aggregate

AGGREGATION_FRAMEWORK_EXAMPLibrary Functions AGGREGATION_FRAMEWORK_EXAMPLES(3)



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                  2016‐03‐30 AGGREGATION_FRAMEWORK_EXAMPLES(3)