Apache Kafka Interview Questions and Answers
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Best Apache Kafka Interview Questions and Answers
Apache Kafka is one of the popular products of Apache Software Foundation and was developed using Scala and Java. It has gained huge popularity in the data stream processing segment with its unique features such as low latency, high-throughput, and handles real-time data feeds. There are a huge number of job opportunities available for certified Kafka professionals. This blog is specifically designed to help you learn top Kafka Interview questions and answers. The core features of Kafka are such as data partitioning, scalability, low latency, and the ability to handle all types of data integration tasks made as a popular platform.
We have collected frequently asked Apache Kafka Interview Questions and Answers based on industry experts. These best Kafka interview questions and answers blog will help you gain the required knowledge and build confidence in you. This Apache Kafka Interview Questions and answers blog covers all the areas of the Kafka right from the basics to advanced level with clear examples. These are the commonly asked Kafka interview questions for beginners and experienced. The following are the frequently asked top Kafka interview questions.
Apache Kafka is one of the popular products of Apache Software Foundation and was developed using Scala and Java. Kafka’s architecture is mainly designed by considering transactional logs. And it comes with unique features such as high throughput replication, scalability, durability, stream process, zero data loss, and much more.
Kafka consumer group is one of the exclusive elements in Kafka. Each Consumer Group in Kafka has been designed to hold one or more number of consumers who can jointly be accessed to the subscribed topics or services.
Kafka mainly works based on the four essential components which are:
- Topic: It is a collection or group of messages
- Producer: It issues and communicates messages to Kafka topic
- Consumer: Kafka consumers are the subscribers to the topics and they are able to read and process messages from Kafka.
Brokers: Brokers components takes the responsibility to manage the storage for messages
All the messages contained in the partitions are allotted with the unique ID numbers and that number is termed as an offset. This Offset ID will enable us to identify each message with partition.
Apache ZooKeeper is a software product of Apache Software. It helps the organizations in managing and coordinating service in a distributed environment. It simplifies and eliminates issues in a distributed environment with its simple architecture and API.
ZooKeeper helps the Kafka by storing all offset messages consumed for a particular use and segregated into a defined consumer group. It allows the client requests to get direct access to the Kafka Server.
Absolutely no! Without the help of ZooKeeper, it won’t be possible to connect with the Kafka Server. You can not even process any type of client request when the ZooKeeper service is down.
Replicas are nothing but a list of nodes and represent a specific log of a particular portion. When it comes to the concept of IRS, it is abbreviated as an In-Sync Replicas and synched with leaders.
In Kafka, we have many partitions and each partition consists of a server that plays a leading role and all other servers act as its followers. It executes the read and writes the request of the partitions. If, at any point in time, the leader fails to execute its work then one of its follower servers will take its position.
In each partition in Kafka we have many servers and each of them is called Leader and the rest are called followers. The main role of the servers is to Replicate the leader. Follower Servers also act as load balancers by executing the role of Leader when it is failed to function.
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The Replications are treated as essential because it ensures that there is no loss in the published messages. Moreover, Replicas also enables the published messages to be consumed in the event of a program error, a machine error, or due to software upgrades.
As we know ZooKeeper is being used by Kafka, it is also important to Initialize the ZooKeeper server and the next thing is to start the Kafka server.
Following is the standard procedure to be followed to start a Kafka server:
- To start the ZooKeeper server: > bin/zookeeper-server-start.sh config/zookeeper.properties
- Next, to start the Kafka server: > bin/kafka-server-start.sh config/server.properties
The major role of the partitioning key within the procedure is to define the destination partition of a message. Users also can use customized Partitions.
The major functionality of the Kafka Procedure API is to wrap the two different procedures which include kafka.producer.async.AsyncProducer and kafka.producer.SyncProducer. The Kafka procedure’s main functionality is to expose all the procedure functionality using a single API to a client.
Kafka and Flume both are real-time processing platforms. Though both are used for the same purpose Kafka gets its hand over Flume with its powerful scalability and durability features.
Following are the major advantages that we get by using Kafka technology:
- It is super fast enough
- Capable enough to handle megabytes of data using brokes
- Highly durable
- Easy to scale
- Power to Analyze large data sets with ease
- Robust distributed design.
Kafka streaming platform contains three major capabilities which are as follows:
- Allows to publish records with ease
- Ease up your record storage process by eliminating issues
- It has the power to process the records automatically
Apache Kafka has four main API’s which are as follows:
- Consumer API
- Procedure API
- Connector API
- Streams API
The maximum size of a message that can be supported by Kafka is 1000000 bytes.
The retention period performs the function of retaining all the published records within the Kafka cluster. It will never consider whether they have been used or not. You can also discard the configuration by using the configuration setting retention period. This would give you extra space in the storage system.
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There are two types of traditional message transfer methods which are:
Publish-Subscribe: In this method, all the messages are broadcasted to all the customers.
Queuing: In this method in which a pool of customers has a chance to read the message from the server and the messages are delivered to them.
The Kafka MirrorMaker offers a Geo-Replication option. MirrorMaker enables the messages to replicate across various cloud regions and data centers. It is highly flexible and can also be used in active/passive scenarios for recovery and data backup. It also satisfies data locality requirements and also places data closure to a user.
Kafka can easily be used as a multi-tenant solution. However, Multi-tenancy can be deployed by configuring the topics that are going to consume or produce data. The multi-tenancy feature also provides operational support for quotas.
The streaming API is defined as a process in which the Input stream is effectively transformed into an output stream.
Consumer API permits an application to subscribe to multiple topics and processes stream of records.
A connector API is something that mainly allows you to run and build reusable procedures or consumers that are capable enough to connect Kafka topics to data systems or applications.
The major functionality of the Kafka is to publish data to the targeted topics. ProcThe procedures’ functionality is that it selects a record and then assigns it to partition within the topic.
RabbitMQ is the competitor of Apache Kafka. So, let’s consider some of the important elements of them.
Features:
Apache Kafka: Highly available, durable, Distributed and allows data sharing and replication
RabbitMQ: This has got no such advanced features
Performance rate:
Apache Kafka: 100,000 messages/second.
RabbitMQ: 20,000 messages/second.
The comparison factors are as follows:
Message Retaining:
Traditional queuing systems: All the messages get deleted from the queue once the process ends.
Apache Kafka: Here messages are persisted even after the process ends. This means Kafka also retains the copy of messages even after messages are delivered to the customers.
Logic-based processing:
Traditional queuing systems: Traditional systems do not provide support for processing logic based on similar events or messages.
Apache Kafka: It permits the logic-based processing on similar events or messages.
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