Message brokers (Kafka, RabbitMQ) in Microservices - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the message broker type in the configuration.

Microservices

config = {"broker": "[1]"}

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

AMySQL

BRedis

CPostgreSQL

DKafka

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a database instead of a message broker.

The configuration must specify the message broker type. Kafka is a popular message broker used in microservices.

2fill in blank

medium

Complete the code to create a durable queue in RabbitMQ.

Microservices

channel.queue_declare(queue='task_queue', durable=[1])

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

A0

BFalse

CTrue

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using False which creates a non-durable queue.

Setting durable=True ensures the queue survives broker restarts, which is important for reliable message delivery.

3fill in blank

hard

Fix the error in the Kafka consumer group configuration.

Microservices

consumer = KafkaConsumer('my_topic', group_id='[1]')

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

Amy_group

B''

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using None or empty string as group_id.

The group_id must be a non-empty string to identify the consumer group. 'my_group' is a valid group id.

4fill in blank

hard

Fill both blanks to configure a Kafka topic with 3 partitions and replication factor 2.

Microservices

topic_config = {'partitions': [1], 'replication_factor': [2]

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

A3

B1

C2

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up partitions and replication factor values.

Kafka topics are configured with the number of partitions and replication factor. Here, 3 partitions and replication factor 2 ensure scalability and fault tolerance.

5fill in blank

hard

Fill all three blanks to create a RabbitMQ exchange with type 'direct', durable true, and auto_delete false.

Microservices

channel.exchange_declare(exchange='logs', exchange_type='[1]', durable=[2], auto_delete=[3])

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

Afanout

Bdirect

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong exchange type or wrong boolean values.

The exchange type 'direct' routes messages with exact matching routing keys. Durable true keeps the exchange after broker restarts. Auto_delete false prevents automatic deletion when no queues are bound.

Practice

1. What is the primary role of a message broker like Kafka or RabbitMQ in a microservices architecture?

easy

A. To store large amounts of user data permanently

B. To enable services to communicate asynchronously by passing messages

C. To replace the database in microservices

D. To directly execute business logic in services

Solution

1. Step 1: Understand message broker function

   Message brokers act as middlemen that help services send and receive messages without waiting for each other.

2. Step 2: Identify correct role in microservices

   They enable asynchronous communication, improving scalability and fault tolerance.

3. Final Answer:

   To enable services to communicate asynchronously by passing messages -> Option B

4. Quick Check:

   Message broker = asynchronous communication [OK]

Hint: Message brokers pass messages between services asynchronously [OK]

Common Mistakes:

• Confusing brokers with databases
• Thinking brokers execute business logic
• Assuming brokers store permanent user data

2. Which of the following is the correct way to declare a RabbitMQ queue in code?

easy

A. channel.queueDeclare('task_queue', true, false, false, null);

B. channel.createQueue('task_queue', durable=true);

C. queue.declare('task_queue', persistent=True);

D. rabbitmq.queue('task_queue', durable=True);

Solution

1. Step 1: Recall RabbitMQ queue declaration syntax

   In RabbitMQ Java client, channel.queueDeclare is used with parameters: queue name, durable, exclusive, autoDelete, and arguments.

2. Step 2: Match correct syntax

   channel.queueDeclare('task_queue', true, false, false, null); matches the official method signature and parameter order correctly.

3. Final Answer:

   channel.queueDeclare('task_queue', true, false, false, null); -> Option A

4. Quick Check:

   RabbitMQ queueDeclare syntax = channel.queueDeclare('task_queue', true, false, false, null); [OK]

Hint: Remember RabbitMQ uses channel.queueDeclare with 5 parameters [OK]

Common Mistakes:

• Using incorrect method names like createQueue
• Passing parameters with wrong names or order
• Confusing RabbitMQ syntax with other brokers

3. Given the following Kafka consumer code snippet, what will be the output if the topic has 3 messages and auto-commit is enabled?

consumer.subscribe(['orders'])
for message in consumer.poll(timeout_ms=1000).values():
    print(message.value.decode('utf-8'))

medium

A. Prints nothing because poll returns a dict of lists

B. Prints only the first message and stops

C. Prints all 3 messages from the 'orders' topic

D. Raises an error due to wrong method usage

Solution

1. Step 1: Analyze Kafka consumer.poll() return type

   The poll() method returns a dictionary where keys are partitions and values are lists of messages.

2. Step 2: Understand iteration over poll().values()

   Iterating over values() gives lists of messages, not individual messages, so calling message.value will cause an error because message is a list, not a message object.

3. Final Answer:

   Raises an error due to wrong method usage -> Option D

4. Quick Check:

   poll() returns dict of lists; iterating directly over values and accessing message.value causes error [OK]

Hint: poll() returns dict of lists, not single messages [OK]

Common Mistakes:

• Assuming poll() returns a flat list of messages
• Not decoding message values properly
• Ignoring that poll() returns per-partition batches

4. A developer wrote this RabbitMQ consumer code but it never receives messages:

channel.basicConsume('task_queue', autoAck=False, callback=process_message)

What is the likely issue?

medium

A. The consumer must call channel.start_consuming() to begin receiving messages

B. The callback function name should be 'on_message' instead of 'process_message'

C. autoAck must be set to True for messages to be received

D. The queue name 'task_queue' is invalid and must be changed

Solution

1. Step 1: Understand RabbitMQ consumer lifecycle

   After setting up basicConsume, the consumer must start the event loop with channel.start_consuming() to receive messages.

2. Step 2: Identify missing call

   The code lacks start_consuming(), so no messages are delivered.

3. Final Answer:

   The consumer must call channel.start_consuming() to begin receiving messages -> Option A

4. Quick Check:

   Missing start_consuming() = The consumer must call channel.start_consuming() to begin receiving messages [OK]

Hint: Remember to call start_consuming() after basicConsume [OK]

Common Mistakes:

• Thinking callback function name must be fixed
• Believing autoAck controls message receipt
• Assuming queue name is invalid without evidence

5. You need to design a scalable order processing system using Kafka. Which approach best ensures message order per customer while allowing parallel processing across customers?

hard

A. Use a single Kafka partition for all orders to keep global order

B. Use multiple topics, one per customer, to isolate order streams

C. Partition messages by customer ID so each customer's orders stay ordered in their partition

D. Send all orders to a single consumer instance to maintain order

Solution

1. Step 1: Understand Kafka partitioning and ordering

   Kafka guarantees order only within a partition, so to keep order per customer, messages must be partitioned by customer ID.

2. Step 2: Evaluate options for scalability and ordering

   Partitioning by customer ID allows parallel processing across partitions (customers) while preserving order per customer.

3. Final Answer:

   Partition messages by customer ID so each customer's orders stay ordered in their partition -> Option C

4. Quick Check:

   Partition by key for order + parallelism = Partition messages by customer ID so each customer's orders stay ordered in their partition [OK]

Hint: Partition by customer ID to keep order and scale processing [OK]

Common Mistakes:

• Using single partition limits scalability
• Creating many topics adds unnecessary complexity
• Using single consumer blocks parallelism