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    <p>There are two approaches to solve this in MongoDB. </p> <ol> <li><p>If your subsets are fairly small, you can just do a query on subset to find all members and use the result of that query as an initial <a href="http://docs.mongodb.org/manual/reference/command/mapReduce/" rel="nofollow">query</a> to a map-reduce call.</p></li> <li><p>However, if you have very large subsets, this may not be possible. What you can do then, is to <a href="http://tebros.com/2011/07/using-mongodb-mapreduce-to-join-2-collections/" rel="nofollow">simulate a join using two map-reduce calls</a> with the 'reduce' output option to reduce into the same target collection. This will create an intermediate collection where the documents look something like this:</p> <pre><code>{Name: Jim, Age: 24, inSubset: true} {Name: Bill, Age: 38, inSubset: false} {Name: Mary, Age: 55, inSubset: true} </code></pre> <p>Finally, you can execute a third map reduce on this intermediate collection to average over all the documents that have <code>inSubset: true</code>. </p></li> </ol> <p>Here is the code for the 2. option (the three map-reduces) in Python, using the pymongo driver:</p> <pre><code>from pymongo import Connection from bson import ObjectId, Code con = Connection(port=30000) # add host/port here if different from default db = con['test'] # or the database name you are using # insert documents db.master.insert({'_id': ObjectId(), 'Name': 'Jim', 'Age': 24}) db.master.insert({'_id': ObjectId(), 'Name': 'Bill', 'Age': 38}) db.master.insert({'_id': ObjectId(), 'Name': 'Mary', 'Age': 55}) db.subset.insert({'_id': ObjectId(), 'Name': 'Jim'}) db.subset.insert({'_id': ObjectId(), 'Name': 'Mary'}) # map function for master collection mapf_master = Code(""" function () { emit(this.Name, {'age': this.Age, 'inSubset': false}); } """) # map function for subset collection mapf_subset = Code(""" function() { emit(this.Name, {'age': 0, 'inSubset': true}); } """) # reduce function for both master and subset reducef = Code(""" function(key, values) { var result = {'age': 0, 'inSubset': false}; values.forEach( function(value) { result.age += value.age; result.inSubset = result.inSubset || value.inSubset; }); return result; } """) # call map-reduce on master and subset (simulates a join) db.master.map_reduce(mapf_master, reducef, out={'reduce': 'join'}) db.subset.map_reduce(mapf_subset, reducef, out={'reduce': 'join'}) # final map function for third map-reduce call mapf_final = Code(""" function() { if (this.value.inSubset) { emit('total', {'age': this.value.age, 'count': 1}); } } """) # final reduce function for third map-reduce call reducef_final = Code(""" function(key, values) { var result = {'age': 0, 'count': 0}; values.forEach( function(value) { result.age += value.age; result.count += value.count; }); return result; } """) # final finalize function, calculates the average finalizef_final = Code(""" function(key, value) { if (value.count &gt; 0) { value.averageAge = value.age / value.count; } return value; } """) # call final map-reduce db.join.map_reduce(mapf_final, reducef_final, finalize=finalizef_final, out={'merge': 'result'}) </code></pre> <p>The result collection looks like this (queried from the mongo shell):</p> <pre><code>&gt; db.result.find() { "_id" : "total", "value" : { "age" : 79, "count" : 2, "averageAge" : 39.5 } } </code></pre> <p>and the final average is stored in the value.averageAge field.</p>
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    1. POFilter map reduce to records that match a different collection
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    1. COThanks, I think I will go with option 1 as my subset is on the order of 1500-2000 names. Is that size what you were thinking is appropriate, and if so would you be so kind as to provide an example of how to effectively pass it into map reduce call?
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      1. USRoger Sanchez
    2. COI think your list sizes should work for option 1--it will be much faster than option 2 for small subsets. You might want to specify just the key fields you need to match with the main collection, so that you don't get any extra stuff back in the query (if there is any extra stuff).
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