Comparison of Apache Storm, Spark Streaming, and Samza for Real‑Time Data Processing

Many distributed computing systems can process large data streams in real time or near real time. This article briefly introduces three Apache frameworks—Storm, Spark Streaming, and Samza—and provides a quick, high‑level overview of their similarities and differences.

Apache Storm

In Storm, you design a graph‑like topology composed of spouts and bolts. The topology is submitted to a cluster where a master node distributes code to worker nodes. Spouts emit tuples, while bolts process, filter, or forward these tuples; bolts can also emit to other bolts. Tuples are immutable key‑value arrays.

Apache Spark

Spark Streaming extends the core Spark API by dividing the incoming stream into micro‑batches based on time intervals. The abstraction for continuous streams is a DStream (Discretized Stream), which is a series of RDDs (Resilient Distributed Datasets). RDDs can be transformed using arbitrary functions or sliding windows.

Apache Samza

Samza processes streams message by message. Each message has a unique offset ID, and streams are partitioned into ordered, read‑only sequences. Samza supports batch processing of the same partition and relies on Hadoop YARN for resource scheduling and Apache Kafka for messaging.

Common Characteristics

All three systems are open‑source, distributed, low‑latency, scalable, and fault‑tolerant. They allow parallel execution of stream‑processing code across multiple machines and provide simple APIs that hide much of the underlying complexity.

The terminology differs, but the underlying concepts are similar.

Comparison Table

Message Delivery Guarantees

At‑most‑once: messages may be lost.

At‑least‑once: messages may be duplicated but not lost.

Exactly‑once: each message is delivered once and only once (hard to achieve universally).

State management also varies: Spark Streaming writes state to distributed file systems (e.g., HDFS), Samza uses an embedded key‑value store, while Storm either handles state in the application layer or via higher‑level abstractions such as Trident.

Use Cases

All three frameworks excel at processing large, continuous streams, but the choice depends on specific requirements.

If you need ultra‑low latency incremental computation, Storm is ideal, offering built‑in distributed RPC (DRPC) and language‑agnostic topology definitions via Apache Thrift. For exactly‑once semantics with micro‑batching, consider Storm’s Trident API.

Companies using Storm include Twitter, Yahoo, Spotify, and The Weather Channel.

If you require stateful computation with exactly‑once delivery and can tolerate higher latency, Spark Streaming is a strong candidate, especially when you also need machine learning, graph processing, or SQL integration (Spark SQL, MLlib, GraphX). Streaming algorithms such as streaming K‑means benefit from Spark’s unified model.

Companies using Spark include Amazon, Yahoo, NASA JPL, eBay, and Baidu.

When you have massive state per partition (billions of tuples) and want to keep processing and storage on the same node, Samza is suitable. Its pluggable APIs allow you to swap execution, messaging, and storage components, making it ideal for large, multi‑team pipelines.

Companies using Samza include LinkedIn, Intuit, Metamarkets, Quantiply, and Fortscale.

Conclusion

This article provided a brief overview of the three Apache frameworks without covering all their features or subtle differences. All three projects evolve rapidly, so readers should stay updated on the latest developments.