioned in question). This way you can use aggregation framework for such queries and it will be faster then map/reduce.
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