1. What is Apache Kafka?
Answer: Apache Kafka is an open-source distributed streaming platform. It is used to build real-time data pipelines and streaming applications. Kafka is horizontally scalable and can be used to process large amounts of data.
2. What are the key features of Apache Kafka?
Answer: Some of the key features of Apache Kafka include:
- Scalability: Kafka can be scaled to handle large amounts of data.
- Durability: Kafka is durable and can withstand failures.
- Throughput: Kafka can achieve high throughput for streaming data.
- Latency: Kafka can achieve low latency for streaming data.
- Replication: Kafka replicates data across multiple brokers to ensure availability.
- Partitioning: Kafka partitions data across multiple brokers to improve performance.
- Topics: Kafka topics are used to organize data.
- Consumers: Kafka consumers are used to read data from topics.
- Producers: Kafka producers are used to write data to topics.
3. What are the different components of Apache Kafka?
Answer: The main components of Apache Kafka are:
- Brokers: Brokers are servers that store data in Kafka.
- Topics: Topics are logical groupings of data.
- Partitions: Partitions are physical divisions of a topic.
- Producers: Producers are applications that write data to Kafka.
- Consumers: Consumers are applications that read data from Kafka.
- Zookeeper: Zookeeper is a coordination service that maintains the metadata for Kafka.
4. What are the different types of Kafka messages?
Answer: There are two types of Kafka messages:
Produced messages: These are messages that are written to Kafka by producers.
Consumed messages: These are messages that are read from Kafka by consumers.
5. What are the different delivery guarantees in Kafka?
Answer: Kafka provides three delivery guarantees:
- At most once: This guarantee means that a message may be delivered once, zero times, or multiple times.
- At least once: This guarantee means that a message will be delivered at least once, but it may be delivered multiple times.
- Exactly once: This guarantee means that a message will be delivered exactly once.
6. What are the different consumer groups in Kafka?
Answer: Consumer groups are used to distribute messages across multiple consumers. Each consumer group has a unique identifier.
7. What are the different consumer offsets in Kafka?
Answer: Consumer offsets are used to track the progress of consumers. Each consumer has a unique offset for each topic and partition that it is consuming.
8. What are the different Kafka APIs?
Answer: Kafka provides two APIs:
- Producer API: This API is used to write data to Kafka.
- Consumer API: This API is used to read data from Kafka.
9. What are the different Kafka tools?
Answer: There are a number of Kafka tools available, including:
- Kafka console producer: This tool is used to write data to Kafka from the command line.
- Kafka console consumer: This tool is used to read data from Kafka from the command line.
- Kafka command-line tool: This tool is used to perform administrative tasks on Kafka.
- Kafka REST proxy: This tool provides a RESTful API for interacting with Kafka.
10. What are the real-world use cases of Apache Kafka?
Answer: Apache Kafka is used in a variety of real-world use cases, including:
- Log aggregation: Kafka can be used to aggregate logs from multiple sources.
- Stream processing: Kafka can be used to process streaming data.
- Event streaming: Kafka can be used to stream events from one system to another.
- Data integration: Kafka can be used to integrate data from different sources.
- Real-time analytics: Kafka can be used to perform real-time analytics on streaming data.
- Machine learning: Kafka can be used to build machine learning models on streaming data.
11. What is a Kafka Producer Acknowledgment?
Answer: Kafka producers can configure acknowledgments for sent messages. There are three acknowledgment modes: “acks=0” (no acknowledgment), “acks=1” (acknowledgment from leader), and “acks=all” (acknowledgment from leader and all in-sync replicas).
12. What is Kafka replication?
Answer: Kafka replication is the process of maintaining redundant copies of data across multiple brokers. It ensures data durability and fault tolerance. Each partition has one leader and multiple followers for replication.
13. Explain the role of a Kafka Consumer Group.
Answer: A Kafka Consumer Group is a set of consumers that work together to consume data from Kafka topics. Each partition within a topic is consumed by only one consumer within the group, enabling parallel processing.
14. What is the significance of the consumer offset in Kafka?
Answer: The consumer offset in Kafka represents the last successfully consumed record’s position within a partition. It allows consumers to resume reading from where they left off in case of failure or restart.
15. What are Kafka Producers and Consumers in terms of message delivery semantics?
Answer: Kafka Producers can choose between three message delivery semantics: “at most once” (no retries), “at least once” (retries with potential duplicates), and “exactly once” (guaranteed delivery without duplicates).
16. What is the role of the Kafka Connect framework?
Answer: Kafka Connect is a framework for easily integrating Kafka with external data sources or sinks. It simplifies the development of connectors that move data in and out of Kafka.
17. What is the purpose of the Kafka Streams library?
Answer: Kafka Streams is a Java library for building real-time stream processing applications on top of Kafka. It enables developers to process, transform, and analyze data streams using a high-level DSL.
18. What is the difference between Apache Kafka and Apache Pulsar?
Answer: Apache Kafka and Apache Pulsar are both messaging systems, but Pulsar provides multi-tenancy, native tiered storage, and better support for geo-replication out of the box, which Kafka may require external tools for.
19. Explain the role of the Kafka Schema Registry.
Answer: The Kafka Schema Registry is used in conjunction with Apache Kafka and Avro serialization to enforce schema compatibility and consistency between producers and consumers.
20. What is the purpose of Kafka Connectors?
Answer: Kafka Connectors are plugins that allow you to easily connect Kafka to various data sources and sinks, such as databases, file systems, and cloud services, to stream data in and out of Kafka.
21. What is the significance of the Replication Tool?
Answer: The Replication Tool in Kafka is a helpful addition to promoting higher availability and better durability. Some of the common types of replication tools include the Create Topic tool, List Topic tool, and Add Partition tool.
22. What is the relationship between Apache Kafka and Java?
Answer: Candidates should also prepare adequately for such insightful Kafka interview questions for better chances of qualifying interviews. The foremost relationship between Java and Apache Kafka is that the former supports the standard requirement of high processing rates in Kafka. In addition, Java also provides exceptional community support for all Kafka consumer clients. Therefore, one of the best practices for implementing Kafka is to choose Java for the implementation.
23. Does Kafka provide any guarantees?
Answer: This is one of the tricky Kafka interview questions that test the deeper knowledge of candidates in Kafka. Kafka provides the guarantee of tolerating up to N-1 server failures without losing any record committed to the log. In addition, Kafka also ensures that the order of messages sent by the producer to the specific topic partition will be the same for multiple messages. Kafka also provides the guarantee that consumer instance can view records in the order of their storage in the log.
24. What are the types of the traditional method of message transfer?
Answer: There are mainly two types of the traditional message transfer method. These types are:
Queuing: In Queuing method, a pool of consumers can read a message from the server, and each message goes to one of them.
Publish-Subscribe: In the Publish-Subscribe method, messages are broadcasted to all consumers.
25. What are the biggest disadvantages of Kafka?
Following is the list of most critical disadvantages of Kafka:
- Answer: When the messages are continuously updated or changed, Kafka performance degrades. Kafka works well when the message does not need to be updated.
- Brokers and consumers reduce Kafka’s performance when they get huge messages because they have to deal with the data by compressing and decompressing the messages. This can reduce the overall Kafka’s throughput and performance.
- Kafka doesn’t support wildcard topic selection. It is necessary to match the exact topic name.
- Kafka doesn’t support certain message paradigms such as point-to-point queues and request/reply.
- Kafka does not have a complete set of monitoring tools.
26. What is the purpose of the retention period in the Kafka cluster?
Answer: Within the Kafka cluster, the retention period is used to retain all the published records without checking whether they have been consumed or not. Using a configuration setting for the retention period, we can easily discard the records. The main purpose of discarding the records from the Kafka cluster is to free up some space.
27. What do you understand by load balancing? What ensures load balancing of the server in Kafka?
Answer: In Apache Kafka, load balancing is a straightforward process that the Kafka producers by default handle. The load balancing process spreads out the message load between partitions while preserving message ordering. Kafka enables users to specify the exact partition for a message.
In Kafka, leaders perform the task of all read and write requests for the partition. On the other hand, followers passively replicate the leader. At the time of leader failure, one of the followers takes over the role of the leader, and this entire process ensures load balancing of the servers.
28. When does the broker leave the ISR?
Answer: ISR is a set of message replicas that are completely synced up with the leaders. It means ISR contains all the committed messages, and ISR always includes all the replicas until it gets a real failure. An ISR can drop a replica if it deviates from the leader.
29. How can you get exactly once messaging from Kafka during data production?
Answer: To get exactly-once messaging during data production from Kafka, we must follow the two things avoiding duplicates during data consumption and avoiding duplication during data production.
Following are the two ways to get exactly one semantics during data production:
- Avail a single writer per partition. Whenever you get a network error, you should check the last message in that partition to see if your last write succeeded.
- In the message, include a primary key (UUID or something) and de-duplicate the consumer.
30. What is the use of Apache Kafka Cluster?
Answer: Apache Kafka Cluster is a messaging system used to overcome the challenges of collecting a large volume of data and analyzing the collected data. The following are the main benefits of Apache Kafka Cluster:
- Using Apache Kafka Cluster, we can track web activities by storing/sending the events for real-time processes.
- By using this, we can alert as well as report the operational metrics.
- Apache Kafka Cluster also facilitates us to transform data into the standard format.
- It allows continuous processing of streaming data to the topics.
- Because of its awesome features, it is ruling over some of the most popular applications such as ActiveMQ, RabbitMQ, AWS etc.