Kafka Interview Question and Answers

Kafka Interview Question and Answers

Kafka Interview Question and Answers

Kafka Interview Question and Answers : Apache Kafka is a distributed streaming platform that is designed to handle high volume, real-time data feeds. It is an open-source technology that provides a way to publish and subscribe to streams of records, much like a message queue or enterprise messaging system. Kafka is optimized for high throughput and low latency, making it an ideal solution for processing large-scale data sets. Here we are Providing Important Kafka Interview Question and Answers.

Q: What is Apache Kafka?

A: Apache Kafka is an open-source distributed streaming platform designed to handle high volume, real-time data feeds.

Q: What are the key components of Kafka?

A: The key components of Kafka are:

Topics: A stream of records, where each record represents a single unit of data.

Producers: Applications that publish data to Kafka topics.

Consumers: Applications that subscribe to Kafka topics and process the data.

Brokers: The Kafka servers that manage the storage and exchange of messages.

ZooKeeper: A centralized service that manages configuration information, naming, and synchronization for distributed systems.

Q: What is the role of ZooKeeper in Kafka?

A: ZooKeeper is used for coordinating distributed systems, such as Kafka. It is responsible for maintaining configuration information, naming, and providing synchronization services.

Q: What is a Kafka topic partition?

A: A topic partition is a portion of a Kafka topic that is stored on a single broker. A topic can have multiple partitions, and each partition can be stored on a different broker.

Q: What is a Kafka consumer group?

A: A consumer group is a set of consumers that work together to consume messages from one or more Kafka topics. Each consumer in a group is responsible for consuming a specific subset of the partitions in the topic.

Q: What is the difference between Kafka and traditional messaging systems like RabbitMQ or ActiveMQ?

A: Kafka is a distributed streaming platform designed to handle high volume, real-time data feeds, while traditional messaging systems like RabbitMQ or ActiveMQ are designed for reliable messaging and queuing. Kafka is optimized for horizontal scalability and can handle millions of events per second with low latency.

Q: What is Kafka Connect?

A: Kafka Connect is a framework for connecting external systems to Kafka. It provides a set of connectors that allow you to easily move data between Kafka and other systems, such as databases, message queues, and Hadoop.

Q: What is Kafka Streams?

A: Kafka Streams is a library for building real-time, data-processing applications on top of Kafka. It allows you to process data streams in real-time using a high-level, functional programming model.

Q: How does Kafka ensure high availability and fault tolerance?

A: Kafka ensures high availability and fault tolerance by replicating each partition across multiple brokers. In the event of a broker failure, the replicas can be used to continue processing data without interruption.

Q: What is the role of the Kafka producer acks configuration?

A: The producer acks configuration determines how many replicas must acknowledge a message before the producer considers it to be successfully written. The acks configuration can be set to “all” for maximum durability, or “1” for higher throughput but lower durability.

Q: What is the role of the Kafka retention period configuration?

A: The retention period configuration determines how long Kafka will retain messages in a topic. After the retention period expires, messages will be deleted from the topic. This configuration is useful for managing disk space and ensuring that data is not retained longer than necessary.

Q: What is the difference between Kafka and Apache Pulsar?

A: Apache Pulsar is another distributed messaging system designed to handle real-time data feeds. While Pulsar and Kafka have similar features, Pulsar has a few key differences, such as support for multiple clusters, built-in tiered storage, and support for multiple messaging models.

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