Here’s a compilation of 50 basic and advanced Kafka interview questions and answers to help you prepare:
1. What is Kafka ?
Kafka is a distributed event-streaming platform developed by Apache Software Foundation. It is designed to handle real-time data feeds with high throughput, fault tolerance, and scalability. Kafka has become a popular tool for building data pipelines, stream processing applications, and real-time data analytics.
2. Kafka Internal Architecture ?
3. Key Features of Kafka ?
- Distributed System: Kafka is a distributed platform with multiple brokers (nodes) that work together to handle large-scale data.
Ensures fault tolerance and scalability. - Publish-Subscribe Messaging:
Kafka follows the pub-sub model where producers publish data to topics, and consumers subscribe to those topics to read data. - Durable and Fault-Tolerant:Kafka persists messages to disk, ensuring durability.
It replicates data across brokers, ensuring fault tolerance.
High Throughput:Capable of processing millions of messages per second with low latency. - Real-Time Stream Processing:
Works seamlessly with frameworks like Apache Spark, Apache Flink, or Kafka Streams for real-time data processing. - Decoupled Producers and Consumers:
Producers and consumers are independent, enabling flexibility in data flow design.
4. Key Components of Kafka ?
Apache Kafka is a distributed event-streaming platform with several key components, each playing a critical role in its architecture. Here’s an overview of Kafka’s components:
- Topics
- Partitions
- Producers
- Consumers
- Consumer Groups
- Brokers
- Cluster
- KRaft (Kafka Raft)
- Replication
- Logs
- Offset
- Producers’ and Consumers’ APIs
5. What are the main use cases of Kafka?
- Real-time data streaming (e.g., activity tracking, user analytics).
- Log aggregation (e.g., centralizing logs from multiple sources).
- Event sourcing (e.g., capturing state changes).
- Data integration pipelines (e.g., ETL pipelines).
- Message queuing (e.g., decoupling producers and consumers).
6. What is a Kafka topic, and how is it partitioned?
A topic is a logical category or stream where records are published. Each topic is divided into partitions, which are distributed across brokers.
Partitions enable parallelism by allowing multiple producers and consumers to write and read simultaneously.
7. What is the role of a Kafka partition?
Partitions allow Kafka to distribute messages across brokers for scalability.
Each partition is an append-only log where messages are stored sequentially.
Consumers within a group are assigned specific partitions for parallel consumption.
8. Explain Kafka’s message delivery semantics ?
Kafka supports three message delivery guarantees:
At-most-once: Messages may be lost but are never redelivered.
At-least-once: Messages are never lost but may be redelivered.
Exactly-once: Messages are neither lost nor redelivered (requires idempotent producer and transactional semantics).
9. What are Kafka’s best practices for production?
Use replication for fault tolerance (minimum replication factor of 3).
Monitor consumer lag to ensure consumers are processing data.
Set appropriate retention policies for topics.
Use compression (e.g., gzip) to reduce data transfer costs.
Secure Kafka with SSL and SASL for encryption and authentication.
10. How do you monitor Kafka in production?
Use metrics exposed via JMX (e.g., producer/consumer throughput, broker health).
Integrate with monitoring tools like Prometheus, Grafana, or Datadog.
Track lag using consumer offset monitoring tools (e.g., Burrow).
Monitor Lag: Use tools like Kafka Metrics or monitoring systems (e.g., Prometheus, Datadog) to track and adjust.
11. How would you handle a large backlog of messages?
Scale Consumers: Increase the number of consumers in the group to process messages in parallel.
Optimize Batching: Increase fetch.max.bytes and max.poll.records to fetch more messages per poll.
12. How would you ensure exactly-once processing in Kafka?
Use idempotent producers (set enable.idempotence=true).
Enable Kafka transactions for atomic writes across partitions or topics.
Use a stateful consumer framework like Kafka Streams or frameworks supporting checkpointing (e.g., Flink).
13. What happens if a Kafka broker fails?
Partitions on the failed broker are reassigned to another broker that contains replicas.
A new leader is elected for each affected partition from the ISR.
Producers and consumers continue working with the new leader.
14. How does Kafka handle backpressure?
Kafka consumers can control the rate of data processing by:
Adjusting fetch.max.bytes and fetch.min.bytes to control the batch size.
Using pause() and resume() methods to temporarily stop or resume fetching.
15. What is the ISR (In-Sync Replica) in Kafka?
ISR is a list of replicas that are fully synchronized with the partition leader. For a message to be considered committed, it must be replicated to all ISRs.
16. How do Kafka consumers achieve load balancing?
Consumers in the same consumer group coordinate to ensure that each partition is consumed by only one consumer at a time. Kafka rebalances partitions among consumers when:
A consumer joins or leaves the group.
A partition count changes.
17. How does Kafka achieve high throughput?
Partitioning enables parallelism.
Batching and compression reduce network overhead.
Sequential writes to disk optimize I/O.
Zero-copy technology minimizes CPU usage.
Efficient replication ensures fast failover without slowing down performance.
18. What is Kafka Streams?
Kafka Streams is a Java library for building real-time stream-processing applications on top of Kafka. It allows developers to process data directly from Kafka topics, perform transformations, and write results back to Kafka or other systems.
19. What is ZooKeeper’s role in Kafka?
In Kafka (pre-2.8), ZooKeeper manages metadata, including:
Broker registration and leadership election.
Partition and topic configurations.
Keeping track of in-sync replicas.
Cluster state and coordination.
Note: Kafka now supports a ZooKeeper-free mode using KRaft
20. What is the difference between Kafka and traditional messaging systems?
Distributed Architecture: Kafka is designed for high throughput with distributed brokers.
Retention: Kafka stores messages for a configurable period, unlike traditional systems that delete messages after delivery.
Consumer Model: Kafka allows multiple consumers to read independently from the same topic using consumer groups.
Log-Based: Kafka uses a log-based storage model for durability and replayability.
21. What is Kafka Retention Policy?
Kafka retains messages for a configurable period or until the log size reaches a configured limit, regardless of consumer acknowledgment.
22. What is Kafka’s Exactly Once Semantics (EOS)?
Kafka EOS ensures that each message is processed exactly once in a fault-tolerant manner. This is achieved through idempotence and transactions.
23. What are Kafka’s Key Configuration Settings?
Producer:
linger.ms, batch.size, acks, retries.
Consumer:
auto.offset.reset, enable.auto.commit, max.poll.records.
Broker:
log.retention.hours, num.partitions, default.replication.factor.
24. What are Kafka Alternatives?
– RabbitMQ
– ActiveMQ
– Pulsar
– Kinesis (AWS)
– Azure Event Hubs
25. How does Kafka ensure fault tolerance?
Kafka uses replication to ensure fault tolerance. Each partition has a replication factor, meaning it is copied to multiple brokers.
If a broker fails, the leader role for the partition is transferred to another in-sync replica (ISR), ensuring no data is lost.