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Over the years, Kafka, the open-source message broker project developed by the Apache Software Foundation, has gained the reputation of being the numero uno data processing tool of choice. The exponential boom in the demand for working professionals with certified expertise in Apache Kafka is an evident proof of its growing value in the technological sphere. Written in the Scala language, Kafka provides a unified, high-throughput, low-latency platform for handling real-time data feeds. Kafka’s popularity can be credited to unique attributes that make it a highly attractive option for data integration. Features like scalability, data partitioning, low latency, and the ability to handle large number of diverse consumers make it a good fit for data integration related use cases.
The popularity of Kafka has brought with it, an array of job opportunities and career prospects around it. Having Kafka on your resume is a fast track to growth. In case you are looking to attend an Apache Kafka interview in the near future, do look at the Apache Kafka interview questions and answers below, that have been specially curated to help you crack your interview successfully. If you have attended Kafka interviews recently, we encourage you to add questions in the comments tab.
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Wikipedia defines Kafka as “an open-source message broker project developed by the Apache Software Foundation written in Scala and is a distributed publish-subscribe messaging system.
|High Throughput||Support for millions of messages with modest hardware|
|Scalability||Highly scalable distributed systems with no downtime|
|Replication||Messages are replicated across the cluster to provide support for multiple subscribers and balances the consumers in case of failures|
|Durability||Provides support for persistence of message to disk|
|Stream Processing||Used with real-time streaming applications like Apache Spark & Storm|
|Data Loss||Kafka with proper configurations can ensure zero data loss|
The four major components of Kafka are:
Messages contained in the partitions are assigned a unique ID number that is called the offset. The role of the offset is to uniquely identify every message within the partition.
Consumer Groups is a concept exclusive to Kafka. Every Kafka consumer group consists of one or more consumers that jointly consume a set of subscribed topics.
Kafka uses Zookeeper to store offsets of messages consumed for a specific topic and partition by a specific Consumer Group.
No, it is not possible to bypass Zookeeper and connect directly to the Kafka server. If, for some reason, ZooKeeper is down, you cannot service any client request.
Every partition in Kafka has one server which plays the role of a Leader, and none or more servers that act as Followers. The Leader performs the task of all read and write requests for the partition, while the role of the Followers is to passively replicate the leader. In the event of the Leader failing, one of the Followers will take on the role of the Leader. This ensures load balancing of the server.
Replicas are essentially a list of nodes that replicate the log for a particular partition irrespective of whether they play the role of the Leader. On the other hand, ISR stands for In-Sync Replicas. It is essentially a set of message replicas that are synced to the leaders.
Replication ensures that published messages are not lost and can be consumed in the event of any machine error, program error or frequent software upgrades.
It means that the Follower is unable to fetch data as fast as data accumulated by the Leader.
Since Kafka uses ZooKeeper, it is essential to initialize the ZooKeeper server, and then fire up the Kafka server.
Within the Producer, the role of a Partitioning Key is to indicate the destination partition of the message. By default, a hashing-based Partitioner is used to determine the partition ID given the key. Alternatively, users can also use customized Partitions.
QueueFullException typically occurs when the Producer attempts to send messages at a pace that the Broker cannot handle. Since the Producer doesn’t block, users will need to add enough brokers to collaboratively handle the increased load.
The role of Kafka’s Producer API is to wrap the two producers – kafka.producer.SyncProducer and the kafka.producer.async.AsyncProducer. The goal is to expose all the producer functionality through a single API to the client.
Even though both are used for real-time processing, Kafka is scalable and ensures message durability.
These are some of the frequently asked Apache Kafka interview questions with answers. You can brush up on your knowledge of Apache Kafka with these blogs.
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