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If you’re about to conduct a Kafka Interview and want to ace it, you’ve come to the right place. Fasten your seatbelts because we will delve deeper into the subject of how to ace your Kafka Interview by presenting you with over 50 Kafka Interview questions and answers guide.
Before we get to the meat of this article, let’s outline what we are going to tackle.
First and foremost, let’s see what is Kafka.
Kafka has the ability to process a significant amount of data in a short period of time. It also has low latency, allowing it to handle data in real-time. Despite the fact that Apache Kafka is written in Scala and Java, it may be utilized with a wide range of computer languages.
Traditional message queues, such as RabbitMQ, are not equivalent to Kafka. RabbitMQ deletes messages as soon as the consumer confirms them, whereas Kafka maintains them for a set amount of time (the default is 7 days) after they are received. RabbitMQ also sends messages to consumers and keeps track of their load. It specifies how many messages each consumer should process at any given moment. Consumers, on the other hand, can pull messages from Kafka.
It is designed to be horizontally scalable by adding more nodes.
It is used for fault-tolerant storage, as well as publishing and subscribing to a record stream. The programs are designed to handle timing and consumption data. Kafka replicates log partitions across several hosts. Developers and users submit coding revisions, which the system saves, reads, and analyzes in real-time. Kafka is used for messaging, online activity tracking, log aggregation, and commit logs. Kafka can be used as a database, however, it lacks a data schema and indexes.
Now, let’s dive into the variety of frequently asked Kafka interview questions and answers guide for both freshers and experienced candidates.
We want to bring to your attention that all these Kafka interview questions and answers guide were carefully handpicked by Huru’s professional hiring managers. But, it would be harmless to conduct more research for better understanding. Let’s go.
Ultimate Kafka Interview Questions and Answers Guide for Freshers
What are the features of Kafka?
As we are all aware, Kafka has so many features that it has brought to the Big Data field including:
- Kafka is a fault-tolerant messaging system with high throughput.
- A Topic is a built-in patriation system in Kafka.
- Kafka also comes with a replication mechanism.
- Kafka is a distributed messaging system that can manage massive volumes of data and transfer messages from one sender to another.
- The messages can also be saved to storage and replicated across the cluster using Kafka.
- Kafka works with Zookeeper for synchronization and collaboration with other services.
- Kafka provides excellent support for Apache Spark.
What is meant by Kafka schema registry?
Avro schemas are stored in a Schema Registry, which is available to both producers and consumers in a Kafka cluster. Avro schemas allow producers and consumers to configure compatibility parameters for easy serialization and de-serialization. The Kafka Schema Registry is used to ensure that the schemas used by the consumer and the producer are the same. When using the Confluent schema registry in Kafka, producers just need to supply the schema ID, not the entire schema. Using the schema ID, the consumer searches the Schema Registry for the appropriate schema.
What are the major components of Kafka?
The following are considered to be the major components of Kafka:
- Topic: A Topic is a type of feed or category where records can be saved and published. All of Kafka’s records are organized by topics. Producer applications publish data to topics, whereas consumer apps read data from them. Records published to the cluster are kept in the cluster for a configurable amount of time. Kafka stores record in a log, and it’s up to the consumers to keep track of where they are (the “offset”). When a consumer reads a message, the offset is usually advanced in a linear fashion. On the other hand, the consumer is in control of the situation because he or she can consume messages in any order. When reprocessing records, for example, a consumer can reset to an older offset.
- Producer: A Kafka producer is a data source that optimizes, writes, and publishes messages for one or more Kafka topics. Kafka producers can use partitioning to serialize, compress, and load balance data among brokers.
- Consumer: Consumers read data by reading messages from topics they have subscribed to. Groups of customers will be formed. Each consumer in a consumer group will be responsible for reading a subset of each subject’s partitions for which they have subscribed.
- Broker: A Kafka broker is a server that is part of a cluster of Kafka servers (in other words, a Kafka cluster is made up of a number of brokers). A Kafka cluster is often built by multiple brokers working together to offer load balancing, reliable redundancy, and failover. Brokers use Apache ZooKeeper to manage and coordinate the cluster. Each broker instance can manage read and write volumes of hundreds of thousands per second without losing performance (and gigabytes of messages). Each broker has a unique ID and is responsible for one or more subject log divisions. Kafka brokers also use ZooKeeper for leader elections, in which a broker is chosen to lead the handling of client requests for a certain partition of a topic. When a customer connects to any broker, the entire Kafka cluster is brought up to speed. To accomplish reliable failover, a minimum of three brokers should be employed; the more brokers used, the more reliable the failover becomes.
Can you explain the four core API architecture that Kafka uses?
In this ultimate Kafka interview questions guide, these are the four core API architecture that Kafka uses:
- Producer API: An application can use Kafka’s Producer API to publish a stream of records to one or more Kafka topics.
- Consumer API: The Kafka Consumer API allows an application to subscribe to one or more Kafka topics. It also allows the program to handle streams of records generated in connection with such topics.
- Streams API: The Kafka Streams API allows an application to process data in Kafka using a stream processing architecture. This API allows an application to take input streams from one or more topics, process them with streams operations, and then generate output streams to send to one or more topics. In this way, the Streams API allows you to turn input streams into output streams.
- Connect API: Kafka topics are linked to applications via the Kafka Connector API. This gives up new opportunities for building and controlling producer and consumer activities, as well as creating reusable links across these solutions. For example, a connector might collect all database updates and make them available in a Kafka topic.
What do we mean by a Partition in Kafka?
Kafka topics are divided into partitions, with each partition containing records in a predetermined order. Each record in a partition is assigned and attributed a unique offset. A single topic can contain many partition logs. This permits several people to read at the same time from the same topic. Partitions, which divide data into a single topic and distribute it among multiple brokers, can be used to parallelize topics.
In Kafka, replication is done at the partition level. A replica is a topic partition’s redundant element. Partitions frequently contain one or more copies, implying that partitions contain messages that have been duplicated across many Kafka brokers in the cluster.
Each partition (replica) has one server that acts as the leader, with the rest acting as followers. The partition’s read-write requests are handled by the leader replica, while the followers replicate the leader. If the leader server fails, one of the followers becomes the new leader. We should aim for a good balance of leaders, with each broker leading an equal number of partitions, to distribute the burden.
What do you mean by a zookeeper in Kafka and what are its uses?
Apache ZooKeeper is a naming registry and a distributed, open-source configuration and synchronization service for distributed applications. It monitors the status of Kafka cluster nodes, as well as Kafka topics, partitions, and other data.
Kafka brokers use ZooKeeper to maintain and coordinate the Kafka cluster. ZooKeeper notifies all nodes when the topology of the Kafka cluster changes, such as when brokers and topics are added or removed. ZooKeeper notifies the cluster when a new broker joins the cluster, as well as when a broker fails.
ZooKeeper also allows brokers and topic partition pairs to elect leaders, allowing them to choose which broker will be the leader for a given partition and which will hold clones of the same data.
Is it possible to use Kafka without Zookeeper?
- As of version 2.8, Kafka can now be utilized without ZooKeeper. When Kafka 2.8.0 was released in April 2021, we all had the opportunity to check it out without ZooKeeper. This version, however, is not yet ready for production and is missing a few crucial features.
- It was not feasible to connect directly to the Kafka broker without using Zookeeper in prior versions. This is because the Zookeeper is unable to fulfill client requests when it is down.
Can you explain the concept of Leader and Follower in Kafka.
In this ultimate Kafka interview questions guide, each partition in Kafka has one server acting as a Leader and one or more servers acting as Followers. The Leader is in control of the partition’s read and writes requests, while the Followers are in charge of passively replicating the leader. If the Leader is unable to lead, one of the Followers will take over. As a result, the server’s load is balanced.
Why is Topic Replication important in Kafka? What do you mean by ISR in Kafka?
Pay attention, this is one of the hardest Kafka interview questions throughout this article. The ability to build Kafka deployments that are both durable and highly available requires topic replication. When one broker fails, topic copies on other brokers continue to serve as backups, ensuring that no data is lost and the Kafka deployment is not affected. The replication factor determines how many copies of a subject are stored across the Kafka cluster. It occurs at the partition level and is controlled by the subject. For each partition, a replication factor of two, for example, will store two copies of a subject.
Each partition has an elected leader, and other brokers keep a backup copy that can be used in the event of a failure. The replication factor can’t be greater than the cluster’s entire number of brokers, logically. An In-Sync Replica (ISR) is a replica that is up to date with the partition’s leader.
Can tell me what do you understand about a consumer group in Kafka?
In Kafka, a consumer group is a group of consumers who collaborate to ingest data from the same topic or set of topics. A customer group is essentially represented by the name of an application. Kafka’s customers usually fall into one of three categories. To consume messages from a consumer group, use the ‘-group’ command.
What is the maximum size of a message that Kafka can receive?
A Kafka message can be up to 1MB in size by default (megabyte). You can change the size in the broker settings. Kafka, on the other hand, is built to deal with 1KB messages.
What does it mean if a replica is not an In-Sync Replica for a long time?
When a replica is out of ISR for an extended length of time, it means the follower is unable to acquire data at the same rate as the leader.
How do you start a Kafka server?
In this ultimate Kafka interview questions guide, we begin by extracting Kafka after downloading the most recent version. In order to execute Kafka, we must ensure that our local environment has Java 8+ installed.
To start the Kafka server and ensure that all services are started in the correct order, run the following commands:
- Start the ZooKeeper service by doing the following:
- To start the Kafka broker service, open a new terminal and type the following commands:
$ bin/kafka-server-start.sh config/server.properties
What do you mean by geo-replication in Kafka?
Kafka’s Geo-Replication functionality allows messages from one cluster to be replicated across many data centers or cloud regions. Geo-replication requires duplicating all of the files and, if necessary, storing them all over the world. With Kafka’s MirrorMaker Tool, geo-replication can be performed. Data backup can be ensured using geo-replication.
What are some of the disadvantages of Kafka?
In this article that is revolving around the ultimate Kafka interview questions and answers guide, this is the disadvantages of Kafka:
- When messages are tweaked, Kafka’s performance suffers. Kafka works well when the message does not need to be updated.
- Kafka does not support wildcard topic selection. It’s crucial to use the appropriate issue name.
- When dealing with large messages, brokers and consumers degrade Kafka’s performance by compressing and decompressing the messages. This has an effect on Kafka’s performance and throughput.
- Kafka does not support several message paradigms, such as point-to-point queues and request/reply.
- Kafka lacks a comprehensive set of the monitoring tools.
Can you tell me about some of the real-world uses of Apache Kafka.
As you can see below, these are the real-world usages of Apache Kafka:
- As a Message Broker: Kafka can handle a large number of similar types of messages or data because of its high throughput value. Kafka can be used as a publish-subscribe messaging system, allowing data to be easily read and published.
- To Monitor operational data: Kafka can be used to track metrics associated with certain technology, such as security logs.
- Website activity tracking: Kafka can be used to ensure that data is successfully sent and received by websites. Kafka is capable of handling the large volumes of data generated by websites for each page as well as user activity.
- Data logging: Kafka’s data replication between nodes feature can be utilized to recover data from failed nodes. Kafka may also be used to gather data from a variety of logs and present it to users.
- Stream Processing with Kafka: Streaming data, which is data that is read from one topic, processed, and then written to another, can be handled using Kafka. A new topic containing the processed data will be available to users and apps.
What are the use cases of Kafka monitoring?
The following are the use cases of Kafka monitoring:
- Track System Resource Consumption: It can be used to keep track of system resource use over time, such as memory, CPU, and disk.
- Monitor threads and JVM usage: Kafka relies on the Java garbage collector to free up memory, so make sure it runs regularly to keep the Kafka cluster alive.
- Maintain an eye on the broker, controller, and replication statistics so that partition and replica statuses can be changed as needed.
- Identifying which applications are producing excessive demand and performance bottlenecks may aid in quickly resolving performance issues.
Explain the traditional methods of message transfer? How is Kafka better from them?
In this ultimate Kafka interview questions guide, The traditional methods of message transmission are as follows:
The message queuing pattern employs a point-to-point technique. A message in the queue will be discarded once it has been consumed, similar to how a message in the Post Office Protocol is removed from the server once it has been delivered. These queues allow for asynchronous messaging.
If a network difficulty prevents a message from being delivered, such as when a consumer is unavailable, the message will be queued until it is transmitted. As a result, messages aren’t always sent in the same order. Instead, they are distributed on a first-come, first-served basis, which in some cases can improve efficiency.
Publish – Subscriber Model:
The publish-subscribe pattern entails publishers producing (“publishing”) messages in multiple categories and subscribers consuming published messages from the various categories to which they are subscribed. A message is only withdrawn once it has been consumed by all category subscribers, unlike point-to-point texting.
Kafka caters to a single consumer abstraction, the consumer group, which contains both of the aforementioned. The advantages of adopting Kafka over standard communications transfer mechanisms are as follows:
- Scalable: The data is partitioned and streamlined using a cluster of devices, which increases storage capacity.
- Faster: A single Kafka broker can serve thousands of customers because it can handle megabytes of reads and writes per second.
- Durability and Fault-Tolerant: By copying the data in the clusters, the data is kept permanent and tolerant of any hardware failures.
What are the benefits of using clusters in Kafka?
In this ultimate Kafka interview questions guide, A Kafka cluster is essentially a collection of brokers. They’re utilized to keep things balanced. Because Kafka brokers are stateless, Zookeeper is used to maintain track of the state of their cluster. Hundreds of thousands of reads and writes per second can be handled by a single Kafka broker instance, and each broker may handle TBs of messages without sacrificing speed. The Kafka broker leader can be chosen using Zookeeper. As a result, having a cluster of Kafka brokers greatly improves performance.
Describe the partitioning key in Kafka.
Messages are referred to as records in Kafka’s language. A key and a value are assigned to each record, with the key being optional. The record’s key is utilized for record splitting. Each topic will be divided into one or more parts. Partitioning is a simple data structure to understand. It’s the append-only record sequence, which is ordered chronologically by the time they were attached. Once a record is written to a partition, it is assigned an offset, which is a sequential id that reflects the record’s position in the partition and uniquely identifies it inside it.
The record’s key is used to partition the data. By default, Kafka producer looks at the key of the record to identify which partition it should be written to. For two recordings with the same key, the producer will always choose the same partition.
This is critical because we may be required to give records to consumers in the order in which they were created. When a customer purchases an eBook from your webshop and then cancels the transaction, you want these events to occur in the sequence they were made. If a cancellation event occurs before a buy event, the cancellation will be rejected as invalid (since the purchase has not yet been recorded in the system), and the system will record the purchase and send the product to the client (and lose you money). To fix this difficulty and ensure order, you may use a customer id as the key of these Kafka records. This ensures that a customer’s purchase events are all grouped together.
Can you tell me what is the purpose of partitions in Kafka?
From the standpoint of the Kafka broker, partitions allow a single topic to be partitioned across many servers. This gives you the ability to store more data in a single topic than on a single server. If you have three brokers and need to store 10TB of data in a topic, you can create a subject with only one partition and store the entire 10TB on one broker. Another option is to create a three-partitioned topic with 10 TB of data distributed across all brokers. From the consumer’s perspective, a partition is a unit of parallelism.
Ultimate Kafka Interview Questions and Answers Guide for Experienced
What do you mean by multi-tenancy in Kafka?
Multi-tenancy is a software operation paradigm in which many instances of one or more applications function independently in a shared environment. Although the instances are physically different, they are logically linked. In a system that supports multi-tenancy, the amount of logical isolation must be complete, but the level of physical integration can vary. Because Kafka allows for the configuration of many topics for both consumption and production on the same cluster, it is multi-tenant.
What is a Replication Tool in Kafka? And explain some of the replication tools available in Kafka?
To establish a high-level design for the replica management process, the Kafka Replication Tool is employed. Some of the replication tools that are accessible are as follows:
- Preferred Replica Leader Election Tool: The Preferred Replica Leader Election Tool distributes partitions to many brokers in a cluster, each of which is known as a replica. The favorite replica is a term used to describe the leader. For various partitions, the brokers generally distribute the leader position fairly across the cluster, but due to failures, planned shutdowns, and other circumstances, an imbalance might develop over time. By reassigning the preferred copies, and hence the leaders, this tool can be utilized to maintain the balance in these instances.
- Topics tool: The Kafka topics tool is in charge of all administration operations relating to topics, including:
- Listing and describing the topics.
- Topic generation.
- Modifying Topics.
- Adding a topic’s dividers.
- Disposing of topics.
- Tool to reassign partitions: This utility allows you to alter the replicas assigned to a partition. Adding or removing followers from a division is referred to as this.
- StateChangeLogMerger tool: The StateChangeLogMerger utility gathers data from brokers in a cluster, formats it into a central log, and assists with state change troubleshooting. There are situations when the election of a leader for a particular split causes problems. This tool can be used to determine the source of the problem.
- Change topic configuration tool: Create new, Alter and delete configuration options with this tool.
What is the difference between Rabbitmq and Kafka?
In this ultimate Apache Kafka interview questions and answers article, here are the main differences between Rabbitmq and Kafka:
Based on Architecture:
- Request/reply, point-to-point, and pub-sub communication patterns are all employed by Rabbitmq, which is a general-purpose message broker.
- It operates on the basis of a smart broker/dumb consumer concept. The broker watches the consumer’s status and sends signals to them at roughly the same time.
- It’s a mature platform with lots of Java, client libraries,.NET, Ruby, and Node.js support. It also comes with a number of plugins.
- Asynchronous or synchronous communication is possible. It also gives you the option of deploying it in a dispersed manner.
- For high-volume publish-subscribe messages and streams, Kafka is a message and stream platform. It’s long-lasting, quick, and scalable.
- It’s a log-like durable message store that runs in a server cluster and keeps streams of records in topics (categories).
- Messages are made up of three parts: a value, a key, and a timestamp in this case.
- Because it does not track which messages are viewed by consumers and only keeps unread messages, it has a dumb broker / smart consumer paradigm.
- Kafka keeps track of all communications for a set amount of time. External services are necessary to run in this case, including Apache Zookeeper in some cases.
Manner of Handling Messages:
Based on Approach :
Kafka:Kafka employs the pull model. Consumers request batches of messages from a specific offset. Kafka allows long-pooling when there are no messages past the offset, which reduces tight loops.
A pull approach makes sense because of Kafka’s partitions. Kafka supports message orders in a partition with no competing customers. Users can now make use of message batching for faster message delivery and increased throughput.
Rabbitmq: RabbitMQ is based on the push paradigm, which imposes a prefetch restriction on users to protect them from becoming overwhelmed. This can be used for low-latency messaging. The purpose of the push model is to disseminate messages individually and quickly, ensuring that work is distributed fairly and messages are processed roughly in the order they arrive.
Based on Performance:
Kafka: Kafka has much greater performance than message brokers like RabbitMQ. Its use of sequential disc I/O improves performance, making it a desirable candidate for queue implementation. It can accomplish high throughput (millions of messages per second) with limited resources, which is critical for massive data use cases.
Rabbitmq: RabbitMQ can handle a million messages per second as well, albeit at the cost of greater resources (around 30 nodes). RabbitMQ may be used for many of the same applications as Kafka, however, it requires the usage of other technologies like Apache Cassandra.
What are the parameters that you should look for while optimizing Kafka for optimal performance?
While tuning for optimal performance, two major measurements are taken into account: latency measures, which refer to the time it takes to process one event, and throughput measures, which refer to the number of events that can be processed in a given amount of time. Most systems are optimized for either delay or throughput, but Kafka can accomplish both.
The following stages have to do with optimizing Kafka’s performance:
Kafka producer tuning: A batch stores the information that producers must submit to brokers. When the batch is ready, the producer sends it to the broker. Two parameters must be considered while adjusting the producers for latency and throughput: batch size and linger time. The batch size must be carefully chosen. To maximize throughput, larger batch size is recommended if the producer is constantly providing messages. If the batch size is set to a large value, however, it may never fill up or take a long time to do so, causing the delay. The batch size should be determined by the nature of the volume of messages sent by the producer.
The linger length is included to create a delay while more records are added to the batch, allowing for the transmission of larger records. With a longer linger period, more messages can be sent in a single batch, but latency may suffer as a result. In contrast, a shorter linger time will result in fewer messages being transmitted faster, resulting in lower latency but also lower throughput.
Tuning the Kafka broker: A leader is assigned to each partition in a topic, and each leader has 0 or more followers. It’s crucial that the leaders be well-balanced, and that particular nodes aren’t overworked compared to others.
Tuning Kafka Consumers: The number of divisions for a topic should be equal to the number of consumers to guarantee that consumers keep up with producers. Consumers in the same consumer category are separated into divisions.
What is the difference between Redis and Kafka.
The table below illustrates the differences between Redis and Kafka:
Describe in what ways Kafka enforces security.
In this ultimate Kafka interview questions guide, these are the ways Kafka enforces the security:
Encryption: The Kafka broker’s communications with its many clients are all encrypted. This ensures that data is not intercepted by other clients. All messages are sent and received between the components in an encrypted format.
Authentication: Apps that use the Kafka broker must be authorized before they may connect to it. Only apps that have been approved will be allowed to send and receive messages. Authorized programs will use unique ids and passwords to identify themselves.
Authentication is followed by authorization. After a client has been validated, it is feasible for it to publish or consume messages. The permission allows writing access to apps to be limited in order to avoid data contamination.
Differentiate between Kafka and Java Messaging Service(JMS).
The following table illustrates the differences between Kafka and Java Messaging Service:
What do you understand about Kafka MirrorMaker?
MirrorMaker is a standalone application for copying data between Apache Kafka clusters. The MirrorMaker reads data from original cluster topics and writes it to a destination cluster with the same name as the original cluster topic. The source and destination clusters are two distinct entities with different partition numbers and offset values.
Can you differentiate between Kafka and Flume?
In this ultimate Kafka interview questions guide, Apache Flume is a trustworthy, distributed, and available software for quickly and efficiently aggregating, collecting, and transferring large amounts of log data. Its architecture, which is built on streaming data flows, is both adaptable and simple. Java is the programming language used to create it. It has its own query processing engine, which allows it to modify each new batch of data before sending it to its destination. It is built to be flexible.
The following table illustrates the differences between Kafka and Flume:
What do you mean by confluent Kafka? What are its advantages?
This is one of the the ultimate Kafka interview questions. Confluent is an Apache Kafka-based data streaming platform that can do more than just publish and subscribe. It can also store and process data within the stream. Confluent Kafka is a more extensive version of Apache Kafka. It improves Kafka’s integration capabilities by adding tools for optimizing and maintaining Kafka clusters, as well as methods for ensuring the security of the streams. Because of the Confluent Platform, Kafka is simple to set up and use. Confluent’s software is available in three flavors:
- A free, open-source streaming platform that makes it simple to get started with real-time data streams;
- An enterprise-grade version with more administration, operations, and monitoring tools;
- A premium cloud-based version.
Following are the advantages of Confluent Kafka :
- It features practically all of Kafka’s characteristics, as well as a few extras.
- It greatly simplifies the administrative operations procedures.
- It relieves data managers of the burden of thinking about data relaying.
Describe message compression in Kafka. What is the need for message compression in Kafka? Also mention if there are any disadvantages of it.
In Kafka, producers send data to brokers in JSON format. The JSON format saves data as strings, which can result in the Kafka topic storing several duplicate records. As a result, the quantity of space used on the disc increases. As a result, data is compressed or delayed before being delivered to Kafka in order to save disk space. No changes to the consumer or broker setup are necessary because message compression is conducted on the producer side.
Because of the following factors, it is advantageous:
- It reduces the size of messages sent to Kafka, lowering their latency.
- With less bandwidth, producers can send more net messages to the broker.
- Data saved in Kafka via cloud platforms can save money in situations where cloud services are compensated.
- Message compression decreases the amount of data stored on disk, allowing read and write operations to be performed more quickly.
The following are some of the drawbacks of message compression:
- To condense their work, producers must spend some CPU cycles.
- For consumers, decompression takes many CPU cycles.
- The CPU is put under more strain during compression and decompression.
Can you tell me about some of the use cases where Kafka is not suitable?
In this ultimate Kafka job interview questions and answers article, we mention the main use cases where Kafka is not suitable.
- Kafka is a data management system designed to handle massive quantities of data. If only a small number of messages need to be handled each day, traditional messaging systems would be more appropriate.
- Despite the fact that Kafka has a streaming API, it is insufficient for data transformations. Kafka should be avoided for ETL (extract, transform, load) jobs.
- For instances where a simple task queue is required, there are better solutions, such as RabbitMQ.
- If long-term storage is required, Kafka is not the best option. It simply allows you to save data for a set amount of time and then delete it.
What do you understand about log compaction and quotas in Kafka?
Kafka uses log compaction to ensure that at least the last known value for each message key inside the log of data is retained for each topic partition. This permits the state to be restored after an application crash or a system failure. It permits refreshing caches after an application restarts during any operational maintenance. Because of the log compaction, every consumer processing the log from the beginning will be able to see at least the final state of all entries in the order in which they were written.
As of Kafka 0.9, a Kafka cluster can impose quotas on producers and retrieve requests. Client-id quotas are byte-rate restrictions defined for each client-id. A client-id is a logical identification for an application that makes requests. As a result, a single client-id can be used to link to several producers and client instances. The quota will be applied uniformly to all of them. By using massive amounts of data, quotas prevent a single application from monopolizing broker resources and triggering network saturation.
Please mention the guarantees that Kafka provides?
In this ultimate Kafka interview questions and answers guide, these are the guarantees that Kafka provides.
- The messages are displayed in the order in which the producers published them. The messages are kept in their original order.
- The number of replicas is determined by the replication factor. The Kafka cluster offers failure tolerance for up to n-1 servers if the replication factor is n.
- Kafka can give “at least one” delivery semantics per partition. This means that even if a partition is delivered multiple times, Kafka guarantees that it will reach at least one customer.
What is meant by an unbalanced cluster in Kafka? How can you balance it?
To add new brokers to an existing Kafka cluster, simply add a unique broker id, listeners, and log directory to the server.properties file. These brokers, on the other hand, will not be assigned any data partitions from the cluster’s existing topics, so they won’t be doing much work unless partitions are relocated or new topics are created. If a cluster has any of the following issues, it is said to be unbalanced.
Consider the following scenario: a topic with three partitions and a three-broker replication factor.
On a partition, the leader receives all reads and writes. Fetch requests are sent to the leaders by their followers in order to receive their most current messages. Followers are only used for redundancy and fail-over.
Consider the example of a failing broker. It’s possible that the failed broker was made up of many different leader partitions. The followers on the other brokers support each failing broker’s leader partition as the leader. Because it is not possible to failover to an out-of-sync replica, the follower must be in sync with the leader in order to be promoted to leader.
There is no redundancy if another broker goes down because all of the leaders are on the same broker.
The divisions acquire some redundancy when both brokers 1 and 3 go live, but the leaders remain concentrated on broker 2.
As a result, there is a leader imbalance among the Kafka brokers. The cluster is in a leader skewed state when a node is a leader for more partitions than the number of partitions/number of brokers.
Solving the Leader Skew problem:
The following are the steps how to solve a Leader Skew problem:
- Using the –generate option, generate the candidate assignment configuration with the partition reassignment tool (Kafka-reassign-partition.sh). Here is the existing and planned replica allotment.
- Make a JSON file with the assignment suggestion.
- Run the partition reassignment tool to update the metadata for balancing.
- After the partition reassignment is complete, run the “Kafka-preferred-replica-election.sh” tool to finish the balancing.
How to expand a cluster in Kafka?
A server just has to be given a unique broker id and Kafka must be started on that server to be added to a Kafka cluster. A new server will not be provided to any of the data divisions until a new topic is formed. As a result, whenever a new machine is added to the cluster, some old data must be transferred to the new machines. The partition reassignment tool is used to move some partitions to the new broker.
The new server will be made a follower of the partition it is migrating to, allowing it to entirely replicate the data on that disk. After all of the data has been duplicated, the new server can join the ISR, and one of the existing replicas will delete the data for that partition.
What do you mean by the graceful shutdown in Kafka?
Any broker shutdown or failure will be detected automatically by the Apache cluster. In this case, new leaders will be picked for partitions previously handled by that device. This can occur as a result of a server failure or even when the server is shut down for maintenance or configuration changes. Kafka provides a graceful approach for ending a server rather than killing it when it is shut down on purpose.
When a server is turned off, the following happens:
- Kafka guarantees that all of its logs are synced onto a disk to avoid having to perform any log recovery when it is restarted. Purposeful restarts can be sped up since log recovery requires time.
- All partitions for which the server is the leader will be migrated to the replicas before the server is shut down. As a result, the leadership transition will be speedier, and the time each partition is unavailable will be reduced to a few milliseconds.
Can the number of partitions for a topic be changed in Kafka?
You can’t currently reduce the number of partitions for a subject in Kafka. The partitions can be shrunk but not expanded. Altering the behavior of a topic and its associated configurations is possible with Apache Kafka’s modify command. Use the modify command to create more partitions.
Use the following command to increase the number of partitions to five:
./bin/kafka-topics.sh –alter –zookeeper localhost:2181 –topic sample-topic –partitions 5
What do you mean by BufferExhaustedException and OutOfMemoryException in Kafka?
A BufferExhaustedException is thrown when the producer can’t assign memory to a record because the buffer is full. If the producer is in non-blocking mode and the pace of production over an extended period of time exceeds the rate at which data is transferred from the buffer, the allocated buffer will be emptied and an exception will be thrown.
An OutOfMemoryException may occur if the consumers send large messages or if the number of messages sent increases faster than the rate of downstream processing. As a result, the message queue becomes overburdened, using RAM.
How will you change the retention time in Kafka at runtime?
In this ultimate Kafka interview questions and answers guide, here is a table that shows the change of the retention time in Kafka at runtime.
What are Znodes in Kafka Zookeeper and how many types of Znodes are there?
Znodes are the nodes in a ZooKeeper tree. Znodes keep version numbers for data modifications, ACL changes, and timestamps in a structure. The version number and timestamp are used by ZooKeeper to verify the cache and ensure that updates are coordinated. When data on Znode changes, the version number associated with it increases.
There are three different types of znodes:
- Persistence Znode: These are znodes that keep working even after the client who created them has been disconnected. By default, all znodes are persistent unless otherwise defined.
- Ephemeral Znode: Ephemeral znodes exist only as long as the client is alive. The ephemeral Znodes are immediately erased when the client that created them disconnects from the ZooKeeper ensemble. They play a vital role in the leader’s election.
- Sequential Znode: The ZooKeeper can be requested to append a growing counter to the path’s end when znodes are built. The counter on the parent znode is unique. Either persistent or ephemeral nodes can be found in a sequence.
Explain the role of the offset.
The offset is a unique ID number assigned to messages contained in the partitions. The offset’s purpose is to identify each message within the partition.
How can large messages be sent in Kafka?
The maximum size of a message that can be sent in Kafka is 1MB by default. A few properties must be changed in order to transmit larger messages using Kafka. The configuration details that need to be modified are listed below.
- At the Consumer end – fetch.message.max.bytes
- At the Broker, end to create replica– replica.fetch.max.bytes
- At the Broker, the end to create a message – message.max.bytes
- At the Broker end for every topic – max.message.bytes
Explain how Kafka provides security.
In this ultimate Kafka interview questions and answers guide, theses how Kafka provides security.
- Encryption: All communications sent between the Kafka broker and its many clients are encrypted. This prevents data from being intercepted by other clients. All messages are shared in an encrypted format between the components.
- Authentication: Before being able to connect to Kafka, apps that use the Kafka broker must be authenticated. Only approved applications will be able to send or receive messages. To identify themselves, authorized applications will have unique ids and passwords.
- After authentication, authorization is carried out. It is possible for a client to publish or consume messages once it has been validated. The permission ensures that write access to apps can be restricted to prevent data contamination.
Can Apache Kafka be considered to be a distributed streaming platform? Elaborate.
Pay attention because this is one of the most challenging and ultimate Kafka interview questions of all time. Yes, Apache Kafka is a platform for distributed streaming data. A streaming platform is defined as one that possesses the following three features:
- The ability to publish and subscribe to data streams.
- Provide services that are similar to those provided by a message queue or an enterprise messaging system.
- Streams of records should be stored in a way that is both durable and fault-tolerant.
Kafka can be regarded as a streaming platform because it fits all three of these criteria.
Furthermore, a Kafka cluster is said to be distributed because it is made up of numerous servers that act as brokers. Kafka topics are divided into many partitions to ensure load balancing. Brokers execute these partitions in parallel, allowing numerous producers and consumers to simultaneously send and receive messages.
At the end of the day, distributed streaming platforms handle large amounts of data in real-time by pushing them to multiple servers for real-time processing.
Can Apache Kafka be integrated with Apache Storm? If yes, explain how.
Yes, Apache Kafka and Apache Storm are designed to work together. Apache Storm is a distributed real-time processing system that can handle massive volumes of data in real-time. Storm works in the background, continuously absorbing data from configured sources and routing it via the data pipeline to specified destinations.
The following components work together in Storm to process streaming data:
- Spout: source of the stream. It is a continuous stream of log data
- Bolt: the bolt consumes input streams, processes them, and possibly emits new streams.
Here are some of the classes that can be used to integrate Apache Storm and Apache Kafka:
- BrokerHosts: BrokerHosts is a graphical user interface. Two of their implementations are ZkHosts and StaticHosts. ZkHosts dynamically tracks Kafka brokers and is used to keep track of their details in ZooKeeper. The Kafka brokers and their details are manually specified using StaticHosts.
- KafkaSpout API: Our spout implementation, KafkaSpout, will be merged with Storm. It retrieves messages from a Kafka topic and emits them as tuples into the Storm environment. SpoutConfig provides KafkaSpout with configuration information.
Why is the Kafka broker said to be “dumb”?
The Kafka broker does not keep track of which messages the consumers have read. It simply keeps all of the messages in its queue for a predetermined amount of time, known as the retention time, before deleting them. It is the consumer’s obligation to remove messages from the queue. As a result, Kafka’s architecture is described as “smart-client, dumn-broker.”
What are the responsibilities of a Controller Broker in Kafka?
The Controller’s primary responsibility is to manage and coordinate the Kafka cluster and Apache ZooKeeper. The controller job can be assumed by any broker in the cluster. However, once the application is up and running, the cluster can only have one controller broker. The broker will attempt to construct a Controller node in ZooKeeper when it first starts up. The controller is the first broker who creates this controller node.
The controller is responsible for:
- creating and deleting topics
- Adding partitions and assigning leaders to the partitions
- Managing the brokers in a cluster
- Leader Election
- Reallocation of partitions.
What causes OutOfMemoryException?
If the consumers are sending huge messages or if there is a spike in the number of messages sent at a rate quicker than the rate of downstream processing, an OutOfMemoryException may arise. As a result, the message queue fills up, consuming RAM.
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