Cursors and the Aggregation Framework
Now that MongoDB 2.6 has been released, the PHP driver for MongoDB has also received amny updates to support the new features. In this series of articles, I will illustrate some of those updates.
In this article, I will introduce command cursors and demonstrate how they can be applied to aggregations. I previously wrote about the Aggregation Framework last year, but since then it has received a lot of updates and improvements. One of those improvements relates to how the Aggregation Framework (A/F) returns results. Before MongoDB 2.6, the A/F could only return one document, with all the results stored under the results key:
<?php
$m = new MongoClient;
$c = $m->demo->cities;
$pipeline = [
[ '$group' => [
'_id' => '$country_code',
'timezones' => [ '$addToSet' => '$timezone' ]
] ],
[ '$sort' => [ '_id' => 1 ] ],
];
$r = $c->aggregate( $pipeline );
var_dump( $r['result'] );
?>
This code would output something like:
array(242) {
[0] =>
array(2) {
'_id' => string(2) "AD"
'timezones' => array(1) { [0] => string(14) "Europe/Andorra" }
}
[1] =>
array(2) {
'_id' => string(2) "AE"
'timezones' => array(1) { [0] => string(10) "Asia/Dubai" }
}
[2] =>
array(2) {
'_id' => string(2) "AF"
'timezones' => array(1) { [0] => string(10) "Asia/Kabul" }
}
…
MongoCollection::aggregate() is implemented under the hood as a database command. The method in the PHP driver merely wraps this, but you can also call A/F through the MongoDB::command() method:
<?php
$m = new MongoClient;
$d = $m->demo;
$pipeline = [
[ '$group' => [
'_id' => '$country_code',
'timezones' => [ '$addToSet' => '$timezone' ]
] ],
[ '$sort' => [ '_id' => 1 ] ],
];
$r = $d->command( [
'aggregate' => 'cities',
'pipeline' => $pipeline,
] );
var_dump( $r['result'] );
?>
Because a database command only returns one document, the result is limited to a maximum of 16MB. This is not a problem for my example, but it can can certainly be a limiting factor for other A/F queries.
MongoDB 2.6 adds support for returning a cursor for an aggregation command. With the raw command interface, you simply add the extra cursor element:
$r = $d->command( [
'aggregate' => 'cities',
'pipeline' => $pipeline,
'cursor' => [ 'batchSize' => 1 ],
] );
var_dump( $r );
Instead of a document with all results inline, you get a cursor definition back:
array(2) {
'cursor' =>
array(3) {
'id' => class MongoInt64#5 (1) {
public $value => string(12) "392201189815"
}
'ns' => string(11) "demo.cities"
'firstBatch' => array(1) {
[0] =>
array(2) {
'_id' => string(2) "AD"
'timezones' => array(1) { [0] => string(14) "Europe/Andorra" }
}
}
}
'ok' => double(1)
}
The cursor definition contains the cursor ID (in id), the namespace (ns), and whether the command succeeded (in ok). The definition also a portion of the results. The number of items in firstBatch is configured by the value given to batchSize in the command.
To create a cursor that you can iterate over in PHP, you need to convert this cursor definition to a MongoCommandCursor object. You can do that with the MongoCommandCursor::createFromDocument() factory method. This factory method takes three arguments: the MongoClient object ($m in my example), the connection hash, and the cursor definition that was returned. The hash is required so that we can fetch new results from the same connection that executed the original command.
To obtain the connection hash, we need to include a by-ref variable as the third argument to MongoCollection::command():
<?php
$m = new MongoClient;
$d = $m->demo;
$pipeline = [
[ '$group' => [
'_id' => '$country_code',
'timezones' => [ '$addToSet' => '$timezone' ]
] ],
[ '$sort' => [ '_id' => 1 ] ],
];
$r = $d->command(
[
'aggregate' => 'cities',
'pipeline' => $pipeline,
'cursor' => [ 'batchSize' => 1 ],
],
null,
$hash
);
var_dump( $hash );
The hash looks like localhost:27017;-;.;26415. Together with the result, you can now construct a MongoCommandCursor:
$cursor = MongoCommandCursor::createFromDocument( $m, $hash, $r );
And iterate over it:
foreach ( $cursor as $result )
{
echo $result['_id'], ': ', join( ', ', $result['timezones'] ), "\n";
}
?>
As this is all a bit cumbersome, we have also added a helper method for this: MongoCollection::aggregateCursor. This internally does the whole MongoCommandCursor creation dance, and simplifies the previous example to:
<?php
$m = new MongoClient;
$c = $m->demo->cities;
$pipeline = [
[ '$group' => [
'_id' => '$country_code',
'timezones' => [ '$addToSet' => '$timezone' ]
] ],
[ '$sort' => [ '_id' => 1 ] ],
];
$r = $c->aggregateCursor( $pipeline );
foreach ( $r as $result )
{
echo $result['_id'], ': ', join( ', ', $result['timezones'] ), "\n";
}
?>
This helper also automatically sets the initial batch size to 101. You can change the batchSize for subsequent batches by using the MongoCommandCursor::batchSize() method, and for the initial batch by specifying an option to MongoCollection::aggregateCursor:
$options = [ 'cursor' => [ 'batchSize' => 5 ] ]; $r = $d->cities->aggregateCursor( $pipeline, $options ); $r->batchSize( 25 );
In general, you probably should not change the default batch sizes.
The Aggregation Framework has some other new features in MongoDB 2.6 as well. Please refer to the release notes for more information. I might write another post on some of those features later, too.
Life Line
I've just finished reading "Snow Crash" by Neal Stephenson. I found this a fun and excellent read.
Updated a restaurant
Updated a restaurant
Updated a restaurant
Updated a restaurant
Updated a brewery
Updated a restaurant
Updated a restaurant
Having dessert before even attempting to go to the restaurant for my (early) birthday dinner.
Enjoying this barrel aged quadrupal, in entirely new Dutch city (another Christmas tradition for us).
Updated a bar
Updated a restaurant
Updated a pub
Updated a bar
Updated a restaurant
Updated an attraction and a museum
Updated a restaurant
Updated a bar
Updated a brewery
I walked 7.4km in 1h17m31s
I walked 1.0km in 8m59s
I know my French is pretty terrible, but I'm sure I'm closer to the correct answer than what's shown here...
I walked 1.2km in 12m04s
I walked 6.4km in 1h11m52s
Merge branch 'v2022'
Merge pull request #169 from psumbera/solaris-2




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