Kafka general questions and answers

Here are general questions and answers about Kafka to help understand its core concepts and functionalities:

1. What is Apache Kafka?

Answer:
Apache Kafka is an open-source distributed event-streaming platform designed for high-throughput, low-latency data transfer. It is frequently utilized for creating real-time data pipelines, processing streams, and storing distributed events.

2. What are the core components of Kafka?

Answer:

Ø Broker: A Kafka server that stores data and serves requests from producers and consumers.

Ø Topic: A category or stream of data to which records are sent by producers and from which records are received by consumers.

Ø Partition: Subdivisions of a topic for scalability and parallelism.

Ø Producer: Responsible for dispatching messages (data) to Kafka topics.

Ø Consumer: Reads messages from Kafka topics.

Ø Consumer Group: A collective of consumers collaborating to retrieve data from topics.

Ø ZooKeeper: Coordinates and manages Kafka brokers (required until Kafka version 3.3.0).

3. What function does ZooKeeper serve in Kafka?

Answer:
ZooKeeper is used in Kafka to:

Ø Manage broker metadata and track their status.

Ø Maintain partition leader election and replication information.

Ø Store offsets for older Kafka versions (modern Kafka uses its internal storage for offsets).

Since Kafka 3.3.0, ZooKeeper can be replaced with Kafka's built-in KRaft (Kafka Raft) protocol.

4. What is a Kafka Topic?

Answer:
A Kafka topic is a logical channel or stream where data is sent by producers and consumed by consumers. Utilizing topics allows for the effective structuring and categorization of data. Each topic may consist of several partitions to enhance scalability.

5. What is a Partition in Kafka? Why is it important?

Answer:
In Kafka, a partition acts as a key element that facilitates both parallelism and scalability. Each partition is an ordered sequence of records, and a topic is divided into one or more partitions. Partitions allow:

Ø Scalability: Kafka can distribute partitions across multiple brokers.

Ø Parallelism: Consumers in a consumer group can process partitions concurrently.

6. What is a Consumer Group?

Answer:
A consumer group is a set of consumers that coordinate to consume data from Kafka partitions. Kafka guarantees that each partition within a topic is accessed by only one consumer from the group, facilitating parallel processing and load distribution.

7. How does Kafka ensure message durability?

Answer:
Kafka ensures durability using:

Ø Replication: Partitions are replicated across multiple brokers. If a broker goes down, the data continues to be available via different brokers.

Ø Acknowledge Mechanism: Producers can specify acknowledgment levels (e.g., acks=all) to ensure data is written to all replicas before the producer considers the write successful.

8. What are Kafka Producers and Consumers?

Answer:

Ø Producer: An application that sends data (records) to Kafka topics. Producers can specify keys to control which partition the record is sent to.

Ø Consumer: An application that retrieves data from Kafka topics. Consumers may join consumer groups for synchronized data consumption.

9. What is Kafka Replication?

Answer:
Replication in Kafka ensures fault tolerance by maintaining multiple copies of each partition across different brokers.

Ø Leader Replica: The main replica tasked with handling read and write requests.

Ø Follower Replica: Keeps in sync with the leader and assumes control if the leader becomes unavailable.

10. How does Kafka handle message ordering?

Answer:

Ø Kafka ensures the order of messages within a partition.

Ø Across partitions, there is no ordering guarantee.

11. What are Kafka offsets?

Answer:
Offsets act as unique identifiers assigned to each record within a partition. Consumers use offsets to track which messages they have read.

12. How does Kafka achieve high throughput?

Answer:
Kafka achieves high throughput by:

Ø Using a distributed architecture.

Ø Messages are written sequentially to disk, minimizing random write operations.

Ø Batching records to minimize network overhead.

Ø Employing zero-copy optimization for data transfer.

13. What is a Kafka Stream?

Answer:
Kafka Streams is a stream-processing library that enables applications to process and analyze data stored in Kafka topics. The system includes APIs that facilitate the filtering, transforming, and aggregating of real-time data.

14. What are Kafka Producer Acknowledgment Levels?

Answer:

Ø acks=0: Producer does not wait for any acknowledgment from the broker.

Ø acks=1:The producer awaits confirmation from the leader replica.

Ø acks=all: The producer waits for confirmation from all replicas.

15. What is Kafka’s log retention policy?

Answer:
Kafka supports two log retention policies for topics:

Ø Time-based Retention: Messages are retained for a specified duration (e.g., 7 days).

Ø Size-based Retention: Retain messages until the log reaches a configured size (e.g., 1 GB).

16. What is Kafka’s Exactly-Once Semantics (EOS)?

Answer:
Kafka provides exactly-once semantics to guarantee that messages are neither lost nor duplicated.:

Ø Idempotent producers.

Ø Transactional APIs.

17. How does Kafka differ from traditional messaging systems?

Answer:

Ø Kafka is distributed and scales horizontally.

Ø It stores messages for a configurable period, allowing consumers to re-read data.

Ø Kafka supports high-throughput streaming use cases.

Ø Kafka decouples producers and consumers using topics.

18. What is Kafka Connect?

Answer:
Kafka Connect is a utility designed to link Kafka with external systems such as databases, file systems, or cloud storage.. The architecture incorporates source connectors that enhance data ingestion into Kafka, in addition to sink connectors that support the export of data from Kafka to external systems.

19. What is the Kafka Raft (KRaft) Protocol?

Answer:
KRaft is Kafka's built-in consensus protocol designed to replace ZooKeeper. It handles metadata management and leader election directly within Kafka brokers, simplifying the architecture.

20. What are Kafka Use Cases?

Answer:

Ø Real-time data pipelines: Streaming data from source systems to target systems.

Ø Event-driven applications: Microservices communicating via events.

Ø Stream processing: Analyzing and processing data streams in real time.

Ø Log aggregation: Collecting and storing logs from distributed systems.

Ø Message queuing: Decoupling producers and consumers in a messaging system.

Let me know if you'd like more in-depth explanations or examples for any of these questions!

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