Provided by: libmongodb-perl_2.2.2-2_all bug

NAME

       MongoDB::Examples - Some examples of MongoDB syntax

VERSION

       version v2.2.2

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 mappings for JavaScript (or another language), see
       <http://docs.mongodb.org/manual/reference/sql-comparison/>.

       In the following examples, $db is a MongoDB::Database object which was retrieved by using
       "get_database". See MongoDB::MongoClient, MongoDB::Database and MongoDB::Collection for
       more on the methods you see below.

       "CREATE TABLE USERS (a Number, b Number)"
               Implicit, can be done explicitly.

       "INSERT INTO USERS VALUES(1,1)"
               $db->get_collection( 'users' )->insert_one( { a => 1, b => 1 } );

       "SELECT a,b FROM users"
               $db->get_collection( 'users')->find( { } )->fields( { 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 } )->fields( { 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 age>33"
               $db->get_collection( 'users' )->find( { age => { '$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 age>33 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)"
               my $indexes = $db->get_collection( 'users' )->indexes;
               $indexes->create_one( [ name => 1 ] );

       "CREATE INDEX myindexname ON users(name,ts DESC)"
               my $indexes = $db->get_collection( 'users' )->indexes;
               $indexes->create_one( [ name => 1, ts => -1 ] );

       "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->get_collection( 'users' )->distinct( 'last_name' );

       "SELECT COUNT(*y) FROM users"
               $db->get_collection( 'users' )->count_documents;

       "SELECT COUNT(*y) FROM users where age > 30"
               $db->get_collection( 'users' )->count_documents( { "age" => { '$gt' => 30 } } );

       "SELECT COUNT(age) from users"
               $db->get_collection( 'users' )->count_documents( { age => { '$exists' => 1 } } );

       "UPDATE users SET a=1 WHERE b='q'"
               $db->get_collection( 'users' )->update_many( { b => "q" }, { '$set' => { a => 1 } } );

       "UPDATE users SET a=a+2 WHERE b='q'"
               $db->get_collection( 'users' )->update_many( { b => "q" }, { '$inc' => { a => 2 } } );

       "DELETE FROM users WHERE z="abc""
               $db->get_database( 'users' )->delete_many( { 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;
           }

       Next, we can translate the important parts into Perl:

           $db->run_command( [ validate => "foo" ] );

   Find-one-and-modify
       The find-one-and-modify commands in MongoDB::Collection are similar to update (or remove),
       but will return the modified document.  They 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 and then a delete_one, but that wouldn't
       be atomic (a write could occur between the query and the remove).  Instead, we could use
       find_one_and_delete:

           my $coll = $db->get_collection('todo');
           my $next_task = $todo->find_one_and_delete(
               {}, # empty filter means any document
               { sort => {priority => -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 a MongoDB::QueryResult, allowing us to iterate through 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 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" 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
       unnecessary 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}
           ]);

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:

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

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

AUTHORS

       •   David Golden <david@mongodb.com>

       •   Rassi <rassi@mongodb.com>

       •   Mike Friedman <friedo@friedo.com>

       •   Kristina Chodorow <k.chodorow@gmail.com>

       •   Florian Ragwitz <rafl@debian.org>

COPYRIGHT AND LICENSE

       This software is Copyright (c) 2020 by MongoDB, Inc.

       This is free software, licensed under:

         The Apache License, Version 2.0, January 2004