Apache Kafka is an open-source distributed event streaming platform that can handle millions of events per second from various sources and deliver them to multiple destinations. It is designed to provide a unified, high-throughput, low-latency platform for real-time data processing, integration, and analytics. In this blog post, I will explain what Apache Kafka is, why it is used, how it is used, and what makes it different from other systems.
What is Apache Kafka?
Apache Kafka is based on the concept of a commit log, which is a data structure that stores a sequence of records in a persistent and immutable way. Each record consists of a key, a value, and a timestamp. Records are appended to the end of the log as they arrive, and are assigned a unique offset that represents their position in the log. The log is partitioned into smaller segments called partitions, which are distributed across multiple servers called brokers. Each partition can be replicated to ensure fault-tolerance and high availability.
A Kafka cluster consists of one or more brokers that store and serve the data, and one or more clients that produce and consume the data. Producers are processes that send data to Kafka, and consumers are processes that read data from Kafka. Producers and consumers communicate with Kafka brokers using a binary TCP-based protocol that is optimized for efficiency and performance.
Kafka organizes data into topics, which are logical categories of records that share a common purpose or domain. For example, a topic can represent a stream of user actions, sensor readings, financial transactions, or any other type of event. Topics are further divided into partitions, which allow parallelism and scalability. Each partition can have one or more replicas, which are copies of the data that are stored on different brokers. One of the replicas is designated as the leader, which handles all the read and write requests for that partition. The other replicas are followers, which replicate the data from the leader and can take over as the leader in case of a failure.
Producers can write data to one or more partitions of a topic, either by specifying the partition explicitly or by letting Kafka assign the partition based on a key or a round-robin algorithm. Consumers can read data from one or more partitions of a topic, either by subscribing to the whole topic or by assigning specific partitions to each consumer instance. Consumers can also belong to a consumer group, which is a logical grouping of consumers that share a common group ID and work together to consume a topic. Kafka ensures that each partition is consumed by only one consumer instance within a group, and automatically balances the partitions among the instances if one of them fails or joins the group.
Kafka also provides two additional features that enhance its functionality and usability: Kafka Connect and Kafka Streams. Kafka Connect is a framework that allows users to easily connect Kafka to external systems, such as databases, message queues, cloud services, etc. Kafka Connect provides a set of connectors that can import data from or export data to various sources and sinks, as well as a REST API that can manage and monitor the connectors. Kafka Streams is a library that allows users to build stream processing applications that can transform, aggregate, join, and enrich data streams using a declarative and functional API. Kafka Streams can run on any Java application, and can leverage Kafka’s scalability, fault-tolerance, and state management capabilities.
Why is Apache Kafka used?
Apache Kafka is used for a variety of use cases that require fast, reliable, and scalable data processing and integration. Some of the common use cases are:
- Messaging: Kafka can be used as a message broker that decouples the producers and consumers of data, and provides high throughput, low latency, and guaranteed delivery. Kafka can also support various messaging patterns, such as point-to-point, publish-subscribe, request-reply, etc.
- Streaming: Kafka can be used as a stream processing platform that can ingest, process, and analyze data streams in real time, and generate insights, alerts, or actions. Kafka can also support complex stream processing logic, such as windowing, joining, aggregating, filtering, etc.
- Integration: Kafka can be used as a data integration platform that can connect various systems and applications, and enable data exchange, transformation, and enrichment. Kafka can also support various data formats, such as JSON, Avro, Protobuf, etc.
- Logging: Kafka can be used as a logging platform that can collect, store, and distribute large volumes of log data from various sources, such as applications, servers, devices, etc. Kafka can also support various log analysis tools, such as Elasticsearch, Logstash, Kibana, etc.
- Event Sourcing: Kafka can be used as an event sourcing platform that can capture the state changes of an application or a system as a series of events, and replay them to reconstruct the state or derive new states. Kafka can also support various event-driven architectures, such as CQRS, Saga, etc.
How is Apache Kafka used?
Apache Kafka can be used in various ways, depending on the use case and the requirements. However, a typical workflow of using Kafka involves the following steps:
- Install and configure Kafka: Users need to download and install Kafka on their machines, and configure the basic settings, such as broker IDs, ports, directories, etc. Users can also use cloud-based or managed Kafka services, such as Confluent Cloud, Amazon MSK, Azure Event Hubs, etc.
- Create and manage topics: Users need to create and manage topics that represent the data streams they want to produce or consume. Users can use the Kafka command-line tools, such as kafka-topics.sh, kafka-configs.sh, etc., or the Kafka Admin API, to create, delete, list, describe, alter, or reassign topics and partitions.
- Produce and consume data: Users need to write code that can produce or consume data to or from Kafka topics. Users can use the Kafka Producer API and the Kafka Consumer API, which are available in various languages, such as Java, Python, C#, etc., to create producer or consumer instances, configure the properties, and send or receive data. Users can also use the Kafka Console Producer and the Kafka Console Consumer, which are command-line tools that can produce or consume data from the standard input or output.
- Connect to external systems: Users need to connect Kafka to external systems that can act as sources or sinks of data. Users can use the Kafka Connect API, which is a framework that can run connectors that can import data from or export data to various systems, such as databases, message queues, cloud services, etc. Users can also use the Kafka Connect REST API, which can manage and monitor the connectors.
- Process data streams: Users need to process data streams that can transform, aggregate, join, or enrich the data. Users can use the Kafka Streams API, which is a library that can run stream processing applications that can perform various operations on the data streams, such as windowing, joining, aggregating, filtering, etc. Users can also use the Kafka Streams DSL, which is a domain-specific language that can express stream processing logic in a declarative and functional way.
What makes Apache Kafka different?
Apache Kafka is different from other systems that can handle data streams, such as message brokers, stream processing frameworks, or data integration platforms, in several ways. Some of the key differences are:
- Architecture: Kafka is based on the commit log, which is a simple and elegant data structure that can store and deliver data in a sequential and immutable way. This allows Kafka to achieve high performance, scalability, durability, and consistency, without compromising on simplicity and flexibility.
- Model: Kafka is based on the event streaming model, which is a paradigm that treats data as a continuous and unbounded stream of events that can be produced, consumed, processed, and integrated in real time. This allows Kafka to support various use cases that require fast, reliable, and scalable data processing and integration, without compromising on generality and versatility.
- Features: Kafka provides various features that enhance its functionality and usability, such as Kafka Connect, Kafka Streams, Kafka Schema Registry, Kafka REST Proxy, etc. These features allow users to easily connect Kafka to external systems, build stream processing applications, manage data schemas, access data via REST, etc., without compromising on quality and reliability.