Your search for Apache Kafka interview questions comes to an end now! This blog contains the most common Kafka interview questions and answers, which are organized into several categories, including Apache Kafka interview questions for beginners, Apache Kafka interview questions for experienced, Apache Kafka Zookeeper interview questions, and more.
Apache Kafka is a streaming platform that can handle massive amounts of data in a short period of time. Interviewers may ask a lot of questions about this subject because software developers and engineers frequently use this program. If you want to work in software development, learning how to answer Apache Kafka interview questions can help. In this article, we explore some of the frequently asked Apache Kafka interview questions that a hiring manager might ask, as well as provide sample questions and responses.
Kafka Interview Questions
Our top 50 interview questions for evaluating candidates’ technical knowledge and hands-on expertise with Apache Kafka are listed below.
During an interview, a hiring manager may ask you generic questions in order to understand more about you as an employee. These questions provide the interviewer with an overall picture of your personality and work ethic. Here are some general Apache Kafka questions that a hiring manager could ask you during an interview:
Apache Kafka Interview Questions for Beginners
This section includes basic yet frequently asked Apache Kafka interview questions. Generally speaking, these Kafka interview questions focus on the core elements of Apache Kafka, including topics, partitions, consumer groups, load balancing, Kafka APIs, etc.
1. What is Apache Kafka?
Apache Kafka is a distributed streaming technology that enables real-time publication, subscription, storage, and processing of record streams. It’s intended to support high-throughput, fault-tolerant, and scalable data pipelines. Kafka is frequently used to create real-time data pipelines and streaming applications.
2. What are the key components of Kafka?
The key components of Kafka include:
- Producer: Disseminates Kafka-related messages.
- Consumer: Reads the published messages and subscribes to subjects.
- Broker: A Kafka server for topic management and storage.
- ZooKeeper: Oversees and plans Kafka brokers.
- Topic: A feed name or category that records are published under.
- Partition: For scalability, topics are separated into partitions.
3. What is a topic in Kafka?
In Kafka, a topic refers to a category or feed name that records are published under. In Kafka, topics are always multi-subscriber, meaning that they might have zero, one, or many subscribers to the data they contain. For better scalability and parallel processing, topics are divided into partitions.
4. Explain partitions in Apache Kafka.
In Kafka, topics are separated into different categories. Reading from each partition allows one or more consumers to read data from a Kafka topic at the same time. The divisions are split in a specific order. Although the number of partitions is optional and can be altered later, you must provide it when creating a subject.
5. How are partitions distributed in an Apache Kafka cluster?
A Kafka cluster consists of servers that share partitions of a Kafka topic. With its own set of partitions, each Kafka server manages the requests and data. To provide fault tolerance, partitions can be duplicated across several servers. Every partition has a single Kafka server that serves as its leader. The leader is responsible for all read and write requests for that particular partition. There is no limit to the number of followers a leader can have. The leader-follower connection is such that followers passively mimic the leader. If the leader fails, one of the followers can step in to take over as leader. This is how the leader-follower notion works in a Kafka cluster.
6. What is the role of ZooKeeper in Kafka?
Kafka brokers are managed and coordinated using ZooKeeper. It functions as a centralized service for group services, distributed synchronization, naming, and configuration information maintenance. Kafka cluster nodes, Kafka topics, and partitions are all monitored by ZooKeeper.
7. What are consumers in Apache Kafka?
Customers read the brokers’ data. By retrieving information from the brokers, consumers can subscribe to one or more subjects and get published messages from these topics. Customers retrieve the information at their own speed.
8. What are producers in Apache Kafka?
Producers can send messages to one or more Kafka topics. The Kafka brokers receive data from producers. Every time a producer sends messages to the broker, the broker appends the messages to a partition. It is also possible for the producer to send messages to whichever partition they choose.
9. What is a broker in Apache Kafka?
A Kafka cluster consists of one or more servers known as brokers. A broker functions as a container for several subjects with varying partitions. A broker in a cluster is only recognized by its integer ID. A connection with one broker in a cluster implies a connection with the entire cluster. Although they do not have access to all of the data, brokers in Kafka are aware of other brokers, topics, and cluster partitions.
10. How does Kafka ensure fault tolerance?
Kafka provides fault tolerance through data replication. To provide fault tolerance, each partition is duplicated across a set number of servers. One of the servers is assigned as the partition’s leader, handling all read and write requests, while the rest act as followers, passively replicating the leader.
11. What is the difference between a Kafka consumer and a consumer group?
An application that reads information from Kafka topics is called a Kafka consumer. A collection of consumers who collaborate to consume data on one or more subjects is known as a consumer group. Each message is sent to a single consumer instance within each subscribing consumer group, which is the main distinction. This enables load balancing of topic consumption and parallel processing.
12. What is the purpose of the offset in Kafka?
The offset is a unique identifier for a record within a partition. It indicates the consumer’s location in the partition. Because Kafka keeps this offset consistent across all consumer groups and partitions, each group can read from a distinct location within the partition. As a result, Kafka may offer both publish-subscribe and queue messaging formats.
13. How does Kafka handle message delivery semantics?
Three message delivery semantics are supported by Kafka:
- At most once: Although messages could be misplaced, they are never resent.
- At least once: Messages can be redelivered but are never lost.
- Exactly once: Every communication is only ever delivered once. Producer and consumer settings can be used to configure the option, which is contingent upon the particular use case.
14. What is the role of the Kafka producer API?
The Kafka producer API is used to publish streams of data to Kafka topics. It manages message partitioning, load balancing across several brokers, and compression. The producer can be set up for varying degrees of delivery guarantees and is also in charge of retrying unsuccessful publishing efforts.
15. How does Kafka support scalability?
Kafka allows for scalability through division and distributed processing. Topics can be distributed among numerous brokers, allowing for parallel processing. To read from several partitions at once, consumers can be aggregated. To expand a cluster’s capacity and enable downtime-free scaling, brokers can be added.
16. What is log compaction in Kafka?
In contrast to time-based retention, which is coarser-grained, log compaction provides finer-grained retention per record. The goal is to eliminate records that have a more recent update with the same main key in a targeted manner. In this manner, it is ensured that each key’s log contains at least its most recent state.
17. How does Kafka handle message ordering?
Kafka maintains order within a partition. Messages sent by a producer to a certain subject division are appended in the order they were sent. A consumer instance will read records in the same order that they are placed in the log. But order across partitions is not guaranteed.
18. What is the significance of the acks parameter in Kafka producers?
In Kafka producers, the acks parameter determines how many acknowledgments the producer needs to receive from the leader before deeming a request finished. It influences how long recordings last and can be configured to: 0: Not being acknowledged 1. Leader recognition only: Recognizing the full ISR (In-Sync Replica).
19. How does Kafka handle data retention?
Kafka uses modifyable retention policies to manage data retention. These can be dependent on size (e.g., retain up to 1GB per partition) or time (e.g., retain data for 7 days). Old messages are deleted once the retention limit has been reached. Additionally, log compaction is supported by Kafka for topics that require only the most recent value for each key.
20. What is the purpose of the Kafka Connect API?
A tool for reliably and scalably streaming data between Apache Kafka and other data systems is called Kafka Connect. It facilitates the rapid development of connections that transfer huge data sets into and out of Kafka. Databases, key-value stores, search indexes, and file systems can all be connected to Kafka in this way.
21. How does Kafka ensure high availability?
Kafka guarantees high availability by:
- Distribute replication among several brokers.
- When a broker fails, the leader is elected automatically.
- The capacity to add brokers to a cluster without experiencing any outages.
- For endurance, the number of in-sync clones can be adjusted.
- ZooKeeper for broker management and distributed coordination.
22. Can Kafka be used without a ZooKeeper?
There is no way to connect to the Apache Kafka Server directly without using the ZooKeeper. Therefore, the response is no. It will be impossible to fulfill any client requests if the ZooKeeper is unavailable for any reason.
23. How is load balancing maintained in Kafka?
In Kafka, the producers manage load balancing. The order of the message is preserved as the message load is distributed among the different partitions. By default, the producer uses a round-robin method to choose the subsequent partition to absorb message data. Users can define precise partitions for a message if another method than round-robin is to be utilized.
24. Explain the retention period in an Apache Kafka cluster.
Messages delivered to Kafka clusters are added to one of several partition logs. These messages are stored in multiple partition logs even after they have been eaten, for a preset amount of time or until a configurable capacity is achieved. The retention period is the customizable amount of time a message remains in the log. The communication will be available for the time period stated by the retention policy. Kafka users can set the message retention duration on a per-topic basis. A message’s default retention time is seven days.
25. How long are messages retained in Apache Kafka?
Messages sent to Kafka are maintained regardless of whether or not they are published for a set amount of time known as the retention period. A topic’s retention period is configurable. The default retention time is seven days.
26. What is the role of the Partitioning Key?
Messages are distributed to various partitions linked with a topic in a round-robin method. If a message must be sent to a certain partition, a key might be associated with it. The key specifies which partition that message will travel to. All messages that include the same key will be routed to the same partition. When a message does not specify a key, the producer will select the partition using a round-robin method.
27. When does QueueFullException occur in the Producer API?
When the producer sends messages to the broker at a rate that is too fast for the broker to manage, the QueueFullException happens. Adding more brokers would help manage the volume of communications coming in from the producer.
28. What is meant by partition offset in Apache Kafka?
When a message or record is assigned to a partition in Kafka, it receives an offset. The offset denotes the record’s position within that partition. The offset value allows a record to be identified uniquely inside a partition. The partition offset has meaning solely within that particular partition. Older records will have a lower offset since records are always added to the ends of partitions.
29. Explain fault tolerance in Apache Kafka.
In Kafka, partition data is transferred to other brokers, which are referred to as replicas. If there is a failure in the partition data in one node, other nodes will offer a backup and ensure that the data remains available. Kafka permits fault tolerance in this way.
30. What is the importance of replication in Kafka?
In Kafka, replication provides fault tolerance by ensuring that published messages are not permanently lost. Even if a node fails and they are lost on one node due to program error, machine error, or even due to software upgrades, then there is a replica present on another node that can be recovered.
31. What is Geo-Replication in Kafka?
Geo-Replication in Kafka allows you to duplicate messages in one cluster across many data centers or cloud locations. If necessary, geo-replication enables the files to be saved globally by replicating them all. Geo-replication can be accomplished in Kafka by utilizing the MirrorMaker Tool. One technique to make sure the data is backed up is through geo-replication.
32. Define the role of Kafka Streams API and Kafka Connector API.
The Streams API allows an application to function as a stream processor, quickly converting input streams into output streams. The Streams API accepts input streams from one or more topics and sends output streams to one or more output topics.
The Connector API connects Kapfka topics to applications. The connector API enables the execution and creation of reusable producers or consumers that connect Kafka topics to existing applications or data systems.
33. How does Kafka handle message compression?
Message compression is supported by Kafka to minimize the amount of data that is stored and delivered. Producer-level configuration of compression is possible, and Kafka supports a number of compression formats, such as gzip, snappy, lz4, and zstd. It is possible to set up the broker to decompress messages in order to verify and convert them to the broker’s version of the message format.
34. How does Kafka handle data replication?
Kafka keeps several copies of every partition on various brokers in order to replicate data. A partition’s leader is a single broker who manages all read and write requests, with followers replicating the leader’s data. One of the followers takes over as leader in the event that the current one fails. Each topic has a variable number of replicas.
35. What is the purpose of the Kafka Mirror Maker?
A technology called Kafka Mirror Maker is used to replicate data between Kafka clusters, possibly even between data centers. Consuming from one Kafka cluster and producing to another is how it operates. You can use this to migrate data between clusters, aggregate data from several datacenters into one place, or keep a backup of your data.
36. What is the purpose of the Kafka Quota API?
To stop a single client from using up too many broker resources, you can impose restrictions on output and fetch requests using the Kafka Quota API. Quotas can restrict the rate of data generation or consumption and can be set for each client or user. This aids in avoiding denial of service situations and guaranteeing equitable resource distribution.
37. How does Kafka handle message serialization and deserialization?
Kafka does not serialize or deserialize message data; instead, it handles it as opaque byte arrays. Nonetheless, serializers and deserializers for keys and values can be set up for Kafka producers and consumers. Avro, String, and Integer are common formats. It is possible to construct bespoke serializers and deserializers for complicated objects.
38. What is the purpose of the Kafka Schema Registry?
Metadata has a serving layer provided by the Kafka Schema Registry. It offers a RESTful interface for Avro schema storage and retrieval. It works in tandem with Kafka to guarantee that the schemas used by producers and consumers are compatible. When data models change over time while still being backward and forward compatible, this is especially helpful.
39. How does Kafka handle message delivery timeouts?
Delivery timeouts can be set up for Kafka producers. Depending on the setup, the producer may retry if a message cannot be successfully acknowledged within this delay period. The maximum amount of time a consumer can go without polling before it is deemed unsuccessful and a rebalance is initiated is controlled by the max.poll.interval.ms option on the consumer side.
40. What is the purpose of the Kafka producer’s max.block.ms parameter?
When using transmit() and metadata() to explicitly request metadata, a Kafka producer’s max.block.ms option determines how long the producer will block. A TimeoutException will be raised if this amount of time passes before the producer can submit the record. By establishing a limit on the amount of time the program will wait in these situations, this parameter helps to avoid infinite stalling.
Kafka Interview Questions for Experienced
The following Apache Kafka interview questions are excellent for individuals with a few years of industry experience aiming for a promotion to a senior position. These interview questions for Kafka go into great detail on the ideas behind Apache Kafka.
1. How can Kafka be tuned for optimal performance?
When tuning for optimal performance, two important metrics must be taken into account: throughput metrics, which indicate how many events can be processed in a given amount of time, and latency metrics, which indicate how long it takes to process one event. Most systems are designed for either delay or throughput, whereas Kafka can do both. The following actions must be taken in order to tune Kafka for best performance:
- Tuning Kafka producers: Data that producers must give to brokers is stored in batches. The producer notifies the broker when the batch is prepared. Batch size and linger time are two criteria that need to be considered in order to adjust the producers for latency and throughput. The batch size must be carefully chosen. To optimize throughput, a higher batch size is better if the producer is constantly sending messages. Nevertheless, if a very big batch size is selected, it might never fill up or take a long time to do so, which would impact the latency. It will be necessary to calculate the batch size while considering the type of messages the producer sends. In order to send larger records, the linger time is added to create a delay while more records are filled up in the batch. More messages can be transmitted in a batch with a longer linger period, but latency may be compromised. Conversely, fewer messages will be sent more quickly with a shorter linger time, which will lower latency but also lower throughput.
- Tuning Kafka broker: Every topic’s partition has a leader who will also have zero or more followers. It is critical that the leaders are correctly balanced and that nodes are not overworked relative to others.
- Tuning Kafka Consumers: To ensure that the consumers can keep up with the producers, it is advised that the number of divisions for a topic be equal to the number of consumers. The divisions are distributed among the consumers in the same consumer group.
2. What is the Kafka MirrorMaker?
A standalone tool called Kafka MirrorMaker makes it possible to move data between Apache Kafka clusters. After reading the data from the original cluster’s topics, the Kafka MirrorMaker will write the topics to a destination cluster that shares the same topic name. The source and destination clusters are separate entities that may differ in offset values and the number of partitions.
3. Is it feasible to get the message offset after production?
A class acting as a producer cannot accomplish this since, as in most queue systems, its function is to forget and fire the messages. You receive the offset from a Kaka broker as a message consumer.
4. What is the process for rebalancing the Kafka cluster?
Partitions are not automatically balanced when a client adds new disks or nodes to already existing nodes. Adding disks won’t aid in rebalancing if a number of nodes in a subject already match the replication factor. Rather, after adding new hosts, the Kafka-reassign-partitions command is advised.
5. How does Kafka interact with clients and servers?
A straightforward, language-neutral, high-performance TCP protocol is used for client-server communication. Backward compatibility with the previous version is maintained by this protocol.
6. In what way is the log cleaner set up?
It initiates the pool of cleaner threads and is enabled by default. Add log.cleanup.policy=compact to enable log cleaning on a specific topic. Either the alter topic command or topic creation time can be used to do this.
7. What are some ways to increase a remote consumer’s throughput?
To compensate for the extended network delay, the socket buffer size must be adjusted if the consumer and broker are not in the same data center.
8. When does the broker leave ISR, and how may churn be decreased there?
All of the committed messages are in ISR. Until there is a genuine failure, it should have all clones. If a replica departs from the leader, it is removed from ISR.
9. What is suggested if the replica remains out of ISR for an extended period of time?
A copy that is out of ISR for an extended period of time suggests that the follower is unable to retrieve data as quickly as the leader can.
10. How can load balancing be ensured in Apache Kafka when one Kafka fails?
In order to maintain load balancing, if a Kafka server that was in charge of any partition fails, one of its followers will step in as the new leader. The topic replication factor must be more than one in order for this to occur, meaning that the leader must have at least one follower who is willing to assume the new leadership role.
11. Where is the meta-information about topics stored in the Kafka cluster?
At the moment, the ZooKeeper in Apache Kafka stores meta-data about topics. In a different Kafka cluster, the ZooKeeper stores information about the partition locations and topic-specific configuration settings.
12. Explain the scalability of Apache Kafka.
Scalability in software refers to an application’s capacity to continue operating even when subjected to variations in processing and application requirements. The messages that pertain to a specific topic are separated into partitions in Apache Kafka. This makes it possible to scale the subject size beyond what one server can accommodate. Kafka can ensure load balancing across several consumer processes by allowing a topic to be partitioned. Furthermore, the idea of the consumer group in Kafka also helps to increase its scalability. There is only one consumer in a consumer group that consumes a specific partition. This facilitates the parallelism of digesting several messages about the same subject.
13. What is meant by Kafka Connect?
Apache Kafka offers a tool called Kafka Connect that makes it possible for scalable and dependable streaming data to transfer between Kafka and other systems. It facilitates the definition of connectors that are in charge of transferring sizable data sets into and out of Kafka. Whole databases can be processed as input via Kafka Connect. Additionally, it has the ability to gather application server metrics into Kafka topics, making the data accessible for Kafka stream processing.
14. Explain producer batch in Apache Kafka.
One by one, producers write messages to Kafka. After waiting for the messages to be transmitted to him, Kafka forms a batch, adds the messages to it, and then waits for the batch to fill up. The batch is delivered to Kafka only after that. This particular batch is referred to as the producer batch. Although it can be changed, a producer batch’s default size is 16KB. The compression and throughput of the producer requests increase with batch size.
15. Define consumer lag in Apache Kafka.
The latency between Kafka producers and consumers is known as “consumer lag.” If the rate of data production is significantly higher than the rate of data consumption, consumer groups will lag behind. The discrepancy between the consumer offset and the most recent offset is known as consumer lag.
16. What do you know about log compaction in Kafka?
Kafka uses a technique called log compaction to make sure that, for a single topic partition, at least the most recent value for each message key in the data log is kept. This enables the state to be restored in the case of a system failure or after an application crashes. During any operational maintenance, it permits cache reloading after an application restarts. Log compaction guarantees that any user who processes the log from the beginning can see all records in their final state in the order that they were originally written.
17. Explain customer serialization and deserialization in Kafka.
In Kafka, a standardized binary message format is used to convey messages between the producer, broker, and consumers. Serialization is the process by which the data is transformed into a stream of bytes for transmission. The process of transforming array bytes into the required data format is known as deserialization. To instruct the producer on how to transform the message into byte arrays, custom serializers are utilized at the producer end. At the consumer end, deserializers are employed to transform the byte arrays back into the message.
18. What role does the Kafka consumer API and Kafka producer API play?
An application can process the stream of records that is supplied to it and subscribe to one or more topics using a consumer API.
The Kafka producer API functions as a wrapper for the two producers, Sync Producer and Async Producer, as you can see. Giving the client access to all producer capabilities through a single API is the aim.
19. How can you write data from Kafka to a database?
There are two different frameworks: Connect Source and Connect Sink. Data can be loaded from source databases, and topics can be sent from Kafka to external databases.
20. What is the best method to determine the number of topics in a single Kafka broker?
To view every subject in a broker, use the list command. The describe command also allows you to access subject details.
Final Words
We hope that these Apache Kafka Interview Questions and Answers have improved your preparation for your upcoming big data job interview and given you a deeper grasp of this cutting-edge distributed streaming and queuing technology. In addition to developing the necessary Kafka skills, working on and practicing big data projects that employ Kafka is a good method to further expand your portfolio and get your ideal job.
It takes more than just learning the required skills or rehearsing a few interview questions and responses on pertinent big data tools and technologies to prepare for a big data interview. It also entails learning these tools practically to the level of an expert.
Frequently Asked Questions
What exactly does Kafka do?
Kafka often develops data-adaptive systems and real-time streaming data pipelines. Kafka enables state restoration, node re-sync, and data replication. Kafka assists with a number of functions, including log aggregation, messaging, load balancing, click-stream tracking, audit trails, and real-time data analytics and stream processing.
How to study for Kafka interview?
Resources like blogs and videos about Kafka interview preparation can help you prepare for the interview. You can use these tools to learn about the kinds of questions that are asked in Kafka interviews and how to respond appropriately. Additionally, you should attempt to obtain practical experience on some authentic Kafka projects on Github, ProjectPro, etc.
What is Kafka used for?
A distributed system called Kafka is used to build pipelines and applications for real-time streaming data.
What are main APIs of Kafka?
The Kafka Producer, Consumer, Streams, Connect, and AdminClient APIs are the five primary Kafka API functions.
What are the major components of Kafka?
Topics, producers, consumers, consumer groups, clusters, brokers, partitions, message replicas, leaders, and followers are some of Kafka’s main elements.
