Resource planning and capacity in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the number of partitions for a Kafka topic.

Kafka

newTopic = NewTopic('my-topic', [1], replication_factor=1)

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

A3

B'3'

Cpartitions

Dreplication_factor

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of an integer for partitions.

Confusing replication_factor with partitions.

The number of partitions must be an integer, so 3 is correct.

2fill in blank

medium

Complete the code to configure the consumer group id.

Kafka

consumer = KafkaConsumer('my-topic', group_id=[1], bootstrap_servers=['localhost:9092'])

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

A'my-group'

B123

CNone

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number instead of a string for group_id.

Setting group_id to None disables consumer group behavior.

The group_id must be a string identifying the consumer group.

3fill in blank

hard

Fix the error in setting the retention time for a topic.

Kafka

topic_config = {'retention.ms': [1]

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

A60000ms

B'60000'

Cretention.ms

D60000

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of an integer for retention.ms.

Adding units like 'ms' inside the value.

Kafka expects retention.ms as an integer representing milliseconds.

4fill in blank

hard

Fill both blanks to create a producer with acks set to all and retries set to 5.

Kafka

producer = KafkaProducer(acks=[1], retries=[2])

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

A'all'

B3

C5

D'none'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number for acks instead of a string.

Setting retries as a string.

Setting acks to 'all' ensures all replicas acknowledge. Retries should be an integer like 5.

5fill in blank

hard

Fill all three blanks to create a topic with 4 partitions, replication factor 2, and cleanup policy set to compact.

Kafka

newTopic = NewTopic('[1]', [2], replication_factor=[3], topic_configs={'cleanup.policy': 'compact'})

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

Amy-topic

B4

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong data types for partitions or replication factor.

Confusing replication factor with partitions.

The topic name is 'my-topic', partitions are 4, and replication factor is 2 for fault tolerance.

Practice

1. What is the main goal of resource planning in Kafka?

easy

A. To reduce the number of topics in Kafka

B. To increase the size of Kafka messages

C. To limit the number of users accessing Kafka

D. To ensure Kafka can handle the expected data load smoothly

Solution

1. Step 1: Understand resource planning purpose

   Resource planning is about preparing Kafka to manage data efficiently without delays or failures.

2. Step 2: Identify the main goal

   The main goal is to handle the expected data load smoothly, avoiding bottlenecks or crashes.

3. Final Answer:

   To ensure Kafka can handle the expected data load smoothly -> Option D

4. Quick Check:

   Resource planning = handle data load smoothly [OK]

Hint: Resource planning means handling data load well [OK]

Common Mistakes:

• Confusing resource planning with limiting users
• Thinking it reduces topics or message sizes
• Ignoring the data load aspect

2. Which of the following is the correct way to increase Kafka capacity?

easy

A. Add more brokers to the Kafka cluster

B. Decrease the number of partitions per topic

C. Reduce the hardware resources of brokers

D. Limit the number of consumers

Solution

1. Step 1: Review capacity factors in Kafka

   Capacity depends on brokers, partitions, and hardware resources.

2. Step 2: Identify correct capacity increase method

   Adding more brokers increases cluster capacity by distributing load.

3. Final Answer:

   Add more brokers to the Kafka cluster -> Option A

4. Quick Check:

   More brokers = more capacity [OK]

Hint: Add brokers to increase capacity [OK]

Common Mistakes:

• Thinking decreasing partitions increases capacity
• Reducing hardware lowers capacity
• Limiting consumers does not increase capacity

3. Given a Kafka cluster with 3 brokers and 6 partitions per topic, what happens if you increase partitions to 12 without adding brokers?

medium

A. The number of consumers must be reduced

B. The cluster automatically adds brokers to handle the load

C. The load per broker increases, possibly causing performance issues

D. Kafka will reject the partition increase request

Solution

1. Step 1: Understand partition increase effect

   Increasing partitions without adding brokers means more load per broker.

2. Step 2: Analyze cluster behavior

   Kafka does not auto-add brokers; load per broker rises, risking performance issues.

3. Final Answer:

   The load per broker increases, possibly causing performance issues -> Option C

4. Quick Check:

   More partitions + same brokers = more load per broker [OK]

Hint: More partitions without brokers = more load per broker [OK]

Common Mistakes:

• Assuming Kafka auto-adds brokers
• Thinking consumers must be reduced
• Believing Kafka rejects partition increase

4. You have a Kafka cluster with 5 brokers but notice high CPU usage on one broker only. What is the likely cause?

medium

A. Uneven partition distribution causing load imbalance

B. All brokers have equal load by design

C. Kafka automatically balances CPU usage evenly

D. The broker hardware is faulty and cannot be fixed

Solution

1. Step 1: Understand load distribution in Kafka

   Partitions are assigned to brokers; uneven distribution causes load imbalance.

2. Step 2: Identify cause of high CPU on one broker

   Uneven partition assignment can overload one broker, causing high CPU usage.

3. Final Answer:

   Uneven partition distribution causing load imbalance -> Option A

4. Quick Check:

   Uneven partitions = uneven CPU load [OK]

Hint: Check partition distribution for load imbalance [OK]

Common Mistakes:

• Assuming Kafka balances CPU automatically
• Blaming hardware without checking partitions
• Thinking all brokers always have equal load

5. You want to plan capacity for a Kafka cluster expected to handle 1 million messages per second. Which combination is best to start with?

hard

A. 5 brokers with 5 partitions each and low memory hardware

B. 10 brokers with 100 partitions each and high-performance SSD storage

C. 2 brokers with 10 partitions each and standard HDD storage

D. 1 broker with 200 partitions and no replication

Solution

1. Step 1: Analyze message load requirements

   1 million messages per second is very high; needs many brokers and partitions.

2. Step 2: Choose hardware and partition setup

   High-performance SSDs and many brokers with many partitions support high throughput and reliability.

3. Final Answer:

   10 brokers with 100 partitions each and high-performance SSD storage -> Option B

4. Quick Check:

   High load needs many brokers, partitions, and fast storage [OK]

Hint: High load needs many brokers, partitions, and SSDs [OK]

Common Mistakes:

• Choosing too few brokers or partitions
• Ignoring storage speed impact
• Not planning for replication and reliability