Why topics organize messages in Kafka - See It in Action

Understanding Kafka Topics and Message Organization

📖 Scenario: You are working on a messaging system where different types of messages need to be organized clearly. Kafka uses topics to keep messages grouped by their purpose or category.

🎯 Goal: Learn how Kafka topics organize messages so that producers send messages to the right place and consumers read only the messages they need.

📋 What You'll Learn

Create a Kafka topic named orders to hold order messages

Create a Kafka topic named payments to hold payment messages

Write a simple producer code snippet to send a message to the orders topic

Write a simple consumer code snippet to read messages from the orders topic

💡 Why This Matters

🌍 Real World

In real systems, topics help separate different kinds of data like orders, payments, and user actions so they don't get mixed up.

💼 Career

Understanding Kafka topics is important for roles in data engineering, backend development, and system architecture where message streaming is used.

1

Create Kafka topics for message organization

Write the Kafka commands to create two topics named orders and payments.

Kafka

# Write Kafka CLI commands to create topics 'orders' and 'payments'
# Your code here

Hint

Use kafka-topics --create command with --topic option to create each topic.

2

Set up a producer to send messages to the orders topic

Write a Kafka producer command to send the message 'Order123 placed' to the orders topic.

Kafka

kafka-topics --create --topic orders --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
kafka-topics --create --topic payments --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
# Write Kafka producer command to send 'Order123 placed' to 'orders' topic
# Your code here

Hint

Use kafka-console-producer with --topic orders and send the message using input redirection.

3

Set up a consumer to read messages from the orders topic

Write a Kafka consumer command to read messages from the orders topic starting from the beginning.

Kafka

kafka-topics --create --topic orders --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
kafka-topics --create --topic payments --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
echo 'Order123 placed' | kafka-console-producer --topic orders --bootstrap-server localhost:9092
# Write Kafka consumer command to read from 'orders' topic from the beginning
# Your code here

Hint

Use kafka-console-consumer with --topic orders and --from-beginning to read all messages.

4

Display the message read from the orders topic

Run the consumer command and write the exact output it shows for the message sent to the orders topic.

Kafka

kafka-topics --create --topic orders --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
kafka-topics --create --topic payments --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1
echo 'Order123 placed' | kafka-console-producer --topic orders --bootstrap-server localhost:9092
kafka-console-consumer --topic orders --bootstrap-server localhost:9092 --from-beginning --max-messages 1
# Write the exact output shown by the consumer
# Your code here

Hint

The consumer will print the message exactly as sent: Order123 placed.

Practice

1. Why does Kafka use topics to organize messages?

easy

A. To group related messages together for easier management

B. To store messages permanently on the producer side

C. To encrypt messages automatically

D. To limit the number of messages sent

Solution

1. Step 1: Understand the purpose of topics in Kafka

   Topics are used to group messages that belong to the same category or stream.

2. Step 2: Identify the role of topics in message management

   Grouping messages helps producers and consumers handle data efficiently and logically.

3. Final Answer:

   To group related messages together for easier management -> Option A

4. Quick Check:

   Topics group messages = A [OK]

Hint: Topics group messages logically for easy handling [OK]

Common Mistakes:

• Thinking topics store messages on producer side
• Confusing topics with encryption features
• Believing topics limit message quantity

2. Which of the following is the correct way to create a topic in Kafka using the command line?

easy

A. kafka topic create myTopic --partitions 3

B. kafka-topics --create --topic myTopic --partitions 3 --replication-factor 1 --bootstrap-server localhost:9092

C. create kafka topic myTopic partitions=3

D. kafka-create-topic --name myTopic --partitions 3

Solution

1. Step 1: Recall the Kafka CLI syntax for topic creation

   The correct command uses 'kafka-topics' with '--create' and required flags.

2. Step 2: Match the command with the correct options

   kafka-topics --create --topic myTopic --partitions 3 --replication-factor 1 --bootstrap-server localhost:9092 matches the official syntax including bootstrap-server, partitions, and replication-factor.

3. Final Answer:

   kafka-topics --create --topic myTopic --partitions 3 --replication-factor 1 --bootstrap-server localhost:9092 -> Option B

4. Quick Check:

   Correct Kafka topic creation syntax = D [OK]

Hint: Use kafka-topics with --create and required flags [OK]

Common Mistakes:

• Using incorrect command names like kafka-create-topic
• Omitting bootstrap-server flag
• Wrong order or missing required flags

3. Given a Kafka topic with 3 partitions, what happens when a producer sends 6 messages without specifying a key?

medium

A. Messages are sent randomly to any partition

B. All messages go to the first partition

C. Messages are distributed evenly across the 3 partitions

D. Messages are rejected because no key is provided

Solution

1. Step 1: Understand default partitioning behavior without a key

   When no key is provided, Kafka uses round-robin to distribute messages evenly across partitions.

2. Step 2: Apply this to 6 messages and 3 partitions

   Messages will be sent 2 per partition, balancing the load.

3. Final Answer:

   Messages are distributed evenly across the 3 partitions -> Option C

4. Quick Check:

   No key means round-robin distribution = A [OK]

Hint: No key means even round-robin distribution [OK]

Common Mistakes:

• Assuming all messages go to one partition
• Thinking messages are rejected without a key
• Believing distribution is random without pattern

4. You have a Kafka topic with 2 partitions but your consumer only reads from partition 0. What is the likely problem?

medium

A. Kafka does not support multiple partitions

B. The topic only has one partition

C. The producer is sending messages only to partition 1

D. The consumer is not configured to subscribe to all partitions

Solution

1. Step 1: Analyze consumer subscription behavior

   If a consumer reads only from partition 0, it likely subscribed only to that partition explicitly.

2. Step 2: Check topic partition count and producer behavior

   The topic has 2 partitions, so consumer should subscribe to both to read all messages.

3. Final Answer:

   The consumer is not configured to subscribe to all partitions -> Option D

4. Quick Check:

   Consumer subscription controls partitions read = C [OK]

Hint: Consumer must subscribe to all partitions to read all messages [OK]

Common Mistakes:

• Assuming topic has only one partition
• Believing Kafka does not support multiple partitions
• Thinking producer controls consumer reading partitions

5. How can partitioning in Kafka topics improve performance when handling large message streams?

hard

A. By allowing parallel processing of messages across multiple consumers

B. By encrypting messages faster

C. By reducing the total number of messages sent

D. By storing messages only on the producer side

Solution

1. Step 1: Understand the role of partitions in Kafka topics

   Partitions split a topic into multiple parts that can be processed independently.

2. Step 2: Connect partitions to performance benefits

   Multiple consumers can read from different partitions in parallel, increasing throughput and speed.

3. Final Answer:

   By allowing parallel processing of messages across multiple consumers -> Option A

4. Quick Check:

   Partitions enable parallelism = B [OK]

Hint: Partitions enable parallel consumer processing [OK]

Common Mistakes:

• Confusing partitioning with encryption
• Thinking partitioning reduces message count
• Believing messages are stored only on producer