# MongoCollection::aggregate

(PECL mongo >=1.3.0)

MongoCollection::aggregate — Perform an aggregation using the aggregation framework

### Description

public MongoCollection::aggregate ( array `$pipeline` [, array `$options` ] ) : array
public MongoCollection::aggregate ( array `$op` [, array `$op` [, array `$...` ]] ) : array

The MongoDB » aggregation framework provides a means to calculate aggregated values without having to use MapReduce. While MapReduce is powerful, it is often more difficult than necessary for many simple aggregation tasks, such as totaling or averaging field values.

This method accepts either a variable amount of pipeline operators, or a single array of operators constituting the pipeline.

### Parameters

`pipeline`

  An array of pipeline operators.

`options`

  Options for the aggregation command. Valid options include:

  - "allowDiskUse"

    Allow aggregation stages to write to temporary files

  - "cursor"

    Options controlling the creation of the cursor object. This option causes the command to return a result document suitable for constructing a MongoCommandCursor. If you need to use this option, you should consider using MongoCollection::aggregateCursor().

  - "explain"

    Return information on the processing of the pipeline.

  - "maxTimeMS"

    Specifies a cumulative time limit in milliseconds for processing the operation on the server (does not include idle time). If the operation is not completed by the server within the timeout period, a MongoExecutionTimeoutException will be thrown.

Or

`op`

  First pipeline operator.

`op`

  The second pipeline operator.

`...`

  Additional pipeline operators.

### Return Values

The result of the aggregation as an array. The ok will be set to 1 on success, 0 on failure.

### Errors/Exceptions

When an error occurs an array with the following keys will be returned:

- errmsg - containing the reason for the failure
- code - the errorcode of the failure
- ok - will be set to 0.

### Changelog

Version | Description
--- | ---
1.5.0 | Added optional `options` argument

### Examples

Example #1 MongoCollection::aggregate() example

The following example aggregation operation pivots data to create a set of author names grouped by tags applied to an article. Call the aggregation framework by issuing the following command:

`<?php
$m = new MongoClient("localhost");
$c = $m->selectDB("examples")->selectCollection("article");
$data = array (
'title' => 'this is my title',
'author' => 'bob',
'posted' => new MongoDate,
'pageViews' => 5,
'tags' => array ( 'fun', 'good', 'fun' ),
'comments' => array (
array (
'author' => 'joe',
'text' => 'this is cool',
),
array (
'author' => 'sam',
'text' => 'this is bad',
),
),
'other' =>array (
'foo' => 5,
),
);
$d = $c->insert($data, array("w" => 1));
$ops = array(
array(
'$project' => array(
"author" => 1,
"tags" => 1,
)
),
array('$unwind' => '$tags'),
array(
'$group' => array(
"_id" => array("tags" => '$tags'),
"authors" => array('$addToSet' => '$author'),
),
),
);
$results = $c->aggregate($ops);
var_dump($results);
?>`

The above example will output:

    array(2) {
      ["result"]=>
      array(2) {
        [0]=>
        array(2) {
          ["_id"]=>
          array(1) {
            ["tags"]=>
            string(4) "good"
          }
          ["authors"]=>
          array(1) {
            [0]=>
            string(3) "bob"
          }
        }
        [1]=>
        array(2) {
          ["_id"]=>
          array(1) {
            ["tags"]=>
            string(3) "fun"
          }
          ["authors"]=>
          array(1) {
            [0]=>
            string(3) "bob"
          }
        }
      }
      ["ok"]=>
      float(1)
    }

The following examples use the » zipcode data set. Use mongoimport to load this data set into your mongod instance.

Example #2 MongoCollection::aggregate() example

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

`<?php
$m = new MongoClient("localhost");
$c = $m->selectDB("test")->selectCollection("zips");
$pipeline = array(
array(
'$group' => array(
'_id' => array('state' => '$state'),
'totalPop' => array('$sum' => '$pop')
)
),
array(
'$match' => array(
'totalPop' => array('$gte' => 10 * 1000 * 1000)
)
),
);
$out = $c->aggregate($pipeline);
var_dump($out);
?>`

The above example will output something similar to:

    array(2) {
      ["result"]=>
      array(7) {
        [0]=>
        array(2) {
          ["_id"]=>
          string(2) "TX"
          ["totalPop"]=>
          int(16986510)
        }
        [1]=>
        array(2) {
          ["_id"]=>
          string(2) "PA"
          ["totalPop"]=>
          int(11881643)
        }
        [2]=>
        array(2) {
          ["_id"]=>
          string(2) "NY"
          ["totalPop"]=>
          int(17990455)
        }
        [3]=>
        array(2) {
          ["_id"]=>
          string(2) "IL"
          ["totalPop"]=>
          int(11430602)
        }
        [4]=>
        array(2) {
          ["_id"]=>
          string(2) "CA"
          ["totalPop"]=>
          int(29760021)
        }
        [5]=>
        array(2) {
          ["_id"]=>
          string(2) "OH"
          ["totalPop"]=>
          int(10847115)
        }
        [6]=>
        array(2) {
          ["_id"]=>
          string(2) "FL"
          ["totalPop"]=>
          int(12937926)
        }
      }
      ["ok"]=>
      float(1)
    }

Example #3 MongoCollection::aggregate() example

To return the average populations for cities in each state, use the following aggregation operation:

`<?php
$m = new MongoClient;
$c = $m->selectDB("test")->selectCollection("zips");
$out = $c->aggregate(
array(
'$group' => array(
'_id' => array('state' => '$state', 'city' => '$city' ),
'pop' => array('$sum' => '$pop' )
)
),
array(
'$group' => array(
'_id' => '$_id.state',
'avgCityPop' => array('$avg' => '$pop')
)
)
);
var_dump($out);
?>`

The above example will output something similar to:

    array(2) {
      ["result"]=>
      array(51) {
        [0]=>
        array(2) {
          ["_id"]=>
          string(2) "DC"
          ["avgCityPop"]=>
          float(303450)
        }
        [1]=>
        array(2) {
          ["_id"]=>
          string(2) "DE"
          ["avgCityPop"]=>
          float(14481.913043478)
        }
    ...
        [49]=>
        array(2) {
          ["_id"]=>
          string(2) "WI"
          ["avgCityPop"]=>
          float(7323.0074850299)
        }
        [50]=>
        array(2) {
          ["_id"]=>
          string(2) "WV"
          ["avgCityPop"]=>
          float(2759.1953846154)
        }
      }
      ["ok"]=>
      float(1)
    }

Example #4 MongoCollection::aggregate() with command options

To return information on how the pipeline will be processed we use the explain command option:

`<?php
$m = new MongoClient;
$c = $m->selectDB("test")->selectCollection("zips");
$pipeline = array(
array(
'$group' => array(
'_id' => '$state',
'totalPop' => array('$sum' => '$pop'),
),
),
array(
'$match' => array(
'totalPop' => array('$gte' => 10 * 1000 * 1000)
)
),
array(
'$sort' => array("totalPop" => -1),
),
);
$options = array("explain" => true);
$out = $c->aggregate($pipeline, $options);
var_dump($out);
?>`

The above example will output something similar to:

    array(2) {
      ["stages"]=>
      array(4) {
        [0]=>
        array(1) {
          ["$cursor"]=>
          array(3) {
            ["query"]=>
            array(0) {
            }
            ["fields"]=>
            array(3) {
              ["pop"]=>
              int(1)
              ["state"]=>
              int(1)
              ["_id"]=>
              int(0)
            }
            ["plan"]=>
            array(4) {
              ["cursor"]=>
              string(11) "BasicCursor"
              ["isMultiKey"]=>
              bool(false)
              ["scanAndOrder"]=>
              bool(false)
              ["allPlans"]=>
              array(1) {
                [0]=>
                array(3) {
                  ["cursor"]=>
                  string(11) "BasicCursor"
                  ["isMultiKey"]=>
                  bool(false)
                  ["scanAndOrder"]=>
                  bool(false)
                }
              }
            }
          }
        }
        [1]=>
        array(1) {
          ["$group"]=>
          array(2) {
            ["_id"]=>
            string(6) "$state"
            ["totalPop"]=>
            array(1) {
              ["$sum"]=>
              string(4) "$pop"
            }
          }
        }
        [2]=>
        array(1) {
          ["$match"]=>
          array(1) {
            ["totalPop"]=>
            array(1) {
              ["$gte"]=>
              int(10000000)
            }
          }
        }
        [3]=>
        array(1) {
          ["$sort"]=>
          array(1) {
            ["sortKey"]=>
            array(1) {
              ["totalPop"]=>
              int(-1)
            }
          }
        }
      }
      ["ok"]=>
      float(1)
    }

### See Also

- MongoCollection::aggregateCursor() - Execute an aggregation pipeline command and retrieve results through a cursor
- The MongoDB » aggregation framework