Aggregation for reporting dashboards in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group documents by the "category" field.

MongoDB

db.sales.aggregate([{ $group: { _id: "$"[1] } }])

Drag options to blanks, or click blank then click option'

Acategory

Bdate

Camount

Dregion

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting the dollar sign before the field name.

Using a field that doesn't exist in the documents.

The $group stage groups documents by the specified field. Here, grouping by category collects sales per category.

2fill in blank

medium

Complete the code to calculate the total sales amount per category.

MongoDB

db.sales.aggregate([{ $group: { _id: "$category", totalSales: { $[1]: "$amount" } } }])

Drag options to blanks, or click blank then click option'

Aavg

Bsum

Cmax

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using $avg instead of $sum for totals.

Missing the dollar sign before the operator name.

The $sum operator adds up all values of the specified field, here summing amount per category.

3fill in blank

hard

Fix the error in the pipeline to filter sales greater than 100 before grouping.

MongoDB

db.sales.aggregate([{ $match: { amount: { $[1]: 100 } } }, { $group: { _id: "$category", totalSales: { $sum: "$amount" } } }])

Drag options to blanks, or click blank then click option'

Aeq

Blt

Cgt

Dne

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt which means less than.

Using $eq which means equal to.

The $gt operator filters documents where amount is greater than 100.

4fill in blank

hard

Fill both blanks to calculate average sales per region for sales above 50.

MongoDB

db.sales.aggregate([{ $match: { amount: { $[1]: 50 } } }, { $group: { _id: "$"[2], avgSales: { $avg: "$amount" } } }])

Drag options to blanks, or click blank then click option'

Agt

Blt

Cregion

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt instead of $gt for filtering.

Grouping by category instead of region.

First, filter sales with $gt 50, then group by region to find average sales per region.

5fill in blank

hard

Fill all three blanks to create a pipeline that filters sales in 2023, groups by product, and counts sales.

MongoDB

db.sales.aggregate([{ $match: { year: [1] } }, { $group: { _id: "$"[2], count: { $[3]: 1 } } }])

Drag options to blanks, or click blank then click option'

A2023

Bproduct

Csum

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string '2023' instead of number 2023.

Grouping by category instead of product.

Using $avg instead of $sum for counting.

The pipeline filters documents where year is 2023, groups by product, and counts sales by summing 1 for each document.

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