Kafka Interview Questions

Table of Contents

Kafka interview questions are a good way to test a candidate’s knowledge about Kafka. The best Kafka interview questions will be based on the technical skills, as well as their ability to work in a team environment. In this article, we have compiled a list of the most popular Kafka interview questions and answers. , along with sample questions and answers to help you prepare for your upcoming interview.

Kafka Interview Questions:

What is Kafka?

Kafka is a system for publishing and subscribing to messages as a central log, as well as handling failures in an asynchronous manner. It was originally introduced by Avro, an Apache project. It provides an easy-to-use API for building applications

What are the advantages and disadvantages of Kafka?

Kafka is a publish-subscribe messaging system. It is designed to handle a large number of messages that are sent between different systems.

The advantages of Kafka are:

– Kafka can be used for real time data analysis

– Kafka can be used for event streaming and transaction processing

– Kafka provides fault tolerance and high availability

The disadvantages of Kafka are:

– Kafka does not provide a built in security mechanism

– it cannot be used as an alternative to SQL databases

What are the Best Practices for Setting up Kafka?

The best practices for setting up Kafka are to set up the Kafka broker and create topics.

Kafka is a distributed and replicated message queue that can be used for a variety of purposes. It is written in Java and is designed to persist messages in order to deliver them at a later time.

The best way to set up Kafka is by installing it on your own hardware or on a virtual machine, although there are hosted solutions available as well.

Kafka brokers are usually clustered together with one acting as the leader, if possible, and the others being followers. The leader handles all reads and writes while the followers just handle reads.

What are the challenges for Kafka?

Kafka has been a popular choice for many companies and developers that need a scalable and reliable messaging system.

The challenges Kafka faces are the following:

– The lack of features in the open source version

– The complexity of the installation process

– The lack of documentation for some features, such as security

What are the best practices for using Kafka?

Kafka is a distributed publish-subscribe messaging system that can be used for streaming data.

The best practices for using Kafka are to use it for the following:

– Publish messages to the system from one or more producers.

– Subscribe to messages from one or more consumers.

– Configure message retention time, which controls how long messages persist in the system before they are deleted and garbage collected.

– Configure message throughput, which controls how many messages are processed per second by the system (or consumed by consumers).

What are Znodes in Kafka Zookeeper? How many types of Znodes are there?

Znodes are the backbone of Kafka. They help maintain data on your Kafka cluster and provide the necessary security for managing data. There are two types of Znodes:

ZkClusterNode – safeguards data and manages the cluster;

ZkNode – is used to create new topics and receive messages.

Differentiate between Kafka streams and Spark Streaming.

Kafka streams and Spark Streaming differ in the way their programs are written. Kafka streams use a pull model where data is read from the Kafka cluster, while Spark Streaming uses a push model where data is pushed from the Spark clusters to the streaming application.

What do you understand about log compaction and quotas in Kafka?

Kafka is an open-source message brokering system that stores messages in log files. Log compaction ensures that the disk space consumed by the log is reduced. Quotas are used to specify limits on the number of bytes in a log file.

What are the guarantees that Kafka provides?

Kafka provides all of the guarantees necessary for a distributed streaming platform. Kafka ensures that data is delivered in a fault tolerant way, in spite of node failures.

There are three guarantees that Kafka provides:

Fault Tolerance Guarantee: Kafka is designed to deliver messages eventually and in spite of node failures. This means that Kafka provides guarantees that the delivery of data will not be affected by the failure of a single or multiple nodes.

Reliability Guarantee: Kafka provides an expectation that one stream will always be readable and delivers data to applications when they request it.

Uncorrectable Error Guarantee: Kafka ensures that messages are delivered in spite of node failures, and even if a message is lost Kafka guarantees that the message will be delivered eventually.

What do you mean by an unbalanced cluster in Kafka? How can you balance it?

An unbalanced cluster in Kafka is a term for a situation where one partition of the cluster has significantly less data than the other partitions. This is usually an indication that you need to rebalance your cluster. The rebalance process will move data from one partition of the cluster to another, depending on which partition has less data.

Kafka Interview Questions for Experienced:

How will you expand a cluster in Kafka?

KSQL is a SQL-like language that can be used to query and update data in Kafka. KSQL can be used to create Kafka topics, define how data should be partitioned across them, and create tables. to store new data.

KSQL includes a number of functions that are similar to SQL, including CREATE TABLE which works in much the same way as CREATE TABLE in SQL. “CREATE AGGREGATE” and “CREATE DETAILED INDEX” can be used to create aggregate tables for the aggregates of a table, and “CREATE REPLICATION” is a function that creates replication sets. “CREATE TABLE” can be used to create new tables in Kafka and “CREATE AGGREGATE” can be used to create new aggregates. “UPDATE statement” in KSQL is almost the same as SQL UPDATE statement.

What do you mean by graceful shutdown in Kafka?

A graceful shutdown is a way of shutting down a Kafka cluster that ensures that the data in the cluster is not corrupted. When shutting down a Kafka cluster, you have to be careful to ensure that all data is written to disk before decommissioning broker nodes. This can be done by executing a command on each broker node before removing it from the cluster. The command will instruct the node to flush all its storage to disk.

Can the number of partitions for a topic be changed in Kafka?

Kafka is a distributed streaming platform for handling real-time data. It’s most commonly used as a publish-subscribe message queue with an open-source release. Advanced topics such as partitioning are handled by Kafka’s administration tools, which can be updated to handle the number of partitions that you need.

How will you change the retention time in Kafka at runtime?

Kafka has a retention time period following which it will delete old messages. To change the retention time, you need to create a topic called TEMP with retention time of your choice and use it in Kafka’s configuration.

What is the Difference Between Compaction and Flush in Apache Kafka?

Compaction is a process that moves data from one location to another in order to reduce the size of the data. Flush is a process that sends all of the data to different nodes in order to make sure that the node has all of the latest data.

Compaction and flush are two different processes with different purposes. Compaction reduces the size of data, while flush sends all of the latest information from one node to another.

What are the Different Types of Offsets in Apache Kafka?

Offsets are used in Apache Kafka to maintain the order of messages. They are also used for message retention and for replication.

There are three types of offsets in Apache Kafka:

1. The Sequence Number Offset, which is the default offset type,

2. The Absolute Offset, which is a timestamp that is not relative to any other offset

3. The Relative Offset, which is a timestamp that can be expressed as an amount of time since another defined offset

Which Technologies can be Connected to Kafka?

Kafka is a distributed, partitioned, replicated commit log service. It can be used to publish and subscribe to streams of records. It can process hundreds of thousands of records per second, making it an ideal candidate for building real-time data pipelines and streaming applications.

Kafka supports the publish/subscribe message model where a producer sends messages to one or more Kafka topics which are then made available in real time for consumption by one or more consumers.

Kafka is written in Scala and runs on the JVM (Java Virtual Machine). In this section, we will look at a few technologies that can be connected to Kafka: –

  • Apache Spark
  • Apache Storm
  • Apache Flink
  • Apache Samza

Apache Spark – It is an open-source cluster computing framework from the Apache Foundation. Spark is designed to work with in-memory data, fast disks, and external storage engines like Apache Kafka. It provides APIs for working with both batch and streaming data. Apache Spark can be used to connect directly to Kafka using the native API.

Apache Storm – It is a real-time distributed computing system. Storm is an in-memory stream processing engine that allows real-time analytics across very large data sets that were previously considered “big data.” Storm was originally released as a topology running on Mesos, but now it can run natively.

Apache Flink – It is a high-performance and scalable open source stream processing platform for large-scale data processing.

Apache Samza – It is a stream processing framework that provides support for coordination, fault tolerance, and memorization.

How to Set Up Kafka for Development or Production?

Kafka is a distributed streaming platform that provides real-time data. It is used in many industries like Finance, Social Media, and IoT. The way Kafka works is by acting as a central hub for all the data coming from different sources. It can be likened to a post office which receives letters from various senders and then distributes them to their respective recipients.

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