Comparing Apache Storm, Spark, and Samza: Which Real‑Time Stream Processor Fits Your Needs?

Apache Storm

In Storm, you design a real‑time computation graph called a topology, which is submitted to a cluster. The master node distributes code to worker nodes that run spouts (which emit tuples) and bolts (which process, filter, or forward tuples). Tuples are immutable key‑value arrays.

Apache Spark

Spark Streaming extends the core Spark API by dividing incoming data into micro‑batches called DStreams (Discretized Streams), which are essentially sequences of RDDs (Resilient Distributed Datasets). RDDs can be transformed by functions or sliding windows.

Apache Samza

Samza processes streams message‑by‑message. Its stream unit is a message with an offset ID, not a tuple or DStream. Samza supports batch processing of partitions and uses pluggable execution and messaging components, relying on Hadoop YARN and Apache Kafka.

Common Features

All three are open‑source distributed systems offering low latency, scalability, and fault tolerance. They allow parallel execution of stream processing tasks across multiple machines and provide simple APIs to abstract underlying complexities.

Comparison Table

The following table highlights key differences:

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 without loss or duplication (hard to guarantee).

State management differs: Spark Streaming writes state to distributed file systems (e.g., HDFS); Samza uses an embedded key‑value store; Storm can manage state in the application layer or via the higher‑level Trident abstraction.

Use Cases and Recommendations

If you need a high‑speed incremental computation engine with low latency, Storm is a strong choice, especially with its built‑in distributed RPC (DRPC) and language‑agnostic Thrift API. For exactly‑once delivery or micro‑batch processing, consider Spark Streaming, which also integrates SQL, machine learning, and graph libraries. If you have massive state per partition and prefer co‑located storage and processing, Samza offers a pluggable API and is well‑suited for large‑scale, multi‑team environments.

Adoption

Storm is used by Twitter, Yahoo, Spotify, The Weather Channel, etc. Spark is used by Amazon, Yahoo, NASA JPL, eBay, Baidu, and others. Samza is used by LinkedIn, Intuit, Metamarkets, Quantiply, Fortscale, among others.

Conclusion

This overview provides a brief comparison of the three Apache frameworks; many additional features and evolving differences exist beyond this summary.