Kafka Streams

Kafka Streams is a client library for building real-time, scalable, and fault-tolerant stream processing applications using Apache Kafka. It enables developers to process and transform data directly within their Kafka infrastructure without needing a separate processing cluster. Kafka Streams can read data from Kafka topics, perform computations or transformations, and write the results back to Kafka—making it ideal for use cases like filtering, aggregating, joining, and windowing of data streams

Also known as: Kafka Streams API, Kafka Stream Processing

Comparisons

• Kafka Streams vs. Apache Flink: Kafka Streams is a lightweight library embedded within applications, while Flink is a full-fledged stream processing engine with its own runtime.
• Kafka Streams vs. ksqlDB: Kafka Streams provides low-level, code-driven stream processing, whereas ksqlDB offers a SQL-like interface for stream processing on Kafka.

Pros

• Integrated with Kafka: No need for a separate cluster—Kafka Streams runs within client applications.

• Scalable and Fault-Tolerant: Automatically handles distributed state and failures using Kafka’s log-based architecture.

• Developer-Friendly: Simple Java API that allows embedding stream logic directly into applications.
• Event-Time Processing: Supports time-based operations like tumbling or sliding windows using event timestamps.

Cons

• Language Limitation: Primarily supports Java and Scala, limiting adoption for developers using other languages.

• Resource Intensive at Scale: As processing is embedded in the application, scaling requires careful planning and resource allocation.

• Operational Complexity: Requires developers to manage application state and scaling manually.

Example

Suppose a ride-sharing platform wants to calculate the average trip duration per city in real time. Using Kafka Streams, the application can:

1. Consume trip data from a Kafka topic (e.g., trips).
2. Group trips by city and apply a tumbling time window.
3. Compute the average duration per window.
4. Publish the results to another Kafka topic (e.g., trip-averages).

This enables near-instant insights into operational metrics without needing a separate analytics pipeline.