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Implementing stateful and stateless stream processing operations

Implementing stateful and stateless stream processing operations is a crucial aspect of building robust and scalable real-time stream processing applications. Apache Kafka provides powerful tools and APIs for processing streams of data efficiently. In this topic, we will explore the implementation of stateful and stateless stream processing operations, empowering learners to leverage these operations effectively in their stream processing pipelines.

Implementing Stateless Stream Processing Operations:

  1. Filtering:
    Stateless filtering operations allow developers to selectively process records based on specific conditions. We will explore how to filter data streams using predicates.

Code Sample 1: Filtering a Kafka Stream

Java
KStream<String, String> inputStream = builder.stream("input-topic");
KStream<String, String> filteredStream = inputStream.filter((key, value) -> value.startsWith("A"));
filteredStream.to("output-topic");
  1. Mapping:
    Stateless mapping operations enable developers to transform the data within a stream without relying on any external state. We will explore how to apply mapping functions to data streams.

Code Sample 2: Mapping a Kafka Stream

Java
KStream<String, String> inputStream = builder.stream("input-topic");
KStream<String, Integer> mappedStream = inputStream.mapValues(value -> value.length());
mappedStream.to("output-topic");
  1. Flat-Mapping:
    Flat-mapping operations allow developers to transform each input record into zero or more output records. We will explore how to apply flat-mapping functions to data streams.

Code Sample 3: Flat-Mapping a Kafka Stream

Java
KStream<String, String> inputStream = builder.stream("input-topic");
KStream<String, String> wordsStream = inputStream.flatMapValues(value -> Arrays.asList(value.split("\\s+")));
wordsStream.to("output-topic");

Implementing Stateful Stream Processing Operations:

  1. Aggregation:
    Stateful aggregation operations enable developers to accumulate and compute aggregations over a stream of data. We will explore how to perform stateful aggregations using Kafka Streams’ groupByKey and aggregate functions.

Code Sample 4: Stateful Aggregation with Kafka Streams

Java
KStream<String, Integer> inputStream = builder.stream("input-topic");
KTable<String, Long> aggregatedTable = inputStream.groupByKey()
    .aggregate(
        () -> 0L,
        (key, value, aggregate) -> aggregate + value,
        Materialized.as("aggregation-store")
    );
aggregatedTable.toStream().to("output-topic");
  1. Joining:
    Stateful joining operations allow developers to combine two or more data streams based on a common key. We will explore how to perform stateful joins using Kafka Streams’ join functions.

Code Sample 5: Stateful Join with Kafka Streams

Java
KStream<String, String> stream1 = builder.stream("stream1-topic");
KStream<String, Integer> stream2 = builder.stream("stream2-topic");
KStream<String, String> joinedStream = stream1.join(
    stream2,
    (value1, value2) -> value1 + value2,
    JoinWindows.of(Duration.ofMinutes(5)),
    Joined.with(Serdes.String(), Serdes.String(), Serdes.Integer())
);
joinedStream.to("output-topic");

Reference Link: Apache Kafka Documentation – Kafka Streams – https://kafka.apache.org/documentation/streams/

Helpful Video: “Kafka Streams in 10 Minutes” by Confluent – https://www.youtube.com/watch?v=VHFg2u_4L6M

Conclusion:

Implementing stateful and stateless stream processing operations is essential for building real-time stream processing applications using Apache Kafka. Stateless operations such as filtering, mapping, and flat-mapping allow developers to selectively process and

transform data within a stream. Stateful operations such as aggregation and joining enable the accumulation and computation of aggregations and joining multiple streams based on a common key.

The provided code samples demonstrate the implementation of stateless and stateful stream processing operations using the Kafka Streams API. The reference link to the official Kafka documentation and the suggested video resource offer additional insights into the usage and best practices of stream processing operations. By mastering these operations, developers can build robust and scalable stream processing applications that process and analyze data in real-time, unlocking the full potential of Apache Kafka.

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About Author
Ozzie Feliciano CTO @ Felpfe Inc.

Ozzie Feliciano is a highly experienced technologist with a remarkable twenty-three years of expertise in the technology industry.

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