Kafka vs RabbitMQ vs Redis Pub/Sub - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Primary Use Case Differences

Which of the following best describes the primary use case difference between Kafka, RabbitMQ, and Redis Pub/Sub?

A Kafka is used for web hosting, RabbitMQ for file storage, Redis Pub/Sub for image processing.

B Kafka is mainly for caching, RabbitMQ for database storage, Redis Pub/Sub for batch processing.

C Kafka is designed for high-throughput event streaming, RabbitMQ for complex routing and message queuing, Redis Pub/Sub for lightweight real-time messaging without persistence.

D Kafka is a relational database, RabbitMQ is a web server, Redis Pub/Sub is a programming language.

💡 Hint

Think about what each system is optimized to do in messaging and data handling.

Kafka is optimized for streaming large volumes of data with durability. RabbitMQ excels at complex routing and guaranteed delivery. Redis Pub/Sub is fast but does not store messages.

❓ Predict Output

intermediate

Message Persistence Behavior

Given the following scenario, which system will retain messages if the consumer is offline and reconnects later?

Kafka

Scenario: A producer sends messages continuously. The consumer disconnects for 10 minutes and then reconnects.

A Kafka will retain messages and deliver them when the consumer reconnects.

B RabbitMQ will not retain messages if the consumer is offline.

C Redis Pub/Sub will retain messages and deliver them when the consumer reconnects.

D None of the systems retain messages for offline consumers.

💡 Hint

Consider which systems support message durability and replay.

Kafka stores messages on disk and allows consumers to read from any offset, so messages are retained. Redis Pub/Sub does not store messages, so offline consumers miss messages. RabbitMQ can retain messages if configured with durable queues, but by default messages may be lost if the consumer is offline.

🔧 Debug

advanced

Unexpected Message Loss in Redis Pub/Sub

You implemented a Redis Pub/Sub system but notice that some messages are lost when subscribers are disconnected briefly. What is the root cause?

A Redis Pub/Sub uses a message broker that crashed.

B Redis Pub/Sub does not queue messages; if a subscriber is offline, it misses messages sent during that time.

C Redis Pub/Sub stores messages on disk but the disk is full.

D Redis Pub/Sub requires explicit message acknowledgments to prevent loss.

💡 Hint

Think about how Redis Pub/Sub handles message delivery and subscriber state.

Redis Pub/Sub delivers messages only to currently connected subscribers. It does not store or queue messages, so any subscriber disconnected during message publishing will miss those messages.

📝 Syntax

advanced

Kafka Consumer Group Configuration

Which Kafka consumer configuration option ensures that multiple consumers share the load of a topic's partitions?

A Setting 'auto.offset.reset' to 'latest'.

B Setting different 'group.id' values for each consumer.

C Setting 'enable.auto.commit' to false.

D Setting the same 'group.id' for all consumers to form a consumer group.

💡 Hint

Consumer groups allow load balancing across consumers.

Consumers with the same 'group.id' form a group and share partitions of the topic, enabling parallel processing. Different 'group.id's mean independent consumers each get all messages.

🚀 Application

expert

Choosing the Right System for Real-Time Analytics

You need to build a real-time analytics pipeline that processes millions of events per second with guaranteed message delivery and the ability to replay events for debugging. Which system is the best choice?

A Kafka, because it supports high throughput, durability, and replayable event streams.

B RabbitMQ, because it is optimized for lightweight real-time messaging without persistence.

C Redis Pub/Sub, because it stores all messages and supports complex routing.

D None of these systems can handle real-time analytics at scale.

💡 Hint

Consider throughput, durability, and replay features.

Kafka is designed for high-throughput event streaming with durable storage and allows consumers to replay events from any point, making it ideal for real-time analytics pipelines.