Apache Kafka's popularity has spawned plenty of job opportunities and career prospects around it. Having Kafka on your résume puts you on the fast track to success. If you're planning on attending an Apache Kafka interview soon, take a look at the Apache Kafka interview questions and answers below, which have been carefully curated to help you ace your interview.
Apache Kafka's popularity is soaring, due to the number of career opportunities available. Having a working understanding of Kafka is a surefire way to advance in this digital age.
Thus, in this blog, we've curated a list of commonly asked Apache Kafka Interview Questions and Answers for beginners and experienced professionals. Let’s get started:
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Apache Kafka is a Scala-based publish-subscribe communicating system created by Apache. It is a logging service that is distributed, segmented, and replicated.
Kafka's four key components are as follows:
The Producer API in Kafka serves as a wrapper for the two producers – Sync Producer and Async Producer. The objective is to provide all producer capabilities to the client via a single API.
Consumer Groups are an Apache Kafka-exclusive notion. Essentially, each Kafka consumer group comprises one or more consumers who consume a collection of committed topics in unison.
Each message in the partitioning is assigned a sequential ID number that refers to as an offset. As a result, we utilize these offsets to distinguish every message in the partitioning individually.
|Also Read Apache Airflow Tutorial|
Typically, a Queue-Full Exception arises when the Producer sends messages at a rate that the Broker might not manage. Due to the Producer's lack of blocking capabilities, users will need to add sufficient brokers to handle the additional demand cooperatively.
Each partition in Kafka contains a single server acting as the Leader and 0 or more servers acting as Followers.
The Leader is responsible for all read and writes operations to the partition, while the Followers are responsible for passively replicating the leader.
Each Kafka broker comes with a limited number of partitions. Additionally, with Kafka, each partition can serve as a leader or a clone of a subject.
Partitions - A solitary fragment of a Kafka theme. The number of partitions per subject is adjustable. Additional divisions provide greater parallelism in reading from the subjects. The number of divisions in a consumer group affects the group of consumers.
Replicas - These are duplicates of the partitions. They are never addressed or read to. Their sole purpose is to provide redundancy for data. When a subject has n copies, n-1 brokers may fail without causing data loss. Additionally, no subject can have a replication factor larger than the number of brokers.
Apache Kafka is a decentralized database that was designed with Zookeeper in mind. However, Zookeeper's primary function is to provide coordination amongst the many nodes in the network, in this case. However, because Zookeeper acts as a regularly committed offset, we can restore from previously committed offsets if any node fails.
Because bypassing Zookeeper and connecting directly to the Kafka server is not feasible, the answer is no. If ZooKeeper is unavailable for whatever reason, it is unable to serve any client request.
There are several advantages of Kafka that make it beneficial to use:
We do not require any significant hardware in Kafka, as it is equipped to handle data at rapid speeds and in enormous volumes. Additionally, it can take numerous messages every second.
Kafka is resilient to cluster node/machine failures
Kafka can accommodate the messages with the millisecond-level latency needed by the majority of new use cases.
One of the majority factors that contribute is durability. Since Kafka allows message replication, messages are never deleted.
A topic is a term that refers to a genre or feed to which data is published. In Kafka, topics can be multi-subscriber; – i.e., a topic may have 0, 1, or many consumers who subscribe to the information stored. The Kafka cluster keeps a partitioned log for each topic.
Flume's primary use case is to ingest data into Hadoop. The Flume is integrated with Hadoop's monitoring system, file types, file system, and tools such as Morphlines. The Flume is the ideal solution when working with non-relational data sources or when streaming a large file into Hadoop.
The primary application of Kafka is as a distributed publish-subscribe messaging service. Kafka was not designed with Hadoop in mind, and utilizing Kafka to gather and analyze data to Hadoop is far more complex than with Flume.
Kafka can be utilized when a highly dependable and scalable corporate messaging system must link several systems, such as Hadoop.
Kafka MirrorMaker supports geo-replication for groups. Messages are duplicated across different cloud data centers using MirrorMaker. This may be used in active/passive settings for regular backups and inactive scenarios to move data adjacent to the users.
As the leader’s primary responsibility is to handle all read and write queries for the partitioning, Followers passively copy the leader.
As a result, when the Leader becomes incapacitated, any of the Followers assumes the position of the Leader. Essentially, this complete procedure guarantees that the server’s load is balanced.
A replica is a collection of nodes that duplicate the log, specifically for a certain division. Additionally, ISR stands for In-Sync Replicas, a group of message replicas synchronized with the leaders.
We can be sure that broadcasted messages are not discarded and can be received in the case of a machine failure, a program failure, or regular software updates due to replication.
Simply said, this means that the Follower cannot acquire data at the same rate as the Leader.
A cluster in Kafka comprises several brokers due to the distributed nature of the system. The system's subject is subdivided into numerous divisions. Each broker maintains one or more divisions, allowing consumers and producers to obtain and publish messages concurrently.
The TCP protocol is used to communicate between clients and servers because it is fast, simple, and language-independent. This protocol is backward compatible with its predecessor.
By default, it is activated and initiates the pool of cleaning threads. To enable log cleaning for a certain subject, add the following: log.cleanup = compact. This may be accomplished using the modify topic command or during the subject creation process.
The conventional technique entails the following:
Queuing - A group of consumers reads messages from the host, and each message is sent to a different consumer.
Publish-subscribe - All consumers are notified when new messages are published.
If the consumer is not situated in the same data center as the broker, the socket buffer size must be adjusted to account for the extended network delay.
This is one of the most often asked topics during advanced Kafka interviews. Kafka may be used in a multi-tenant environment. Multi-Tenancy is the setup of distinct topics for data consumption or production.
Data is stored in Kafka across several cluster nodes. There is a good chance that one of the nodes will fail. Fault tolerance means that the system remains secured and accessible even if one or more of the cluster's nodes fails.
The load balancer balances loads across various systems if the workload is raised as a result of message replication across numerous systems.
The Connector API is an API that enables the running of and the development of repeatable producers that link Kafka topics to application code or data systems.
To meet the high processing rates required by Kafka, we can use the Java programming language. Furthermore, Java provides excellent community support for Kafka consumer applications. Thus, we may conclude that implementing Kafka in Java is the correct decision.
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Constant, real-time, simultaneous, and record-by-record processing of data is referred to as Kafka Stream processing.
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Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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