How the engine optimizes pipelines in MongoDB - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start an aggregation pipeline with the correct stage operator.

MongoDB

db.collection.aggregate([[1]])

Drag options to blanks, or click blank then click option'

A{ $match: { status: 'A' } }

B{ $filter: { input: '$items', as: 'item', cond: { $gt: ['$$item.qty', 10] } } }

C{ $group: { _id: '$cust_id', total: { $sum: '$amount' } } }

D{ $sort: { date: 1 } }

Attempts:

3 left

💡 Hint

Common Mistakes

Using a stage that manipulates arrays as the first stage.

Starting with a grouping stage without filtering first.

The $match stage filters documents and is commonly the first stage in a pipeline for optimization.

2fill in blank

medium

Complete the code to optimize the pipeline by placing the correct stage early.

MongoDB

db.collection.aggregate([[1], { $group: { _id: '$category', total: { $sum: '$price' } } }])

Drag options to blanks, or click blank then click option'

A{ $sort: { price: -1 } }

B{ $project: { price: 1, category: 1 } }

C{ $match: { price: { $gt: 100 } } }

D{ $limit: 5 }

Attempts:

3 left

💡 Hint

Common Mistakes

Using $project before filtering, which does not reduce documents.

Sorting before filtering, which can be inefficient.

Placing $match early filters documents, reducing the workload for later stages.

3fill in blank

hard

Fix the error in the pipeline by choosing the correct stage operator.

MongoDB

db.collection.aggregate([{ $match: { status: 'A' } }, [1]])

Drag options to blanks, or click blank then click option'

A{ $group: { _id: '$cust_id', total: { $sum: '$amount' } } }

B{ $sortByCount: '$category' }

C{ $filter: { input: '$items', as: 'item', cond: { $gt: ['$$item.qty', 10] } } }

D{ $unwind: '$items' }

Attempts:

3 left

💡 Hint

Common Mistakes

Using $filter as a pipeline stage, which is an expression, not a stage.

Using $unwind incorrectly without context.

The $group stage correctly aggregates documents after filtering with $match.

4fill in blank

hard

Fill both blanks to optimize the pipeline by filtering and projecting fields.

MongoDB

db.collection.aggregate([[1], [2]])

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A{ $match: { status: 'active' } }

B{ $project: { name: 1, status: 1, _id: 0 } }

C{ $group: { _id: '$status', count: { $sum: 1 } } }

D{ $sort: { name: 1 } }

Attempts:

3 left

💡 Hint

Common Mistakes

Projecting fields before filtering, which processes unnecessary data.

Grouping before filtering, which can be inefficient.

First, $match filters documents, then $project selects fields to keep, optimizing data flow.

5fill in blank

hard

Fill all three blanks to build an optimized pipeline with filtering, unwinding, and grouping.

MongoDB

db.collection.aggregate([[1], [2], [3]])

Drag options to blanks, or click blank then click option'

A{ $match: { tags: 'mongodb' } }

B{ $unwind: '$tags' }

C{ $group: { _id: '$tags', count: { $sum: 1 } } }

D{ $sort: { count: -1 } }

Attempts:

3 left

💡 Hint

Common Mistakes

Unwinding before filtering, which processes more data than needed.

Grouping before unwinding, which groups arrays instead of elements.

The pipeline filters documents with $match, expands array elements with $unwind, then groups by tags with $group.