Kafka Streams vs Flink: Key Differences and When to Use Each

Kafka Streams is a lightweight library for building stream processing apps directly on Kafka, ideal for simple to moderate workloads. Apache Flink is a powerful, standalone stream processing framework that supports complex event processing and large-scale data pipelines with advanced features.

Quick Comparison

Here is a quick side-by-side comparison of Kafka Streams and Apache Flink based on key factors.

Factor	Kafka Streams	Apache Flink
Deployment	Library embedded in Java apps	Standalone cluster or cloud service
Integration	Tightly integrated with Kafka	Supports Kafka and many other sources/sinks
State Management	Built-in Kafka-backed state store	Advanced state backend with snapshots and recovery
Latency	Low latency for simple pipelines	Very low latency with complex event processing
Scalability	Scales with Kafka partitions	Highly scalable with distributed cluster
Use Cases	Simple to moderate stream processing	Complex event processing and batch + stream

Key Differences

Kafka Streams is a Java library designed to be embedded inside applications that consume and process data from Kafka topics. It is simple to set up because it runs within your app and uses Kafka's own storage for state management. This makes it ideal for straightforward stream processing tasks tightly coupled with Kafka.

Apache Flink, on the other hand, is a full-fledged stream processing framework that runs as a separate cluster. It supports complex event processing, windowing, and exactly-once state consistency with advanced state backends. Flink can consume from Kafka but also from many other data sources, making it more flexible for diverse pipelines.

While Kafka Streams scales by increasing Kafka partitions and app instances, Flink scales horizontally across a distributed cluster with fine-grained control over parallelism. Flink also supports batch processing alongside streaming, making it a unified engine for both.

Code Comparison

This example shows how to count words from a Kafka topic using Kafka Streams.

java

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.Materialized;
import org.apache.kafka.streams.kstream.Produced;
import java.util.Properties;

public class WordCountApp {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-app");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

        StreamsBuilder builder = new StreamsBuilder();
        KStream<String, String> textLines = builder.stream("input-topic");

        textLines.flatMapValues(textLine -> java.util.Arrays.asList(textLine.toLowerCase().split(" ")))
                 .groupBy((key, word) -> word)
                 .count(Materialized.as("counts-store"))
                 .toStream()
                 .to("output-topic", Produced.with(Serdes.String(), Serdes.Long()));

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        streams.start();
    }
}

Output

The application reads lines from 'input-topic', counts word occurrences, and writes counts to 'output-topic'.

Apache Flink Equivalent

This example shows how to count words from a Kafka topic using Apache Flink.

java

import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import java.util.Properties;

public class FlinkWordCount {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        Properties props = new Properties();
        props.setProperty("bootstrap.servers", "localhost:9092");
        props.setProperty("group.id", "flink-group");

        FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("input-topic", new SimpleStringSchema(), props);
        DataStream<String> stream = env.addSource(consumer);

        DataStream<Tuple2<String, Integer>> counts = stream
            .flatMap((String line, org.apache.flink.util.Collector<Tuple2<String, Integer>> out) -> {
                for (String word : line.toLowerCase().split(" ")) {
                    out.collect(new Tuple2<>(word, 1));
                }
            })
            .returns(org.apache.flink.api.java.typeutils.TypeExtractor.getForClass(Tuple2.class))
            .keyBy(value -> value.f0)
            .sum(1);

        FlinkKafkaProducer<String> producer = new FlinkKafkaProducer<>("output-topic", new SimpleStringSchema(), props);
        counts.map(value -> value.f0 + ": " + value.f1).addSink(producer);

        env.execute("Flink Kafka WordCount");
    }
}

Output

The Flink job reads from 'input-topic', counts words, and writes results as strings to 'output-topic'.

When to Use Which

Choose Kafka Streams when you want a simple, lightweight solution embedded in your Java app that tightly integrates with Kafka and handles moderate stream processing tasks with low latency.

Choose Apache Flink when you need a powerful, scalable, and flexible stream processing engine that supports complex event processing, fault tolerance, and multiple data sources beyond Kafka, especially for large-scale or mixed batch/stream workloads.

Key Takeaways

Kafka Streams is a lightweight library embedded in apps, best for simple Kafka-centric stream processing.

Apache Flink is a standalone, scalable framework suited for complex event processing and diverse data sources.

Kafka Streams uses Kafka for state management; Flink offers advanced state backends with snapshots.

Flink supports both batch and stream processing; Kafka Streams focuses only on streaming.

Choose Kafka Streams for simplicity and tight Kafka integration; choose Flink for power and flexibility.