MONGOC_AGGREGATE(3)

NAME
mongoc_aggregate - Aggregation Framework Examples

This document provides a number of practical examples that display the capabilities of the aggregation framework.

The Aggregations using the Zip Codes Data Set examples uses a publicly available data set of all zipcodes and populations in the United States. These data are available at: zips.json.

REQUIREMENTS

Let's check if everything is installed.

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

        $ mongoimport --drop -d test -c zipcodes zips.json

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

        $ mongo test
        MongoDB shell version: 2.6.1
        connecting to: test
        > db.zipcodes.count()
        29467
        > db.zipcodes.findOne()
        {
              "_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:

    •   The _id field holds the zipcode as a string.

    •   The city field holds the city name.

    •   The state field holds the two letter state abbreviation.

    •   The pop field holds the population.

    •   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: aggregation1.c.INDENT 0.0

    #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_canonical_extended_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?appname=aggregation-example");
       mongoc_client_set_error_api (client, 2);
       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.

AUTHOR
MongoDB, Inc

COPYRIGHT
2018, MongoDB, Inc