(PECL mongo >=1.3.0)
MongoCollection::aggregate — Perform an aggregation using the aggregation framework
$pipeline
[, array $options
] )$op
[, array $op
[, 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.
pipelineAn array of pipeline operators.
optionsArray of command arguments, such as allowDiskUse, explain or cursor.
Or
opFirst pipeline operator.
opThe second pipeline operator.
...Additional pipeline operators.
The result of the aggregation as an array. The ok will be set to 1 on success, 0 on failure.
When an error occurs an array with the following keys will be returned:
| Version | Description |
|---|---|
| 1.5.0 |
Added optional options argument
|
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' => '$state', 'city' => '$city' ),
'pop' => array('$sum' => '$pop' )
)
),
array(
'$group' => array(
'_id' => '$_id.state',
'avgCityPop' => array('$avg' => '$pop')
)
)
);
$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 comman 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)
}