Pipeline execution order matters in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start an aggregation pipeline with a match stage filtering documents where age is greater than 25.

MongoDB

db.users.aggregate([ { $[1]: { age: { $gt: 25 } } } ])

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

Amatch

Bgroup

Csort

Dproject

Attempts:

3 left

💡 Hint

Common Mistakes

Using $group instead of $match will cause an error because grouping requires a different structure.

Using $sort or $project here won't filter documents.

The $match stage filters documents based on the given condition. Here, it selects users older than 25.

2fill in blank

medium

Complete the code to sort the documents by score in descending order after filtering.

MongoDB

db.users.aggregate([ { $match: { active: true } }, { $[1]: { score: -1 } } ])

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

Aproject

Bgroup

Csort

Dlimit

Attempts:

3 left

💡 Hint

Common Mistakes

Using $group instead of $sort will aggregate documents, not order them.

Using $limit will restrict the number of documents but not sort them.

The $sort stage orders documents. Using { score: -1 } sorts by score descending.

3fill in blank

hard

Fix the error in the pipeline by placing the $project stage correctly to include only name and score fields.

MongoDB

db.users.aggregate([ { $[1]: { score: { $gt: 50 } } }, { $project: { name: 1, score: 1 } } ])

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

Agroup

Bmatch

Csort

Dlimit

Attempts:

3 left

💡 Hint

Common Mistakes

Placing $match after $project can cause unexpected results or errors.

Using $group here is incorrect because we want to filter, not aggregate.

The $match stage should come before $project to filter documents before selecting fields.

4fill in blank

hard

Fill both blanks to correctly filter active users and then group them by city counting the number of users per city.

MongoDB

db.users.aggregate([ { $[1]: { active: true } }, { $[2]: { _id: "$city", count: { $sum: 1 } } } ])

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

Amatch

Bproject

Cgroup

Dsort

Attempts:

3 left

💡 Hint

Common Mistakes

Using $project instead of $match will not filter documents.

Using $sort instead of $group will not aggregate counts.

First, $match filters active users. Then, $group groups by city and counts users.

5fill in blank

hard

Fill all three blanks to filter users older than 30, sort by age ascending, and project only name and age fields.

MongoDB

db.users.aggregate([ { $[1]: { age: { $gt: 30 } } }, { $[2]: { age: 1 } }, { $[3]: { name: 1, age: 1, _id: 0 } } ])

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

Amatch

Bsort

Cproject

Dgroup

Attempts:

3 left

💡 Hint

Common Mistakes

Changing the order of stages can lead to wrong results.

Using $group instead of $project will aggregate instead of selecting fields.

The pipeline first filters with $match, then sorts with $sort, and finally selects fields with $project.

Practice

1. In MongoDB aggregation pipelines, why does the order of stages matter?

easy

A. Because stages run in the order they are written, affecting results and performance

B. Because MongoDB runs all stages in parallel regardless of order

C. Because only the last stage affects the output

D. Because stages can be reordered automatically by MongoDB

Solution

1. Step 1: Understand pipeline execution

   MongoDB executes pipeline stages sequentially in the order they appear.

2. Step 2: Impact of order on results and performance

   Changing the order can change the output and how fast the query runs, especially filtering early helps performance.

3. Final Answer:

   Because stages run in the order they are written, affecting results and performance -> Option A

4. Quick Check:

   Pipeline order affects output and speed [OK]

Hint: Remember: pipeline stages run top to bottom [OK]

Common Mistakes:

• Thinking MongoDB runs stages in parallel
• Believing order does not affect output
• Assuming MongoDB reorders stages automatically

2. Which of the following is the correct syntax to start a MongoDB aggregation pipeline with a filter stage?

easy

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

B. db.collection.aggregate({ $match: { status: 'A' } })

C. db.collection.find({ $match: { status: 'A' } })

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

Solution

1. Step 1: Identify correct aggregation syntax

   The aggregate method takes an array of stages, each stage is an object with a stage operator like $match.

2. Step 2: Check each option

   db.collection.aggregate([{ $match: { status: 'A' } }]) correctly uses an array with $match stage. db.collection.aggregate({ $match: { status: 'A' } }) misses array brackets. db.collection.find({ $match: { status: 'A' } }) uses find() which is not aggregation. db.collection.aggregate([{ match: { status: 'A' } }]) misses $ in $match.

3. Final Answer:

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

4. Quick Check:

   Aggregation stages are in an array [OK]

Hint: Aggregation stages must be in an array [OK]

Common Mistakes:

• Omitting the array brackets around stages
• Using find() instead of aggregate() for pipelines
• Missing $ in stage operators like $match

3. Given this pipeline:

db.orders.aggregate([
  { $sort: { price: -1 } },
  { $match: { status: 'A' } }
])

What is the main issue with this pipeline?

medium

A. It sorts ascending instead of descending

B. It will cause a syntax error due to stage order

C. It will return no results because $match must come first

D. It filters after sorting, which is less efficient

Solution

1. Step 1: Analyze stage order

   The pipeline sorts all documents first, then filters with $match.

2. Step 2: Understand performance impact

   Filtering first reduces documents early, making sorting faster. Sorting before filtering wastes resources.

3. Final Answer:

   It filters after sorting, which is less efficient -> Option D

4. Quick Check:

   Filter early, then sort for best performance [OK]

Hint: Filter before sort to improve speed [OK]

Common Mistakes:

• Thinking stage order causes syntax errors
• Believing $match must always be first to work
• Confusing sort order direction

4. You wrote this pipeline but it returns an error:

db.sales.aggregate([
  { $group: { _id: "$item", total: { $sum: "$amount" } } },
  { $match: { $gt: ["$total", 100] } }
])

What is the likely cause?

medium

A. The $match stage cannot filter on fields created by $group

B. The $match stage should use $expr to filter on computed fields

C. The $match stage must come before $group to filter correctly

D. The $group stage syntax is incorrect

Solution

1. Step 1: Identify the invalid $match syntax

   The $match stage uses aggregation expression syntax { $gt: ["$total", 100] } directly, which is not valid query selector syntax.

2. Step 2: Use $expr for aggregation expressions

   To use expression operators like $gt with field paths and literals in array form, wrap in $expr: { $match: { $expr: { $gt: ["$total", 100] } } }.

3. Final Answer:

   The $match stage should use $expr to filter on computed fields -> Option B

4. Quick Check:

   $expr enables agg expressions in $match [OK]

Hint: Use $expr in $match after $group [OK]

Common Mistakes:

• Trying to filter grouped fields without $expr
• Placing $match before $group expecting same results
• Assuming $group syntax is wrong

5. You want to find the top 3 most expensive products with status 'A'. Which pipeline correctly applies the stages for best performance?

hard

A. db.products.aggregate([
{ $sort: { price: -1 } },
{ $limit: 3 },
{ $match: { status: 'A' } }
])

B. db.products.aggregate([
{ $limit: 3 },
{ $match: { status: 'A' } },
{ $sort: { price: -1 } }
])

C. db.products.aggregate([
{ $match: { status: 'A' } },
{ $sort: { price: -1 } },
{ $limit: 3 }
])

D. db.products.aggregate([
{ $match: { status: 'A' } },
{ $limit: 3 },
{ $sort: { price: -1 } }
])

Solution

1. Step 1: Filter early for performance

   Applying $match first reduces documents to only those with status 'A'.

2. Step 2: Sort and limit after filtering

   Sorting after filtering is faster, then $limit picks top 3.

3. Step 3: Check other options

   db.products.aggregate([ { $sort: { price: -1 } }, { $limit: 3 }, { $match: { status: 'A' } } ]) sorts all documents before filtering, wasting resources. db.products.aggregate([ { $limit: 3 }, { $match: { status: 'A' } }, { $sort: { price: -1 } } ]) limits before filtering, wrong results. db.products.aggregate([ { $match: { status: 'A' } }, { $limit: 3 }, { $sort: { price: -1 } } ]) limits before sorting, wrong order.

4. Final Answer:

   db.products.aggregate([ { $match: { status: 'A' } }, { $sort: { price: -1 } }, { $limit: 3 } ]) -> Option C

5. Quick Check:

   Filter, then sort, then limit [OK]

Hint: Filter first, then sort, then limit [OK]

Common Mistakes:

• Sorting before filtering wastes resources
• Limiting before sorting gives wrong top results
• Filtering after limiting misses correct documents