MongoCollection::aggregate
(PECL mongo >=1.3.0)
MongoCollection::aggregate — Perform an aggregation using the aggregation framework
Описание
$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.
Список параметров
-
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"
Указывает суммарный лимит времени в миллисекундах на обработку операции (не включая время простоя). Если операция не завершилась за это время, то бросается MongoExecutionTimeoutException.
-
Or
-
op -
First pipeline operator.
-
op -
The second pipeline operator.
-
... -
Additional pipeline operators.
Возвращаемые значения
The result of the aggregation as an array. The ok will be set to 1 on success, 0 on failure.
Ошибки
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.
Список изменений
| Версия | Описание |
|---|---|
| 1.5.0 |
Added optional options argument
|
Примеры
Пример #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);
?>
Результат выполнения данного примера:
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.
Пример #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);
?>
Результатом выполнения данного примера будет что-то подобное:
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)
}
Пример #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);
?>
Результатом выполнения данного примера будет что-то подобное:
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)
}
Пример #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);
?>
Результатом выполнения данного примера будет что-то подобное:
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)
}
Смотрите также
- MongoCollection::aggregateCursor() - Execute an aggregation pipeline command and retrieve results through a cursor
- The MongoDB » aggregation framework
- PHP Руководство
- Функции по категориям
- Индекс функций
- Справочник функций
- Расширения для работы с базами данных
- Расширения для работы с базами данных отдельных производителей
- MongoDB
- Базовые классы
- Функция MongoCollection::aggregate() - Perform an aggregation using the aggregation framework
- Функция MongoCollection::aggregateCursor() - Execute an aggregation pipeline command and retrieve results through a cursor
- Функция MongoCollection::batchInsert() - Inserts multiple documents into this collection
- Функция MongoCollection::__construct() - Creates a new collection
- Функция MongoCollection::count() - Counts the number of documents in this collection
- Функция MongoCollection::createDBRef() - Creates a database reference
- Функция MongoCollection::createIndex() - Creates an index on the specified field(s) if it does not already exist.
- Функция MongoCollection::deleteIndex() - Deletes an index from this collection
- Функция MongoCollection::deleteIndexes() - Delete all indices for this collection
- Функция MongoCollection::distinct() - Retrieve a list of distinct values for the given key across a collection.
- Функция MongoCollection::drop() - Drops this collection
- Функция MongoCollection::ensureIndex() - Creates an index on the specified field(s) if it does not already exist.
- MongoCollection::find
- Функция MongoCollection::findAndModify() - Update a document and return it
- Функция MongoCollection::findOne() - Queries this collection, returning a single element
- Функция MongoCollection::__get() - Gets a collection
- Функция MongoCollection::getDBRef() - Fetches the document pointed to by a database reference
- Функция MongoCollection::getIndexInfo() - Returns information about indexes on this collection
- Функция MongoCollection::getName() - Returns this collection's name
- Функция MongoCollection::getReadPreference() - Get the read preference for this collection
- Функция MongoCollection::getSlaveOkay() - Get slaveOkay setting for this collection
- Функция MongoCollection::getWriteConcern() - Get the write concern for this collection
- Функция MongoCollection::group() - Performs an operation similar to SQL's GROUP BY command
- Функция MongoCollection::insert() - Inserts a document into the collection
- Функция MongoCollection::parallelCollectionScan() - Returns an array of cursors to iterator over a full collection in parallel
- Функция MongoCollection::remove() - Remove records from this collection
- Функция MongoCollection::save() - Saves a document to this collection
- Функция MongoCollection::setReadPreference() - Set the read preference for this collection
- Функция MongoCollection::setSlaveOkay() - Change slaveOkay setting for this collection
- Функция MongoCollection::setWriteConcern() - Set the write concern for this database
- Функция MongoCollection::toIndexString() - Converts keys specifying an index to its identifying string
- Функция MongoCollection::__toString() - String representation of this collection
- Функция MongoCollection::update() - Update records based on a given criteria
- Функция MongoCollection::validate() - Validates this collection
Коментарии
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