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NAME ^

MongoDB::Examples - Some examples of MongoDB syntax

VERSION ^

version v0.704.0.0

MAPPING SQL TO MONGODB ^

For developers familiar with SQL, the following chart should help you see how many common SQL queries could be expressed in MongoDB.

These are Perl-specific examples of translating SQL queries to MongoDB's query language. To see the JavaScript (or other languages') mappings, see http://dochub.mongodb.org/core/sqlToMongo.

In the following examples, $db is a MongoDB::Database object which was retrieved by using get_database. See MongoDB::MongoClient for more.

CREATE TABLE USERS (a Number, b Number)
    Implicit, can be done explicitly.
INSERT INTO USERS VALUES(1,1)
    $db->get_collection( 'users' )->insert( { a => 1, b => 1 } );
SELECT a,b FROM users
    $db->get_collection( 'users')->find( { }, { a => 1, b => 1 } );
SELECT * FROM users
    $db->get_collection( 'users' )->find;
SELECT * FROM users WHERE age=33
    $db->get_collection( 'users' )->find( { age => 33 } )
SELECT a,b FROM users WHERE age=33
    $db->get_collection( 'users' )->find( { age => 33 }, { a => 1, b => 1 } );
SELECT * FROM users WHERE age=33 ORDER BY name
    $db->get_collection( 'users' )->find( { age => 33 } )->sort( { name => 1 } );
<SELECT * FROM users WHERE age33>>
    $db->get_collection( 'users' )->find( { age => { '$gt' => 33 } } );
<SELECT * FROM users WHERE age<33>
    $db->get_collection( 'users' )->find( { age => { '$lt' => 33 } } );
SELECT * FROM users WHERE name LIKE "%Joe%"
    $db->get_collection( 'users' )->find( { name => qr/Joe/ } );
SELECT * FROM users WHERE name LIKE "Joe%"
    $db->get_collection( 'users' )->find( {name => qr/^Joe/ } );
<SELECT * FROM users WHERE age33 AND age<=40>>
    $db->get_collection( 'users' )->find( { age => { '$gt' => 33, '$lte' => 40 } } );
SELECT * FROM users ORDER BY name DESC
    $db->get_collection( 'users' )->find->sort( { name => -1 } );
CREATE INDEX myindexname ON users(name)
    $db->get_collection( 'users' )->ensure_index( { name => 1 } );
CREATE INDEX myindexname ON users(name,ts DESC)
    $db->get_collection( 'users' )->ensure_index( Tie::IxHash->new( name => 1, ts => -1 ) );

In this example, we must use Tie::IxHash to preserve the ordering of the arguments to ensureIndex.

SELECT * FROM users WHERE a=1 and b='q'
    $db->get_collection( 'users' )->find( {a => 1, b => "q" } );
SELECT * FROM users LIMIT 10 SKIP 20
    $db->get_collection( 'users' )->find->limit(10)->skip(20);
SELECT * FROM users WHERE a=1 or b=2
    $db->get_collection( 'users' )->find( { '$or' => [ {a => 1 }, { b => 2 } ] } );
SELECT * FROM users LIMIT 1
    $db->get_collection( 'users' )->find->limit(1);
EXPLAIN SELECT * FROM users WHERE z=3
    $db->get_collection( 'users' )->find( { z => 3 } )->explain;
SELECT DISTINCT last_name FROM users
    $db->run_command( { distinct => "users", key => "last_name" } );
SELECT COUNT(*y) FROM users
    $db->get_collection( 'users' )->count;
<SELECT COUNT(*y) FROM users where age 30>>
    $db->get_collection( 'users' )->find( { "age" => { '$gt' => 30 } } )->count;
SELECT COUNT(age) from users
    $db->get_collection( 'users' )->find( { age => { '$exists' => 1 } } )->count;
UPDATE users SET a=1 WHERE b='q'
    $db->get_collection( 'users' )->update( { b => "q" }, { '$set' => { a => 1 } } );
UPDATE users SET a=a+2 WHERE b='q'
    $db->get_collection( 'users' )->update( { b => "q" }, { '$inc' => { a => 2 } } );
DELETE FROM users WHERE z="abc"
    $db->get_database( 'users' )->remove( { z => "abc" } );

DATABASE COMMANDS ^

If you do something in the MongoDB shell and you would like to translate it to Perl, the best way is to run the function in the shell without parentheses, which will print the source. You can then generally translate the source into Perl fairly easily.

For example, suppose we want to use db.foo.validate in Perl. We could run:

    > db.foo.validate
    function (full) {
        var cmd = {validate:this.getName()};
        if (typeof full == "object") {
            Object.extend(cmd, full);
        } else {
            cmd.full = full;
        }
        var res = this._db.runCommand(cmd);
        if (typeof res.valid == "undefined") {
            res.valid = false;
            var raw = res.result || res.raw;
            if (raw) {
                var str = "-" + tojson(raw);
                res.valid = !(str.match(/exception/) || str.match(/corrupt/));
                var p = /lastExtentSize:(\d+)/;
                var r = p.exec(str);
                if (r) {
                    res.lastExtentSize = Number(r[1]);
                }
            }
        }
        return res;
    }

Thus, we can translate the important parts into Perl:

    $db->run_command( { validate => "foo" } );

Find-and-modify

The find-and-modify command is similar to update (or remove), but it will return the modified document. It can be useful for implementing queues or locks.

For example, suppose we had a list of things to do, and we wanted to remove the highest-priority item for processing. We could do a "find" in MongoDB::Collection and then a "remove" in MongoDB::Collection, but that wouldn't be atomic (a write could occur between the query and the remove). Instead, we can use find and modify.

    my $next_task = $db->run_command({
        findAndModify => "todo",
        sort => {priority => -1},
        remove => 1
    });

This will atomically find and pop the next-highest-priority task.

See http://www.mongodb.org/display/DOCS/findAndModify+Command for more details on find-and-modify.

AGGREGATION ^

The aggregation framework is MongoDB's analogy for SQL GROUP BY queries, but more generic and more powerful. An invocation of the aggregation framework specifies a series of stages in a pipeline to be executed in order by the server. Each stage of the pipeline is drawn from one of the following so-called "pipeline operators": $project, $match, $limit, $skip, $unwind, $group, $sort, and $geoNear.

The aggregation framework is the preferred way of performing most aggregation tasks. New in version 2.2, it has largely obviated mapReduce (http://docs.mongodb.org/manual/reference/command/mapReduce/#dbcmd.mapReduce), and group (http://docs.mongodb.org/manual/reference/command/group/#dbcmd.group).

See the MongoDB aggregation framework documentation for more information (http://docs.mongodb.org/manual/aggregation/).

$match and $group

The $group pipeline operator is used like GROUP BY in SQL. For example, suppose we have a number of local businesses stored in a "business" collection. If we wanted to find the number of coffeeshops in each neighborhood, we could do:

    my $out = $db->get_collection('business')->aggregate(
        [
            {'$match' => {'type' => 'coffeeshop'}},
            {'$group' => {'_id' => '$neighborhood', 'num_coffeshops' => {'$sum' => 1}}}
        ]
    );

The SQL equivalent is SELECT neighborhood, COUNT(*) FROM business GROUP BY neighborhood WHERE type = 'coffeeshop'. After executing the above aggregation query, $out will contain an array of result documents such as the following:

    [
         {
             '_id' => 'Soho',
             'num_coffeshops' => 23
         },
         {
             '_id' => 'Chinatown',
             'num_coffeshops' => 14 
         },
         {
             '_id' => 'Upper East Side',
             'num_coffeshops' => 10
         },
         {
             '_id' => 'East Village',
             'num_coffeshops' => 87
         }
    ]

Note that "aggregate" in MongoDB::Collection takes an array reference as an argument. Each element of the array is document which specifies a stage in the aggregation pipeline. Here our aggregation query consists of a $match phase followed by a $group phase. Use $match to filter the documents in the collection prior to aggregation. The _id field in the $group stage specifies the key to group by; the $ in '$neighborhood' indicates that we are referencing the name of a key. Finally, we use the $sum operator to add one for every document in a particular neighborhood. There are other operators, such as $avg, $max, $min, $push, and $addToSet, which can be used in the $group phase and work much like $sum.

$project and $unwind

Now let's look at a more complex example of the aggregation framework that makes use of the $project and $unwind pipeline operators. Suppose we have a collection called 'courses' which contains information on college courses. An example document in the collection looks like this:

    {
        '_id' => 'CSCI0170',
        'name' => 'Computer Science 17',
        'description' => 'An Integrated Introduction to Computer Science',
        'instructor_id' => 29823498,
        'instructor_name' => 'A. Greenwald',
        'students' => [
            { 'student_id' => 91736114, 'student_name' => 'D. Storch' },
            { 'student_id' => 89100891, 'student_name' => 'J. Rassi' }
        ]
    }

We wish to generate a report containing one document per student that indicates the courses in which each student is enrolled. The following call to "aggregate" in MongoDB::Collection will do the trick:

    my $out = $db->get_collection('courses')->aggregate([
        {'$unwind' => '$students'},
        {'$project' => {
                '_id' => 0,
                'course' => '$_id',
                'student_id' => '$students.student_id',
            }
        },
        {'$group' => {
                '_id' => '$student_id',
                'courses' => {'$addToSet' => '$course'}
            }
        }
    ]);

The output documents will each have a student ID number and an array of the courses in which that student is enrolled:

    [
        {
            '_id' => 91736114,
            'courses' => ['CSCI0170', 'CSCI0220', 'APMA1650', 'HIST1230']
        }
        {
            '_id' => 89100891,
            'courses' => ['CSCI0170', 'CSCI1670', 'CSCI1690']
        }
    ]

The $unwind stage of the aggregation query "peels off" elements of the courses array one-by-one and places them in their own documents. After this phase completes, there is a separate document for each (course, student) pair. The $project stage then throws out unecessary fields and keeps the ones we are interested in. It also pulls the student ID field out of its subdocument and creates a top-level field with the key student_id. Last, we group by student ID, using $addToSet in order to add the unique courses for each student to the courses array.

$sort, $skip, and $limit

The $sort, $skip, and $limit pipeline operators work much like their companion methods in MongoDB::Cursor. Returning to the previous students and courses example, suppose that we were particularly interested in the student with the ID that is numerically third-to-highest. We could retrieve the course list for that student by adding $sort, $skip, and $limit phases to the pipeline:

    my $out = $db->get_collection('courses')->aggregate([
        {'$unwind' => '$students'},
        {'$project' => {
                '_id' => 0,
                'course' => '$_id',
                'student_id' => '$students.student_id',
            }
        },
        {'$group' => {
                '_id' => '$student_id',
                'courses' => {'$addToSet' => '$course'}
            }
        },
        {'$sort' => {'_id' => -1}},
        {'$skip' => 2},
        {'$limit' => 1}
    ]);

Group

In addition to the aggregation framework, MongoDB offers a few special commands for common aggregation tasks: group, distinct, and count.

Returning to the coffeeshop example, the same result could be obtained using group with the following code:

    my $reduce = <<REDUCE;
    function(doc, prev) {
        if (doc.type == "coffeeshop") {
            prev["num coffeeshops"]++;
        }
    }
    REDUCE

    my $result = $db->run_command({group => {
        'ns' => "business",
        'key' => {"neighborhood" => 1},
        'initial' => {"num coffeeshops" => 0},
        '$reduce' => MongoDB::Code->new(code => $reduce)

Modern code should generally prefer the $group aggregation pipeline operator to the group database command.

Distinct

The distinct command returns all values for a given key in a collection. For example, suppose we had a collection with the following documents (_id value ignored):

    { 'name' => 'a', code => 1 }
    { 'name' => 'b', code => 1 }
    { 'name' => 'c', code => 2 }
    { 'name' => 'd', code => "3" }

If we wanted to see all of values in the "code" field, we could run:

    my $result = $db->run_command([
       "distinct" => "collection_name",
       "key"      => "code",
       "query"    => { }
    ]);

Notice that the arguments are in an array, to ensure that their order is preserved. You could also use a Tie::IxHash.

query is an optional argument, which can be used to only run distinct on specific documents. It takes a hash (or Tie::IxHash or array) in the same form as "find($query)" in MongoDB::Collection.

Running distinct on the above collection would give you:

    {
        'ok' => '1',
        'values' => [
                      1,
                      2,
                      "3"
                    ]
    };

MapReduce

For some special purpose aggregation tasks, the aggregation framework may not be sufficient. In this case, the database server can execute special MapReduce jobs written in JavaScript. Be warned: MapReduce is generally slower than the aggregation framework and should be avoided unless your application requires the flexibility that it provides.

This example counts the number of occurrences of each tag in a collection. Each document contains a "tags" array that contains zero or more strings.

    my $map = <<MAP;
    function() {
        this.tags.forEach(function(tag) {
            emit(tag, {count : 1});
        });
    }
    MAP

    my $reduce = <<REDUCE;
    function(prev, current) {
        result = {count : 0};
        current.forEach(function(item) {
            result.count += item.count;
        });
        return result;
    }
    REDUCE

    my $cmd = Tie::IxHash->new("mapreduce" => "foo",
        "map" => $map,
        "reduce" => $reduce);

    my $result = $db->run_command($cmd);

See the MongoDB documentation on MapReduce for more information (http://docs.mongodb.org/manual/core/map-reduce).

QUERYING ^

Nested Fields

MongoDB allows you to store deeply nested structures and then query for fields within them using dot-notation. For example, suppose we have a users collection with documents that look like:

    {
        "userId" => 12345,
        "address" => {
            "street" => "123 Main St",
            "city" => "Springfield",
            "state" => "MN",
            "zip" => "43213"
        }
    }

If we want to query for all users from Springfield, we can do:

    my $cursor = $users->find({"address.city" => "Springfield"});

This will search documents for an "address" field that is a subdocument and a "city" field within the subdocument.

UPDATING ^

Positional Operator

In MongoDB 1.3.4 and later, you can use positional operator, $, to update elements of an array. For instance, suppose you have an array of user information and you want to update a user's name.

A sample document in JavaScript:

    {
        "users" : [
            {
                "name" : "bill",
                "age" : 60
            },
            {
                "name" : "fred",
                "age" : 29
            },
        ]
    }

The update:

    $coll->update({"users.name" => "fred"}, {'users.$.name' => "george"});

This will update the array so that the element containing "name" => "fred" now has "name" => "george".

AUTHORS ^

COPYRIGHT AND LICENSE ^

This software is Copyright (c) 2014 by MongoDB, Inc..

This is free software, licensed under:

  The Apache License, Version 2.0, January 2004
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