Why expressions matter in pipelines in MongoDB - Challenge Your Understanding

Challenge - 5 Problems

❓ query_result

intermediate

Output of $project with expression in MongoDB pipeline

Given the collection sales with documents like { item: "apple", price: 10, quantity: 2 }, what is the output of this aggregation pipeline?

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

MongoDB

db.sales.aggregate([{ $project: { totalCost: { $multiply: ["$price", "$quantity"] } } }])

A [{ "totalCost": null }]

B [{ "totalCost": 12 }]

C [{ "totalCost": 10 }]

D [{ "totalCost": 20 }]

💡 Hint

Think about how $multiply works with the fields price and quantity.

The $multiply expression multiplies the values of price (10) and quantity (2), resulting in 20.

🧠 Conceptual

intermediate

Why use expressions in MongoDB pipelines?

Which of the following best explains why expressions are important in MongoDB aggregation pipelines?

A They allow transforming and computing new fields dynamically during data processing.

B They automatically index the collection for faster queries.

C They replace the need for a database schema.

D They encrypt data during aggregation.

💡 Hint

Think about what expressions do inside a pipeline stage.

Expressions let you compute new values or transform existing fields as data flows through the pipeline.

📝 Syntax

advanced

Identify the syntax error in this pipeline expression

What error does this MongoDB aggregation pipeline produce?

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

MongoDB

db.collection.aggregate([{ $project: { total: { $add: ["$price", "$quantity" } } } }])

A TypeError: Cannot add string and number

B SyntaxError: Missing closing bracket in $add array

C RuntimeError: Field price does not exist

D No error, runs successfully

💡 Hint

Check the brackets around the $add array.

The $add operator expects an array, but the closing bracket is missing, causing a syntax error.

❓ optimization

advanced

Optimizing pipeline expressions for performance

Which pipeline expression is more efficient when calculating a discount price as 90% of the original price in MongoDB?

A { $project: { discountPrice: { $multiply: ["$price", 0.9] } } }

B { $project: { discountPrice: { $subtract: ["$price", { $multiply: ["$price", 0.1] }] } } }

C { $project: { discountPrice: { $add: ["$price", -0.1] } } }

D { $project: { discountPrice: { $divide: ["$price", 1.1] } } }

💡 Hint

Consider the number of operations and clarity.

Multiplying price by 0.9 is a single, clear operation and more efficient than subtracting a computed value or dividing.

🔧 Debug

expert

Why does this pipeline produce unexpected results?

Consider this pipeline:

[{ $project: { total: { $add: ["$price", "$quantity"] } } }, { $match: { total: { $gt: 10 } } }]

Why might the $match stage not filter documents as expected?

A Because $gt operator cannot be used inside $match.

B Because $add cannot add two fields together.

C Because $project creates a new field total but does not persist it for $match to use.

D Because $match must come before $project in the pipeline.

💡 Hint

Think about how fields created in $project are available to later stages.

Fields created in $project are available to subsequent stages, but if the pipeline is run incorrectly or the field is missing, $match may not filter as expected. However, the main issue is that $project replaces the document fields, so if total is the only field projected, $match can use it. If other fields are missing, it might cause confusion.