Kafka
Apache Kafka is an open-source stream-processing software platform developed by LinkedIn and donated to the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Overview of Kafka
Kafka is a distributed publish-subscribe messaging system that is designed to be fast, scalable, and durable. It operates as a broker between producers and consumers, making it highly effective for managing large volumes of data streams.
Usage of Kafka
Kafka is used primarily for building real-time streaming data pipelines and applications that require reliable, high-speed processing of data streams. Its performance and scalability make it an excellent choice for applications like event sourcing, commit logs, and operational metrics handling.
Advantages of Using Kafka
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High Throughput: Capable of handling millions of messages per second, supporting high-volume data streams.
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Scalability: Easily scales out horizontally to handle more streams by adding more nodes to the Kafka cluster.
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Durability and Reliability: Stores streams of records with disk durability and replication, ensuring data is not lost.
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Fault Tolerance: Handles failures with the brokers in a cluster transparently, maintaining service continuity.
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Low Latency: Processes messages with very low latency, typically measured in milliseconds.
Communication in Kafka
Kafka uses a simple communication protocol over TCP/IP. Here’s how communication typically happens:
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Producers: Applications that publish (send) events to Kafka topics.
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Consumers: Applications or systems that subscribe to topics and process the feeds published by producers.
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Brokers: Servers where the data is stored, and that manage the storage of messages in topics.
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ZooKeeper: Manages and coordinates Kafka brokers and consumers.
Messages are categorized into topics, and each message within a topic is identified by a sequential ID number called an offset.
Use Cases
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Messaging System: Kafka is widely used as a replacement for more traditional message brokers, like AMQP and JMS, due to its higher throughput, replication, and fault tolerance.
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Activity Tracking: Kafka is used to collect user activity data, operational metrics, and in-application events to provide real-time analytics and monitoring.
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Log Aggregation Solution: It provides a common data source for all log data, making it easier for organizations to process logs.
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Stream Processing: Often used as part of a real-time stream processing system to transform or react to streams of data between systems.
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Event Sourcing: Kafka can be used to record events in a durable way, supporting event-driven architectures.
In summary, Apache Kafka is essential for businesses needing a robust, scalable, and efficient way to manage real-time data streams across distributed systems.