Computed pattern for pre-aggregation in MongoDB

Introduction

Pre-aggregation helps speed up queries by calculating and storing results ahead of time.

When you want to quickly get totals or averages without recalculating every time.

When your data is large and running calculations on the fly is slow.

When you need to show summary reports that update regularly.

When you want to reduce the load on your database during busy times.

Syntax

MongoDB

db.collection.aggregate([
  {
    $group: {
      _id: <grouping_key>,
      <computed_field>: { $<aggregation_operator>: "$<field>" }
    }
  }
])

The $group stage groups documents by a key and computes values.

You can use operators like $sum, $avg, $max, and $min to compute fields.

Examples

Groups sales by product and sums the amount for each product.

MongoDB

db.sales.aggregate([
  {
    $group: {
      _id: "$product",
      totalSales: { $sum: "$amount" }
    }
  }
])

Calculates the average order total for each customer.

MongoDB

db.orders.aggregate([
  {
    $group: {
      _id: "$customerId",
      averageOrder: { $avg: "$total" }
    }
  }
])

Sample Program

This query groups orders by category, sums the quantity sold, and calculates the average price per category.

MongoDB

db.orders.aggregate([
  {
    $group: {
      _id: "$category",
      totalQuantity: { $sum: "$quantity" },
      averagePrice: { $avg: "$price" }
    }
  }
])

Important Notes

Pre-aggregation stores computed results to avoid repeating calculations.

Use indexes on grouping keys to improve aggregation speed.

Summary

Pre-aggregation calculates and stores summary data ahead of time.

Use $group with aggregation operators to compute values.

This pattern helps queries run faster on large data sets.

Practice

1. What is the main purpose of using a computed pattern for pre-aggregation in MongoDB?

easy

A. To delete all raw data after aggregation

B. To create indexes on every field automatically

C. To calculate and store summary data ahead of time for faster queries

D. To replicate data across multiple servers

Solution

1. Step 1: Understand pre-aggregation concept

   Pre-aggregation means computing summary data before queries run, so queries are faster.

2. Step 2: Identify the main goal

   The goal is to store computed summaries to avoid recalculating on every query.

3. Final Answer:

   To calculate and store summary data ahead of time for faster queries -> Option C

4. Quick Check:

   Pre-aggregation = faster queries [OK]

Hint: Pre-aggregation means compute summaries before queries [OK]

Common Mistakes:

• Confusing pre-aggregation with data deletion
• Thinking it automatically creates indexes
• Mixing it with data replication

2. Which MongoDB aggregation stage is primarily used to compute grouped summary data for pre-aggregation?

easy

A. $match

B. $group

C. $project

D. $sort

Solution

1. Step 1: Recall aggregation stages

   $group is used to group documents and compute summary values like sums or counts.

2. Step 2: Identify correct stage for pre-aggregation

   Pre-aggregation requires grouping and computing, so $group is the right stage.

3. Final Answer:

   $group -> Option B

4. Quick Check:

   $group = grouping and computing summaries [OK]

Hint: Use $group to compute grouped summaries [OK]

Common Mistakes:

• Choosing $match which filters but does not compute
• Confusing $project with grouping
• Selecting $sort which only orders data

3. Given the following aggregation pipeline, what will be the output?

[{ $group: { _id: "$category", total: { $sum: "$amount" } } }]

Assuming the collection has documents: {category: "A", amount: 10}, {category: "B", amount: 5}, {category: "A", amount: 15}.

medium

A. [{_id: null, total: 30}]

B. [{_id: "A", total: 10}, {_id: "B", total: 5}, {_id: "A", total: 15}]

C. [{_id: "A", total: 15}, {_id: "B", total: 5}]

D. [{_id: "A", total: 25}, {_id: "B", total: 5}]

Solution

1. Step 1: Group documents by category

   Documents with category "A" are grouped together, and category "B" separately.

2. Step 2: Sum amounts per group

   For "A": 10 + 15 = 25; for "B": 5.

3. Final Answer:

   [{_id: "A", total: 25}, {_id: "B", total: 5}] -> Option D

4. Quick Check:

   Sum amounts by category = correct totals [OK]

Hint: Sum amounts grouped by _id field [OK]

Common Mistakes:

• Not grouping sums correctly
• Returning individual documents instead of grouped
• Using null _id which sums all documents

4. Identify the error in this aggregation pipeline for pre-aggregation:

[{ $group: { _id: "$category", total: { $sum: "amount" } } }]

medium

A. Missing $ before amount in $sum operator

B. Incorrect use of $group stage

C. Missing $match stage before $group

D. Using _id field incorrectly

Solution

1. Step 1: Check $sum operator syntax

   $sum requires a field path starting with $, but "amount" is missing the $.

2. Step 2: Confirm correct syntax

   It should be $sum: "$amount" to sum the amount field values.

3. Final Answer:

   Missing $ before amount in $sum operator -> Option A

4. Quick Check:

   $sum needs $field syntax [OK]

Hint: Field names in aggregation need $ prefix [OK]

Common Mistakes:

• Forgetting $ before field names in operators
• Assuming $match is mandatory before $group
• Misusing _id field in $group

5. You want to pre-aggregate sales data by month and product category, storing total sales and average price. Which aggregation pipeline correctly computes these values?

hard

A. [{ $group: { _id: { month: { $month: "$date" }, category: "$category" }, totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }]

B. [{ $group: { _id: "$date", totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }]

C. [{ $group: { _id: { month: "$date", category: "$category" }, totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }]

D. [{ $group: { _id: { month: { $month: "$date" }, category: "$category" }, totalSales: { $sum: "$price" }, avgPrice: { $avg: "$amount" } } }]

Solution

1. Step 1: Group by month and category correctly

   Use $month operator on "$date" to extract month, and include "$category" as second key.

2. Step 2: Compute total sales and average price correctly

   Sum the "$amount" field for totalSales and average the "$price" field for avgPrice.

3. Final Answer:

   [{ $group: { _id: { month: { $month: "$date" }, category: "$category" }, totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }] -> Option A

4. Quick Check:

   Group by month+category, sum amount, avg price [OK]

Hint: Use $month for date grouping and correct fields for sum/avg [OK]

Common Mistakes:

• Grouping by full date instead of month
• Swapping sum and avg fields
• Not using $month operator for month extraction