MongoCollection::aggregate

(PECL mongo >=1.3.0)

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

Descrierea

public MongoCollection::aggregate ( array $pipeline , array $options = ? ) : array

public MongoCollection::aggregate ( array $op , array ...$ops ) : 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.

Parametri

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"

Specifică o limită de timp cumulativă în milisecunde pentru procesarea operațiunii pe server (nu include timpul de așteptare). Dacă operațiunea nu este terminată pe server în timpul rezervat, va fi emisă o MongoExecutionTimeoutException.

op: First pipeline operator.
ops: Additional pipeline operators.

Valorile întoarse

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

Erori/Excepții

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.

Istoricul schimbărilor

Versiune	Descriere
PECL mongo 1.5.0	Added optional `options` argument

Exemple

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);
?>

Exemplul de mai sus va afișa:

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);
?>

Exemplul de mai sus va afișa ceva similar cu:

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);
?>

Exemplul de mai sus va afișa ceva similar cu:

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);
?>

Exemplul de mai sus va afișa ceva similar cu:

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)
}

A se vedea și

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

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User Contributed Notes 1 note

down

mike at eastghost dot com ¶

8 years ago


COPY ONE COLLECTION TO ANOTHER COLLECTION IN SAME DATABASE

db.myoriginal.aggregate( [ [ $match: [] ], [ $out: "mycopy" ] ] )

a LOT faster than doing many inserts in a forEach loop.
< 2 seconds to copy 50,000 documents each a few KB.
12GB of data in 1-2 minutes on a i5 PC.
Best part : it's non-blocking!
Target can't be a capped collection.

ymmv

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