Kafka (Optional)

Practice 48 Kafka (Optional) interview questions with detailed answers. Part of the Kafka track on Java Interview Hub.

• What is Apache Kafka?
• What are some common use cases of Kafka?
• How does Kafka differ from traditional messaging systems?
• What components make up the Kafka architecture?
• What is a Kafka Topic?
• How do you create a topic in Kafka?
• How can topics be partitioned and why is this important?
• What happens when a topic is replicated in Kafka?
• Explain the role of the Zookeeper in Kafka.
• Why is Zookeeper critical for Kafka?
• What would happen if Zookeeper were to fail?
• How does Kafka handle Zookeeper outages?
• What are Kafka Producers and Consumers?
• How do producers send data to Kafka?
• What are some of the strategies consumers use to read data from Kafka?
• How can consumer groups enhance the scalability of Kafka?
• Discuss how Kafka achieves fault tolerance.
• What is the role of replication in Kafka?
• How does Kafka ensure data is not lost?
• What is the significance of the "acknowledgement" setting in producers?
• Explain Kafka Streams and its use cases.
• What differentiates Kafka Streams from other stream processing libraries?
• How does Kafka Streams handle state?
• What are some of the challenges associated with using Kafka Streams?
• How do you secure a Kafka cluster?
• What security mechanisms are available in Kafka?
• How would you implement encryption in Kafka?
• What are the best practices for securing Kafka at scale?
• Discuss Kafka Connect.
• What is Kafka Connect and why is it useful?
• How do you scale Kafka Connect?
• What are some common issues you might encounter while using Kafka Connect?
• You have a Kafka topic with multiple partitions, and you need to ensure that messages with the same key are processed in the order they were sent. How do you achieve this?
• You notice that your Kafka consumers are lagging behind, unable to keep up with the rate at which messages are being produced. What steps would you take to address this issue?
• Your application requires exactly-once processing semantics. How do you configure Kafka to achieve this?
• You need to update the schema of the messages being produced to a Kafka topic without disrupting the existing consumers. How do you handle schema evolution in Kafka?
• Your application requires high availability and fault tolerance for the Kafka cluster. How do you configure Kafka to meet these requirements?
• How would you handle a situation where Kafka is causing message duplication due to consumer rebalancing or producer retries?
• What strategies would you use if your Kafka messages are larger than the default size limit (1 MB) and causing performance issues?
• How would you handle a situation where your Kafka consumer group is significantly lagging behind in consuming messages?
• What steps would you take to ensure high availability if a Kafka broker in a cluster fails unexpectedly?
• How do you ensure data consistency when multiple consumers are reading from the same Kafka topic?
• What are the key metrics you would monitor to ensure optimal Kafka cluster performance, and how would you troubleshoot issues like throughput drops?
• What happens if a Kafka partition leader fails, and how does Kafka handle leader election?
• Why does a consumer group sometimes take a long time to rebalance when a new consumer joins or leaves, and how would you reduce this time?
• Is it possible to lose data in Kafka despite having replication set up? If so, how?
• When would you prefer using a compacted topic over a regular topic, and what are the trade-offs?
• Kafka guarantees ordering of messages, but under what conditions could this guarantee be broken?