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of 37Explain the core components of Kafka architecture.
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Kafka architecture consists of several key components:
- Producer: Applications that publish (write) events to Kafka topics
- Consumer: Applications that subscribe to (read) events from topics
- Broker: Kafka servers that store data and serve client requests
- Topic: Categories or feed names to which records are published
- Partition: Sub-divisions of topics for parallelism and scalability
- Zookeeper: Coordinates and manages Kafka cluster metadata (being replaced by KRaft)
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What is a Kafka topic and how does partitioning work?
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A topic is a category or stream name to which producers send messages and from which consumers read messages. Topics are divided into partitions for scalability and parallelism.
Partitioning benefits:
- Enables parallel processing by multiple consumers
- Distributes data across multiple brokers
- Provides ordering guarantees within each partition
- Allows horizontal scaling
Example: A topic "user-events" with 3 partitions can have messages distributed across partitions 0, 1, and 2 based on a key or round-robin.
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What is the role of Zookeeper in Kafka?
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Zookeeper manages Kafka cluster coordination and metadata:
Responsibilities:
- Broker discovery and health monitoring
- Topic and partition metadata storage
- Leader election for partitions
- Consumer group coordination (legacy)
- Configuration management
Note: Kafka is moving away from Zookeeper dependency with KRaft (Kafka Raft) mode, which handles metadata management internally.
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What are Kafka Headers and when would you use them?
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Kafka Headers are optional metadata key-value pairs attached to each message record.
Use cases:
- Routing: Route messages based on headers
- Tracing: Add correlation IDs for distributed tracing
- Security: Include authentication tokens
- Content metadata: MIME types, encoding information
- Source identification: Origin system information
// Producer adding headers
ProducerRecord<String, String> record = new ProducerRecord<>(
"my-topic", "key", "value");
record.headers().add("correlation-id", "12345".getBytes());
record.headers().add("source-system", "order-service".getBytes());
Benefits: Headers don't affect partitioning and allow metadata without modifying message payload.
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What is Kafka's log retention and cleanup policies?
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Kafka supports two cleanup policies:
1. Delete policy (cleanup.policy=delete):
- Deletes old log segments based on time/size
log.retention.hours=168(7 days default)log.retention.bytes=-1(unlimited size default)log.segment.bytes=1GB(segment size)
2. Compact policy (cleanup.policy=compact):
- Keeps the latest value for each key
- Useful for changelog topics
- Background compaction process
- Maintains ordering within partitions
Combined policy:
cleanup.policy=compact,delete
# Both compaction and time-based deletion
Monitoring: Track log size and compaction metrics to ensure proper cleanup.
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What is the difference between Kafka and traditional message queues?
Explain Kafka producers and their key configurations.
How do Kafka consumers work and what is a consumer group?
What is offset management in Kafka?
Explain Kafka's replication mechanism.
What are Kafka's delivery semantics?
What is Kafka Streams and when would you use it?
Explain Kafka Connect and its purpose.
How does Kafka ensure high throughput?
Explain Kafka's log compaction feature.
What is the difference between `poll()` and `fetch()` in Kafka consumer?
How do you handle schema evolution in Kafka?
Explain Kafka's security features.
What is the purpose of `__consumer_offsets` topic?
How do you monitor Kafka cluster performance?
What causes consumer lag and how do you address it?
What is the difference between Kafka and Apache Pulsar?
What is Kafka MirrorMaker and its use cases?
What is Kafka's approach to backward compatibility?
Explain Kafka's compression and its trade-offs.
What is the difference between at-rest and in-transit encryption in Kafka?
How do you handle duplicate messages in Kafka?
What are Kafka's consistency guarantees?
What are Kafka transactions and how do they work?
Explain Kafka's ISR (In-Sync Replica) mechanism.
How do you tune Kafka for maximum performance?
Explain Kafka's exactly-once semantics implementation.
How does Kafka handle network partitions and split-brain scenarios?
What are some common Kafka anti-patterns to avoid?
How do you implement event sourcing with Kafka?
What is the role of the Kafka Controller?
How do you implement CQRS (Command Query Responsibility Segregation) with Kafka?
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Apache Kafka cheatsheet
Apache Kafka Interview Cheat Sheet
- Summary01
- 1. Core Concepts02
- 2. Architecture Components03
- 3. Key Concepts for Interviews04
- 4. Producer Configuration05
- 5. Consumer Configuration06
- 6. Advanced Topics07
- 7. Performance Optimization08
- 8. Key Concepts & Comparisons09
- 9. Best Practices10
- 10. Quick Reference & Best Practices11
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