Top Kafka Interview Questions And Answers (2023 Update)

Apache Kafka has emerged as a leading distributed streaming platform, enabling organizations to build scalable, real-time data pipelines and applications. With its growing popularity and adoption, the demand for skilled Kafka professionals is on the rise. 

Whether you’re a developer, architect, data engineer, or DevOps professional working with Kafka or looking for a job at an organization that uses Kafka, this guide will help you navigate your interview process with confidence.

We’ve organized Kafka interview questions and answers into the following sections:

Let’s get started!

Common Kafka Interview Questions And Answers

Q) What is Apache Kafka, and what are its primary use cases?

Apache Kafka is a distributed streaming platform that allows you to publish, store, and process streams of records in a fault-tolerant, scalable, and low-latency manner. It was originally developed at LinkedIn and later open-sourced under the Apache Software Foundation.

Kafka’s primary use cases include:

  • Messaging: Kafka can be used as a high-throughput, scalable, and fault-tolerant messaging system to facilitate communication between applications and microservices. For example, Uber, that has one of the largest deployments of Kafka in the world, uses Kafka for its internal messaging system, enabling communication among various microservices within its platform.
  • Event streaming: Kafka naturally supports event-driven architectures, enabling applications to publish, subscribe, and process events in real-time. Netflix utilizes Kafka to build its Keystone pipeline, which processes trillions of events and petabytes worth of data daily.
  • Log aggregation: Kafka can aggregate and process large volumes of log data from various sources (e.g., application logs, system logs) for centralized storage, analysis, and monitoring. LinkedIn, where it all started, employs Kafka for log aggregation and real-time data processing across its massive infrastructure.
  • Data integration: Kafka serves as a powerful data pipeline, enabling data ingestion and integration across different sources and sinks, such as databases, data warehouses, and analytics platforms. Spotify uses Kafka to integrate data from various sources, ensuring seamless data flow across its infrastructure.
  • Real-time data processing: Kafka can be combined with stream processing frameworks like Apache Flink or Apache Samza to build real-time data processing applications, such as real-time analytics, fraud detection, and recommendation engines. Pinterest leverages Kafka Streams at scale to build a real-time budgeting system for its advertising platform.
  • Data synchronization: Kafka can synchronize data between various systems and services, making it ideal for data replication, cache synchronization, and backup purposes. Shopify, whose data lake couldn’t satisfy the ever-growing demand for new and enhanced merchant analytics, now utilizes Kafka to synchronize data between its primary database and analytics data platform.

These use cases make Apache Kafka a popular choice for organizations looking to build scalable, distributed, and real-time data-driven applications and pipelines.

Q) Can you explain the architecture of Apache Kafka and its key components?

Kafka architecture consists of the following key components, which work together to provide high throughput, low latency, and durability guarantees –

  1. Producers: Producers are the client applications that publish (write) records to Kafka topics. They are responsible for serializing the data into the appropriate format (e.g., JSON, Avro), adding relevant headers, and sending the record to Kafka brokers.
  2. Brokers: Kafka brokers are “the core” of a Kafka cluster. They are responsible for receiving records from producers, storing them on disk, and serving them to consumers. Each broker can handle multiple topics and partitions and can support multiple simultaneous producer and consumer connections. Multiple brokers in a kafka cluster work together to ensure fault-tolerance and scalability of the cluster.
  3. Topics: Topics are logical channels to which records are published. They are a core abstraction in Kafka and allow categorization and organization of records based on their content or source. Topics are split into multiple partitions to enable parallelism and scalability.
  4. Partitions: Partitions are the basic unit of parallelism in Kafka. Each topic is divided into one or more ordered, immutable, and append-only partitions. Each partition is replicated across multiple brokers to ensure fault-tolerance. Producers write records to the last offset of a partition, and consumers read records from a specific offset.
  5. Replicas: To ensure fault-tolerance and durability, each partition is replicated across multiple brokers, creating replica partitions. There are two types of replicas: the leader replica and the follower replicas. The leader replica handles all read and write requests, while the follower replicas passively replicate the leader’s data.
  6. Consumers: Consumers are the client applications that read records from Kafka topics. They are organized into consumer groups, allowing multiple consumers to work together to process records from the same topic concurrently. Each consumer group tracks its offset in each partition, allowing it to continue reading from where it left off in case of failure or restart.
  7. Consumer Groups: Consumer groups consist of one or more consumers that collaborate to process records from a set of topic partitions. Each partition is exclusively consumed by a single consumer in the group, allowing for load balancing and fault-tolerance. If a consumer fails or is added to the group, the group rebalances the partition assignment among the available consumers.
  8. Zookeeper: Apache Zookeeper is a distributed coordination service used by Kafka to manage configuration data, monitor the health of brokers, and perform leader election for partitions. In Kafka 2.8.0 and later, a built-in Kafka controller can replace Zookeeper for managing metadata, but many deployments still use Zookeeper for historical reasons.

Q) What is offset management in kafka and where/why is it important?

Offset management in Kafka refers to the process of tracking and controlling the position of a consumer within a partition of a topic. In Kafka, every record within a partition is assigned a unique, sequential identifier called the “offset.” The offset is used to specify the position of a record in the partition, enabling consumers to keep track of the records they have already processed and the ones they still need to process.

Offset management is important for several reasons:

  1. Durability and fault-tolerance: By managing offsets, Kafka consumers can resume processing from the last processed record in case of failures, crashes, or restarts. This ensures that the consumers do not miss any records or process the same record multiple times.
  2. Load balancing and parallelism: Since each partition is consumed by a single consumer within a consumer group, managing offsets enables multiple consumers to work together, sharing the load of processing records from a topic. This parallelism is at the heart of Kafka’s high throughput and overall efficiency.
  3. Consumer progress tracking: Offset management allows you to monitor the progress of consumers as they process records. You can measure the lag between the latest available offset and the current consumer offset to identify slow or stuck consumers and take appropriate actions.
  4. Seeking to specific positions: In some use cases, you might need to start processing records from a specific point in time or offset. Managing offsets enables you to seek to a desired position within a partition and start consuming records from that point onwards.

Q) What role does kafka play in cloud native architectures?

In cloud-native architectures, Kafka plays a significant role as a highly scalable, fault-tolerant, and distributed messaging system. It enables real-time data streaming, processing, and integration among various microservices, applications, and data stores, contributing to the overall flexibility, resilience, and responsiveness of the architecture. One could also argue that Kafka enables nimble architectures and high velocity development.

Here are some of the patterns in cloud-native architectures where Kafka plays a key role:

  1. Decoupling microservices: In a cloud-native, microservices architecture, kafka acts as a communication backbone, enabling microservices to exchange data in a decoupled and asynchronous manner. This approach improves the scalability, fault tolerance, and maintainability of the overall system.
  2. Event-driven architecture: Kafka naturally supports event-driven architectures, where microservices and applications react to events in real time. By using Kafka as the event backbone, applications can publish, subscribe, and process events efficiently, leading to improved responsiveness and agility in the system.
  3. Data integration: Kafka can be used as a centralized data pipeline to integrate data from various sources and sinks in a cloud-native environment. Kafka Connect, a part of the Kafka ecosystem, facilitates easy integration with various data stores, systems, and APIs, allowing data to flow seamlessly across the architecture.
  4. Stream processing: Kafka enables real-time stream processing through its Kafka Streams API and integration with stream processing frameworks like Apache Flink or Apache Samza. This capability allows cloud-native applications to process, transform, and analyze data in real-time as it flows through the system, enabling timely insights and decision-making.
  5. Scalability and elasticity: Kafka is designed to scale horizontally, making it an excellent fit for cloud-native architectures that require elasticity and adaptability. Kafka’s distributed and partitioned nature allows it to handle large volumes of data and traffic while maintaining low latency and high throughput.

Q) How does Kafka use log data for message storage and retrieval?

Kafka uses a distributed, append-only log data structure for message storage and retrieval. Each topic is divided into multiple, ordered partitions, and each partition is a separate log. Messages are stored as a sequence of records in these logs, with each record assigned a unique, increasing offset within its partition.

For message storage, when a producer writes a message to a topic, Kafka appends the message to the leader replica of the target partition. The message is then replicated to the follower replicas, ensuring data redundancy.

For message retrieval, consumers fetch messages from the leader replica of their assigned partitions. They specify the desired starting offset, and the broker reads messages from the corresponding log file, sending them in batches. Consumers process the messages and periodically commit their progress by updating their offsets, allowing the broker to track the consumer’s position within the partition.

By using the log data structure, Kafka ensures high-throughput, fault-tolerant, and scalable storage and retrieval of messages.

Q) How does Kafka manage multiple consumer instances within the same consumer group?

Kafka manages multiple consumer instances within the same consumer group by assigning each partition to a single consumer within the group. This ensures parallelism and load balancing during message processing. If a consumer joins or leaves the group, Kafka triggers a rebalance, automatically reassigning partitions among the available consumers, maintaining a balanced load distribution and preventing duplicate or missed message processing.

Q) What is the significance of the message key in Kafka message production and consumption?

The message key in Kafka plays a crucial role in message production and consumption by influencing partitioning, maintaining message order, and supporting message semantics.

1. Partitioning: During production, the key is used to determine the target partition for a message. Producers typically use a partitioning strategy, such as consistent hashing, to ensure that messages with the same key are written to the same partition. This helps in evenly distributing the load across partitions and brokers.

2. Message order: Kafka guarantees order within a partition. By directing messages with the same key to the same partition, Kafka maintains the order of those messages, which is important for processing events in sequence or preserving temporal relationships between messages.

3. Message semantics: The key can be used to represent the relationship between messages or to group related messages. During consumption, consumers can leverage the key to process messages with the same key together, preserving business logic or data relationships. This is particularly useful in scenarios like stream processing, where aggregations or computations are performed on related data.

Q) How does Kafka prevent data loss in case of broker failures or network issues?

Kafka prevents data loss in case of broker failures or network issues through several mechanisms:

1. Partition replication: Kafka replicates each partition across multiple brokers, designating one as the leader and the others as followers. This redundancy ensures data availability even if a broker fails or experiences network issues.

2. Acknowledgment levels: Producers can configure the acknowledgment level (`acks`) to control how many replicas must acknowledge the receipt of a message before considering it as sent. Higher acknowledgment levels (e.g., `acks=all`) increase data durability but may impact throughput.

3. Unclean leader election: By default, Kafka only allows a follower that is in-sync with the leader to become the new leader if the current leader fails. This prevents data loss from electing an out-of-sync follower as the new leader.

4. Min in-sync replicas (ISR): Kafka allows configuring the minimum number of in-sync replicas required to acknowledge a write (`min.insync.replicas`). This setting ensures that a certain number of replicas are always in-sync, increasing data durability.

5. Consumer offsets: Consumers periodically commit their processed offsets to Kafka, enabling them to resume from the last committed offset in case of failures or network issues.

These mechanisms together ensure Kafka’s fault-tolerance and minimize data loss during broker failures or network problems.

Q) What are the different message processing semantics that kafka supports?

Kafka supports the following three message processing semantics, which determine how messages are delivered and processed by consumers: At-most-once, At-least-once and Exactly-once.

These semantics define the trade-offs between message delivery guarantees and processing efficiency.

1. At-most-once: Messages are delivered to consumers at most once, prioritizing processing efficiency and low latency but potentially resulting in message loss.

2. At-least-once: Messages are guaranteed to be delivered to consumers at least once, ensuring no message loss, but possibly causing duplicate processing.

3. Exactly-once: Messages are delivered and processed exactly once, eliminating message loss and duplicate processing, but potentially introducing overhead in terms of latency and complexity.

Kafka Interview Questions for Java Developers

Q) How do you produce and consume messages in Java using the Kafka API?

To produce and consume messages in Java using the Kafka API, you need to leverage the Kafka producer and consumer APIs provided by the “kafka-clients library. 

At a high level, to produce messages on a topic, the producer must leverage the KafkaProducer and ProdcerRecord classes. Transport of the message from the producer client to the broker is attempted when the client invokes ProducerRecord.send()

Similarly, to consume messages from a kafka topic, the consumer client must instantiate a KafkaConsumer object, invoke the subscribe() with the topic name, followed by poll() (typically in a loop) to retrieve messages.

Q) What is the importance of exception handling in Java when working with Kafka producers and consumers?

Not handling exceptions properly in Kafka producers and consumers can lead to various side effects that can cause data loss or impact the stability, reliability, and data integrity of your application. Some of the noteworthy side effects of improper or lack of exception handling in Kaka client applications include:

  1. Data loss: If exceptions aren’t handled correctly, you might lose messages that haven’t been acknowledged by the broker. For example, if a producer encounters an error while sending a message and the exception isn’t caught or retried, that message may never be sent to the Kafka topic.
  2. Data corruption: Improper handling of deserialization and serialization exceptions can lead to data corruption. If deserialization fails and the error isn’t handled, the consumer might incorrectly process the message, resulting in corrupted data in the downstream system.
  3. Infinite loops: If you don’t handle exceptions properly in a poll loop, your consumer might get stuck in an infinite loop, continually trying to process the same record and never making progress. This can lead to high resource utilization and impact the overall performance of your application.
  4. Out-of-order processing: If you don’t handle exceptions in a producer’s callback, you might not be aware of failed message sends. This could result in out-of-order processing of messages, as the producer might continue to send subsequent messages successfully.
  5. Resource leaks: Failing to handle exceptions during producer or consumer closure can lead to resource leaks. For example, if an InterruptException isn’t handled during closure, the producer or consumer might not release its resources, leading to increased memory usage and potential performance issues.
  6. Stalled consumers: If you don’t handle exceptions related to rebalancing, consumers might stall or stop receiving messages. For instance, if an exception occurs during partition assignment and isn’t handled, the consumer might not start processing messages from the newly assigned partitions.
  7. Difficulty in monitoring and troubleshooting: When exceptions are not properly handled or logged, it becomes challenging to monitor the health of the application, particularly the Kafka clients. This lack of visibility can make it difficult to identify and resolve production issues in a timely manner.
  8. Unexpected application crashes: Failing to handle exceptions might lead to uncaught exceptions propagating up the call stack and causing your entire application to crash. This can lead to unexpected downtime and affect the availability of your services. SLA breaches due to application downtime can have significant negative consequences in sensitive environments such as banking and financial organizations.

Q) What is rebalancing and how do you handle it in a Java based Kafka client?

Rebalancing in Kafka is the process of redistributing the partitions among the consumers in a consumer group. This occurs when a new consumer joins the group, an existing consumer leaves the group, or when the number of partitions for a topic changes. Rebalancing ensures that each consumer in the group gets an equal share of the partitions.

Handling rebalancing in a Java-based Kafka client involves using a ConsumerRebalanceListener with the subscribe method of the KafkaConsumer class. The ConsumerRebalanceListener interface has two methods: onPartitionsRevoked and onPartitionsAssigned.

  1. onPartitionsRevoked: This method is called before the rebalancing starts and after the consumer has stopped consuming messages. It allows you to perform any necessary cleanup, such as committing offsets or closing resources related to the partitions being revoked.
  2. onPartitionsAssigned: This method is called after the rebalancing is complete and the consumer has been assigned new partitions. It allows you to perform any necessary initialization, such as seeking to a specific offset or setting up resources for the newly assigned partitions.

Here’s an example of how to handle rebalancing in a Java-based Kafka client:

import org.apache.kafka.clients.consumer.ConsumerRebalanceListener;

import org.apache.kafka.clients.consumer.KafkaConsumer;

import org.apache.kafka.common.TopicPartition;

import java.time.Duration;

import java.util.Collection;

import java.util.Collections;

import java.util.Properties;

public class MyKafkaConsumer {

  public static void main(String[] args) {

    Properties props = new Properties();

    props.put("bootstrap.servers", "localhost:9092");

    props.put("", "my-group-id");

    props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

    props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

    KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);

    consumer.subscribe(Collections.singletonList("my-topic"), new ConsumerRebalanceListener() {


      public void onPartitionsRevoked(Collection<TopicPartition> partitions) {

        // Commit the current offsets before rebalancing




      public void onPartitionsAssigned(Collection<TopicPartition> partitions) {

        // Perform any necessary initialization for the newly assigned partitions



    try {

      while (true) {

        // Poll for new messages and process them


    } finally {





In this example, the onPartitionsRevoked method is used to commit the current offsets before the rebalancing takes place. The onPartitionsAssigned method can be used to perform any necessary initialization for the newly assigned partitions. This approach ensures that the consumer can gracefully handle rebalancing events and maintain consistency and reliability.

Spring Boot Kafka Interview Questions

Q) How does Spring Boot simplify working with Kafka in Java applications?

Spring Boot simplifies working with Kafka in Java applications by providing auto-configuration, abstraction layers, and integrations with other Spring components. It leverages the Spring for Apache Kafka and Spring Kafka projects, which offer high-level abstractions for Kafka producers, consumers, and streams.

Here are some ways Spring Boot makes it easier to work with Kafka in Java applications:

  1. Auto-configuration: Spring Boot automatically configures Kafka producers and consumers with sensible defaults based on the application properties. You can easily customize these configurations by providing your own settings in the or application.yml file.
  2. High-level abstractions: Spring Boot provides high-level abstractions for Kafka producers and consumers through the KafkaTemplate and @KafkaListener annotation, respectively. These abstractions simplify message sending and consumption by handling common boilerplate code, allowing you to focus on your application logic.
  3. Error handling: Spring Boot provides a rich error-handling mechanism for Kafka consumers. By using the ErrorHandler interface and its implementations, you can easily handle deserialization exceptions, record processing exceptions, and more.
  4. Message conversion: Spring Boot allows you to automatically convert messages between different formats using the MessageConverter interface. This simplifies the serialization and deserialization process for producers and consumers.
  5. Integration with other Spring components: Spring Boot offers seamless integration with other Spring components, such as Spring Data, Spring Cloud Stream, and Spring Security. This allows you to build more complex applications that leverage Kafka alongside other Spring-based technologies.
  6. Testing support: Spring Boot provides testing support for Kafka applications through the spring-kafka-test module. This includes embedded Kafka brokers and consumer/producer factories, making it easy to write integration tests for your Kafka-based applications.

Related Reading: Deploy Spring Boot Applications to AWS

Q) Can you explain the use of @KafkaListener and its role in a Spring Boot Kafka application?

@KafkaListener is an annotation provided by the Spring for Apache Kafka project, which simplifies the creation of Kafka consumers in a Spring Boot application. It enables you to define methods in your application that automatically get called when new messages arrive in the specified Kafka topics.

The @KafkaListener annotation abstracts away the underlying details of creating and managing Kafka consumers, allowing you to focus on your application logic.

Here are some key aspects of the @KafkaListener annotation:

  1. Message consumption: When a method is annotated with @KafkaListener, Spring automatically subscribes the method to the specified topic(s) and calls the method when new messages arrive.
  2. Concurrency: The @KafkaListener annotation supports a concurrency attribute, which defines the number of concurrent consumer threads that will be created for the listener. This makes it easy to scale your consumer to handle a higher volume of messages.
  3. Message deserialization: Spring Boot takes care of message deserialization based on the configuration provided in the or application.yml file. The deserialized messages are passed as method arguments to the listener method.
  4. Error handling: You can define custom error handling logic for exceptions that occur while processing messages. By implementing the ErrorHandler interface and configuring it in your application, you can handle exceptions gracefully and avoid losing messages due to processing errors.
  5. Message filtering: The @KafkaListener annotation supports message filtering using the RecordFilterStrategy interface. By implementing this interface, you can filter out unwanted messages before they are passed to the listener method.

Confluent Kafka Interview Questions

Q) What is Confluent Kafka, and how does it differ from Apache Kafka?

Confluent Kafka is a data streaming platform built around Apache Kafka, developed and maintained by Confluent Inc., a company founded by the original creators of Apache Kafka. Confluent Kafka extends Apache Kafka with additional tools, services, and components that make it easier to deploy, manage, and scale Kafka-based data streaming applications.

Confluent offers both an open-source distribution called Confluent Platform and a fully managed cloud-based service called Confluent Cloud.

Here are some key differences between Confluent Kafka and Apache Kafka:

  1. Additional Components: Confluent Kafka includes several components not present in Apache Kafka, such as:
    • Confluent Schema Registry: A centralized service for managing and sharing Avro schemas across multiple producers and consumers.
    • Confluent REST Proxy: A RESTful interface for producing and consuming Kafka messages, which enables non-Java clients to interact with Kafka.
    • Confluent ksqlDB: A streaming SQL engine that enables real-time data processing and querying directly on Kafka topics.
    • Confluent Connect: A framework for building and managing Kafka connectors that integrate with various data sources and sinks.
  2. Enterprise Features: Confluent Platform offers additional features and enhancements aimed at enterprise users, including:
    • Multi-Region Clusters: Support for deploying Kafka clusters across multiple regions for improved disaster recovery and high availability.
    • Tiered Storage: Allows older Kafka messages to be offloaded to more cost-effective storage systems while still being available for querying and processing.
    • Role-Based Access Control (RBAC): Provides granular access control to Kafka resources based on user roles and permissions.
    • Confluent Control Center: A web-based UI for managing, monitoring, and securing Kafka clusters and Confluent components.
  3. Commercial Support: Confluent Inc. offers commercial support and professional services for Confluent Platform and Confluent Cloud, which can be valuable for organizations that require assistance with deploying, managing, and maintaining Kafka-based solutions.
  4. Managed Cloud Service: Confluent Cloud is a fully managed Kafka service that runs on major cloud providers like AWS, Azure, and GCP. It takes care of the operational aspects of running Kafka, such as provisioning, scaling, and monitoring, allowing you to focus on building and deploying your applications.

While Apache Kafka is the core distributed streaming platform that provides the fundamental capabilities for data streaming, Confluent Kafka extends it with additional tools, components, and services that make it easier to build, deploy, and manage Kafka-based applications. Depending on your requirements and use case, Confluent Kafka can provide added value and convenience compared to using Apache Kafka alone, especially for large Kafka deployments and enterprise environments.

Q) Explain the role of Confluent Schema Registry in building a streaming data or event-driven platform.

Confluent Schema Registry is a centralized service for storing, managing, and sharing Avro schemas in Kafka-based applications. The role of Confluent Schema Registry in Kafka applications is to store, manage, and share Avro schemas, ensuring data compatibility and consistency across different producers and consumers. By centralizing schema management and providing features like schema versioning and compatibility checking, the Schema Registry helps you build more robust and maintainable Kafka-based data streaming applications.