Pipeline mental model (stages flow) in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start an aggregation pipeline with the first stage.

MongoDB

db.collection.aggregate([ { [1]: { age: { $gt: 25 } } } ])

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

A$match

B$group

C$sort

D$project

Attempts:

3 left

💡 Hint

Common Mistakes

Using $group as the first stage without filtering.

Using $sort before filtering documents.

The $match stage filters documents to pass only those that match the condition. It is usually the first stage in a pipeline.

2fill in blank

medium

Complete the code to add a stage that groups documents by 'department' and counts them.

MongoDB

db.collection.aggregate([ { $match: { status: 'active' } }, { [1]: { _id: "$department", count: { $sum: 1 } } } ])

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

A$project

B$group

C$sort

D$limit

Attempts:

3 left

💡 Hint

Common Mistakes

Using $project instead of $group for grouping.

Trying to count documents in $match stage.

The $group stage groups documents by a specified key and can perform accumulations like counting.

3fill in blank

hard

Fix the error in the pipeline stage that sorts documents by 'score' descending.

MongoDB

db.collection.aggregate([ { $match: { active: true } }, { [1]: { score: -1 } } ])

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

A$sort

B$limit

C$match

D$group

Attempts:

3 left

💡 Hint

Common Mistakes

Using $group or $match instead of $sort for ordering.

Using positive 1 for descending order.

The $sort stage orders documents by specified fields. Using $sort with { score: -1 } sorts descending by score.

4fill in blank

hard

Fill both blanks to project only the 'name' and 'total' fields in the output.

MongoDB

db.collection.aggregate([ { $match: { status: 'complete' } }, { [1]: [2] } ])

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

A$project

B{ name: 1, total: 1 }

C{ name: 0, total: 0 }

D$group

Attempts:

3 left

💡 Hint

Common Mistakes

Using $group instead of $project to select fields.

Setting fields to 0 to include them.

The $project stage selects which fields to include or exclude. Using { name: 1, total: 1 } includes only those fields.

5fill in blank

hard

Fill all three blanks to create a pipeline that filters active users, groups by city counting users, and sorts by count descending.

MongoDB

db.users.aggregate([ { [1]: { active: true } }, { [2]: { _id: "$city", userCount: { $sum: 1 } } }, { [3]: { userCount: -1 } } ])

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

A$match

B$group

C$sort

D$project

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing the order of stages.

Using $project instead of $group for counting.

This pipeline first filters active users with $match, then groups them by city counting users with $group, and finally sorts the results by userCount descending with $sort.

Practice

1. What does each stage in a MongoDB aggregation pipeline do?

easy

A. Runs a SQL query on the data

B. Creates a new database

C. Transforms or filters data before passing it to the next stage

D. Deletes the entire collection

Solution

1. Step 1: Understand the pipeline concept

   Each stage in a pipeline processes data step-by-step, modifying or filtering it.

2. Step 2: Identify the role of stages

   Stages do not create databases or delete collections; they only transform or filter data.

3. Final Answer:

   Transforms or filters data before passing it to the next stage -> Option C

4. Quick Check:

   Pipeline stages transform or filter data [OK]

Hint: Remember: pipeline stages change data step-by-step [OK]

Common Mistakes:

• Thinking stages create or delete collections
• Confusing pipeline with database commands
• Assuming stages run SQL queries

2. Which of the following is the correct syntax to start an aggregation pipeline in MongoDB?

easy

A. db.collection.find({ $match: { status: 'A' } })

B. db.collection.update({ $match: { status: 'A' } })

C. db.collection.insert([{ $match: { status: 'A' } }])

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

Solution

1. Step 1: Identify aggregation pipeline syntax

   The aggregation pipeline uses the aggregate() method with an array of stages.

2. Step 2: Compare options

   Only db.collection.aggregate([ { $match: { status: 'A' } } ]) uses aggregate() with a pipeline stage ($match). Others use find, insert, or update incorrectly.

3. Final Answer:

   db.collection.aggregate([ { $match: { status: 'A' } } ]) -> Option D

4. Quick Check:

   aggregate() starts pipelines [OK]

Hint: aggregate() method always starts a pipeline [OK]

Common Mistakes:

• Using find() instead of aggregate() for pipelines
• Trying to insert pipeline stages
• Using update() with pipeline syntax

3. Given the pipeline: [{ $match: { age: { $gte: 18 } } }, { $group: { _id: '$city', count: { $sum: 1 } } }], what does the output represent?

medium

A. Count of people grouped by city who are 18 or older

B. List of all people younger than 18

C. Sum of ages for each city

D. All documents without filtering

Solution

1. Step 1: Analyze the $match stage

   This stage filters documents where age is 18 or more.

2. Step 2: Analyze the $group stage

   Groups filtered documents by city and counts how many per city.

3. Final Answer:

   Count of people grouped by city who are 18 or older -> Option A

4. Quick Check:

   $match filters, $group counts by city [OK]

Hint: $match filters, $group aggregates data [OK]

Common Mistakes:

• Confusing $sum with sum of ages
• Ignoring the filter condition
• Assuming output lists all people

4. What is wrong with this pipeline? [{ $match: { score: { $gt: 50 } } }, { $project: { score: 1, name: 1 } }, { $match: { name: 'Alice' } }]

medium

A. Using two $match stages is not allowed

B. The pipeline syntax is correct and will work as expected

C. The $project stage must include _id field explicitly

D. The second $match stage should come before $project

Solution

1. Step 1: Check multiple $match stages

   MongoDB allows multiple $match stages anywhere in the pipeline.

2. Step 2: Check $project usage

   $project can select fields; _id is included by default unless excluded explicitly.

3. Final Answer:

   The pipeline syntax is correct and will work as expected -> Option B

4. Quick Check:

   Multiple $match and $project usage is valid [OK]

Hint: Multiple $match stages are allowed anywhere [OK]

Common Mistakes:

• Thinking multiple $match stages are invalid
• Assuming _id must be included explicitly
• Believing $project must come last

5. You want to find the average price of products in each category but only for products with stock greater than 0. Which pipeline correctly achieves this?

hard

A. [{ $match: { stock: { $gt: 0 } } }, { $group: { _id: '$category', avgPrice: { $avg: '$price' } } }]

B. [{ $group: { _id: '$category', avgPrice: { $avg: '$price' } } }, { $match: { stock: { $gt: 0 } } }]

C. [{ $project: { category: 1, price: 1, stock: 1 } }, { $group: { _id: '$category', avgPrice: { $avg: '$price' } } }]

D. [{ $match: { price: { $gt: 0 } } }, { $group: { _id: '$category', avgPrice: { $avg: '$stock' } } }]

Solution

1. Step 1: Filter products with stock > 0 first

   The $match stage must come first to filter only products in stock.

2. Step 2: Group by category and calculate average price

   Use $group with _id as category and $avg on price to get average price per category.

3. Final Answer:

   [{ $match: { stock: { $gt: 0 } } }, { $group: { _id: '$category', avgPrice: { $avg: '$price' } } }] -> Option A

4. Quick Check:

   Filter then group for correct average [OK]

Hint: Filter first, then group to aggregate correctly [OK]

Common Mistakes:

• Grouping before filtering causes wrong results
• Averaging wrong fields
• Filtering on price instead of stock