NAME

       Aggregation_Framework_Examples - None

REQUIREMENTS

       MongoDB , version 2.2.0 or later.  MongoDB C driver , version 0.96.0 or later.

       Let's check if everything is installed.

       Use the following command to load zips.json data set into mongod instance:

       $

       Let's use the MongoDB shell to verify that everything was imported successfully.

       $
       MongoDB shell version: 2.6.1
       connecting to: test
       > 29467 >
       {
            "_id" : "35004",
            "city" : "ACMAR",
            "loc" : [
                 ‐86.51557,
                 33.584132
            ],
            "pop" : 6055,
            "state" : "AL"
       }

AGGREGATIONS USING THE ZIP CODES DATA SET

       Each document in this collection has the following form:

       {
         "_id" : "35004",
         "city" : "Acmar",
         "state" : "AL",
         "pop" : 6055,
         "loc" : [‐86.51557, 33.584132]
       }

       In these documents:

       \[bu]
         The _id field holds the zipcode as a string.

       \[bu]
         The city field holds the city name.

       \[bu]
         The state field holds the two letter state abbreviation.

       \[bu]
         The pop field holds the population.

       \[bu]
         The loc field holds the location as a [latitude, longitude] array.

STATES WITH POPULATIONS OVER 10 MILLION

       To get all states with a population greater than 10 million, use the following aggregation pipeline:

       #include <mongoc.h>
       #include <stdio.h>

       static void
       print_pipeline (mongoc_collection_t *collection)
       {
          mongoc_cursor_t *cursor;
          bson_error_t error;
          const bson_t *doc;
          bson_t *pipeline;
          char *str;

          pipeline = BCON_NEW ("pipeline", "[",
             "{", "$group", "{", "_id", "$state", "total_pop", "{", "$sum", "$pop", "}", "}", "}",
             "{", "$match", "{", "total_pop", "{", "$gte", BCON_INT32 (10000000), "}", "}", "}",
          "]");

          cursor = mongoc_collection_aggregate (collection, MONGOC_QUERY_NONE, pipeline, NULL, NULL);

          while (mongoc_cursor_next (cursor, &doc)) {
             str = bson_as_json (doc, NULL);
             printf ("%s\n", str);
             bson_free (str);
          }

          if (mongoc_cursor_error (cursor, &error)) {
             fprintf (stderr, "Cursor Failure: %s\n", error.message);
          }

          mongoc_cursor_destroy (cursor);
          bson_destroy (pipeline);
       }

       int
       main (int argc,
             char *argv[])
       {
          mongoc_client_t *client;
          mongoc_collection_t *collection;

          mongoc_init ();

          client = mongoc_client_new ("mongodb://localhost:27017");
          collection = mongoc_client_get_collection (client, "test", "zipcodes");

          print_pipeline (collection);

          mongoc_collection_destroy (collection);
          mongoc_client_destroy (client);

          mongoc_cleanup ();

          return 0;
       }

       You should see a result like the following:

       { "_id" : "PA", "total_pop" : 11881643 }
       { "_id" : "OH", "total_pop" : 10847115 }
       { "_id" : "NY", "total_pop" : 17990455 }
       { "_id" : "FL", "total_pop" : 12937284 }
       { "_id" : "TX", "total_pop" : 16986510 }
       { "_id" : "IL", "total_pop" : 11430472 }
       { "_id" : "CA", "total_pop" : 29760021 }

       The above aggregation pipeline is build from two pipeline operators: $group and $match \&.

       The  $group  pipeline operator requires _id field where we specify grouping; remaining fields specify how
       to generate composite value and must use one of the group aggregation functions:  $addToSet  ,  $first  ,
       $last , $max , $min , $avg , $push , $sum \&. The $match pipeline operator syntax is the same as the read
       operation query syntax.

       The $group process reads all documents and for each state it creates a separate document, for example:

       { "_id" : "WA", "total_pop" : 4866692 }

       The  total_pop field uses the $sum aggregation function to sum the values of all pop fields in the source
       documents.

       Documents created by $group are piped to the $match pipeline operator. It returns the documents with  the
       value of total_pop field greater than or equal to 10 million.

AVERAGE CITY POPULATION BY STATE

       To  get  the  first  three  states  with  the  greatest  average  population  per city, use the following
       aggregation:

       pipeline = BCON_NEW ("pipeline", "[",
          "{", "$group", "{", "_id", "{", "state", "$state", "city", "$city", "}", "pop", "{", "$sum", "$pop", "}", "}", "}",
          "{", "$group", "{", "_id", "$_id.state", "avg_city_pop", "{", "$avg", "$pop", "}", "}", "}",
          "{", "$sort", "{", "avg_city_pop", BCON_INT32 (‐1), "}", "}",
          "{", "$limit", BCON_INT32 (3) "}",
       "]");

       This aggregate pipeline produces:

       { "_id" : "DC", "avg_city_pop" : 303450.0 }
       { "_id" : "FL", "avg_city_pop" : 27942.29805615551 }
       { "_id" : "CA", "avg_city_pop" : 27735.341099720412 }

       The above aggregation pipeline is build from three pipeline operators: $group , $sort and $limit \&.

       The first $group operator creates the following documents:

       { "_id" : { "state" : "WY", "city" : "Smoot" }, "pop" : 414 }

       Note, that the $group operator can't use nested documents except the _id field.

       The second $group uses these documents to create the following documents:

       { "_id" : "FL", "avg_city_pop" : 27942.29805615551 }

       These documents are sorted by the avg_city_pop field in descending order. Finally,  the  $limit  pipeline
       operator returns the first 3 documents from the sorted set.

COLOPHON

       This     page     is     part     of    MongoDB    C    Driver.     Please    report    any    bugs    at
       https://jira.mongodb.org/browse/CDRIVER.

MongoDB C Driver                                   2016‐01‐18                  AGGREGATION_FRAMEWORK_EXAMPLES(3)