Kafka is a well known and widely used publish-subscribe messaging and event streaming system. It is used as a distributed messaging system in different types of projects to achieve real-time data streaming and messaging goals. Kafka skills are high in demand and it is among the much needed skills in the job market. In this Kafka Interview Questions Answers article, we will discuss important Kafka Related Job Interview Questions and Answers for beginners, intermediate as well as advance levels.

Top 20 Kafka Interview Questions Answers For Developers Job Interviews

It is important to clarify here that reading job interview questions isn’t sufficient to crack the interviews. You should have enough practical experience on real projects to achieve desired level of confidence and expertise. However, such interview questions will help you to gain some insights into the possible questions and answers. You will also get some refresh of your Kafka related knowledge. I will highly recommend to also go through my article Kafka Introduction, Kafka Architecture Overview, Use-Cases & and Basic Concepts Explanation to equip yourself with some much needed Kafka related knowledge.

Now, let’s jump to the Kafka Interview Questions Answers straight.

Explain What is Kafka and Why It is Used?

It is a distributed, high throughput messaging and event streaming system which can be used in a wide variety of projects to stream and publish data efficiently. Kafka can be used for different use cases including real-time messaging, event streaming, IoT based applications and also for standard pub-sub requirements.

Name Some of the Key Components of Apache Kafka

Some of the key components of Apache Kafka are:

• Producer
• Consumer
• Broker
• Topic
• ZooKeeper

What is the Role of Broker in Kafka Architecture?

A Kafka broker is a server that stores and manages Kafka topics and partitions, and that receives and serves requests from producers and consumers. It essentially acts as a middle-man for the entire message communication that happens between message producers and message consumers in the Kafka cluster.

Explain the Flow of Messages in Kafka Environment.

In a typical message flow, producers send messages to specific topics. Messages are stored in partitions and can spread across multiple brokers in a cluster. The messages are consumed by the consumers based on their topic subscriptions. Consumer message consumption is tracked based on consumer offsets.

What is the purpose of Partitions in Kafka?

In Kafka, topics are distributed into sub-units known as partitions. Partitions enable Kafka to provide high-throughput, fault-tolerant messaging by distributing the load and storage of a topic across multiple brokers in a cluster. Each partition is stored on a single broker, and multiple partitions can be stored on multiple brokers in a cluster.

What is the role of ZooKeeper in Kafka?

Kafka Zookeeper is used to manage the configuration, coordination, and synchronization of Kafka brokers in a cluster. In case of any changes in the Kafka cluster, Zookeeper is responsible to notify all the nodes. ZooKeeper is used by Kafka to keep track of the location of partitions, the status of brokers, and the metadata of the cluster.

How Fault Tolerance is achieved in Apache Kafka?

Fault tolerance is achieved in Kafka using replication. With such replica based environment, even if a node fails; messages from another node in the replica can be made available. Each partition in Kafka has one leader replica that is responsible for all read and write operations, and one or more follower replicas that replicate data from the leader. If the leader fails, one of the followers is elected as the new leader.

Explain the role of Producers and Consumers in Kafka.

Producers are the systems or applications which produce and send messages to the topics in Kafka. On the other hand, Consumers are the systems or applications which pull messages from Kafka topics based on their subscriptions.

Mention few steps for performance tuning and higher throughput in Apache Kafka.

The throughput of Kafka can be increased by increasing the number of partitions for a topic, adding more Kafka brokers, and configuring the producer and consumer settings for optimal performance.

At Producer side, some of the options to consider include setting a proper Batch Size and Linger time. Optimal configuration of these two parameters can help achieving better throughput by sending batch of messages at an ideal size.

Tuning at consumer side can be achieved to have a proper correlation between number of consumers and the number of partitions.

What is the role of Offsets in Kafka?

When messages are stored in a partition, each message is assigned a unique identifier known as offset. It starts with 0 and then incremented by 1 for each subsequent message.

Offsets enable consumers to track their progress in reading messages from a topic partition. A consumer can specify an offset to start reading messages from a specific point in the partition, and it can keep track of the last offset it read to resume reading from that point if it is interrupted or restarted.

Explain About Kafka Cluster and its benefits.

A Kafka cluster is formed by having multiple brokers as a logical group to achieve load balancing. Multiple brokers in the cluster work together to provide a distributed messaging system. Clustering of Kafka brokers helps in handling large volumes of data and ensuring reliable message processing even in the face of failures.

What is meant by Consumer Groups?

In Kafka, consumer group is a set of consumers that pull data from the same topic or same set of topics. Consumers in a consumer group coordinate with each other to ensure that each partition is assigned to a single consumer within the group.

What is the significance of Kafka Replica?

Replicas in Kafka are used to achieve fault tolerance. Essentially, Replicas are copies of partitions that are maintained on multiple brokers for fault tolerance and high availability. If a broker hosting a partition fails or becomes unavailable, Kafka can automatically switch to a replica on a different broker, ensuring that messages are not lost and that processing continues uninterrupted.

Explain the concepts of leaders and followers in Kafka Architecture.

In a Kafka cluster, partitions are distributed among multiple brokers. One of these servers acts as a leader while others act as followers. The leader is responsible for all read and write operations for the partition, including handling requests from producers and consumers. All messages for the partition are written to the leader first, and then replicated to the followers for redundancy and fault tolerance.

How do we achieve Load Balancing in Kafka environment?

Partitioning is used to achieve load balancing. In Kafka, a topic is partitioned into multiple sub-units known as partitions for this purpose. By distributing the partitions across multiple brokers and consumers, Kafka can parallelize processing and handle high-volume data streams.

Mention some of the benefits of using Kafka in any project.

Kafka being a distributed messaging and event-streaming system comes with a wide range of benefits when using in different types of projects. Some of the key benefits of Apache Kafka are:

• It is highly scalable, fault tolerant with load balancing capabilities and can help achieving greater throughput in medium to large enterprise scale projects.
• Kafka is a highly Compatible system as it supports a variety of programming languages and can integrate with other data processing tools and frameworks, making it easy to use in existing data ecosystems.
• It can serve a variety of use-cases for both pub-sub messaging and event streaming scenarios.

How we achieve data retention in Apache Kafka?

Data retention is achieved in Kafka based on retention policy configurations. Retention policy can be configured at topic level which can be either time based on size based. A timer based retention is set to specify how long messages should be retained while size based retention configurations specify how much data should be retained.

Is Apache Kafka Pull Based or Push Based System?

Apache Kafka is a Pull Based messaging platform. Messages are pulled by the consumers from the Kafka topics based on the subscriptions.

Explain Kafka Multi Tenancy.

In Apache Kafka, Multi-tenancy is ability of a single Kafka cluster to support multiple independent groups of users, applications or tenants, who can each have their own isolated and secure messaging environment within the same shared infrastructure. Each tenant in such an environment is allocated a sub-set of available resources with separate tenant based administration capabilities.

Which Protocol is used by Apache Kafka?

Kafka uses a custom binary protocol on top of TCP. The Kafka protocol defines a set of messages, each with a unique identifier, that clients can use to interact with brokers. This protocol is designed to support multiple versions, allowing clients and brokers to negotiate the most appropriate version for their needs.

For Kafka as well as Other related technologies related tutorials, you can refer to TutorialsPedia YouTube Channel as well which contains plenty of useful videos.