Kafka Streams vs Spark Streaming: Key Differences and Use Cases

Kafka Streams is a lightweight library for building stream processing apps directly on Kafka with low latency, while Spark Streaming is a scalable, batch-based stream processing engine that integrates with many data sources. Kafka Streams suits real-time, event-driven apps; Spark Streaming fits complex analytics and large-scale data processing.

Quick Comparison

This table summarizes key factors to help you quickly see the differences between Kafka Streams and Spark Streaming.

Factor	Kafka Streams	Spark Streaming
Architecture	Library integrated with Kafka clients	Distributed processing engine with micro-batch model
Latency	Low (milliseconds)	Higher (seconds, depends on batch interval)
Ease of Use	Simple API, Java/Scala only	Rich API, supports Java, Scala, Python
Scalability	Scales with Kafka partitions	Highly scalable with cluster resources
Fault Tolerance	Built-in Kafka offset management	Checkpointing and write-ahead logs
Use Cases	Real-time event processing	Complex analytics and batch + streaming

Key Differences

Kafka Streams is a client library that runs within your application. It processes data as it arrives in Kafka topics, offering very low latency and exactly-once processing. It is lightweight and easy to embed in microservices, making it ideal for real-time event-driven applications.

Spark Streaming, on the other hand, is part of the Apache Spark ecosystem. It processes data in small batches (micro-batches), which introduces some latency but allows complex computations and integration with many data sources beyond Kafka. It supports multiple languages and is designed for large-scale data processing and analytics.

While Kafka Streams tightly couples with Kafka and is simpler to deploy, Spark Streaming requires a cluster setup but offers more flexibility and power for heavy data transformations and machine learning pipelines.

Code Comparison

Here is a simple example that reads from a Kafka topic, transforms the data by converting messages to uppercase, and writes back to another 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 java.util.Properties;

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

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

        KStream<String, String> uppercased = source.mapValues(value -> value.toUpperCase());

        uppercased.to("output-topic");

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

        Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
    }
}

Output

Messages from 'input-topic' are read, converted to uppercase, and written to 'output-topic' in real-time.

Spark Streaming Equivalent

Below is a similar example using Spark Streaming with Kafka integration. It reads messages, converts them to uppercase, and writes them to another Kafka topic using micro-batches.

java

import org.apache.spark.SparkConf;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

public class SparkStreamingExample {
    public static void main(String[] args) throws InterruptedException {
        SparkConf conf = new SparkConf().setAppName("SparkStreamingApp").setMaster("local[*]");
        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));

        Map<String, Object> kafkaParams = new HashMap<>();
        kafkaParams.put("bootstrap.servers", "localhost:9092");
        kafkaParams.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        kafkaParams.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        kafkaParams.put("group.id", "spark-streaming-group");
        kafkaParams.put("auto.offset.reset", "latest");
        kafkaParams.put("enable.auto.commit", false);

        JavaInputDStream<org.apache.kafka.clients.consumer.ConsumerRecord<String, String>> stream =
            KafkaUtils.createDirectStream(
                jssc,
                LocationStrategies.PreferConsistent(),
                ConsumerStrategies.Subscribe(Collections.singletonList("input-topic"), kafkaParams)
            );

        stream.foreachRDD(rdd -> {
            rdd.foreachPartition(partition -> {
                KafkaProducer<String, String> producer = new KafkaProducer<>(kafkaParams);
                partition.forEachRemaining(record -> {
                    String upper = record.value().toUpperCase();
                    producer.send(new ProducerRecord<>("output-topic", record.key(), upper));
                });
                producer.close();
            });
        });

        jssc.start();
        jssc.awaitTermination();
    }
}

Output

Every 5 seconds, messages from 'input-topic' are read, converted to uppercase, and sent to 'output-topic' in batches.

When to Use Which

Choose Kafka Streams when you need low-latency, real-time processing tightly integrated with Kafka, especially for event-driven microservices or simple stream transformations.

Choose Spark Streaming when you require complex analytics, integration with multiple data sources, or large-scale batch and streaming workloads that benefit from Spark's ecosystem and scalability.

Key Takeaways

Kafka Streams offers low-latency, lightweight stream processing embedded in Kafka clients.

Spark Streaming uses micro-batches, suitable for complex analytics and large-scale data.

Kafka Streams is simpler to deploy for Kafka-only pipelines; Spark Streaming supports diverse data sources.

Choose Kafka Streams for real-time event processing; choose Spark Streaming for heavy analytics.

Both support fault tolerance but differ in architecture and latency trade-offs.