Kafka Streams

Kafka Streams is a lightweight library for building real-time, highly scalable, and fault-tolerant stream processing applications. It allows you to process and analyze data stored in Kafka topics using a high-level DSL (Domain-Specific Language) or a low-level Processor API.

Key Features

  1. Stream Processing Topology: Kafka Streams allows you to define a processing topology that describes how data flows through a stream processing application. The topology consists of nodes representing stream processors and edges representing the data flow between them.

  2. Stateful Processing: Kafka Streams provides built-in support for stateful processing, allowing you to store and manage state within your stream processing application. This enables tasks like windowed aggregations, joins, and stateful transformations.

  3. Fault Tolerance: Kafka Streams leverages Kafka’s fault-tolerance capabilities to ensure reliable and consistent processing. It automatically handles failures and recovers state from Kafka topics, ensuring that processing can resume from where it left off.

  4. Exactly-Once Processing: Kafka Streams supports exactly-once processing semantics, guaranteeing that each record is processed exactly once, even in the presence of failures. This is achieved through the use of transaction IDs and commit offsets.

  5. Scalability: Kafka Streams applications can be scaled horizontally by adding more instances of the application. The library automatically handles the partitioning and distribution of data among the instances, allowing for seamless scaling.

Use Cases

Kafka Streams is well-suited for a wide range of real-time data processing scenarios, such as:

  1. Real-time Analytics: Kafka Streams can be used to perform real-time analytics on streaming data, such as calculating metrics, aggregating data, and detecting anomalies.

  2. Event-Driven Applications: Kafka Streams enables the development of event-driven applications that react to and process events in real-time, such as user interactions, sensor data, or financial transactions.

  3. Data Enrichment and Transformation: Kafka Streams allows you to enrich and transform streaming data by joining it with other data sources, applying transformations, and deriving new insights.

  4. Stream Processing Pipelines: Kafka Streams can be used to build end-to-end stream processing pipelines that ingest, process, and output data in real-time, enabling complex data flows and real-time decision making.


Here’s a simple example of a Kafka Streams application that performs a word count:

// Create a StreamsBuilder
StreamsBuilder builder = new StreamsBuilder();

// Define the input topic
KStream<String, String> textLines = builder.stream("input-topic");

// Perform the word count
KTable<String, Long> wordCounts = textLines
    .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
    .groupBy((key, value) -> value)

// Write the word counts to an output topic
wordCounts.toStream().to("output-topic", Produced.with(Serdes.String(), Serdes.Long()));

// Create and start the Kafka Streams application
KafkaStreams streams = new KafkaStreams(builder.build(), config);

In this example, the Kafka Streams application reads from an input topic, performs a word count on the incoming text, and writes the word counts to an output topic. The processing topology consists of a KStream for the input text lines, which is then transformed and aggregated into a KTable representing the word counts.