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👐 $match and $project

MongoDB’s Aggregation Framework allows for powerful data transformations and analysis. The $match, $project, and $sort stages are fundamental building blocks of an aggregation pipeline.

🔹 $match → Filtering data​

Just like .find() based on the query mentioned, the $match stage filters documents from the collection.

Syntax​

{ $match: { <query> } }

Example: Get all the books that were published after the year 2010​

db.books.aggregate([
  {
    $match: { year: { $gt: 2010 } },
  },
]);
info

Place $match as early as possible in the pipeline to reduce the number of documents processed in later stages.

🔹 $project → Selecting fields​

  • The $project stage controls which fields are included in the output.
  • It can also be used for adding computed fields to the results.

Syntax​

{
  $project: {
    field1: 1,
    field2: 1,
    _id: 0
  }
}
  • 1: Include the field
  • 0: Exclude the field

Example: Get all the books published after the year 2010. The output should only include the title, year, and page count of the book.​

db.books.aggregate([
  {
    $match: { year: { $gt: 2010 } },
  },
  {
    $project: {
      title: 1,
      year: 1,
      pages: 1,
      _id: 0,
    },
  },
]);

Equivalent SQL query​

SELECT title, year, pages FROM books WHERE year>2010;

Computed fields example: Along with the title and authors, also output the count of authors for every book in the database.​

db.books.aggregate([
  {
    $project: {
      title: 1,
      authors: 1,
      authorCount: { $size: "$authors" },
    },
  },
]);

Challenge 🚀​

👐 1. Find books with more than 2 available copies.​

Answer
  db.books.aggregate([
    { $match: {available: {$gt: 2}}}
  ]);

👐 2. Find books with more than 2 available copies. Return only book titles and publication year.​

Answer
  db.books.aggregate([
    { $match: {available: {$gt: 2}}},
    { $project: {title: 1, year: 1, _id: 0}}
  ]);