$facet for multiple pipelines in MongoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the $facet stage do in a MongoDB aggregation pipeline?

The $facet stage allows running multiple aggregation pipelines within a single stage. It outputs a document where each field contains the results of one pipeline, letting you perform different analyses on the same input data simultaneously.

beginner

How does $facet help when you want to get multiple summaries from the same collection?

$facet runs multiple pipelines in parallel on the same input documents. This means you can get different summaries, like counts, averages, or filtered lists, all in one query without running multiple separate queries.

intermediate

Can you explain the output format of a $facet stage?

The output of $facet is a single document with fields named after each pipeline. Each field contains an array of documents that are the results of that pipeline. For example, { counts: [...], averages: [...] }.

intermediate

Why might you use $facet instead of running multiple aggregation queries?

Using $facet reduces the number of queries to the database, improving performance and consistency. It also simplifies code by combining multiple analyses into one pipeline.

beginner

Give an example use case for $facet in a real-world scenario.

Imagine an online store wanting to see total sales, average order value, and a list of top products all at once. $facet can run these three pipelines in parallel on the orders collection and return all results in one response.

What is the main purpose of the $facet stage in MongoDB?

A Filter documents based on a condition

B Run multiple aggregation pipelines in parallel on the same input

C Sort documents by a field

D Join two collections

$facet runs multiple pipelines simultaneously on the same input documents.

What type of output does $facet produce?

A A document with fields containing arrays of results from each pipeline

B A single array of documents

C A count of documents

D A sorted list of documents

$facet outputs a document where each field holds an array of results from its pipeline.

Which of these is NOT a benefit of using $facet?

A Automatically indexing fields

B Reducing the number of database queries

C Getting consistent results from the same data snapshot

D Combining multiple analyses in one query

$facet does not create indexes; it runs multiple pipelines in one aggregation.

If you want to get both total count and average value from a collection in one query, which stage helps?

A <code>$match</code>

B <code>$group</code>

C <code>$facet</code>

D <code>$sort</code>

$facet can run separate pipelines for count and average simultaneously.

Inside a $facet stage, how do you define each pipeline?

A As a single aggregation stage

B As a query filter

C As a JavaScript function

D As an array of aggregation stages under a named field

Each pipeline is an array of stages assigned to a field name inside $facet.

Describe how the $facet stage works and why it is useful in MongoDB aggregation.

Think about running many analyses on the same data at once.

Explain the structure of the output document from a $facet stage with two pipelines named 'counts' and 'averages'.

Imagine the output as a box with two labeled compartments holding lists.