Kafka Streams API for Confluent Platform (2024)

The Kafka Streams API in a Nutshell¶

The Streams API of Kafka, available through a Java library, can be used to build highly scalable, elastic,fault-tolerant, distributed applications, and microservices. First and foremost, the Kafka Streams API allows youto create real-time applications that power your core business.It is the easiest yet the most powerful technology to process data stored in Kafka. It builds upon importantconcepts for stream processing such as efficient management of application state, fast andefficient aggregations and joins, properly distinguishing between event-time and processing-time, and seamless handlingof out-of-order data.

A unique feature of the Kafka Streams API is that the applications you build with it are normal Java applications.These applications can be packaged, deployed, and monitored like any other Java application – there isno need to install separate processing clusters or similar special-purpose and expensive infrastructure!

Use Case Examples¶

The Kafka Streams API is applicable to a wide range of use cases and industries.

• Travel companies can build applications with the Kafka Streams API to make real-time decisions to find best suitablepricing for individual customers, to cross-sell additional services, and to process bookings and reservations.
• The finance industry can build applications to aggregate data sources for real-time views of potential exposures andfor detecting and minimizing fraudulent transactions.
• Logistics companies can build applications to track their shipments fast, reliably, and in real-time.
• Retailers can build applications to decide in real-time on next best offers, personalized promotions, pricing, andinventory management.
• Automotive and manufacturing companies can build applications to ensure their production lines perform optimally, togain real-time insights into their supply chains, and to monitor telemetry data from connected cars to decide if aninspection is needed.
• And many more.

A Closer Look¶

Before you dive into the Concepts andArchitecture, get your feet wet by walkingthrough your first Kafka Streams application,let’s take a closer look.

A key motivation of the Kafka Streams API is to bring stream processing out of the Big Data niche into the world ofmainstream application development, and to radically improve the developer and operations experience bymaking stream processing simple and easy.Using the Kafka Streams API you can implement standard Java applications to solve your stream processing needs –whether at small or at large scale – and then run these applications on client machines at the perimeter of yourKafka cluster. Your applications are fully elastic: you can run one or more instances of your application, and theywill automatically discover each other and collaboratively process the data. Your applications are also fault-tolerant:if one of the instances dies, then the remaining instances will automatically take over its work – without any dataloss! Deployment-wise, you are free to choose from any technology that can deploy Java applications, includingbut not limited to Puppet, Chef, Ansible, Docker, Mesos, YARN, Kubernetes, and so on.This lightweight and integrative approach of the Kafka Streams API – “Build applications, not infrastructure!” – is instark contrast to other stream processing tools that require you to install and operate separate processing clusters andsimilar heavy-weight infrastructure that come with their own special set of rules on how to use and interact with them.

The following list highlights several key capabilities and aspects of the Kafka StreamsAPI that make it a compelling choice for use cases such as microservices, event-driven systems, reactive applications,and continuous queries and transformations.

Powerful

• Makes your applications highly scalable, elastic, distributed, fault-tolerant
• Supports exactly-once processing semantics
• Stateful and stateless processing
• Event-time processing with windowing, joins, aggregations
• Supports Kafka Streams Interactive Queries for Confluent Platform to unify the worlds of streamsand databases
• Choose between a declarative, functional API and a lower-levelimperative API for maximum control and flexibility

Lightweight

• Low barrier to entry
• Equally viable for small, medium, large, and very large use cases
• Smooth path from local development to large-scale production
• No processing cluster required
• No external dependencies other than Kafka

Fully integrated

• 100% compatible with Kafka 0.11.0 and 1.0.0
• Easy to integrate into existing applications and microservices
• No artificial rules for packaging, deploying, and monitoring your applications
• Runs everywhere: on-premises, public clouds, private clouds, containers, etc.
• Integrates with databases through continuous change data capture (CDC) performed byKafka Connect

Real-time

• Millisecond processing latency
• Record-at-a-time processing (no micro-batching)
• Seamlessly handles out-of-order data
• High throughput

Secure

• Supports encryption of data-in-transit
• Supports authentication and authorization

In summary, the Kafka Streams API is a compelling choice for building mission-critical stream processing applicationsand microservices. Give it a try with this step-by-step tutorial to build your first Kafka Streams application!The next sections, Concepts, Architecture,and the Developer Guide will help to get you started.

Requirements¶