Q: What is the difference between Kafka Streams, Apache Flink, and Apache Spark Streaming?
Answer:
All three are stream processing frameworks, but they serve different niches.
Kafka Streams
A lightweight client library (not a cluster/framework) for building stream processing applications that read from and write to Kafka.
StreamsBuilder builder = new StreamsBuilder();
builder.stream("input-topic")
.filter((key, value) -> value.contains("important"))
.mapValues(value -> value.toUpperCase())
.to("output-topic");
KafkaStreams streams = new KafkaStreams(builder.build(), props);
streams.start();
Key characteristics:
- Runs as a regular Java application — no separate cluster to deploy.
- Exactly-once semantics built in.
- Supports stateful operations (aggregations, joins, windowing) with local state stores (RocksDB).
- Scales by simply running more instances of the application.
Apache Flink
A distributed stream processing framework designed for complex, low-latency event processing at massive scale.
Key characteristics:
- Runs on its own cluster (JobManager + TaskManagers).
- True event-time processing with watermarks.
- Advanced windowing (tumbling, sliding, session windows).
- Exactly-once semantics via checkpointing.
- Can process both streams and batch data (unified model).
- Supports multiple languages (Java, Scala, Python, SQL).
Apache Spark Streaming (Structured Streaming)
A micro-batch streaming engine built on top of Spark. It processes data in small batches rather than true record-at-a-time streaming.
Key characteristics:
- Runs on a Spark cluster.
- Processes streams as a series of small batch jobs.
- Shares Spark's batch processing ecosystem (MLlib, SQL, DataFrames).
- Higher latency than Flink (seconds vs milliseconds).
Comparison
| Feature | Kafka Streams | Flink | Spark Streaming |
|---|---|---|---|
| Deployment | Library (no cluster) | Dedicated cluster | Spark cluster |
| Latency | Low (ms) | Very low (ms) | Higher (seconds) |
| Model | True streaming | True streaming | Micro-batch |
| State management | RocksDB (local) | Managed state + checkpoints | Spark state store |
| Exactly-once | ✅ (Kafka-only) | ✅ (with any source) | ✅ |
| Source/Sink | Kafka only | Kafka, HDFS, DBs, etc. | Kafka, HDFS, DBs, etc. |
| Complexity | Low | Medium-High | Medium |
| Best for | Kafka-centric microservices | Complex CEP, large-scale | Batch + streaming unified |
When to Use What?
- Kafka Streams: Your data is in Kafka and goes back to Kafka. You want simplicity and don't want to manage a separate cluster.
- Flink: You need sub-millisecond latency, complex event processing (CEP), or reading from non-Kafka sources.
- Spark Streaming: Your team already uses Spark for batch and wants to add streaming. Latency of seconds is acceptable.
[!NOTE] Apache Flink is increasingly becoming the industry standard for large-scale stream processing. Many companies are migrating from Spark Streaming to Flink for its true streaming model and lower latency.