Kafka Architecture & Concepts
Q1: Explain Kafka architecture. How does it achieve high throughput?
A: Kafka is a distributed publish-subscribe messaging system with components:
Producers: Publish data to topics.
Consumers: Subscribe and consume data from topics.
Brokers: Kafka servers that store and forward messages.
ZooKeeper (pre-2.8): Manages cluster metadata (now optional post-KRaft).
Kafka achieves high throughput through:
Sequential disk writes (append-only log).
Zero-copy transfer via sendfile.
Batching and compression.
Partitioning for parallelism.
Q2: What is a partition in Kafka? Why is it important?
A: A partition is an ordered, immutable sequence of records. Partitions:
Allow parallelism by distributing data across brokers.
Help scale consumers (each partition can be read by one consumer in a group).
Provide data ordering within a partition.
Q3: How does Kafka manage message offsets? Can offsets be reset?
A: Offsets track the position of a consumer within a partition:
Stored in Kafka's internal topic: __consumer_offsets.
Managed automatically or manually by consumers.
Can be reset using the CLI tool:
kafka-consumer-groups.sh --reset-offsets
Offset reset strategies: earliest, latest, none.
Q4: How does Kafka ensure message delivery guarantees (at-most-once, at-least-once, exactly-once)?
A: At-most-once: No retries, auto-commit enabled.
At-least-once: Retries enabled, offset committed after processing.
Exactly-once (since 0.11): Requires idempotent producers and transactional writes:
enable.idempotence=true
transactional.id
Q5: What happens when a Kafka consumer fails?
A: Kafka uses consumer groups:
Other consumers in the group will rebalance and take over the partitions.
During rebalance, consumption is temporarily paused.
Offset management ensures the consumer resumes from the last checkpoint.
✅ Kafka Reliability, Fault Tolerance & Performance
Q6: What is ISR (In-Sync Replica)? Why is it important?
A: ISR is a list of replicas that are fully caught up with the leader:
Only messages acknowledged by all ISR members are considered committed.
Ensures high availability and data durability.
If ISR size drops below the min.insync.replicas, producers can be blocked (based on config).
Q7: How do you tune Kafka for high throughput?
A: Key tuning parameters:
Producer: batch.size, linger.ms, compression.type, acks
Broker: num.network.threads, num.io.threads, log.segment.bytes
Consumer: fetch.min.bytes, fetch.max.wait.ms
Use compression (snappy, lz4).
Increase partition count for parallelism.
✅ Kafka Streams, Connect, and Ecosystem
Q8: What is Kafka Streams and how is it different from Kafka Consumer API?
A: Kafka Streams is a lightweight stream processing library:
Built on top of Kafka Consumer API.
Provides stateful transformations (e.g., joins, aggregations).
Supports windowing, fault-tolerance, exactly-once semantics.
No need for a separate cluster (unlike Spark or Flink).
Q9: How do you use Kafka Connect?
A: Kafka Connect is a framework for integrating Kafka with external systems:
Source connectors: Ingest data from DBs, files, etc. into Kafka.
Sink connectors: Export Kafka data to systems like Elasticsearch, S3, etc.
Supports distributed mode for scalability.
Easily configured via JSON and REST API.
✅ Security, Monitoring, and Troubleshooting
Q10: How do you secure a Kafka cluster?
A: Kafka supports:
Authentication: SASL/PLAIN, SCRAM, GSSAPI.
Authorization: ACLs on topics and consumer groups.
Encryption: TLS for data-in-transit.
Ensure brokers, producers, consumers use the right keystore/truststore configs.
Q11: How do you monitor a Kafka cluster? What are the key metrics?
A: Use tools like Prometheus, Grafana, Kafka Manager, or Confluent Control Center.
Key metrics:
Under-replicated partitions
ISR shrink/expansion rate
Broker disk usage
Consumer lag
Message rate (ingest/egress)
Q12: Describe how Kafka handles back pressure.
A: Kafka doesn't throttle producers by default. You can handle back pressure by:
Limiting max.in.flight.requests.per.connection
Using bounded queue.buffering.max.ms
Consumers may lag behind—monitor lag and scale consumers or optimize processing.
========================Advanced Concepts ==================
1. What is the role of the offset?
In partitions, messages are assigned a unique ID number called the offset. The role is to identify each message in the partition uniquely.
2. Can Kafka be used without ZooKeeper?
It is not possible to connect directly to the Kafka Server by bypassing ZooKeeper. Any client request cannot be serviced if ZooKeeper is down.
3. In Kafka, why are replications critical?
Replications are critical as they ensure published messages can be consumed in the event of any program error or machine error and are not lost.
4. What is a partitioning key?
Ans. The partitioning key indicates the destination partition of the message within the producer. A hashing based partitioner determines the partition ID when the key is given.
5. What is the critical difference between Flume and Kafka?
Kafka ensures more durability and is scalable even though both are used for real-time processing.
6. When does QueueFullException occur in the producer?
QueueFullException occurs when the producer attempts to send messages at a pace not handleable by the broker.
7. What is a partition of a topic in Kafka Cluster?
Partition is a single piece of Kafka topic. More partitions allow excellent parallelism when reading from the topics. The number of partitions is configured based on per topic.
8. Explain Geo-replication in Kafka?
The Kafka MirrorMaker provides Geo-replication support for clusters. The messages are replicated across multiple cloud regions or datacenters. This can be used in passive/active scenarios for recovery and backup.
9. What do you mean by ISR in Kafka environment?
ISR is the abbreviation of In sync replicas. They are a set of message replicas that are synced to be leaders.
10. How can you get precisely one messaging during data production?
To get precisely one messaging from data production, you have to follow two things avoiding duplicates during data production and avoiding duplicates during data consumption. For this, include a primary key in the message and de-duplicate on the consumer.
11. How do consumers consumes messages in Kafka?
The transfer of messages is done in Kafka by making use of send file API. The transfer of bytes occurs using this file through the kernel-space and the calls between back to the kernel and kernel user.
12. What is Zookeeper in Kafka?
One of the basic Kafka interview questions is about Zookeeper. It is a high performance and open source complete coordination service used for distributed applications adapted by Kafka. It lets Kafka manage sources properly.
13. What is a replica in the Kafka environment?
The replica is a list of essential nodes needed for logging for any particular partition. It can play the role of a follower or leader.
14. What does follower and leader in Kafka mean?
Partitions are created in Kafka based on consumer groups and offset. One server in the partition serves as the leader, and one or more servers act as a follower. The leader assigns itself tasks that read and write partition requests. Followers follow the leader and replicate what is being told.
15. Name various components of Kafka?
The main components are:
Producer – produces messages and can communicate to a specific topic
Topic: a bunch of messages that come under the same topic
Consumer: One who consumes the published data and subscribes to different topics
Brokers: act as a channel between consumers and producers.
16. Why is Kafka so popular?
Kafka acts as the central nervous system that makes streaming data available to applications. It builds real-time data pipelines responsible for data processing and transferring between different systems that need to use it.
17. What are consumers in Kafka?
Kafka tags itself with a user group, and every communication on the topic is distributed to one use case. Kafka provides a single-customer abstraction that discovers both publish-subscribe consumer group and queuing.
18. What is a consumer group?
When more than one consumer consumes a bunch of subscribed topics jointly, it forms a consumer group.
19. How is a Kafka Server started?
To start a Kafka Server, the Zookeeper has to be powered up by using the following steps:
> bin/zookeeper-server-start.sh config/zookeeper.properties
> bin/kafka-server-start.sh config/server.properties
20. How does Kafka work?
Kafka combines two messaging models, queues them, publishes, and subscribes to be made accessible to several consumer instances.
21. What are replications dangerous in Kafka?
This is because duplication assures that issued messages are absorbed in plan fault, appliance mistake or recurrent software promotions.
22. What is the role of Kafka Producer API play?
It covers two producers: kafka.producer.async.AsyncProducer and kafka.producer.SyncProducer. The API provides all producer performance through a single API to its clients.
23. Discuss the architecture of Kafka?
A cluster in Kafka contains multiple brokers as the system is distributed. The topic in the system is divided into multiple partitions. Each broker stores one or multiple partitions so that consumers and producers can retrieve and publish messages simultaneously.
24. What advantages does Kafka have over Flume?
Kafka is not explicitly developed for Hadoop. Using it for writing and reading data is trickier than it is with Flume. However, Kafka is a highly reliable and scalable system used to connect multiple systems like Hadoop.
25. Why are the benefits of using Kafka?
Kafka has the following advantages:
Scalable- Data is streamlined over a cluster of machines and partitioned to enable large information.
Fast- Kafka has brokers which can serve thousands of clients
Durable- message is replicated in the cluster to prevent record loss.
Distributed- provides robustness and fault tolerance.
============Advanced Kafka Interview Questions ========================
1. Is getting message offset possible after producing?
This is not possible from a class behaving as a producer because, like in most queue systems, its role is to forget and fire the messages. As a message consumer, you get the offset from a Kaka broker.
2. How can the Kafka cluster be rebalanced?
When a customer adds new disks or nodes to existing nodes, partitions are not automatically balanced. If several nodes in a topic are already equal to the replication factor, adding disks will not help in rebalancing. Instead, the Kafka-reassign-partitions command is recommended after adding new hosts.
3. How does Kafka communicate with servers and clients?
The communication between the clients and servers is done with a high-performance, simple, language-agnostic TCP protocol. This protocol maintains backwards compatibility with the earlier version.
4. How is the log cleaner configured?
It is enabled by default and starts the pool of cleaner threads. For enabling log cleaning on particular topic, add: log.cleanup.policy=compact. This can be done either by using alter topic command or at topic creation time.
5. What are the three broker configuration files?
The essential configuration files are broker.id, log.dirs, zookeeper.connect.
6. What are the traditional methods of message transfer?
The traditional method includes:
Queuing- a pool of consumers read a message from the server, and each message goes to one of the consumers.
Publish-subscribe: Messages are broadcasted to all consumers.
7. What is a broker in Kafka?
The broker term is used to refer to Server in Kafka cluster.
8. What maximum message size can the Kafka server receive?
The maximum message size that Kafka server can receive is 10 lakh bytes.
9. How can the throughput of a remote consumer be improved?
If the consumer is not located in the same data center as the broker, it requires tuning the socket buffer size to amortize the long network latency.
10. How can churn be reduced in ISR, and when does the broker leave it?
ISR has all the committed messages. It should have all replicas till there is a real failure. A replica is dropped out of ISR if it deviates from the leader.
11. If replica stays out of ISR for a long time, what is indicated?
If a replica is staying out of ISR for a long time, it indicates the follower cannot fetch data as fast as data is accumulated at the leader.
12. What happens if the preferred replica is not in the ISR?
The controller will fail to move leadership to the preferred replica if it is not in the ISR.
13. What is meant by SerDes?
SerDes (Serializer and Deserializer) materializes the data whenever necessary for any Kafka stream when SerDes is provided for all record and record values.
14. What do you understand by multi-tenancy?
This is one of the most asked advanced Kafka interview questions. Kafka can be deployed as a multi-tenant solution. The configuration for different topics on which data is to be consumed or produced is enabled.
15. How is Kafka tuned for optimal performance?
To tune Kafka, it is essential to tune different components first. This includes tuning Kafka producers, brokers and consumers.
16. What are the benefits of creating Kafka Cluster?
When we expand the cluster, the Kafka cluster has zero downtime. The cluster manages the replication and persistence of message data. The cluster also offers strong durability because of cluster centric design.
17. Who is the producer in Kafka?
The producer is a client who publishes and sends the record. The producer sends data to the broker service. The producer applications write data to topics that are ready by consumer applications.
18. Tell us the cases where Kafka does not fit?
Kafka ecosystem is a bit difficult to configure, and one needs implementation knowledge. It does not fit in situations where there is a lack of monitoring tool, and a wildcard option is not available to select topics.
19. What is the consumer lag?
Ans Reads in Kafka lag behind Writes as there is always some delay between writing and consuming the message. This delta between the consuming offset and the latest offset is called consumer lag.
20. What do you know about Kafka Mirror Maker?
Kafka Mirror Maker is a utility that helps in replicating data between two Kafka clusters within the different or identical data centres.
21. What is fault tolerance?
In Kafka, data is stored across multiple nodes in the cluster. There is a high probability of one of the nodes failing. Fault tolerance means that the system is protected and available even when nodes in the cluster fail.
22. What is Kafka producer Acknowledgement?
An acknowledgement or ack is sent to the producer by a broker to acknowledge receipt of the message. Ack level defines the number of acknowledgements that the producer requires before considering a request complete.
23. What is load balancing?
The load balancer distributes loads across multiple systems in caseload gets increased by replicating messages on different systems.
24. What is a Smart producer/ dumb broker?
A smart producer/dumb broker is a broker that does not attempt to track which messages have been read by consumers. It only retains unread messages.
25. What is meant by partition offset?
The offset uniquely identifies a record within a partition. Topics can have multiple partition logs that allow consumers to read in parallel. Consumers can read messages from a specific as well as an offset print of their choice.
26. What is Apache Kafka?
Apache Kafka is a powerful, open-source distributed event-streaming platform. Originally developed by LinkedIn as a messaging queue, it has evolved into a tool for handling data streams across various scenarios.
Kafka's distributed system architecture allows horizontal scalability, enabling consumers to retrieve messages at their own pace and making it easy to add Kafka nodes (servers) to the cluster.
Kafka is designed to process large amounts of data quickly with low latency. Although it is written in Scala and Java, it supports a wide range of programming languages.
27. What are some of Kafka's features?
Apache Kafka is an open-source distributed streaming platform widely used for building real-time data pipelines and streaming applications. It offers the following features:
1. High throughput
Kafka is capable of handling massive volumes of data. It is designed to read and write hundreds of gigabytes from source clients efficiently.
2. Distributed architecture
Apache Kafka has a cluster-centric architecture and inherently supports message partitioning across Kafka servers. This design also enables distributed consumption across a cluster of consumer machines, all while preserving the order of messages within each partition. Additionally, a Kafka cluster can scale elastically and transparently, without requiring any downtime.
3. Supporting various clients
Apache Kafka supports the integration of clients from different platforms, such as .NET, JAVA, PHP, and Python.
4. Real-Time messages
Kafka produces real-time messages that should be visible to consumers; this is important for complex event processing systems.
28.How do Partitions work in Kafka?
In Kafka, a topic serves as a storage space where all messages from producers are kept. Typically, related data is stored in separate topics. For instance, a topic named "transactions" would store details of user purchases on an e-commerce site, while a topic called "customers" would hold customer information.
Topics are divided into partitions. By default, a topic has one partition, but you can configure it to have more. Messages are distributed across these partitions, with each partition having its own offset and being stored on a different server in the Kafka cluster.
For example, if a topic has three partitions across three brokers, and a producer sends 15 messages, the messages are distributed in sequence:
Record 1 goes to Partition 0
Record 2 goes to Partition 1
Record 3 goes to Partition 2
Then the cycle repeats, with Record 4 going back to Partition 0, and so on.
29 .Why would you choose Kafka over other messaging services?
Choosing Kafka over other messaging services often comes down to its unique strengths, especially for use cases that need high throughput and real-time data processing. Here’s why Kafka stands out:
High throughput and scalability: Kafka can handle large volumes of data efficiently. Its architecture supports horizontal scaling, allowing you to add more brokers and partitions to manage increasing data loads without sacrificing performance.
Real-time processing: It’s excellent for real-time data streaming, making it perfect for use cases like activity tracking and operational monitoring. For example, LinkedIn created Kafka to handle its activity tracking pipeline, allowing it to publish real-time feeds of user interactions like clicks and likes.
Message replay: Kafka allows consumers to replay messages, which is helpful if a consumer encounters an error or becomes overloaded. This ensures no data is lost—consumers can recover and replay missed messages to maintain data integrity.
Durability and fault tolerance: The tool replicates data across multiple brokers, ensuring reliability even if some brokers fail. This fault-tolerant design keeps data accessible, providing security for critical operations.
30. Explain all the APIs provided by Apache Kafka?
An API (Application Programming Interface) enables communication between different services, microservices, and systems. Kafka provides a set of APIs designed to build event streaming platforms and interact with its messaging system.
The core Kafka APIs are:
Producer API: Used to send real-time data to Kafka topics. Producers determine which partition within a topic each message should go to, with callbacks handling the success or failure of send operations.
Consumer API: Allows reading real-time data from Kafka topics. Consumers can be part of a consumer group for load balancing and parallel processing, subscribing to topics and continuously polling Kafka for new messages.
Streams API: Supports building real-time applications that transform, aggregate, and analyze data within Kafka. It offers a high-level DSL (Domain Specific Language) to define complex stream processing logic.
Kafka Connect API: Used to create and run reusable connectors for importing and exporting data between Kafka and external systems.
Admin API: Provides tools for managing and configuring Kafka topics, brokers, and other resources to ensure smooth operation and scalability.
These APIs enable seamless data production, consumption, processing, and management within Kafka.
31. What are the implications of increasing the number of partitions in a Kafka topic?
Increasing the number of partitions in a Kafka topic can improve concurrency and throughput by allowing more consumers to read in parallel. However, it also introduces certain challenges:
Increased cluster overhead: More partitions consume additional cluster resources, leading to higher network traffic for replication and increased storage requirements.
Potential data imbalance: As partitions increase, data may not be evenly distributed, potentially causing some partitions to be overloaded while others remain underutilized.
More complex consumer group management: With more partitions, managing consumer group assignments and tracking offsets becomes more complicated.
Longer rebalancing times: When consumers join or leave, rebalancing partitions across the group can take longer, affecting overall system responsiveness.
32. Explain the four components of Kafka architecture ?
Kafka is a distributed architecture made up of several key components:
Broker nodes
Kafka's distributed architecture comprises several key components, one of which is the broker node. Brokers handle the heavy lifting of input/output operations and manage the durable storage of messages. They receive data from producers, store it, and make it available to consumers. Each broker is part of a Kafka cluster and has a unique ID to help with coordination.
In older versions of Kafka, the cluster is managed using Zookeeper, which ensures proper coordination between brokers and tracks metadata such as partition locations (though Kafka is moving toward an internal KRaft mode for this). Kafka brokers are highly scalable and capable of handling large volumes of read and write requests for messages across distributed systems.
ZooKeeper nodes
ZooKeeper plays a crucial role in Kafka by managing broker registration and electing the Kafka controller, which handles administrative operations for the cluster. ZooKeeper operates as a cluster itself, called an ensemble, where multiple processes work together to ensure only one broker at a time is assigned as the controller.
If the current controller fails, ZooKeeper quickly elects a new broker to take over. Although ZooKeeper has been an essential part of Kafka’s architecture, Kafka is transitioning to a new KRaft mode that removes the need for ZooKeeper. ZooKeeper is an independent open-source project and not a native Kafka component.
Producers
A Kafka producer is a client application that serves as a data source for Kafka, publishing records to one or more Kafka topics via persistent TCP connections to brokers.
Multiple producers can send records to the same topic concurrently. Kafka topics are append-only, meaning that producers can write new data, but neither producers nor consumers can modify or delete existing records, which ensures data immutability.
Consumers
A Kafka consumer is a client application that subscribes to one or more Kafka topics to consume streams of records. Consumers typically work in consumer groups, where the load of reading and processing records is distributed across multiple consumers.
Each consumer tracks its progress through the stream by maintaining offsets, ensuring that no data is processed twice and unprocessed records are not lost. Kafka consumers act as the final step in the data pipeline, where records are processed or forwarded to downstream systems.
33. What is the primary purpose of log compaction in Kafka? How does log compaction impact the performance of Kafka consumers?
The main goal of log compaction in Kafka is to retain the most recent value for each unique key in a topic's log, ensuring that the latest state of the data is preserved and reducing storage usage. This allows consumers to access the current value more efficiently without having to process older duplicates.
Unlike Kafka's traditional retention policy, which deletes messages after a certain time period, log compaction deletes older records only for each key, retaining the latest value for that key. This feature helps ensure consumers always have access to the current state while maintaining a compact log for better storage efficiency and faster lookups.
34. What is the difference between Partitions and Replicas in a Kafka cluster?
Partitions and replicas are key components of Kafka's architecture, ensuring both performance and fault tolerance. Partitions increase throughput by allowing a topic to be split into multiple parts, enabling consumers to read from different partitions in parallel, which improves Kafka's scalability and efficiency.
Replicas, on the other hand, provide redundancy by creating copies of partitions across multiple brokers. This ensures fault tolerance because, in the event of a leader broker failure (the broker managing read and write operations for a partition), one of the follower replicas can be promoted to take over as the new leader.
Kafka maintains multiple replicas of each partition to ensure high availability and data durability, minimizing the risk of data loss during failures, though full durability depends on replication settings.
35. What is a schema in Kafka, and why is it important for distributed systems?
In Kafka, a schema defines the structure and format of data, such as fields like CustomerID (integer), CustomerName (string), and Designation (string).
In distributed systems like Kafka, producers and consumers must agree on the data format to avoid errors when exchanging data. The Kafka Schema Registry manages and enforces these schemas, ensuring that both producers and consumers use compatible versions.
The Schema Registry stores schemas (commonly in formats like Avro, Protobuf, or JSON Schema) and supports schema evolution, allowing data formats to change without breaking existing consumers. This ensures smooth data exchange and system reliability as schemas evolve.
36. How does Kafka ensure message loss prevention?
Kafka employs several mechanisms to prevent message loss and ensure reliable message delivery:
Manual offset commit: Consumers can disable automatic offset commits and manually commit offsets after successfully processing messages to avoid losing unprocessed messages in case of a consumer crash.
Producer acknowledgments (acks=all): Setting acks=all on the producer ensures that messages are acknowledged by all in-sync replicas before being considered successfully written, reducing the risk of message loss in case of broker failure.
Replication (min.insync.replicas and replication.factor): Kafka replicates each partition’s data across multiple brokers. A replication factor greater than 1 ensures fault tolerance, allowing data to remain available even if a broker fails. The min.insync.replicas setting ensures that a minimum number of replicas acknowledge a message before it is written, ensuring better data durability. For example, with a replication factor of 3, if one broker goes down, the data remains available on the other two brokers.
Producer retries: Configuring retries to a high value ensures that producers will retry sending messages if a transient failure occurs. Combined with careful configuration, like max.in.flight.requests.per.connection=1, this reduces the chance of message reordering and ensures that messages eventually get delivered without being lost.
37. How does Kafka different from RabbitMq?
Kafka and RabbitMQ are two popular messaging systems and different in terms of architecture and usage. You can see how they compare in key areas:
1. Usage and design
Kafka is designed to handle large-scale data streams and real-time pipelines, optimized for high throughput and low latency. Its log-based architecture ensures durability and allows data to be reprocessed, making it ideal for use cases like event sourcing and stream processing.
RabbitMQ is a general-purpose message broker that supports complex routing and is typically used for messaging between microservices or distributing tasks among workers. It excels in environments where reliable message delivery, routing flexibility, and interaction between services are essential.
2. Architecture
Kafka categorizes messages into topics, which are divided into partitions. Each partition can be processed by multiple consumers, enabling parallel processing and scaling. Data is stored on disk with a configurable retention period, ensuring durability and allowing reprocessing of messages when needed.
RabbitMQ sends messages to queues, where they are consumed by one or more consumers. It ensures reliable delivery through message acknowledgments, retries, and dead-letter exchanges for handling failed messages. This architecture focuses on message integrity and flexibility in routing.
3. Performance and scalability
Kafka is built for horizontal scalability by adding more brokers and partitions, enabling it to handle millions of messages per second. Its architecture supports parallel processing and high throughput, making it ideal for large-scale data streaming.
RabbitMQ can scale, though not as efficiently as Kafka when dealing with very large volumes of data. It is suitable for moderate to high throughput scenarios but is not optimized for the extreme throughput Kafka can handle in large-scale streaming applications.
Summary: Both Kafka and RabbitMQ are powerful tools for messaging, but they excel in different use cases. Kafka is ideal for high-throughput, real-time data streaming, and event sourcing applications where large-scale, parallel processing is needed.
RabbitMQ is well-suited for reliable message delivery, task distribution in microservice architectures, and complex message routing. It excels in scenarios requiring loose coupling between services, asynchronous processing, and reliability.
38 .How does Kafka help in developing microservice-based applications?
Apache Kafka is a valuable tool for building microservice architectures due to its capabilities in real-time data handling, event-driven communication, and reliable messaging. Here's how Kafka supports microservice development:
1. Event-driven architecture
Service decoupling: Kafka allows services to communicate through events instead of direct calls, reducing dependencies between them. This decoupling enables services to produce and consume events independently, simplifying service evolution and maintenance.
Asynchronous processing: Services can publish events and continue processing without waiting for other services to respond, improving system responsiveness and efficiency.
2. Scalability
Horizontal scalability: Kafka can handle large data volumes by distributing the load across multiple brokers and partitions. Each partition can be processed by different consumer instances, allowing the system to scale horizontally.
Parallel consumption: Multiple consumers can read from different partitions simultaneously, increasing throughput and performance.
3. Reliability and fault tolerance
Data replication: Kafka replicates data across multiple brokers, ensuring high availability and fault tolerance. If one broker fails, another can take over with the replicated data. Kafka stores data on disk with configurable retention periods, ensuring events are not lost and can be reprocessed if necessary.
4. Data integration
Central data hub: Kafka acts as a central hub for data flow within a microservice architecture, facilitating integration between various data sources and sinks. This simplifies maintaining data consistency across services.
Kafka Connect: Kafka Connect provides connectors for integrating Kafka with numerous databases, key-value stores, search indexes, and other systems, streamlining data movement between microservices and external systems.
39. What is Kafka Zookeeper, and how does it work?
Apache ZooKeeper is an open-source service that helps coordinate and manage configuration data, synchronization, and group services in distributed systems. In older versions of Kafka, ZooKeeper played a critical role in managing metadata, leader election, and broker coordination.
However, Kafka has been transitioning to KRaft mode since version 2.8, which eliminates the need for ZooKeeper. ZooKeeper is now considered obsolete and will be removed in the 4.0 release, which is expected in 2024.
ZooKeeper stores data in a hierarchical structure of nodes called "znodes," where each znode can store metadata and have child znodes, similar to a file system. This structure is crucial for maintaining metadata related to Kafka brokers, topics, and partitions. ZooKeeper's quorum-based mechanism ensures consistency: a majority of nodes (quorum) must agree on any change, making ZooKeeper highly reliable and fault-tolerant.
In older versions of Kafka, ZooKeeper is used for several critical functions:
Leader election: ZooKeeper elects a controller node, which manages partition leader assignments and other cluster-wide tasks.
Broker metadata management: It maintains a centralized registry of active brokers and their status.
Partition reassignment: ZooKeeper helps manage the rebalancing of partitions across brokers during failures or cluster expansions.
ZooKeeper’s ZAB protocol (ZooKeeper Atomic Broadcast) guarantees consistency across nodes, even in the face of network partitions, ensuring that Kafka remains fault-tolerant. This consistency and reliability make ZooKeeper crucial for managing the distributed nature of Kafka, though the future of Kafka’s architecture will rely less on ZooKeeper as KRaft becomes the default mode.
40 .How can you reduce disk usage in Kafka?
Kafka provides several ways to effectively reduce disk usage. Here are key strategies:
1. Adjust log retention settings
Modify the log retention policy to reduce the amount of time messages are retained on disk. This can be done by adjusting retention.ms (time-based retention) or retention.bytes (size-based retention) to limit how long or how much data Kafka stores before it deletes older messages.
2. Implement log compaction
Use log compaction to retain only the most recent message for each unique key, removing outdated or redundant data. This is particularly useful for scenarios where only the latest state is relevant, reducing disk space usage without losing important information.
3. Configure cleanup policies
Set effective cleanup policies based on your use case. You can configure both time-based (retention.ms) and size-based (retention.bytes) retention policies to delete older messages automatically and manage Kafka’s disk footprint.
4. Compress messages
Enable message compression on the producer side using formats like GZIP, Snappy, or LZ4. Compression reduces message size, leading to lower disk space consumption. However, it’s important to consider the trade-off between reduced disk usage and increased CPU overhead due to compression and decompression.
5. Adjust log segment size
Configure Kafka’s log segment size (segment.bytes) to control how often log segments are rolled. Smaller segment sizes allow for more frequent log rolling, which can help in more efficient disk usage by enabling quicker deletion of older data.
6. Delete unnecessary topics or partitions
Periodically review and delete unused or unnecessary topics and partitions. This can free up disk space and help keep Kafka’s disk usage under control.
41. What are the differences between leader replica and follower replica in Kafka?
Leader replica :
The leader replica handles all client read and write requests. It manages the partition’s state and is the primary point of interaction for producers and consumers. If the leader fails, its role is transferred to one of the follower replicas to maintain availability.
Follower replica:
The follower replica replicates data from the leader but does not directly handle client requests. Its role is to ensure fault tolerance by keeping an up-to-date copy of the partition’s data. Starting from Kafka 2.4, under certain conditions, follower replicas can also handle read requests to help distribute the load and improve read throughput. If the leader fails, a follower is elected as the new leader to maintain data consistency and availability.
42. Why do we use clusters in Kafka, and what are their benefits?
A Kafka cluster is made up of multiple brokers that distribute data across several instances, allowing for scalability without downtime. These clusters are designed to minimize delays, and in case the primary cluster fails, other Kafka clusters can take over to maintain service continuity.
The architecture of a Kafka cluster includes Topics, Brokers, Producers, and Consumers. It efficiently manages data streams, making it ideal for big data applications and the development of data-driven applications.
43. How does Kafka ensure message ordering?
Kafka ensures message ordering in two main ways:
Using message keys: When a message is assigned a key, all messages with the same key are sent to the same partition. This ensures that messages with the same key are processed in the order they are received, as Kafka preserves ordering within each partition.
Single-threaded consumer processing: To maintain order, the consumer should process messages from a partition in a single thread. If multiple threads are used, set up separate in-memory queues for each partition to ensure that messages are processed in the correct order during concurrent handling.
44. What Do ISR and AR represent in Kafka? What does ISR expansion mean?
ISR (in-sync replicas)
ISR refers to the replicas that are fully synchronized with the leader replica. These replicas have the latest data and are considered reliable for both read and write operations.
AR (assigned replicas)
AR includes all replicas assigned to a partition, both in-sync and out-of-sync replicas. It represents the complete set of replicas for a partition.
ISR expansion
ISR expansion occurs when new replicas catch up with the leader and are added to the ISR list. This increases the number of up-to-date replicas, improving fault tolerance and reliability.
45. What is a zero-copy usage scenario in Kafka?
Kafka uses zero-copy to efficiently transfer large volumes of data between producers and consumers. It leverages the FileChannel.transferTo method to move data directly from the file system to network sockets without additional copying, improving performance and throughput.
This technique utilizes memory-mapped files (mmap) for reading index files, allowing data buffers to be shared between user space and kernel space, further reducing the need for extra data copying. This makes Kafka well-suited for handling large-scale real-time data streams efficiently.
=============================Advanced Questions=========================
1. What is Apache Kafka and why is it used?
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications.
2. How does Kafka differ from traditional messaging systems?
Kafka is designed for fault tolerance, high throughput, and scalability, unlike traditional messaging systems that may not handle large data streams efficiently.
3. What are Producers and Consumers in Kafka?
Producers publish messages to Kafka topics. Consumers read messages from topics.
// Producer
producer.send(new ProducerRecord<String, String>("topic", "key", "value"));
// Consumer
consumer.subscribe(Arrays.asList("topic"));
4. What is a Kafka Topic?
A Topic is a category to which records are published by producers and from which records are consumed by consumers.
kafka-topics.sh --create --topic my_topic --bootstrap-server localhost:9092
5. How does Kafka ensure durability and fault-tolerance?
Kafka replicates data across multiple brokers. Consumers read from leader replicas, and follower replicas synchronize data.
6. What is a Kafka Partition?
Partitions allow Kafka to horizontally scale as each partition can be hosted on a different server.
7. What is Zookeeper’s role in a Kafka ecosystem?
Zookeeper manages brokers, maintains metadata, and helps in leader election for partitions.
8. How can you secure Kafka?
Kafka can be secured using SSL for encryption, SASL for authentication, and ACLs for authorization.
9. What is Kafka Streams?
Kafka Streams is a client library for building real-time, highly scalable, fault-tolerant stream processing applications.
KStream<String, String> stream = builder.stream(“input-topic”);
stream.to(“output-topic”);
10. What are some use-cases for Kafka?
Kafka is used for real-time analytics, data lakes, aggregating data from different sources, and acting as a buffer to handle burst data loads.
11. How do you integrate Kafka with Spring Boot?
You can use the Spring Kafka library, which provides `@KafkaListener` for consumers and `KafkaTemplate` for producers.
@KafkaListener(topics = “myTopic”)
public void listen(String message) {
// Handle message
}
12. How do you send a message to a Kafka topic using Spring Kafka?
Use `KafkaTemplate` to send messages.
kafkaTemplate.send("myTopic", "myMessage");
13. How do you consume messages from a Kafka topic in Spring?
Use the `@KafkaListener` annotation to mark a method as a Kafka message consumer.
@KafkaListener(topics = “myTopic”)
public void consume(String message) {
// Process message
}
14. How do you handle message deserialization errors in Spring Kafka?
Use the `ErrorHandlingDeserializer` to wrap the actual deserializer and catch deserialization errors.
15. How do you ensure ordered message processing in Spring Kafka?
Set the `concurrency` property of `@KafkaListener` to 1 to ensure single-threaded consumption for each partition.
@KafkaListener(topics = “myTopic”, concurrency = “1”)
16. How do you batch-process messages from Kafka in Spring?
Use the `@KafkaListener` annotation with the `batchListener` property set to `true`.
@KafkaListener(topics = “myTopic”, batchListener = true)
public void consume(List<String> messages) {
// Process messages
}
17. How do you filter messages in Spring Kafka?
Implement a `RecordFilterStrategy` to filter out unwanted records before they reach the `@KafkaListener`.
Create a class that implements RecordFilterStrategy:
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.springframework.kafka.listener.adapter.RecordFilterStrategy;
public class MyRecordFilterStrategy implements RecordFilterStrategy<String, String> {
@Override
public boolean filter(ConsumerRecord<String, String> consumerRecord) {
// Return true to filter out the record, false to include it
return !consumerRecord.value().contains("important");
}
}
Now, configure your ConcurrentKafkaListenerContainerFactory to use this filter:
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
@Configuration
public class KafkaConsumerConfig {
@Bean
public ConcurrentKafkaListenerContainerFactory<String, String> kafkaListenerContainerFactory(
ConsumerFactory<String, String> consumerFactory) {
ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
factory.setConsumerFactory(consumerFactory);
factory.setRecordFilterStrategy(new MyRecordFilterStrategy());
return factory;
}
}
Finally, use the @KafkaListener annotation to consume messages:
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.stereotype.Service;
@Service
public class MyKafkaConsumer {
@KafkaListener(topics = "myTopic")
public void consume(String message) {
System.out.println("Consumed message: " + message);
}
}
18. How do you handle retries for message processing in Spring Kafka?
Configure a `SeekToCurrentErrorHandler` or implement a custom error handler to manage retries.
19. How can you produce and consume Avro messages in Spring Kafka?
Use the Apache Avro serializer and deserializer along with Spring Kafka’s `KafkaTemplate` and `@KafkaListener`.
20. How do you secure Kafka communication in a Spring application?
Configure SSL properties in the `application.yml` or `application.properties` file for secure communication.
spring.kafka.properties.security.protocol: SSL
21. What are the key differences between Spring AMQP and Spring Pub-Sub?
Spring AMQP is based on the AMQP protocol and is often used with RabbitMQ. It supports complex routing and is suitable for enterprise-level applications. Spring Pub-Sub is generally used with messaging systems like Kafka and is more geared towards high-throughput data streaming.
22. How do message delivery semantics differ between Spring AMQP and Spring Pub-Sub?
Spring AMQP provides more granular control over message acknowledgment and transactions. Spring Pub-Sub, especially with Kafka, focuses on high-throughput and allows at-least-once, at-most-once, and exactly-once semantics.
Configure the producer for exactly-once semantics by setting the transactional.id and acks properties:
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;
public class ExactlyOnceProducer {
public static void main(String[] args) {
Properties props = new Properties();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
props.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG, "my-transactional-id");
props.put(ProducerConfig.ACKS_CONFIG, "all");
Producer<String, String> producer = new KafkaProducer<>(props);
producer.initTransactions();
try {
producer.beginTransaction();
for (int i = 0; i < 100; i++) {
producer.send(new ProducerRecord<>("my-topic", Integer.toString(i), Integer.toString(i)));
}
producer.commitTransaction();
} catch (Exception e) {
producer.abortTransaction();
}
producer.close();
}
}
Configure the consumer to read committed messages:
import org.apache.kafka.clients.consumer.Consumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;
public class ExactlyOnceConsumer {
public static void main(String[] args) {
Properties props = new Properties();
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
props.put(ConsumerConfig.GROUP_ID_CONFIG, "my-group");
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
props.put(ConsumerConfig.ISOLATION_LEVEL_CONFIG, "read_committed");
Consumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("my-topic"));
while (true) {
ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
records.forEach(record -> {
System.out.printf("Consumed record with key %s and value %s%n", record.key(), record.value());
});
}
}
}
23. How do you handle message ordering in Spring AMQP and Spring Pub-Sub?
In Spring AMQP, message ordering is generally maintained within a single queue. In Spring Pub-Sub with Kafka, message ordering is maintained within a partition.
24. How do you implement dead-letter queues in Spring AMQP and Spring Pub-Sub?
Spring AMQP has built-in support for dead-letter exchanges and queues. In Spring Pub-Sub with Kafka, you’d typically use a separate topic as a dead-letter queue.
Consumer Configuration
@KafkaListener(topics = "my-topic", errorHandler = "myErrorHandler")
public void listen(String message) {
// Process message or throw an exception
}
@Bean
public KafkaTemplate<String, String> kafkaTemplate() {
return new KafkaTemplate<>(producerFactory());
}
@Bean
public ProducerFactory<String, String> producerFactory() {
// Configure producer factory
}
@Bean
public MyErrorHandler myErrorHandler(KafkaTemplate<String, String> template) {
return new MyErrorHandler(template);
}
Custom Error Handler
public class MyErrorHandler implements ErrorHandler {
private final KafkaTemplate<String, String> template;
public MyErrorHandler(KafkaTemplate<String, String> template) {
this.template = template;
}
@Override
public void handle(Exception thrownException, ConsumerRecord<?, ?> record) {
template.send("my-dead-letter-topic", record.key().toString(), record.value().toString());
}
}
25. How do Spring AMQP and Spring Pub-Sub handle message filtering?
Spring AMQP supports various routing options including direct, topic, fanout, and headers for message filtering. Spring Pub-Sub with Kafka generally relies on consumer logic for filtering or uses Kafka Streams for more complex scenarios.
In the ever-evolving landscape of backend engineering, Apache Kafka stands as a beacon for real-time data processing and streaming. As Java backend engineers, understanding Kafka is not just a skill but a necessity in today’s data-driven world. From the simplicity of producing and consuming messages to the complexities of ensuring data durability and fault tolerance, Kafka offers a robust platform for scalable applications. As we continue to explore the depths of real-time data streaming, may our understanding of Kafka deepen, and our applications become more resilient. Until next time, keep streaming and stay curious!
======================More Attention on Interview Questions==============
1. Can you describe the basic architecture of Kafka?
Kafka is a distributed, high-throughput, fault-tolerant stream-processing platform. It’s designed with a storage layer and a compute layer. The key components of its architecture are:
Producers – These are applications that publish data to Kafka topics. For example, producers may send website user activity information such as page views or clicks to Kafka.
Consumers – These are applications that subscribe to topics and then process the data. Consumers may work on an analytics dashboard to process user-activity data in real time.
Brokers – These are servers for storing and distributing data. Brokers are like hard drives that hold data over different partitions.
Topics – These are categories or feeds that are used to organize data. "user_activity" or "financial_transactions" are examples of topics.
Partitions – Each topic is divided into different partitions, which allows for parallel processing.
The ZooKeeper (or Kafka Raft) – This manages clusters of Kafka metadata and coordinates brokers.
key components of apache kafka architecture graphic
Pro tip: Look for candidates who can describe how Kafka enables real-time data feeding and stream processing.
2. What is a topic in Kafka?
A topic is a category or feed name to which records are published. They’re partitionable and log-based, enabling the distribution and parallel processing of data across multiple brokers.
For example, a social media app will publish actions such as likes, shares, and comments to separate Kafka topics, ensuring scalability and efficient processing.
3. What is a partition in a Kafka topic?
Partitions are subsections of a topic where data is stored. Partitions enable Kafka to scale horizontally and support multiple consumers by dividing the data across different brokers. This helps with fault tolerance and scalability.
For example, let’s say you have a “website traffic” topic with one million visitors a day, and you set up ten partitions across five brokers. This spreads the amount of data one broker holds and replicates it across other brokers. A consumer group then processes each partition concurrently, while replication ensures no data is lost if a broker goes down.
4. What are producers and consumers in Kafka?
Producers – These are applications that publish data to Kafka topics. They’re responsible for serializing data into JSON or Avro so Kafka can store it and send it to the appropriate topic. For example, a mobile app may send user location data to a Kafka topic.
Consumers – These are applications that subscribe to one or more topics and then process the data. For instance, a real-time fraud detection system might have consumers read transaction data to spot suspicious patterns.
Consumer Groups – These allow multiple consumers to share their workload by reading across different partitions.
5. What is a Kafka broker?
A broker is a server in the Kafka cluster that stores data and serves client requests.
Brokers are responsible for:
Data storage, or holding data on topic partitions.
Handling requests from producers to write data and consumers to read data.
Message distribution across partitions.
Replication, which ensures fault tolerance by replicating partitions across multiple brokers.
For example, say a bank stores transaction logs across multiple brokers using a Kafka cluster. It replicates the data across multiple brokers so if one fails due to a hardware issue, the data isn’t lost.
6. What are ISR in Kafka?
ISR (short for In-Sync Replicas) are replicas of a Kafka partition that are fully in sync with the leader.
They’re critical for ensuring data durability and consistency. If a leader fails, one of the ISRs can become the new leader.
For example, a stock trading platform might keep trade execution logs consistently available by maintaining at least three in-sync replicas per partition.
7. How does partitioning work in Kafka?
Partitioning involves the division of topics into multiple segments across brokers to:
Enable parallel processing – Enables multiple consumers to read data at the same time.
Increase throughput – Distributes the load for scalability.
Enhance fault tolerance – Ensures resilience through replication .
Pro tip: Strong candidates will explain how Kafka assigns partitions to consumers in a group and how partitions ensure load balancing within a cluster.
8. How does Kafka ensure message durability?
Kafka uses several tools to ensure message durability:
Replication – Partitions are replicated across multiple brokers, and copies are stored on different servers to limit lost data.
Write-ahead log (WAL) – Messages are first recorded on logs before they are persisted to the disk. This means if a broker crashes before a message is fully written to the disk, it can be recovered from the log.
Retention policies – Kafka gives administrators the control to set storage parameters over time and size. This helps to ensure only necessary data is retained.
9. Describe an instance where Kafka might lose data and how you would prevent it?
Kafka can lose data in rare situations. Here’s how it happens – and how to stop it.
Risks:
Unclean Leader Elections – A lagging replica becomes leader, missing data.
Broker Failures – Hardware dies before replication finishes.
Config Errors – Weak settings skip safety checks or incorrect topic configurations.
Prevention:
Replication Factor: Set to 3+ for redundant copies.
Min.insync.replicas: Require 2+ in-sync replicas before acknowledging writes.
Acks=All: Require confirmation from all replicas before acknowledging writes.
Backups: Snapshot data regularly.
Monitoring: Watch for replica lag or broker downtime.
How Kafka loses information and how to prevent it graphic?
10. What is the role of the server.properties file?
The server.properties file is the primary configuration file for a Kafka broker. It includes settings related to the broker, network, logs, replication, and other operational parameters.
Pro tip: Look for candidates to mention specific configurable properties that are crucial for setting up and managing Kafka brokers, including:
broker.id – Unique ID for each broker
log.dirs – Storage location
zookeeper.connect – Manages cluster metadata
11. How do you install Kafka on a server?
To install Kafka on a server, an engineer would need to:
Download Kafka – Download the latest Kafka release from the official website.
Configure ZooKeeper – If you're using ZooKeeper for coordination (recommended), you must have a running ZooKeeper ensemble. Configure the zookeeper.connect property in the Kafka server configuration file (server.properties) to point to your ZooKeeper ensemble.
Configure Kafka – Modify the server.properties file and adjust to your required settings.
Start the Broker – Execute the kafka-server-start.sh script (or kafka-server-start.bat on Windows) to start the Kafka broker.
For example, on a Linux server, you’d grab Kafka 3.6 from the Apache site, unzip it, and ensure ZooKeeper’s running (e.g., bin/zookeeper-server-start.sh). Then, you’d edit server.properties to set broker.id=1 and log.dirs=/data/kafka, then start Kafka with bin/kafka-server-start.sh. If it fails, check the logs for Java heap issues.
12. How would you secure a Kafka cluster?
Top candidates would use multiple layers of security and strategies, such as:
Encryption: SSL/TLS for encryption of data in transit
Authentication: SASL/SCRAM for authentication
A Kerberos integration
OAuth mechanisms
Authorization: Network policies for controlling access to the Kafka cluster.
ACLs (Access Control Lists) for authorizing actions by users or groups on specific topics.
For example, a financial institution might use TLS and ACLs to prevent unauthorized access to sensitive customer data.
13. How do you monitor the health of a Kafka cluster?
Here are some ways to monitor the health of clusters:
Checking broker states
Verifying consumer group status
Assessing replication factors
Checking logs for errors using JMX (Java Management Extensions) to monitor performance metrics.
Pro tip: Top applicants will know how to use the Kafka command-line tools such as kafka-topics.sh to view topic details and kafka-consumer-groups.sh to get consumer group information.
14. What tools can you use for Kafka monitoring?
Skilled candidates will mention several tools, including:
Kafka's own JMX metrics for in-depth monitoring at the JVM level.
Prometheus with Grafana for visualizing metrics.
Elastic Stack (Elasticsearch, Logstash, Kibana) for log aggregation and analysis.
Datadog, New Relic, or Dynatrace for integrated cloud monitoring are used mainly in commercial platforms for dashboards and anomaly detection .
For example, a music streaming service might use JMX to monitor broker latency, Prometheus to scrape metrics every ten seconds, and Grafana to graph “songs-played” topic throughput. If a broker slows down, Kibana’s log view (via Elastic Stack) will pinpoint the error and alert the music streaming service that the disk is full.
Pro tip: Use our Elasticsearch test to evaluate candidates’ proficiency with Elasticsearch.
15. How would you upgrade a Kafka cluster with minimal downtime?
Strong candidates will describe their hands-on experience with Kafka. Here’s what their answers might include:
Perform a rolling upgrade – Stop brokers gracefully, updating them one at a time to prevent downtime.
Backup configurations & data before upgrading – Ensure all data and metadata are backed up safely.
Test the upgrade in a staging environment before deploying.
Monitor cluster performance closely during the upgrading process.
Pro tip: Look for candidates with a strong understanding of version compatibility and configuration changes (e.g., 2.8 to 3.0 shifts) between Kafka versions.
16. Explain the concept of Kafka MirrorMaker?
Kafka MirrorMaker is a tool used for cross-cluster data replication. It enables data mirroring between two Kafka clusters. The primary applications of MirrorMaker include:
Disaster recovery, where data is backed up in another cluster.
Geo-replication, which ensures low-latency access for users in different locations.
MirrorMaker works by using consumer and producer configurations to pull data from a source cluster and push it to a destination cluster.
For example, a global video streaming service might use MirrorMaker to synchronize content across multiple countries and ensure a seamless user experience.
17. What is exactly-once processing in Kafka?
Here are the key components of this feature:
No duplicate messages – Prevents multiple processing of the same message.
No data loss, even in failure scenarios – Ensures messages are never lost.
Transactional guarantees, which use Kafka’s transactional APIs for:
Idempotent Producers
Transactional Consumers
Transaction Coordinator
For example, a payments app might use exactly-once processing to prevent duplicate transactions from being logged.
18. What might cause latency issues in Kafka?
There are several potential causes of latency, such as:
High volume of network traffic or inadequate network hardware
Disk I/O bottlenecks due to high throughput or slow disks.
Large batch sizes or infrequent commits causing delays.
Inefficient consumer configurations or slow processing of messages.
Pro tip: Look for candidates who can explain how they’d diagnose and mitigate these issues, such as by adjusting configurations and upgrading hardware.
19. How can you reduce disk usage in Kafka?
Some of the best ways to reduce disk usage are to:
Adjust log retention settings to keep messages for a shorter duration.
Use log compaction to only retain the last message for each key.
Configure message cleanup policies effectively.
Compress messages before sending them to Kafka.
20. What are some best practices for scaling a Kafka deployment?
To scale a deployment successfully, developers should:
Size and partition topics to distribute load evenly across the cluster.
Use adequate hardware to support the intended load.
Monitor performance and adjust configurations as necessary.
Use replication to improve availability and fault tolerance
Use Kafka Streams or Kafka Connect for integrating and processing data at scale.
21. What are the implications of increasing the number of partitions in a Kafka topic?
Increasing partitions can improve concurrency and throughput but also has its downsides, as it might:
Increase overhead on the cluster due to more open file handles and additional replication traffic.
Lead to possible imbalance in data distribution.
Lead to longer rebalancing times and make managing consumer groups more difficult.
Pro tip: Strong candidates will know that careful planning and testing before altering partitions is key.
22. How do you reassign partitions in Kafka?
Reassigning partitions involves: Using kafka-reassign-partitions.sh to generate a reassignment plan.
Executing the reassignment JSON file.
Balancing partitions across brokers.
23. What are some security risks when working with Kafka?
Some of Kafka’s security risks are:
Unauthorized data access
Data tampering
Service disruption.
Pro tip: Take this question further by asking candidates how to mitigate these risks. They should be able to tell you that it’s essential to secure network access to Kafka, protect data at rest and in transit, and put in place robust authentication and authorization procedures.
24. How does Kafka support GDPR compliance?
Kafka ensures robust data protection thanks to its:
Data encryption features, both in transit using SSL/TLS and at rest .
Ability to handle data retention policies .
Deletion capabilities that can be used to comply with GDPR’s right to erasure.
Logging and auditing features to track data access and modifications.
Pro tip: Need to evaluate candidates’ GDPR knowledge and their ability to handle sensitive data? Use our GDPR and Privacy test.
25. What authentication mechanisms can you use in Kafka?
Kafka supports:
SSL/TLS for encrypting data and optionally authenticating clients using certificates.
SASL (Simple Authentication and Security Layer) which supports mechanisms like GSSAPI (Kerberos), PLAIN, and SCRAM to secure Kafka brokers against unauthorized access.
Integration with enterprise authentication systems like LDAP.
26. How can you use Kafka’s quota feature to control client traffic?
Kafka quotas can be set to limit the byte rate for producing and consuming messages, which prevents the overloading of Kafka brokers by aggressive clients.
There are three types of quotas:
Producer Quotas – Limits the rate at which a producer sends messages.
Consumer Quotas – Restricts how quickly consumers can fetch data.
Replication Quotas – Controls the bandwidth used for replicating data across brokers.
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