mirror of
https://github.com/sigmasternchen/php-doc-en
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26b9276e07
- Update all options to include the WriteConcern option - and refactored those options into entities git-svn-id: https://svn.php.net/repository/phpdoc/en/trunk@328543 c90b9560-bf6c-de11-be94-00142212c4b1
436 lines
9.2 KiB
XML
436 lines
9.2 KiB
XML
<?xml version="1.0" encoding="utf-8"?>
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<!-- $Revision$ -->
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<refentry xml:id="mongocollection.aggregate" xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink">
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<refnamediv>
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<refname>MongoCollection::aggregate</refname>
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<refpurpose>Perform an aggregation using the aggregation framework</refpurpose>
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</refnamediv>
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<refsect1 role="description">
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&reftitle.description;
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<methodsynopsis>
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<modifier>public</modifier> <type>array</type><methodname>MongoCollection::aggregate</methodname>
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<methodparam><type>array</type><parameter>pipeline</parameter></methodparam>
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<methodparam choice="opt"><type>array</type><parameter>op</parameter></methodparam>
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<methodparam choice="opt"><type>array</type><parameter>...</parameter></methodparam>
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</methodsynopsis>
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<para>
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The MongoDB
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<link xlink:href="&url.mongodb.docs.aggregation;">aggregation framework</link>
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provides a means to calculate aggregated values without having to use
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MapReduce. While MapReduce is powerful, it is often more difficult than
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necessary for many simple aggregation tasks, such as totaling or averaging
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field values.
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</para>
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<para>
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This method accepts either a variable amount of pipeline operators, or a
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single array of operators constituting the pipeline.
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</para>
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</refsect1>
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<refsect1 role="parameters">
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&reftitle.parameters;
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<variablelist>
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<varlistentry>
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<term><parameter>pipeline</parameter></term>
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<listitem>
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<para>
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An array of pipeline operators, or just the first operator.
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</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term><parameter>op</parameter></term>
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<listitem>
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<para>
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The second pipeline operator.
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</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term><parameter>...</parameter></term>
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<listitem>
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<para>
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Additional pipeline operators.
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</para>
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</listitem>
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</varlistentry>
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</variablelist>
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</refsect1>
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<refsect1 role="returnvalues">
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&reftitle.returnvalues;
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<para>
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The result of the aggregation as an array. The <varname>ok</varname> will
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be set to <literal>1</literal> on success, <literal>0</literal> on failure.
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</para>
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</refsect1>
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<refsect1 role="errors">
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&reftitle.errors;
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<para>
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When an error occurs an array with the following keys will be returned:
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<itemizedlist>
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<listitem>
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<simpara>
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<varname>errmsg</varname> - containing the reason for the failure
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</simpara>
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</listitem>
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<listitem>
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<simpara>
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<varname>code</varname> - the errorcode of the failure
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</simpara>
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</listitem>
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<listitem>
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<simpara>
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<varname>ok</varname> - will be set to 0.
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</simpara>
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</listitem>
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</itemizedlist>
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</para>
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</refsect1>
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<refsect1 role="examples">
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&reftitle.examples;
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<example xml:id="mongocollection.aggregate.example.basic">
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<title><methodname>MongoCollection::aggregate</methodname> example</title>
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<para>
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The following example aggregation operation pivots data to create a set of
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author names grouped by tags applied to an article. Call the aggregation
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framework by issuing the following command:
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</para>
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<programlisting role="php">
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<![CDATA[
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<?php
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$m = new Mongo;
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$c = $m->selectDB("examples")->selectCollection("article");
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$data = array (
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'title' => 'this is my title',
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'author' => 'bob',
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'posted' => new MongoDate,
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'pageViews' => 5,
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'tags' => array ( 'fun', 'good', 'fun' ),
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'comments' => array (
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array (
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'author' => 'joe',
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'text' => 'this is cool',
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),
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array (
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'author' => 'sam',
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'text' => 'this is bad',
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),
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),
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'other' =>array (
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'foo' => 5,
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),
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);
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$d = $c->insert($data, array("w" => 1));
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$ops = array(
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array(
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'$project' => array(
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"author" => 1,
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"tags" => 1,
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)
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),
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array('$unwind' => '$tags'),
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array(
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'$group' => array(
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"_id" => array("tags" => '$tags'),
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"authors" => array('$addToSet' => '$author'),
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),
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),
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);
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$results = $c->aggregate($ops);
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var_dump($results);
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?>
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]]>
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</programlisting>
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&example.outputs;
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<screen>
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<![CDATA[
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array(2) {
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["result"]=>
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array(2) {
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[0]=>
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array(2) {
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["_id"]=>
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array(1) {
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["tags"]=>
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string(4) "good"
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}
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["authors"]=>
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array(1) {
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[0]=>
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string(3) "bob"
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}
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}
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[1]=>
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array(2) {
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["_id"]=>
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array(1) {
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["tags"]=>
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string(3) "fun"
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}
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["authors"]=>
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array(1) {
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[0]=>
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string(3) "bob"
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}
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}
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}
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["ok"]=>
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float(1)
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}
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]]>
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</screen>
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</example>
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<para>
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The following examples use the <link xlink:href="&url.mongodb.examples.zipcode;">zipcode data set</link>.
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Use mongoimport to load this data set into your mongod instance.
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</para>
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<example xml:id="mongocollection.aggregate.example.zipcode.population">
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<title><methodname>MongoCollection::aggregate</methodname> example</title>
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<para>
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To return all states with a population greater than 10 million, use the following aggregation operation:
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</para>
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<programlisting role="php">
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<![CDATA[
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<?php
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$m = new Mongo;
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$c = $m->selectDB("test")->selectCollection("zips");
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$out = $c->aggregate(array(
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'$group' => array(
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'_id' => '$state',
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'totalPop' => array('$sum' => '$pop')
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)
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),
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array(
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'$match' => array('totalPop' => array('$gte' => 10*1000*1000))
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)
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);
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var_dump($out);
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?>
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]]>
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</programlisting>
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&example.outputs.similar;
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<screen>
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<![CDATA[
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array(2) {
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["result"]=>
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array(7) {
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[0]=>
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array(2) {
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["_id"]=>
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string(2) "TX"
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["totalPop"]=>
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int(16986510)
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}
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[1]=>
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array(2) {
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["_id"]=>
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string(2) "PA"
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["totalPop"]=>
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int(11881643)
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}
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[2]=>
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array(2) {
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["_id"]=>
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string(2) "NY"
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["totalPop"]=>
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int(17990455)
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}
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[3]=>
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array(2) {
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["_id"]=>
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string(2) "IL"
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["totalPop"]=>
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int(11430602)
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}
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[4]=>
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array(2) {
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["_id"]=>
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string(2) "CA"
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["totalPop"]=>
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int(29760021)
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}
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[5]=>
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array(2) {
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["_id"]=>
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string(2) "OH"
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["totalPop"]=>
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int(10847115)
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}
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[6]=>
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array(2) {
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["_id"]=>
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string(2) "FL"
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["totalPop"]=>
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int(12937926)
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}
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}
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["ok"]=>
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float(1)
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}
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]]>
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</screen>
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</example>
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<example xml:id="mongocollection.aggregate.example.zipcode.population.average">
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<title><methodname>MongoCollection::aggregate</methodname> example</title>
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<para>
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To return the average populations for cities in each state, use the following aggregation operation:
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</para>
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<programlisting role="php">
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<![CDATA[
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<?php
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$m = new Mongo;
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$c = $m->selectDB("test")->selectCollection("zips");
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$out = $c->aggregate(
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array(
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'$group' => array(
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'_id' => array('state' => '$state', 'city' => '$city' ),
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'pop' => array('$sum' => '$pop' )
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)
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),
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array(
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'$group' => array(
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'_id' => '$_id.state',
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'avgCityPop' => array('$avg' => '$pop')
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)
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)
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);
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var_dump($out);
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]]>
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</programlisting>
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&example.outputs.similar;
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<screen>
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<![CDATA[
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array(2) {
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["result"]=>
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array(51) {
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[0]=>
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array(2) {
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["_id"]=>
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string(2) "DC"
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["avgCityPop"]=>
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float(303450)
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}
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[1]=>
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array(2) {
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["_id"]=>
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string(2) "DE"
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["avgCityPop"]=>
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float(14481.913043478)
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}
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[2]=>
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array(2) {
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["_id"]=>
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string(2) "RI"
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["avgCityPop"]=>
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float(18933.283018868)
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}
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[3]=>
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array(2) {
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["_id"]=>
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string(2) "AL"
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["avgCityPop"]=>
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float(7907.2152641879)
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}
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[4]=>
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array(2) {
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["_id"]=>
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string(2) "NH"
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["avgCityPop"]=>
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float(5232.320754717)
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}
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...
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[45]=>
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array(2) {
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["_id"]=>
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string(2) "WY"
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["avgCityPop"]=>
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float(3359.9111111111)
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}
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[46]=>
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array(2) {
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["_id"]=>
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string(2) "MN"
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["avgCityPop"]=>
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float(5335.4865853659)
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}
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[47]=>
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array(2) {
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["_id"]=>
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string(2) "OK"
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["avgCityPop"]=>
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float(6155.7436399217)
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}
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[48]=>
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array(2) {
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["_id"]=>
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string(2) "IL"
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["avgCityPop"]=>
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float(9931.0182450043)
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}
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[49]=>
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array(2) {
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["_id"]=>
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string(2) "WI"
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["avgCityPop"]=>
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float(7323.0074850299)
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}
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[50]=>
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array(2) {
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["_id"]=>
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string(2) "WV"
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["avgCityPop"]=>
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float(2759.1953846154)
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}
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}
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["ok"]=>
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float(1)
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}
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]]>
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</screen>
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</example>
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</refsect1>
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<refsect1 role="seealso">
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&reftitle.seealso;
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<simplelist>
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<member>The MongoDB <link xlink:href="&url.mongodb.docs.aggregation;">aggregation framework</link></member>
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</simplelist>
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</refsect1>
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</refentry>
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<!-- Keep this comment at the end of the file
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Local variables:
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mode: sgml
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sgml-omittag:t
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sgml-shorttag:t
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sgml-minimize-attributes:nil
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sgml-always-quote-attributes:t
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sgml-indent-step:1
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sgml-indent-data:t
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indent-tabs-mode:nil
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sgml-parent-document:nil
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sgml-default-dtd-file:"~/.phpdoc/manual.ced"
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sgml-exposed-tags:nil
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sgml-local-catalogs:nil
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sgml-local-ecat-files:nil
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End:
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vim600: syn=xml fen fdm=syntax fdl=2 si
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vim: et tw=78 syn=sgml
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vi: ts=1 sw=1
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-->
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