Pipeline mental model (stages flow) in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of this MongoDB aggregation pipeline?

Consider a collection sales with documents like { item: 'apple', quantity: 5, price: 10 }. What will be the output of this pipeline?

[{ $match: { quantity: { $gt: 3 } } }, { $project: { item: 1, total: { $multiply: ["$quantity", "$price"] } } }]

MongoDB

[{ $match: { quantity: { $gt: 3 } } }, { $project: { item: 1, total: { $multiply: ["$quantity", "$price"] } } }]

A [{ item: 'apple', quantity: 5, price: 10, total: 50 }]

B [{ item: 'apple', total: 50 }]

C [{ item: 'apple', total: 15 }]

D [{ item: 'apple', quantity: 5, price: 10 }]

💡 Hint

Remember that $project controls which fields appear in the output.

The $match stage filters documents where quantity > 3. The $project stage includes only item and a new field total which is quantity multiplied by price. Other fields are excluded.

🧠 Conceptual

intermediate

Which stage in a MongoDB pipeline filters documents?

In the MongoDB aggregation pipeline, which stage is used to filter documents based on a condition?

A $group

B $sort

C $project

D $match

💡 Hint

Think about which stage selects documents by condition.

The $match stage filters documents to pass only those that meet the condition to the next stage.

📝 Syntax

advanced

Identify the syntax error in this pipeline stage

Which option contains a syntax error in the MongoDB aggregation pipeline stage?

{ $project: { total: { $multiply: ["$quantity", "$price"] } }

A { $project: { total: { $multiply: "$quantity", "$price" } } }

B } } } ]"ecirp$" ,"ytitnauq$"[ :ylpitlum$ { :latot { :tcejorp$ {

C { $project: { total: { $multiply: ["$quantity", "$price"] } } }

D $project: { total: { $multiply: ["$quantity", "$price"] } } }

💡 Hint

Check the syntax of the $multiply operator.

The $multiply operator requires an array of expressions. Option A passes two separate arguments instead of an array.

❓ optimization

advanced

Which pipeline order improves performance by reducing documents early?

You want to optimize a pipeline that calculates total sales and then filters for totals above 100. Which pipeline order is better?

A [{ $project: { total: { $multiply: ["$quantity", "$price"] } } }, { $match: { total: { $gt: 100 } } }]

B [{ $match: { total: { $gt: 100 } } }, { $project: { total: { $multiply: ["$quantity", "$price"] } } }]

C [{ $match: { quantity: { $gt: 0 } } }, { $project: { total: { $multiply: ["$quantity", "$price"] } } }, { $match: { total: { $gt: 100 } } }]

D [{ $project: { total: { $multiply: ["$quantity", "$price"] } } }, { $match: { quantity: { $gt: 0 } } }]

💡 Hint

Filtering early reduces the number of documents processed in later stages.

Option C filters documents early by quantity > 0 before calculating total and filtering by total > 100, reducing workload.

🔧 Debug

expert

Why does this pipeline produce no results?

Given this pipeline:

[{ $match: { total: { $gt: 100 } } }, { $project: { total: { $multiply: ["$quantity", "$price"] } } }]

Why might it return an empty result even if some documents have quantity and price values?

A Because $match runs before $project, so 'total' does not exist yet

B Because $project removes all original fields except those specified, so $match cannot see 'total'

C Because $match cannot filter on fields created in previous $project stages

D Because $multiply operator is invalid and causes the pipeline to fail silently

💡 Hint

Think about the order stages run and when fields become available.

The pipeline has $match before $project. Stages process sequentially, so when $match executes, the 'total' field has not yet been created by the subsequent $project stage. Thus, no documents match the filter on 'total', resulting in empty output.

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