Comparative Overview of Apache Storm, Spark Streaming, and Samza for Real-Time Data Processing

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

Apache Storm

In Storm, you first design a topology for real‑time computation, which is submitted to a cluster where a master node distributes code to worker nodes. A topology consists of spouts and bolts: spouts emit immutable tuples that carry data, while bolts process, filter, or transform these tuples and can forward them to other bolts.

Apache Spark

Spark Streaming extends the core Spark API; instead of processing each record individually like Storm, it slices 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) that can be transformed using functions or sliding windows.

Apache Samza

Samza processes streams message‑by‑message; its unit is a message rather than a tuple or DStream. Streams are partitioned into ordered sequences of read‑only messages, each identified by an offset. Samza supports batch processing of the same partition and relies on Hadoop YARN for resource scheduling and Apache Kafka for messaging.

Commonalities

All three systems are open‑source, distributed, low‑latency, scalable, and fault‑tolerant. They allow you to assign computation tasks across a set of fault‑tolerant machines and provide simple APIs that hide much of the underlying complexity.

The terminology differs, but the underlying concepts are similar.

Comparison Chart

The table below summarizes some key differences.

Data delivery guarantees fall into three categories:

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.

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

Use Cases

All three frameworks excel at processing continuous high‑volume real‑time data, but the choice depends on specific requirements.

If you need a high‑speed event‑processing system with incremental computation, Storm is ideal, especially with its built‑in distributed RPC (DRPC) and language‑agnostic Thrift API. For exactly‑once delivery and stateful processing, consider Storm’s Trident API, which also offers micro‑batching.

When you require stateful computation with exactly‑once guarantees and can tolerate higher latency, Spark Streaming is a good fit, especially if you also need graph processing, machine learning, or SQL support via Spark’s integrated libraries (Spark SQL, MLlib, GraphX).

If you have massive state per partition and want to keep storage and processing on the same machine, Samza is suitable. Its pluggable APIs allow you to swap execution, messaging, and storage engines, and its fine‑grained job model works well for large, multi‑team pipelines.

Companies Using Storm : Twitter, Yahoo, Spotify, The Weather Channel, etc.

Companies Using Spark : Amazon, Yahoo, NASA JPL, eBay, Baidu, etc.

Companies Using Samza : LinkedIn, Intuit, Metamarkets, Quantiply, Fortscale, etc.

Conclusion

This article provided a brief overview of the three Apache frameworks without covering all their features or subtle differences; each framework continues to evolve, so readers should stay updated on the latest developments.