What is Kafka?
Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and real-time data processing. It is commonly used to build real-time data pipelines and streaming applications. Kafka was originally developed at LinkedIn and later open-sourced as part of the Apache Software Foundation.
Kafka serves three primary purposes:
1. Message Broker: Decouples producers (senders) and consumers (receivers) by acting as an intermediary.
2. Stream Processor: Provides tools to process streams of data in real-time.
3. Storage System: Durable storage of message streams for later retrieval and processing.
Core Concepts of Kafka
1. Producers:
• Applications or services that write data (messages) to Kafka topics.
2. Consumers:
• Applications or services that read data from Kafka topics.
3. Topics:
• Categories or logical channels to which producers send messages and consumers subscribe.
4. Partitions:
• A topic is divided into one or more partitions for scalability and parallelism. Each partition stores an ordered log of messages.
5. Brokers:
• Kafka servers that store topic data. A Kafka cluster consists of multiple brokers.
6. Replication:
• Kafka ensures fault tolerance by replicating partitions across multiple brokers.
7. Zookeeper (or KRaft in newer versions):
• Used to manage Kafka cluster metadata, leader elections, and configurations (KRaft is Kafka’s native replacement for Zookeeper).
Why is Kafka Fast?
Kafka’s exceptional performance stems from its architectural design and optimized use of resources. Here are the key reasons:
1. Sequential I/O and Batching
• Kafka writes and reads messages sequentially to/from disk using append-only logs.
• Sequential writes are much faster than random writes due to reduced disk seek overhead.
• Messages are batched before being written to disk or sent over the network, further reducing I/O operations.
2. Zero-Copy Mechanism
• Kafka uses the sendfile() system call to transfer data directly from disk to network sockets without copying it into application memory.
• This reduces context switches between user space and kernel space, leading to lower latency and higher throughput.
3. Efficient Data Storage
• Kafka does not track individual message acknowledgments; instead, it uses offset-based tracking.
• Each message in a partition has a unique offset, and consumers maintain their own offsets.
• This simplifies the storage model, reduces overhead, and increases throughput.
4. Partitioning and Parallelism
• Topics are split into partitions, which allow parallel processing.
• Each partition can be independently processed by different consumers in a consumer group, making Kafka highly scalable.
5. Replication with Leader-Follower Model
• Kafka partitions have a leader that handles all reads and writes, while replicas act as backups.
• The leader handles client requests, ensuring consistent and efficient operations.
6. Asynchronous Processing
• Producers and consumers communicate asynchronously, reducing blocking and latency.
• Producers do not wait for consumers to process messages, and vice versa.
7. Backpressure Handling
• Kafka can handle a large number of messages without overwhelming the system by allowing consumers to process messages at their own pace.
• Messages remain in Kafka until consumers explicitly commit that they’ve been processed.
8. High Compression
• Kafka supports message compression (e.g., GZIP, Snappy, LZ4) at the producer level.
• Compressed messages reduce storage and network bandwidth usage.
9. Minimized Coordination Overhead
• Kafka reduces inter-node coordination by storing and replicating data at the partition level rather than managing individual messages.
• Consumers maintain their own offsets, eliminating the need for Kafka to track message delivery statuses.
Kafka vs Traditional Message Brokers
| Feature | Kafka | Traditional Brokers (e.g., RabbitMQ) |
| Data Persistence | Stores messages persistently by default | Often optimized for in-memory storage |
| Scalability | Horizontally scalable with partitions | Limited scalability |
| Message Consumption | Pull-based (consumers fetch messages) | Push-based (brokers send messages) |
| Performance | Optimized for high throughput and low latency | Generally slower |
| Fault Tolerance | Built-in replication | Depends on implementation |
Typical Use Cases for Kafka
1. Log Aggregation:
• Collect and centralize application logs for monitoring or analysis.
2. Real-Time Data Pipelines:
• Stream data between microservices or systems (e.g., ETL pipelines).
3. Stream Processing:
• Process data in real-time using Kafka Streams or tools like Apache Flink.
4. Event Sourcing:
• Store a record of every change to an application state.
5. IoT and Sensor Data:
• Collect and analyze data from IoT devices.