Why performance tuning matters in MongoDB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find all documents in the 'users' collection.

MongoDB

db.users.[1]()

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

Afind

Binsert

Cupdate

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using insert() instead of find()

Using update() when you want to read data

The find() method retrieves documents from a collection. Here, it fetches all documents from 'users'.

2fill in blank

medium

Complete the code to find users older than 25.

MongoDB

db.users.find({ age: { [1]: 25 } })

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

A$gt

B$eq

C$ne

D$lt

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt (less than) instead of $gt

Using $eq which means equal

The $gt operator means 'greater than'. So this query finds users with age greater than 25.

3fill in blank

hard

Fix the error in the query to find users with name 'Alice'.

MongoDB

db.users.find({ name: [1] })

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

A{name: 'Alice'}

BAlice

C"Alice"

D'Alice'

Attempts:

3 left

💡 Hint

Common Mistakes

Not quoting the string

Using an object instead of a string

String values in MongoDB queries must be quoted. Using 'Alice' correctly specifies the string.

4fill in blank

hard

Fill both blanks to find users with age between 20 and 30.

MongoDB

db.users.find({ age: { [1]: 20, [2]: 30 } })

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

A$gte

B$lt

C$lte

D$gt

Attempts:

3 left

💡 Hint

Common Mistakes

Using $gt and $lt which exclude 20 and 30

Mixing up the operators

$gte means 'greater than or equal to' and $lte means 'less than or equal to'. Together they find ages between 20 and 30 inclusive.

5fill in blank

hard

Fill all three blanks to create an index on the 'email' field in ascending order.

MongoDB

db.users.createIndex({ [1]: [2] }, { unique: [3] })

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

Aemail

B1

Ctrue

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 instead of 1 for ascending

Setting unique to false or 0

Creating an index on 'email' with ascending order (1) and setting unique to true ensures no duplicate emails.

Practice

1. Why is performance tuning important in MongoDB databases?

easy

A. It helps queries run faster and reduces resource use.

B. It increases the size of the database automatically.

C. It deletes old data to save space.

D. It changes the database schema without user input.

Solution

1. Step 1: Understand performance tuning purpose

   Performance tuning aims to make database operations faster and more efficient.

2. Step 2: Identify the correct benefit in MongoDB context

   Faster queries and less resource use directly improve application speed and cost.

3. Final Answer:

   It helps queries run faster and reduces resource use. -> Option A

4. Quick Check:

   Performance tuning = faster queries [OK]

Hint: Performance tuning improves speed and resource use [OK]

Common Mistakes:

• Confusing tuning with data deletion
• Thinking tuning changes schema automatically
• Believing tuning increases database size

2. Which of the following is the correct way to create an index on the username field in MongoDB?

easy

A. db.collection.index({username: 'asc'})

B. db.collection.create_index('username')

C. db.collection.createIndex({username: 1})

D. db.collection.addIndex(username)

Solution

1. Step 1: Recall MongoDB index creation syntax

   MongoDB uses createIndex with a document specifying fields and sort order (1 for ascending).

2. Step 2: Match syntax with options

   db.collection.createIndex({username: 1}) matches the correct syntax: db.collection.createIndex({username: 1}).

3. Final Answer:

   db.collection.createIndex({username: 1}) -> Option C

4. Quick Check:

   Correct index syntax = db.collection.createIndex({username: 1}) [OK]

Hint: Use createIndex with field and 1 or -1 [OK]

Common Mistakes:

• Using wrong method names like create_index
• Passing field name as string without object
• Using addIndex which does not exist

3. Given the following MongoDB query, what is the expected result?

db.users.find({age: {$gt: 30}}).explain('executionStats')

What does the executionStats output help you understand?

medium

A. It creates an index on the age field automatically.

B. It shows how many documents matched and how long the query took.

C. It changes the age field to greater than 30.

D. It deletes documents older than 30 years.

Solution

1. Step 1: Understand the purpose of explain('executionStats')

   This command returns detailed info about query performance, including matched documents and time taken.

2. Step 2: Identify what the output shows

   The output helps analyze query efficiency, not modify data or create indexes.

3. Final Answer:

   It shows how many documents matched and how long the query took. -> Option B

4. Quick Check:

   Explain executionStats = query performance details [OK]

Hint: Explain shows query stats, not data changes [OK]

Common Mistakes:

• Thinking explain modifies data
• Assuming explain creates indexes
• Confusing query filter with data update

4. You wrote this MongoDB query to find users older than 25:

db.users.find({age: {$gt: 25}})

But it runs very slowly. What is the most likely reason?

medium

A. The age field is not indexed.

B. The query syntax is incorrect.

C. MongoDB does not support the $gt operator.

D. The collection name is wrong.

Solution

1. Step 1: Check query syntax correctness

   The syntax is correct; $gt is a valid operator and collection name seems fine.

2. Step 2: Identify performance cause

   Without an index on age, MongoDB must scan all documents, causing slow queries.

3. Final Answer:

   The age field is not indexed. -> Option A

4. Quick Check:

   Missing index = slow query [OK]

Hint: Slow queries often mean missing indexes [OK]

Common Mistakes:

• Blaming syntax when it's correct
• Thinking $gt is unsupported
• Assuming wrong collection name without checking

5. You have a large MongoDB collection with millions of documents. You want to improve query speed for searches on email and status. Which approach best improves performance?

hard

A. Remove all indexes to speed up writes.

B. Create separate indexes on email and status fields.

C. Store email and status in a single string field.

D. Create a compound index on {email: 1, status: 1}.

Solution

1. Step 1: Understand compound vs separate indexes

   A compound index on both fields allows efficient queries filtering on both email and status together.

2. Step 2: Evaluate options for best performance

   Separate indexes help single-field queries but compound index is better for combined filters. Storing combined fields as string or removing indexes harms performance.

3. Final Answer:

   Create a compound index on {email: 1, status: 1}. -> Option D

4. Quick Check:

   Compound index = best multi-field query speed [OK]

Hint: Use compound indexes for multi-field queries [OK]

Common Mistakes:

• Using separate indexes for combined queries
• Combining fields into one string reduces query flexibility
• Removing indexes slows queries