python - MapReduce in PyMongo -

my mongo collection : impressions has docs in following format:-

   {         _uid: 10,         "impressions": [             {                 "pos": 6,                 "id": 123,                 "service": "furniture"             },             {                 "pos": 0,                 "id": 128,                 "service": "electronics"             },             {                 "pos": 2,                 "id": 127,                 "service": "furniture"             },             {                 "pos": 2,                 "id": 125,                 "service": "electronics"             },             {                 "pos": 10,                 "id": 124,                 "service": "electronics"             }         ]       },      {         _uid: 11,         "impressions": [             {                 "pos": 1,                 "id": 124,                 "service": "furniture"             },             {                 "pos": 10,                 "id": 124,                 "service": "electronics"             },             {                 "pos": 1,                 "id": 123,                 "service": "furniture"             },             {                 "pos": 21,                 "id": 122,                 "service": "furniture"             },             {                 "pos": 3,                 "id": 125,                 "service": "electronics"             },             {                 "pos": 10,                 "id": 121,                 "service": "electronics"             }             ]          },             .             .             .             .             . 

each of doc in collection has "impressions" key array of dictionaries. in each dictionary "id" id of entity, "service" service type , "pos"is position of item in search page results. aim find out count of number of impressions every "id" in each category. above data "service" == "furniture", want have aggregation results:-

[ {"id": 123,"impressions_count":2}, {"id": 127,"impressions_count":1}, {"id": 124,"impressions_count":1}, {"id": 122,"impressions_count":1} ] 

i tried aggregate on "id" using mapreduce via following function in python script

def fetch_impressions():     try:         imp_collection = get_mongo_connection('impressions')         map = code("""                 function(){                     for( x in this.impressions){                         var flat_id = x['id'];                         var service_type = x['service']                         emit(parseint(flat_id),1);                         }                     };                 """)                          """)         reduce = code("""                         function(a,b){                             return array.sum(b);                             };                         """)          results = imp_collection.map_reduce(map, reduce, 'aggregation_result')         return results     except exception e:         raise exception(e) 

but i'm getting results none, because of faulty map function.i'm new javascript , mongo kindly help!

you can use aggregation framework

import pymongo conn = pymongo.mongoclient() db = conn.test col =  db.collection  doc in col.aggregate([{'$unwind': '$impressions'},      {'$match': {'impressions.service': 'furniture'}},      {'$group': {'_id': '$impressions.id', 'impressions_count': {'$sum': 1}}},      ]):     print(doc) 

or more efficiently using $map , $setdifference operators.

col.aggregate([     { "$project": { "impressions": {"$setdifference": [{ "$map": { "input": "$impressions", "as": "imp", "in": { "$cond": { "if": { "$eq": [ "$$imp.service", "furniture" ] }, "then": "$$imp.id", "else": 0 }}}}, [0]]}}},      { "$unwind": "$impressions" },      { "$group": { "_id": "$impressions", "impressions_count": { "$sum": 1 }}} ]) 

which yields:

{'_id': 122.0, 'impressions_count': 1} {'_id': 124.0, 'impressions_count': 1} {'_id': 127.0, 'impressions_count': 1} {'_id': 123.0, 'impressions_count': 2}