In the nine years since its release in 2011, Kafka has established itself as one of the most valuable tools for data processing in the technological sphere. Airbnb, Goldman Sachs, Netflix, LinkedIn, Microsoft, Target and The New York Times are just a few companies built on Kafka.
But what is Kafka? The simple answer to that would be — it is what helps an Uber driver match with a potential passenger or help LinkedIn perform millions of real-time analytical or predictable services. In short, Apache is a highly scalable, open-sourced, fault-tolerant distributed event streaming platform created by LinkedIn in 2011. It uses a commit log you can subscribe to, which can then be published on a number of streaming applications.
Its low latency, data integration and high throughput contribute to its growing popularity, so much so that an expertise in Kafka is considered to be a glowing addition to a candidate’s resume and professionals with a certified qualification in it are in high demand today. This has also resulted in an increase in job opportunities centered around Kafka.
In this article, we have compiled a list of Kafka interview questions and answers that are most likely to come up in your next interview session. You might want to look these up to brush up your knowledge before you go in for your interview. So, here we go!
Top 11 Kafka Interview Questions and Answers
1. What is Apache Kafka?
Kafka is a free, open-source data processing tool created by Apache Software Foundation. It is written in Scala and Java, and is a distributed, real-time data store designed to process streaming data. It offers a high throughput working on a decent hardware.
When thousands of data sources continuously send data records at the same time, streaming data is generated. To handle this streaming data, a streaming platform would need to process this data both sequentially and incrementally while handling the non-stop influx of data.
Kafka takes this incoming data influx and builds streaming data pipelines that process and move data from system to system.
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Functions of Kafka:
- It is responsible for publishing streams of data records and subscribing to them
- It handles effective storage of data streams in the order that they are generated
- It takes care of real-time days processing
Uses of Kafka:
- Data integration
- Real-time analytics
- Real-time storage
- Message broker solution
- Fraud detection
- Stock trading
2. Why Do We Use Kafka?
Apache Kafka serves as the central nervous system making streaming data available to all streaming applications (an application that uses streaming data is called a streaming application). It does so by building real-time pipelines of data that are responsible for processing and transferring data between different systems that need to use it.
Kafka acts as a message broker system between two applications by processing and mediating communication.
It has a diverse range of uses which include messaging, processing, storing, transportation, integration and analytics of real-time data.
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3. What are the key Features of Apache Kafka?
The salient features of Kafka include the following:
1. Durability – Kafka allows seamless support for the distribution and replication of data partitions across servers which are then written to disk. This reduces the chance of servers failing, makes the data persistent and tolerant of faults and increases its durability.
2. Scalability – Kafka can be disturbed and replaced across many servers which make it highly scalable, beyond the capacity of a single server. Kafka’s data partitions have no downtime due to this.
3. Zero Data Loss – With proper support and the right configurations, the loss of data can be reduced to zero.
4. Speed – Since there is extremely low latency due to the decoupling of data streams, Apache Kafka is very fast. It is used with Apache Spark, Apache Apex, Apache Flink, Apache Storm, etc, all of which are real-time external streaming applications.
5. High Throughput & Replication – Kafka has the capacity to support millions of messages which are replicated across multiple servers to provide access to multiple subscribers.
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4. How does Kafka Work?
Kafka works by combining two messaging models, thereby queuing them, and publishing and subscribing to them so it can be made accessible to many consumer instances.
Queuing promotes scalability by allowing data to be processed and distributed to multiple consumer servers. However, these queues are not fit to be multi-subscribers. This is where the publishing and subscribing approach steps in. However, since every message instance would then be sent to every subscriber, this approach cannot be used for the distribution of data across multiple processes.
Therefore, Kafka employs data partitions to combine the two approaches. It uses a partitioned log model in which each log, a sequence of data records, is split into smaller segments (partitions), to cater to multiple subscribers.
This enables different subscribers to have access to the same topic, making it scalable since each subscriber is provided a partition.
Kafka’s partitioned log model is also replayable, allowing different applications to function independently while still reading from data streams.
5. What are the Major Four Components of Kafka?
There are four components of Kafka. They are:
Topics are streams of messages that are of the same type.
Producers are capable of publishing messages to a given topic.
Brokers are servers wherein the streams of messages published by producers are stored.
Consumers are subscribers that subscribe to topics and access the data stored by the brokers.
6. How many APIs does Kafka Have?
Kafka has five main APIs which are:
– Producer API: responsible for publishing messages or stream of records to a given topic.
– Consumer API: known as subscribers of topics that pull the messages published by producers.
– Streams API: allows applications to process streams; this involves processing any given topic’s input stream and transforming it to an output stream. This output stream may then be sent to different output topics.
– Connector API: acts as an automating system to enable the addition of different applications to their existing Kafka topics.
– Admin API: Kafka topics are managed by the Admin API, as are brokers and several other Kafka objects.
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7. What is the Importance of the Offset?
The unique identification number that is allocated to messages stored in partitions is known as the Offset. An offset serves as an identification number for every message contained in a partition.
8. Define a Consumer Group.
When a bunch of subscribed topics are jointly consumed by more than one consumer, it is called a Consumer Group.
9. Explain the Importance of the Zookeeper. Can Kafka be used Without Zookeeper?
Offsets (unique ID numbers) for a particular topic as well as partitions consumed by a particular consumer group are stored with the help of Zookeeper. It serves as the coordination channel between users. It is impossible to use Kafka that doesn’t have Zookeeper. It makes the Kafka server inaccessible and client requests can’t be processed if the Zookeeper is bypassed.
10. What do Leader and Follower In Kafka Mean?
Each of the partitions in Kafka are assigned a server which serves as the Leader. Every read/write request is processed by the Leader. The role of the Followers is to follow in the footsteps of the Leader. If the system causes the Leader to fail, one of the Followers will stop replicating and fill in as the Leader to take care of load balancing.
11. How do You Start a Kafka Server?
Before you start the Kafka server, power up the Zookeeper. Follow the steps below:
> bin/zookeeper-server-start.sh config/zookeeper.properties
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12. Why should one prefer Apache Kafka over the other traditional techniques?
The following is a list of advantages Apache Kafka has over other conventional messaging methods:
- Kafta is quick: To process megabytes of reads and writes, a single Kafka broker may serve thousands of clients.
- Kafka is Scalable: To enable greater data, we can partition data in Kafka and streamline it across a cluster of devices.
- Kafka is Reliable: To avoid data loss, Kafka uses persistent messages that are replicated throughout the cluster. Due to this, Kafka is resilient.
- Kafka is distributed by design, which guarantees fault tolerance features as well as long-term dependability.
13. What is the role of Kafka producer API?
The producer functionality is handled by the Kafka process API through a single API call to the client. The work of the Kafka.producer is coordinated, in particular, by the Kafka producer API.Kafka.producer.async.Async Producer and SyncProducer are two examples.
14. What is the maximum limit of Kafka messages?
Kafka messages can have a maximum size of 1MB (megabyte), but we can change it as needed. We are able to change the size thanks to the broker settings.
Kafka Interview Questions for Experienced Professionals
To increase your chances of getting hired, check Kafka interview questions and answers for experience below.
15. What are the conventional means of communication in Kafka?
There are two ways to use Apache Kafka’s conventional message transport method:
- Messages from the server are read by a pool of consumers using the queuing mechanism, and each message is sent to a different consumer.
- Publish-Subscribe: Messages are distributed to all users when using the Publish-Subscribe approach.
16. What exactly do you mean by “load balancing”? What makes sure that the Kafka server is load balanced?
Load balancing in Apache Kafka is a simple operation that is handled by the Kafka producers by default. The load balancing procedure maintains message ordering while distributing the message load across partitions. Users of Kafka can define the precise partition for a message.
Leaders handle all read and write requests for the partition in Kafka. However, followers simply imitate the leader in a passive manner. The procedure ensures server load balancing by having one of the followers assume the leadership role in the event of a leader failure.
17. What are the use cases of Kafka monitoring?
The use cases for Apache Kafka monitoring are as follows:
- The utilisation of system resources like memory, CPU, and disc may be monitored over time with Apache Kafka.
- Threads and JVM utilisation are monitored using Apache Kafka. In order to release memory, it relies on the Java garbage collector, which ensures that it runs frequently, making the Kafka cluster more active.
- It can be used to detect which apps are generating a lot of demand, and pinpointing performance bottlenecks could aid in finding quick solutions to performance problems.
- It continuously monitors the broker, controller, and replication statistics to adjust the status of the partitions and replicas as needed.
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What is a Zookeeper in Kafka?
Kafka is a decentralized system developed by Apache. The ZooKeeper holds offset-related information inside the Kafka environment, which is utilized to consume a certain topic and by a specified consumer group. Its primary function is to establish coordination amongst nodes in a cluster. Still, it may also be used to recuperate from previously committed offsets if any node fails since it functions as a periodically committed offset. It is not feasible to disable Zookeeper and connect to the Kafka server directly. As a result, we won't be able to use Apache Kafka without ZooKeeper. We can't service any client requests in Kafka if the ZooKeeper is offline.
Why do we need Kafka?
The Apache software created Kafka, which was developed in the Scala programming language. Kafka is a centralized platform which is used for processing data extracted from real-time sources. It permits low-latency message delivery and also ensures tolerance to faults in case of a machine failure. It can manage a large number of different types of customers. Kafka publishes all data to a disc, which effectively implies that all writes go to the operating system's page cache (RAM). Transferring data from a page cache to a networking socket becomes much faster as a result of this.
What are the real-life use cases of Kafka?
In the actual world, Kafka is well-known. To start with, it is employed in metrics as Kafka is frequently used for operational data monitoring. This entails compiling statistics from scattered apps into centralized operational data streams. Since Kafka can be used throughout an enterprise to gather logs from many services and make them accessible in a consistent format to multiple customers, it is also utilized in Log Aggregation Solutions. Finally, it is applicable in stream processing, where popular frameworks like Storm and Spark Streaming take data from a topic, process it, and publish the processed data to a new topic where it can be accessed by users and applications. The high durability of Kafka is also highly valuable in stream processing.