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Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in a Kafka cluster. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka’s server-side cluster technology.
- Quick Start Guide
- The Kafka Streams Quick Start demonstrates how to run your first Java application that uses the Kafka Streams library by showcasing a simple end-to-end data pipeline powered by Kafka.
- Streams API Screencasts
Watch the Intro to Streams API on YouTube.
Table of Contents
- Kafka Streams Quick Start
- Kafka Streams Demo Application
- Code Examples
- Developer Guide
- Writing a Streams Application
- Configuring a Streams Application
- Streams DSL
- Processor API
- Data Types and Serialization
- Interactive Queries
- Memory Management
- Running Streams Applications
- Managing Streams Application Topics
- Streams Security
- Application Reset Tool
- Upgrade Guide
- Upgrading from CP 3.3.x (Kafka 0.11.0.x-cp1) to CP 4.0.0 (Kafka 1.0.0-cp1)
- Upgrading older Kafka Streams applications to CP 4.0.0
- Is Kafka Streams a project separate from Apache Kafka?
- Is Kafka Streams a proprietary library of Confluent?
- Do Kafka Streams applications run inside the Kafka brokers?
- Why Does My Kafka Streams Application Use So Much Memory?
- What are the system dependencies of Kafka Streams?
- How do I migrate my older Kafka Streams applications to the latest Confluent Platform version?
- Which versions of Kafka clusters are supported by Kafka Streams?
- What programming languages are supported?
- Why is my application re-processing data from the beginning?
- Failure and exception handling
- Interactive Queries
- Troubleshooting and debugging
- Easier to interpret Java stacktraces?
- Visualizing topologies?
- Inspecting streams and tables?
- Invalid Timestamp Exception
- Why do I get an
IllegalStateExceptionwhen accessing record metadata?
- Why is
- Scala: compile error “no type parameter”, “Java-defined trait is invariant in type T”
- How can I convert a KStream to a KTable without an aggregation step?
- RocksDB behavior in 1-core environments