55+ Best Kafka Interview Questions & Answers
Nowadays, many engineers use the distributed streaming platform Apache Kafka for developing their sites. But what exactly is Apache Kafka? Why is Apache Kafka commonly covered in technical interviews when hiring engineers? In this guide, we’ll be covering everything an engineer or hiring manager needs to know about this platform and what key answers to look for in interviews. The absolute best guide to answering Kafka interview questions.
What is Apache Kafka?
Apache Kafka is a distributed streaming platform. It was originally developed by Linkedin and was later donated to the Apache Software Foundation, known for creating open-source high-impact projects like Cassandra, Cordova, and Flex.
The Apache Kafka project aims to provide a consolidated low-latency platform for dealing with data feeds in real-time.
So we know that Apache Kafka is a streaming platform, but what exactly does a streaming platform do?
A streaming platform can do three core things:
• Publish and read information to streams of records, not unlike an enterprise messaging system or queue.
• Store records in a way that they will be unaffected by system malfunctions or component failure.
• Process streams of records in real time.
Apache Kafka specifically is used for building streaming data pipelines that push and pull data between applications or systems in a way that’s fully reliable. It is also used for building streaming applications that modify or react to incoming data.
Apache Kafka is run as a cluster that spans more than one data center via one or more servers. This cluster stores streams of records and are categorized via "topics." Each individual record has a key, a value, and a timestamp.
Apache Kafka has multiple core application programming interfaces (a.k.a. APIs):
Producer. This API allows applications to publish streams of records to various topics.
Consumer. This API allows applications to subscribe to one or multiple topics and process the records in them.
Connector. This API allows applications to act as a type of “stream processor,” which means it allows Apache Kafka to consume an input stream from a topic and subsequently produce an output stream to an output topic. This is essentially used to change input streams into output streams.
Streams. This API allows for the production and running of reusable consumers or producers that are able to link Apache Kafka topics to applications or data systems that already exist.
Essentially, it allows a relational database to be updated in real-time when a change happens to a table.
The communication that occurs between clients and servers is done with a very simplistic and high-performing language diagnostic Transmission Control Protocol (TCP). TCP sustains backward compatibility with older versions. Apache also provides a Java client, though users can use preferred clients in a variety of languages.
Apache Kafka provides several product guarantees:
• Messages that are sent by the producer to a specific topic partition will be affixed in the order that they are sent out.
• Topics with replication fact N will be tolerated up to N1 server failures without the risk of deleting records that have been committed to the log.
• The consumer instance will see the records in the order that they were stored in the log.
As a messaging system, Apache Kafka is also innovative when compared to more traditional enterprise messaging systems.
Traditional messaging uses queuing and publish-subscribe models. Queues allow groups of consumers to read from a particular server and each record goes to one of them. Publish-subscribe models allow the record to be simply broadcast to all of the consumers. There are certainly pros and cons to both models, but Apache Kafka utilizes the best of both worlds.
Apache Kafka uses a generalized form of these two models as a consumer group concept. You can use Apache Kafka to divide up processing over a group of processes as well as broadcast messages to more than one consumer group. The advantage here is that each topic in Apache Kafka has both of these properties. It can scale processing but also be a multi-subscriber without the need to choose between the two. Apache Kafka also boasts stronger ordering guarantees that the typical enterprise messaging system as well.
As a storage system, Apache Kafka is also quite advanced. Data that has been written to Apache Kafka is written to the disk and doubled in the case of system or component failure. Producers will then wait to move on to the acknowledgment stage so that a write isn’t quite considered finalized until it is completely replicated and guaranteed to operate in case the server written to fails. It’s the perfect platform to use in order to protect data from loss.
Apache Kafka’s disk structures also scale quite well. The platform performs the same whether you have 10 KB or 10 TB of data on the server.
Why Do Engineers Use Apache Kafka?
Developers and software engineers use Apache Kafka in order to allow apps to process records as they execute. It’s a brilliantly fast platform that uses input/output (a.k.a. IO) in an efficient way by batching and compressing records.
Engineers also use Apache Kafka for decoupling data streams and to stream data into real-time analytics systems, data lakes, and applications.
Why is Apache Kafka Covered in Technical Interviews?
Companies are hiring Apache Kafka engineers and developers more than ever before. This innovative technology has many solutions to common data streaming problems that tech companies face, so it’s becoming vital to hire engineers that know how to use the platform to stay ahead. Needless to say, if a developer or engineer is in need of employment opportunities, they will need to learn Apache Kafka. If a tech company wants to ensure the safety of their data in the case of a major malfunction with their system, they will need to implement Apache Kafka.
Apache Kafka interviews involve a substantial amount of investigation and proper questioning in order to establish seasoned Apache Kafka engineers from novices.
55 Apache Kafka Interview Questions & Answers
1. What is Apache Kafka?
Apache Kafka is an open-source distributed streaming platform. It’s used for a number of things, including as a messaging system, for data backup in the case of system failure, and for use with streaming applications like Apache Storm and Spark.
2. What are the major components in Apache Kafka?
Topic, which allows for the stream of messages of the same type. Consumer, for subscribing to different topics and pulling data from brokers. Brokers, a group of servers where messages can be stored. Producer, for publishing messages to a particular topic.
3. Describe what a “consumer group” is.
A consumer group is a unique Apache Kafka concept in which one or multiple consumers that use a set of subscribed topics are grouped together.
4. What is the purpose of the Apache Kafka Producer API?
Apache Kafka’s Producer API is used to wrap the system’s two producers, “Apache Kafka.producer.async.AsyncProducer” and “Apache Kafka.producer.SyncProducer,” in order to expose the Producer’s functionality through one API to the client.
5. What is the purpose of “offset?”
Messages inside of partitions have a specific and unique identification number, which is called the offset. The role of this is to identify each and every message within the partition.
6. What does the “ZooKeeper” do?
The ZooKeeper is used to store offsets of different messages that are consumed for a particular topic and partition by a unique consumer group.
7. Can Apache Kafka be used without ZooKeeper?
Apache Kafka cannot be used without ZooKeeper. One cannot bypass it and connect to Apache Kafka’s server. If ZooKeeper is down for whatever reason, a client request cannot be served until it is functional again.
8. What are “Replicas” and the “ISR?”
Replicas are pretty much a list of nodes that duplicate the log for a specific partition, no matter if they play the role of the Leader or not. In-Sync Replicas (IRS) are a group of message replicas that are synced to the Leader servers.
9. Describe in detail the roles of “Leader” and “Follower.”
The partitions in Apache Kafka have a server that takes on the role of a Leader and zero to multiple servers that play the role of Followers. The Leader server takes on the task of all read and write requests for a particular partition. The Followers replicate the Leader in a passive fashion. If the Leader server fails for whatever reason, one of the Follower servers will take over as the Leader server. This is used to make sure that the load is balanced across servers.
10. Are replications vital to Apache Kafka?
Yes. Replication is used to make sure that published messages are not deleted and can be used in the event of component malfunction, multiple software upgrades, or system failure.
11. What does it mean if a Replica remains outside of the ISR for a period of time?
It means that the Follower server is unable to grab data as quickly as data is being accumulated by the Leader.
12. Describe how one would start an Apache Kafka server.
Start by initializing the ZooKeeper server, as Apache Kafka requires it to run. After that, launch the Apache Kafka server. There are two codes required to perform these tasks.
13. In the Producer, at what time does QueueFullException happen?
QueueFullException usually occurs when the Producer tries to send messages at a speed at which the Broker cannot keep up with. Because the Producer cannot block, the developer will need to add enough Brokers to work together to handle the heavy load.
14. Name the major differences between Flume and Apache Kafka.
Both Apache Kafka and Flume are used for processing in real-time. However, Apache Kafka is scalable and can ensure message safety and durability, which Flume cannot currently do. Flume utilizes Push for its data flow, while Apache Kafka uses “pull.” Flume uses tight Hadoop integration, while Apache Kafka used loose Hadoop integration. Flume’s functionality revolves around being a system for data collection, aggregation, and movement. Apache Kafka’s functionality revolves around being a publish-subscribe model messaging system.
15. Define a “Partitioning Key.”
In the Producer, the Partition Key is used to find the destination partition of the message. A hashing based Partitioner is automatically used to find the partition identification given the key. Developers can also utilize custom Partitions instead.
16. Describe what a partition is in Apache Kafka.
Each Apache Kafka broker contains a handful of partitions. Each partition across the system can be a leader or replica of a particular topic, but they are mutually exclusive and cannot be both or neither.
17. What do you believe are some significant advantages to implementing Apache Kafka into a system?
There are five major advantages to using Apache Kafka. These include high-throughput, low latency, fault-tolerance, durability, and scalability.
18. Describe high-throughput in the context of Apache Kafka.
There is no need for substantially large hardware in Apache Kafka. This is because Apache Kafka is capable of taking on very high-velocity and very high-volume data. It can also take care of message throughput of thousands of messages per second. In summary, Apache Kafka is very fast and efficient.
19. Describe low latency in the context of Apache Kafka.
Apache Kafka is able to take on all these messages with very low latency, usually in the range of milliseconds.
20. Describe fault-tolerance in the context of Apache Kafka.
Probably one of the biggest benefits of Apache Kafka that make the platform so attractive to tech companies is its ability to keep data safe in the event of a total system failure, major update, or component malfunction. This is known as fault-tolerance. Apache Kafka is fault-tolerant because it replicates every message within the system to store in case of malfunction.
21. Describe durability in the context of Apache Kafka.
Messages are essentially immortal because Apache Kafka duplicates its messages.
22. Describe scalability in the context of Apache Kafka.
Apache Kafka has the ability to be scaled out without causing any semblance of downtime by tacking on nodes.
23. What do you believe is the biggest advantage of using Apache Kafka?
Just about everything about Apache Kafka is useful, but it’s especially useful to use for major websites that can’t afford the chance for a system malfunction and loss of data. LinkedIn, Yahoo, Twitter, Netflix, Spotify, Uber, and Pinterest are just a few massive names that utilize Apache Kafka. You can imagine if they didn’t use Apache Kafka how catastrophic it would be for their administration and user base if suddenly all of their data was lost. Very few platforms are available to prevent this, but Apache Kafka is one of the best solutions to this looming issue with mega-enterprise.
24. What do you believe is the biggest disadvantage of using Apache Kafka?
There are very few disadvantages of using Apache Kafka, but there are a few small problems that the platform presents. First, Apache Kafka does not contain a complete set of monitoring tools. Management and monitoring tools are vital for running a successful website, so it's understandable that enterprise support staff would feel uneasy about implementing Apache Kafka in the long-term. Apache Kafka also has some problems with message tweaking. The Broker uses specific system calls to send messages to the consumer. Apache Kafka's performance does reduce quite significantly if the message requires some adjustments or tweaking. Apache Kafka doesn't support wildcard topic selection, meaning it can only match up the exact topic name. Apache Kafka also suffers from a lack of pace, occasional clumsiness, and a lack of some useful messaging paradigms like request and reply and point-to-point queueing.
25. What is the purpose of a retention period within an Apache Kafka cluster?
The retention period retains the published records inside of the Apache Kafka cluster. These records can be discarded by using a particular configuration setting for the retention period. This allows some space to be free up.
26. What is the maximum message size that can be handled and received by Apache Kafka?
The maximum message size that Apache Kafka can receive and process is approximately one million bytes, or one megabyte.
27. What is multi-tenancy?
Apache Kafka can definitely be used as a multi-tenant product. Through configuring what topics can create or consume data, multi-tenancy is enabled and provides operational support for meeting quotas.
28. What does the Streams API do?
The Streams API allows an application to essentially act as a stream processor. It produces an output stream to one or multiple output topics, transforming the input streams into outputs streams easily and quickly.
29. What does the Connector API do?
The Connector API allows the reusable producers or consumers that connect Apache Kafka topics to other applications or system to be built and run.
30. Explain what geo-replication is within Apache Kafka.
For the Apache Kafka cluster, Apache Kafka MirrorMaker allows for geo-replication. Through this, messages are duplicated across various data centers or cloud regions. Geo-replication can be used in active or passive scenarios for the purpose of backup and recovery. It is also used to get data closer to users and support data locality needs.
31. Compare and contrast Apache Kafka and RabbitMQ.
RabbitMQ is considered a viable alternative to Apache Kafka. Apache Kafka is considered to be highly available, durable, and distributed. Apache Kafka allows for data to be replicated as well as shared. RabbitMQ can do none of these things and do not offer similar features. Apache Kafka has a performance rate of approximately 100,000 messages per second, which RabbitMQ has a performance rate of approximately 20,000 messages per second. In a majority of ways, Apache Kafka’s features are substantially superior to RabbitMQ as it currently stands.
32. Compare and contrast traditional queuing systems with Apache Kafka.
The best way to compare the two platform concepts is to break them down into their features. When it comes to messages retaining, traditional queuing systems usually delete the messages after finalized processing, usually from the very end of the queue. Apache Kafka allows messages to be preserved after being processed and aren’t automatically destroyed after consumers receive them. When it comes to logic-based processing, traditional queuing systems usually don’t process logic based on messages or events that are interchangeable and alike. Apache Kafka allows logic processes based on messages or events that are interchangeable and alike.
33. Why should our company use Apache Kafka Cluster?
Being able to collect large volumes of data and analyze that data is a challenge within most major tech companies. To do this efficiently, we would need a messaging system. Apache Kafka Cluster is a superior product to use because it allows for web activity tracking via storing and sending the events in real time to be processed. Apache Kafka Cluster is fantastic for alerting and reporting operational metrics, transforming data into a standard format, and continuous processing of data to the necessary topics. When you compare Apache Kafka Cluster to applications like RabbitMQ or AWS, it’s clear that it is more widely used and for good reason.
34. What is “log anatomy?”
Log anatomy is essentially the partitions within Apache Kafka. A data source will write messages directly onto the log. The advantage to log anatomy is that at any time, multiple consumers can read from the log that they have selected.
35. What is “data log” in Apache Kafka?
As it has been established, Apache Kafka retains messages for quite a long time. This makes it much more flexible for consumers because they can read messages at their convenience.
36. In your own personal way, explain how you would tune Apache Kafka for peak performance.
You’ll receive various responses to this question depending on the developer’s preferences and experience with Apache Kafka. However, look for any mention of configuring compression, batch size, and sync or async as well as “fetch size.” This ensures that the developer knows that a lot of fine tuning for Apache Kafka is focused on the Producer and Consumer side of the platform.
37. How do Apache Kafka Use Cases work?
There are many use cases available within Apache Kafka. Three common cases would be Apache Kafka metrics, Apache Kafka log aggregation, and stream processing. Apache Kafka metrics involves using Apache Kafka for operational monitoring data and to produce localized feeds of produced data via aggregating statistics from various applications. Apache Kafka log integration is used to access logs from various Leader and Follower servers across the organization. Stream processing is used to solidify Apache Kafka’s durability processes.
38. Name some applications that use Apache Kafka.
There are dozens of applications that use Apache Kafka in real time. Some well-known applications include LinkedIn (the creator of Apache Kafka), Uber, Pinterest, Yahoo, Twitter, Square, Spotify, Netflix, Goldman Sachs, Tumblr, Airbnb, Mozilla, Etsy, Foursquare, Paypal, and Shopify.
39. Describe the notable features of Apache Kafka Streams.
Apache Kafka Streams have many great features. They are very scalable and tolerant to fault. They deploy to cloud, bare metal, and containers. They are quite equally usable for small, medium, and large use cases. They are fully integrated with Apache Kafka security. They write a typical Java application and utilize "exactly-once" processing semantics. Essentially, there is no reason or need to separate processing clusters.
40. What are all of the operations within Apache Kafka?
Finding the exact position of the consumer, expanding the Apache Kafka Cluster, migrating data automatically, retiring the servers, data centers, addition and deletion of topics, modification of the topics, distinguishing turnoff, and mirroring data between Apache Kafka clusters.
41. What are the three main system tools within Apache Kafka?
The three main system tools in Apache Kafka include Apache Kafka Migration Tool, Consumer Offset Checker, and Mirror Maker. Apache Kafka Migration Tool is used to move a broker from a specific version to another version. Consumer Offset Checker is used to show topics, partitions, and owners within a specific set of topics or consumer group. Mirror maker is used to mirror an Apache Kafka cluster to another Apache Kafka cluster.
42. What is a replication tool and what are the three types?
A replication tool is used to improve durability and increase availability. The three types of replication tools for Apache Kafka are Create Topic Tool, Add Partition Tool, and List Topic Tool.
43. Describe stream processing in the context of use with Apache Kafka.
Apache Kafka stream processing is a way to process data continuously in real time, concurrently.
44. What is “Variety of Use Cases” in the context of Apache Kafka’s features?
Variety of Use Cases allows Apache Kafka to manage a massive amount of use cases that are common for a particular data lake. This allows for log assembly and website activity tracking, among others.
45. Why is Java important to use in Apache Kafka?
Apache Kafka has high processing rates, and this is done via Java language. Apache Kafka consumer clients can gain great community support with Java. It’s usually recommended to always integrate Apache Kafka in Java.
46. What is “Topic Replication Factor?”
Topic Replication Factor is a very important factor within Apache Kafka systems. If a broke goes down for whatever reason, another broker’s replicated topics can pick up the slack without data loss.
47. Name three well-known Apache Kafka Streams use cases.
The New York Times uses Apache Kafka to store and send published content to different applications to reach readers. LINE uses Apache Kafka to make it possible for users to communicate with each other using a central data hub. Zalando used Apache Kafka as an enterprise service bus.
48. Why did you decide to learn how to use Apache Kafka?
There will be quite a variety of answers to this question. Be sure to look for answers that reflect an insistence on keeping up with new tech and solutions. This ensures that you'll be hiring a programmer who is willing to learn about new platforms as well as how to implement new platforms to existing applications and systems.
49. What are some common alternatives to Apache Kafka that are currently available?
Jocko, NATS Streaming, Azure Event Hub, Active MQ, RabbitMQ, Apache Spark, Apache Samza, Amazon Kinesis, and Amazon SQS.
50. Describe the architecture Apache Kafka.
Apache Kafka as a product has a distributed design in which a cluster has many brokers or servers associated with it. The topic is divided into many partitions to store messages within, and one consumer group is designated to retrieve the messages from brokers.
51. Within the producer, when will a “queue fullness” situation come into play?
Queue fullness occurs when there are not enough Followers servers currently added on for load balancing.
52. Is message duplication necessary or unnecessary in Apache Kafka?
Duplicating or replicating messages in Apache Kafka is actually a great practice. It ensures that all messages will never be lost, even if the main or producer server suffers a failure.
53. When is it not appropriate to use Apache Kafka.
If you need to number your messages, Apache Kafka isn't capable of doing so. It's the responsibility of the consumer or consumer group to keep track of all message consumption. Kafka also does not have the ability to delete, and all messages will remain via logs in Apache Kafka until the retention time previously defined, which will result in its expiration. If this is an issue with a particular system or product, Apache Kafka may not be an ideal choice for that system or product.
54. What is Hadoop?
Apache Hadoop is an open-source software framework that allows for the storage of data, as well as the running of applications on clusters. It's essentially a massive storage platform for all types of data and boasts massive processing power as well as the ability to handle an endless stream of jobs and tasks. Hadoop can be used in conjunction with Apache Kafka for storage space, and its integration is often recommended.
55. Have you previously integrated or used Apache Kafka with a previous product?
Regardless of the various responses you will receive from interviewees, look for an affirmative answer. Apache Kafka is a complicated platform that requires seasoned and experienced developers to run. It’s also worth asking what type of data streaming platforms the interviewee has previously used before focusing their development skills on Apache Kafka.
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