Computed pattern for pre-aggregation in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Output of a pre-aggregation computed field

Given a MongoDB collection sales with documents containing price and quantity, what is the output of this aggregation pipeline?

MongoDB

db.sales.aggregate([
  {
    $project: {
      totalSale: { $multiply: ["$price", "$quantity"] },
      item: 1
    }
  }
])

A [{ "item": "apple", "totalSale": null }, { "item": "banana", "totalSale": null }]

B [{ "item": "apple", "totalSale": 3 }, { "item": "banana", "totalSale": 2 }]

C [{ "item": "apple", "totalSale": 30 }, { "item": "banana", "totalSale": 20 }]

D [{ "item": "apple", "totalSale": "30" }, { "item": "banana", "totalSale": "20" }]

💡 Hint

Remember that $multiply computes the product of numeric fields.

The $project stage computes a new field totalSale by multiplying price and quantity for each document, preserving the item field.

📝 Syntax

intermediate

Identify the syntax error in computed pre-aggregation

Which option contains a syntax error in the MongoDB aggregation pipeline that computes a new field?

MongoDB

db.orders.aggregate([
  {
    $project: {
      total: { $add: ["$price", "$tax"] },
      item: 1
    }
  }
])

A No syntax error; the pipeline is correct

B Missing comma between fields inside $project

C Field names must not be quoted inside $add

D Using $add with an array is invalid syntax

💡 Hint

Check the use of $add operator and field quoting.

The $add operator accepts an array of expressions, and field names are correctly quoted as strings with $ prefix. The pipeline syntax is valid.

❓ optimization

advanced

Optimize pre-aggregation for repeated computed fields

You want to optimize a MongoDB aggregation pipeline that computes the same complex field multiple times. Which approach is best?

A Use $group to compute the field multiple times

B Compute the field once in a $addFields stage and reuse it later

C Repeat the computation in every $project stage where needed

D Store the computed field in the original documents manually

💡 Hint

Avoid repeating expensive computations in multiple stages.

Computing the field once in $addFields and reusing it avoids redundant calculations and improves performance.

🔧 Debug

advanced

Debug incorrect computed field in pre-aggregation

Why does this pipeline produce null for the computed field totalCost?

MongoDB

db.products.aggregate([
  {
    $project: {
      totalCost: { $multiply: ["$cost", "$quantity"] },
      name: 1
    }
  }
])

A Field names cost and quantity do not exist in documents

B Using $multiply with strings causes null output

C Missing $match stage before $project

D Incorrect use of $project operator

💡 Hint

Check if the fields used in $multiply exist in the documents.

If the fields cost or quantity are missing or named differently, $multiply returns null for totalCost.

🧠 Conceptual

expert

Understanding computed pattern for pre-aggregation in MongoDB

Which statement best describes the computed pattern for pre-aggregation in MongoDB?

A It only works with $group stages and cannot use $project or $addFields

B It requires storing all computed fields permanently in the database documents

C It uses map-reduce exclusively to compute aggregated values

D It involves computing derived fields during aggregation to avoid repeated calculations later

💡 Hint

Think about how computed fields help with performance in aggregation pipelines.

The computed pattern pre-calculates fields during aggregation to optimize queries and reduce repeated work.