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  1. PO
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    <p>After thinking too much, I figured out a solution using map-reduce since use aggregation framework seems to be not possible.</p> <p>Here are the reduce function, that simple group documents by <code>user_id</code>.</p> <pre><code>var mapf = function () { emit(this.user_id, { _id: this._id, creation_time: this.creation_time }) } </code></pre> <p>On reduce function, I check if there is at least four entries. If true, <code>values</code> array is sorted by <code>creation_time</code> and the first 4 documents are skipped.</p> <pre><code>var redf = function (key, values) { var result = {}; if (values.length &gt; 4) { values.sort(function (a, b) { return a.creation_time &gt; b.creation_time; }); // unfortunately, mongodb doesn't support array as result of reduce function result['oids'] = values.slice(3); } return result; } </code></pre> <p>Now it's time to run map-reduce command. The results will be inserted in <code>plus_four_users</code> collection.</p> <pre><code>db.newsdb.mapReduce(mapf, redf, { out : "plus_four_users" }) </code></pre> <p>This will result in something like that:</p> <pre><code>&gt; db.newsdb.find({}, { user_id : 1, creation_time : 1 }) { "_id" : ObjectId("51d612423dab6225ca6e6d36"), "creation_time" : 100, "user_id" : 10 } { "_id" : ObjectId("51d612503dab6225ca6e6d37"), "creation_time" : 200, "user_id" : 10 } { "_id" : ObjectId("51d612553dab6225ca6e6d38"), "creation_time" : 300, "user_id" : 10 } { "_id" : ObjectId("51d612593dab6225ca6e6d39"), "creation_time" : 400, "user_id" : 10 } { "_id" : ObjectId("51d6125d3dab6225ca6e6d3a"), "creation_time" : 500, "user_id" : 10 } { "_id" : ObjectId("51d6126f55ebf2ff5a13d1c9"), "creation_time" : 600, "user_id" : 10 } { "_id" : ObjectId("51d6127455ebf2ff5a13d1ca"), "creation_time" : 300, "user_id" : 11 } { "_id" : ObjectId("51d6127955ebf2ff5a13d1cb"), "creation_time" : 400, "user_id" : 11 } { "_id" : ObjectId("51d6127c55ebf2ff5a13d1cc"), "creation_time" : 500, "user_id" : 11 } { "_id" : ObjectId("51d6127f55ebf2ff5a13d1cd"), "creation_time" : 600, "user_id" : 11 } { "_id" : ObjectId("51d6128555ebf2ff5a13d1ce"), "creation_time" : 700, "user_id" : 11 } &gt; db.plus_four_users.find().pretty() { "_id": 10, "value": { "oids": [ { "_id": ObjectId("51d6125d3dab6225ca6e6d3a"), "creation_time": 500 }, { "_id": ObjectId("51d6126f55ebf2ff5a13d1c9"), "creation_time": 600 } ] } } { "_id": 11, "value": { "oids": [ { "_id": ObjectId("51d6128555ebf2ff5a13d1ce"), "creation_time": 700 } ] } } </code></pre> <p>Hope that can help you!</p>
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    1. POMongo DB - Sort and Skip within a group
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    1. USMiguel Cartagena
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    1. COit's true this can be done with map-reduce but your implementation is not correct. As a simple test, try to run it with a sample data set where one user_id happens to have only one document. It's also not going to work correctly on very large data set, and it will definitely break on a sharded collection.
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      1. USAsya Kamsky
    2. COYou're right @AsyaKamsky. The problem with this MR function is the lack of Idempotence on reduce function. Actually, Yatendra Goel made some moodifications and found the solution, available here: http://stackoverflow.com/questions/17485957/mongodb-map-reduce-complete-data-is-not-returned
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      1. USMiguel Cartagena
    3. COactually I gave him the solution here: https://groups.google.com/d/msg/mongodb-user/EhNEbyB9UBA/YgwELC3e91sJ
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      1. USAsya Kamsky
 

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