Comparing MongoDB New Aggregation Framework and SQL

MongoDB 2.1 introduced the aggregation framework, a faster alternative to Map/Reduce for common aggregation operations. If you took a look at the documentation and examples, you may have found the feature intimidating. Once you tame it, this new feature reveals itself as a very powerful beast. So read on to discover its true power through a series of examples.

A Brief Introduction About The Pipeline Syntax

A MongoDB aggregation is a series of special operators applied to a collection. An operator is a JavaScript object with a single property, the operator name, which value is an option object:

{ $name: { /* options */ } }

Supported operator names are: $project, $match, $limit, $skip, $unwind, $group, and $sort, each with their own set of options. A series of operators is called a pipeline:

[{ $project: { /* options */ } }, { $match: { /* options */ } }, { $group: { /* options */ } }]

When executing a pipeline, MongoDB pipes operators into each other. “Pipe” here takes the Linux meaning: the output of an operator becomes the input of the following operator. The result of each operator is a new collection of documents. So Mongo executes the previous pipeline as follows:

collection | $project | $match | $group => result

You can add as many operators to a pipeline as you like, even twice the same one, at different positions:

collection | $match | $group | $match | $project | $group => result

That explains why a pipeline is not written as a simple JavaScript object, but rather as a collection of objects: in an object, the same operator couldn’t appear twice:

// The first appearance of $match and $group would be ignored with this syntax
{
  $match:   { /* options */ },
  $group:   { /* options */ },
  $match:   { /* options */ },
  $project: { /* options */ },
  $group:   { /* options */ }
}
// So MongoDB imposes a collection of JavaScript objects instead
[
  { $match:   { /* options */ } },
  { $group:   { /* options */ } },
  { $match:   { /* options */ } },
  { $project: { /* options */ } },
  { $group:   { /* options */ } }
]
// That's longer and cumbersome to read, but you'll get used to it

To execute a pipeline on a MongoDB collection, use the aggregate() function on that collection:

db.books.aggregate([{ $project: { title: 1 } }]);

Tip: If you're using Node.js, both the native adapter (since v0.9.9.2) and the ODM (since v3.1.0) support the new aggregation framework. For instance, to execute the previous pipeline on a Mongoose model, you just need to write:

Books.aggregate([{ $project: { title: 1 } }], function(err, results) {
  // do something with the result
});

The main benefit of the aggregation framework is that MongoDB executes it without the overhead of the JavaScript engine. It's implemented directly in C++, and therefore it's very fast. The main limitation - as compared to classical SQL aggregation - is that it’s limited to a single collection. In other terms, you can’t do a Mongo aggregation on several collections using a JOIN-like operation. Apart from that, it’s very powerful.

In this post, I’ll illustrate the power of pipeline operators by example, and compare them to their SQL counterpart. For a detailed reference, go to docs.mongodb.org.

Select, alias, compose

Use the $project operator to select or rename properties from a collection - similar to what you would do with the SQL SELECT clause.

/ sample data
> db.books.find();
[
  { _id: 147, title: "War and Peace", ISBN: 9780307266934 },
  { _id: 148, title: "Anna Karenina", ISBN: 9781593080273 },
  { _id: 149, title: "Pride and Prejudice", ISBN: 9783526419358 },
]

	

# sample data
> SELECT * FROM book;
+-----+-----------------------+---------------+
| id  | title                 | ISBN          |
+-----+-----------------------+---------------+
| 147 | 'War and Peace'       | 9780307266934 |
| 148 | 'Anna Karenina'       | 9781593080273 |
| 149 | 'Pride and Prejudice' | 9783526419358 |
+-----+-----------------------+---------------+

> db.books.aggregate([
  { $project: {
    title: 0,           // eliminate from the output
    reference: "$ISBN"  // use ISBN as source
  } }
]);
[
  { _id: 147, reference: 9780307266934 },
  { _id: 148, reference: 9781593080273 },
  { _id: 149, reference: 9783526419358 },
]

	

> SELECT id, ISBN AS reference FROM book;
+-----+---------------+
| id  | reference     |
+-----+---------------+
| 147 | 9780307266934 |
| 148 | 9781593080273 |
| 149 | 9783526419358 |
+-----+---------------+

The $project operator can also create composed fields and sub-documents using any of the supported expression operators ($and, $or, $gt, $lt, $eq, $add, $mod, $substr, $toLower, $toUpper, $dayOfWeek, $hour, $cond, $ifNull, to name a few).

Grouping documents

Group documents with, as you would have guessed, the $group operator.

// fastest way
> db.books.count();
3
// if you really want to use aggregation
> db.books.aggregate([
  { $group: {
    // _id is required, so give it a constant value 
    // to group all the collection into one result
    _id: null,
    // increment nbBooks for each document
    nbBooks: { $sum: 1 }
  } }
]);
[
  { _id: null, nbBooks: 3 }
]

	

> SELECT COUNT(*) FROM book;
+----------+
| COUNT(*) |
+----------+
| 3        |
+----------+

// sample data
> db.books.find()
[
  { _id: 147, title: "War and Peace", author_id: 72347 },
  { _id: 148, title: "Anna Karenina", author_id: 72347 },
  { _id: 149, title: "Pride and Prejudice", author_id: 42345 }
]

	

# sample data
> SELECT * FROM book
+-----+---------------------+-----------+
| id  | title               | author_id |
+-----+---------------------+-----------+
| 147 | War and Peace       | 72347     |
| 148 | Anna Karenina       | 72347     |
| 149 | Pride and Prejudice | 42345     |
+-----+---------------------+-----------+

> db.books.aggregate([
  { $group: {
    // group by author_id
    _id: "$author_id",
    // increment nbBooks for each document
    nbBooks: { $sum: 1 }
  } }
]);
[
  { _id: 72347, nbBooks: 2 },
  { _id: 42345, nbBooks: 1 }
]

	

> SELECT author_id, COUNT(*)
  FROM book
  GROUP BY author_id;
+-----------+----------+
| author_id | COUNT(*) |
+-----------+----------+
| 72347     | 2        |
| 42345     | 1        |
+-----------+----------+

Multi Operator Pipeline

A pipeline can feature more than one operator. Here is a combination of $group and $project:

> db.books.aggregate([
  { $group: {
    _id: "$author_id",
    nbBooks: { $sum: 1 }
  } },
  { $project: {
    _id: 0,
    authorId: "$_id",
    nbBooks: 1
  } }
]);
[
  { authorId: 72347, nbBooks: 2 },
  { authorId: 42345, nbBooks: 1 }
]

	

> SELECT author_id AS author, COUNT(*) AS nb_books
  FROM book
  GROUP BY author_id;
+--------+----------+
| author | nb_books |
+--------+----------+
| 72347  | 2        |
| 42345  | 1        |
+--------+----------+

More complex aggregations

$group supports a lot of aggregation functions: $first, $last, $min, $max, $avg, $sum, $push, and $addToSet. Check the MongoDB documentation for a complete reference.

// sample data
> db.reviews.find();
[
  { _id: "455", bookId: "974147",
    date: new Date("2012-07-10"), score: 1 },
  { _id: "456", bookId: "345335",
    date: new Date("2012-07-12"), score: 5 },
  { _id: "457", bookId: "345335",
    date: new Date("2012-07-13"), score: 2 },
  { _id: "458", bookId: "974147",
    date: new Date("2012-07-16"), score: 3 }
]

	

# sample data
> SELECT * FROM review;
+-----+---------+--------------+-------+
| id  | book_id | date         | score |
+-----+---------+--------------+-------+
| 455 | 974147  | "2012-07-10" | 1     |
| 456 | 345335  | "2012-07-12" | 5     |
| 457 | 345335  | "2012-07-13" | 2     |
| 458 | 974147  | "2012-07-16" | 3     |
+-----+---------+--------------+-------+

> db.reviews.aggregate([
  { $group: {
    _id: "$bookId",
    avgScore:  { $avg: "$score" },
    maxScore:  { $max: "$score" },
    nbReviews: { $sum: 1 }
  } }
]);
[
  { _id: 345335, avgScore: 3.5, maxScore: 5, nbReviews: 2 },
  { _id: 974147, avgScore: 3, maxScore: 3, nbReviews: 2 }
]

	

> SELECT book_id,
         AVG(score) as avg_score,
         MAX(score) as max_score,
         COUNT(*) as nb_reviews
  FROM review
  GROUP BY book_id ;
+---------+------------+----------+------------+
| book_id | avg_score | max_score | nb_reviews |
+---------+------------+----------+------------+
| 345335  | 3.5       | 5         | 2          |
| 974147  | 2         | 3         | 2          |
+---------+------------+----------+------------+

Conditions

You can restrict the collection to be processed using a query object, passed to the $match operator. Whether you place this operator before or after a $group operator, it becomes the equivalent of WHERE or HAVING in SQL.

> db.reviews.aggregate([
  { $match : {
    date: { $gte: new Date("2012-07-11") }
  } },
  { $group: {
    _id: "$bookId",
    avgScore: { $avg: "$score" }
  } }
]);
[
  { _id: 345335, avgScore: 3.5 },
  { _id: 974147, avgScore: 3 }
]

	

> SELECT book_id, AVG(score)
  FROM review
  WHERE review.date > "2012-07-11"
  GROUP BY review.book_id ;
+---------+------------+
| book_id | AVG(score) |
+---------+------------+
| 345335  | 3.5        |
| 974147  | 3          |
+---------+------------+

> db.reviews.aggregate([
  { $group: {
    _id: "$bookId",
    avgScore: { $avg: "$score" }
  } },
  { $match : {
    avgScore: { $gt: 3 }
  } }
]);
[
  { _id: 345335, avgScore: 3.5 }
]

	

> SELECT book_id, AVG(score) AS avg_score
  FROM review
  GROUP BY review.book_id
  HAVING avg_score > 3;
+---------+------------+
| book_id | AVG(score) |
+---------+------------+
| 345335  | 3.5        |
+---------+------------+

Develop Embedded Arrays

If documents inside a collection contain arrays, you can develop ("unwind") these arrays into several unique documents using the $unwind operator.

// sample data
> db.articles.find();
[
  {
    _id: 12351254,
    title: "Space Is Getting Closer",
    tags: ["science", "space", "iss"]
  },
  {
    _id: 22956492,
    title: "Computer Solves Rubiks Cube",
    tags: ["computing", "science"]
  }
]

	

# sample data
> SELECT * FROM article;
+------------+---------------------------+
| id       | title                       |
+----------+-----------------------------+
| 12351254 | Space Is Getting Closer     |
| 22956492 | Computer Solves Rubiks Cube |
+------------+---------------------------+
> SELECT * FROM tag;
+-----+------------+-----------+
| id  | article_id | name      |
+-----+------------+-----------+
| 534 | 12351254   | science   |
| 535 | 12351254   | space     |
| 536 | 12351254   | iss       |
| 816 | 22956492   | computing |
| 817 | 22956492   | science   |
+-----+------------+-----------+

> db.articles.aggregate([
  { $unwind: "$tags" }
]);
[
  {
    _id: 12351254,
    title: "Space Is Getting Closer",
    tags: "science"
  },
  {
    _id: 12351254,
    title: "Space Is Getting Closer",
    tags: "space"
  },
  {
    _id: 22956492,
    title: "Computer Solves Rubiks Cube",
    tags: "computing"
  },
  {
    _id: 22956492,
    title: "Computer Solves Rubiks Cube",
    tags: "science"
  }
]

	

> SELECT article.id, article.title, tag.name
  FROM article LEFT JOIN tag
  ON article.id = tag.article_id;
+------------+-----------------------------+-----------+
| article.id | article.title               | tag.name  |
+------------+-----------------------------+-----------+
| 12351254   | Space Is Getting Closer     | science   |
| 12351254   | Space Is Getting Closer     | space     |
| 22956492   | Computer Solves Rubiks Cube | computing |
| 22956492   | Computer Solves Rubiks Cube | science   |
+------------+-----------------------------+-----------+

Aggregate Developed Arrays

The true power of the aggregation framework reveals when you pipe $unwind to $group. This is similar to using LEFT JOIN ... GROUP BY in SQL.

> db.articles.aggregate([
  { $unwind: "$tags" },
  { $group: {
    _id: "$tags",
    nbArticles: { $sum: 1 }
  } }
]);
[
  { _id: "science", nbArticles: 2 },
  { _id: "space", nbArticles: 1 },
  { _id: "computing", nbArticles: 1 },
]

	

> SELECT tag.name, COUNT(article.id) AS nb_articles
  FROM article LEFT JOIN tag
  ON article.id = tag.article_id
  GROUP BY tag.name;
+-----------+-------------+
| tqg.name  | nb_articles |
+-----------+-------------+
| science   | 2           |
| space     | 1           |
| computing | 1           |
+-------------+-----------+

> db.articles.aggregate([
  { $unwind: "$tags" },
  { $group: {
    _id: "$tags",
    articles: { $addToSet: "$_id" }
  } }
]);
[
  { _id: "science", articles: [12351254, 22956492] },
  { _id: "space", articles: [12351254] },
  { _id: "computing", articles: [22956492] },
]

	

> SELECT tag.name, GROUP_CONCAT(article.id) AS articles
  FROM article LEFT JOIN tag
  ON article.id = tag.article_id
  GROUP BY tag.name;
+-----------+-------------------+
| tqg.name  | articles          |
+-----------+-------------------+
| science   | 12351254,22956492 |
| space     | 12351254          |
| computing | 22956492          |
+-------------+-----------------+

Conclusion

Imagine what you can do with this system... Pipe operators one after the other, group, sort, limit, etc. The ultimate example, taken from the MongoDB documentation itself, shows a pipeline with two successive $group operators. An SQL database can only do that with subqueries.

If your Map/Reduce functions are simple enough, refactor your Mongo code to the new aggregation framework. It will execute faster, and open to a new realm of possibility.