Aggregation for reporting dashboards in MongoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the MongoDB aggregation framework in reporting dashboards?

The aggregation framework processes data records and returns computed results, helping to summarize, group, and transform data for clear reporting dashboards.

beginner

Name three common stages used in MongoDB aggregation pipelines for dashboards.

Common stages include $match (filters data), $group (groups data and calculates aggregates), and $sort (orders results).

intermediate

How does the $group stage work in MongoDB aggregation?

The $group stage groups documents by a specified key and can calculate sums, averages, counts, and other aggregates for each group.

intermediate

Why is the $match stage important before grouping in aggregation pipelines?

Using $match early filters data to only relevant records, making grouping faster and results more accurate for dashboards.

intermediate

What does the $project stage do in an aggregation pipeline?

The $project stage reshapes each document by including, excluding, or adding new fields, helping format data for dashboard display.

Which aggregation stage filters documents in MongoDB?

A $group

B $project

C $sort

D $match

The $match stage filters documents based on specified criteria.

What does the $group stage typically do?

A Groups documents and calculates aggregates

B Filters documents

C Sorts documents

D Projects fields

The $group stage groups documents by a key and calculates aggregates like sum or average.

Why use $project in an aggregation pipeline?

A To filter documents

B To group documents

C To reshape documents by selecting or adding fields

D To join collections

$project reshapes documents by including, excluding, or adding fields.

Which stage should come first for better performance in dashboards?

A $match

B $sort

C $project

D $group

Filtering early with $match reduces data volume for later stages, improving performance.

What is the output of an aggregation pipeline?

A A single document

B Transformed and summarized documents

C Raw documents only

D Only counts

Aggregation pipelines output transformed and summarized data suitable for dashboards.

Explain how you would use MongoDB aggregation stages to create a sales summary dashboard.

Think about filtering first, then grouping, sorting, and finally shaping the output.

Describe the role of the $group stage and why it is important in reporting dashboards.

Focus on how grouping helps turn many records into useful summaries.

Practice

1. What is the main purpose of using the $group stage in a MongoDB aggregation pipeline for reporting dashboards?

easy

A. To add new fields to documents without changing existing ones

B. To combine documents and calculate summary values like totals or averages

C. To sort documents in ascending or descending order

D. To filter documents based on specific conditions

Solution

1. Step 1: Understand the role of $group

   The $group stage collects documents into groups and calculates summary statistics like sums or averages.

2. Step 2: Compare with other stages

   $match filters, $sort orders, and $addFields adds fields, but only $group aggregates data.

3. Final Answer:

   To combine documents and calculate summary values like totals or averages -> Option B

4. Quick Check:

   $group = summary aggregation [OK]

Hint: Remember $group is for summarizing data by categories [OK]

Common Mistakes:

• Confusing $group with $match filtering
• Thinking $group sorts data
• Assuming $group adds fields without aggregation

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

easy

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

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

C. db.collection.group({ status: 'active' })

D. db.collection.aggregateMatch({ status: 'active' })

Solution

1. Step 1: Identify the aggregation method

   The correct method to start aggregation is aggregate() with an array of stages.

2. Step 2: Check syntax correctness

   db.collection.aggregate([{ $match: { status: 'active' } }]) is valid; others use wrong methods or syntax.

3. Final Answer:

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

4. Quick Check:

   aggregate() starts pipeline [OK]

Hint: Use aggregate() with an array of stages to start [OK]

Common Mistakes:

• Using find() instead of aggregate() for pipelines
• Trying to use group() as a method
• Incorrect method names like aggregateMatch()

3. Given the collection sales with documents like { product: 'A', quantity: 5 } and { product: 'B', quantity: 3 }, what will this aggregation return?

db.sales.aggregate([
  { $group: { _id: '$product', totalQuantity: { $sum: '$quantity' } } },
  { $sort: { totalQuantity: -1 } }
])

medium

A. [{ _id: 'A', totalQuantity: 8 }]

B. [{ _id: 'B', totalQuantity: 3 }, { _id: 'A', totalQuantity: 5 }]

C. [{ _id: 'A', totalQuantity: 5 }, { _id: 'B', totalQuantity: 3 }]

D. SyntaxError due to missing fields

Solution

1. Step 1: Group by product and sum quantity

   The $group stage groups documents by product and sums their quantities: 'A' -> 5, 'B' -> 3.

2. Step 2: Sort by totalQuantity descending

   The $sort orders results by totalQuantity descending, so 'A' (5) comes before 'B' (3).

3. Final Answer:

   [{ _id: 'A', totalQuantity: 5 }, { _id: 'B', totalQuantity: 3 }] -> Option C

4. Quick Check:

   Group sums then sort desc = correct order [OK]

Hint: Group sums then sort descending by total [OK]

Common Mistakes:

• Mixing up sort order ascending vs descending
• Expecting combined total instead of grouped sums
• Thinking syntax error occurs

4. Identify the error in this aggregation pipeline:

db.orders.aggregate([
  { $match: { status: 'completed' } },
  { $group: { _id: '$customerId', total: { $sum: '$amount' } } },
  { $sort: { total: 1 } },
  { $project: { customerId: 1, total: 1 } }
])

medium

A. The $sort stage must come before $group

B. The $sum operator cannot be used inside $group

C. The $match stage should be after $group

D. The $project stage uses a field not present in the grouped output

Solution

1. Step 1: Check fields after $group

   After grouping by _id: '$customerId', the output fields are _id and total. The original customerId field is renamed to _id.

2. Step 2: Analyze $project stage

   The $project tries to include customerId, which does not exist; it should use _id instead.

3. Final Answer:

   The $project stage uses a field not present in the grouped output -> Option D

4. Quick Check:

   Use _id after $group, not original field [OK]

Hint: After $group, use _id field names in $project [OK]

Common Mistakes:

• Assuming original fields remain after $group
• Placing $sort before $group incorrectly
• Misusing $sum outside $group

5. You want to create a dashboard showing the average order amount per customer, but only for customers with more than 3 orders. Which aggregation pipeline correctly achieves this?

hard

A. [{ $group: { _id: '$customerId', avgAmount: { $avg: '$amount' }, count: { $sum: 1 } } }, { $match: { count: { $gt: 3 } } }]

B. [{ $match: { count: { $gt: 3 } } }, { $group: { _id: '$customerId', avgAmount: { $avg: '$amount' } } }]

C. [{ $group: { _id: '$customerId', avgAmount: { $avg: '$amount' } } }, { $match: { avgAmount: { $gt: 3 } } }]

D. [{ $group: { _id: '$customerId', avgAmount: { $avg: '$amount' } } }, { $sort: { avgAmount: -1 } }]

Solution

1. Step 1: Group by customer and calculate average and count

   Use $group to get average order amount and count of orders per customer.

2. Step 2: Filter customers with more than 3 orders

   Apply $match after grouping to keep only customers where count > 3.

3. Final Answer:

   [{ $group: { _id: '$customerId', avgAmount: { $avg: '$amount' }, count: { $sum: 1 } } }, { $match: { count: { $gt: 3 } } }] -> Option A

4. Quick Check:

   Group then filter by count > 3 [OK]

Hint: Filter after grouping to use aggregated counts [OK]

Common Mistakes:

• Filtering by count before grouping
• Filtering by avgAmount instead of count
• Missing count calculation in $group