The DSL API’s Stateless Operations: Overview

Kafka Streams DSL API

Kafka Streams provides a domain-specific language (DSL) API that’s designed to build complex stream processing applications in a simple and concise way. This is because the DSL API is based on functional programming principles, which makes it highly composable and modular. It supports a wide range of stream processing operations, including:

• Stateless transformations (e.g., map and filter)

• Stateful operations (e.g., aggregations)

• Join and windowing operations

Let’s quickly review these operations.

Kafka Streams DSL API operations

Stateless transformations

Stateless operations in Kafka Streams are a set of operations that are designed to operate independently on each data record in a stream. These operations do not require any internal state to be maintained across data records. Therefore, they are ideal for simple transformations or filtering operations. Stateless operations in Kafka Streams can be used to perform a wide range of data transformations such as filtering, mapping, flattening, and more. In addition, stateless operations can be easily combined with other stateful operations in Kafka Streams to create complex processing pipelines that can handle a wide range of data processing tasks.

Stateful operations

Stateful operations in Kafka Streams are a set of operations that require the internal state to be maintained across multiple data records. These operations are typically used for more complex transformations or aggregations that require data to be processed over time, rather than on a record-by-record basis. Stateful operations are important because they allow developers to perform complex computations on data streams that require context and historical information. For example, an aggregation operation that computes the average temperature over the last hour requires historical information about the temperature readings from the previous hour.

Join and windowing operations

Join ...