Apache Storm

Overview

Apache Storm is a distributed system for processing data streams that continue without a defined endpoint. It is built for ongoing computation across a cluster, with an emphasis on scaling, fault tolerance and reliable processing. Supported workloads include real-time analytics, online machine learning, continuous computation, distributed RPC and ETL.

Storm organizes applications as topologies made from spouts and bolts. Spouts supply streams of data; bolts process them. Together, these components can form arbitrarily complex, multi-stage computations. The project reports a benchmark exceeding one million tuples per second per node, though that figure is a project benchmark rather than a general performance guarantee.

The current release is 3.1.0, available in source and binary distributions. Storm is also available through Maven Central and as an official Docker image. It is licensed under Apache License, Version 2.0. For related categories, see Stream Processing Software and Streaming Analytics Software.

Key features

Composable stream processing

Storm's spout-and-bolt model lets developers arrange stream sources and processing steps into multi-stage topologies. This supports complex computations that need to run continuously over incoming data.

Connectors and deployment modes

Out-of-the-box integrations cover Apache Kafka, HDFS, JDBC databases, JMS and Redis. Storm provides a local mode for in-process development and a remote mode for submitting topologies to a cluster.

Reliability and security controls

Storm is designed to scale, tolerate faults and guarantee data processing. Its security options include pluggable Kerberos/SASL authentication and SimpleACLAuthorizer authorization, both disabled by default. TLS is supported for Thrift RPC and for messaging between Netty workers through mutual TLS. Release files include OpenPGP signatures and SHA-512 checksums.

Storm supports stateful processing. The listed supported languages are JavaScript, Python, Ruby and C#. Users seeking assistance can contact the user mailing list at [email protected].

Pricing

Apache Storm is free and open source under the Apache License, Version 2.0. The listed plan is free; the pricing model is free, with a free plan available.

Platforms

Storm is listed for API, Linux, macOS, self-hosted, web and Windows environments. Its deployment model is self-hosted, so teams operate their own installation. Distribution options include source and binary releases, Maven Central and the official Docker image.

Who it's for

Apache Storm is suited to teams building distributed applications that must process unbounded streams continuously. Its use cases span analytics, online machine learning, continuous computation, distributed RPC and ETL. The topology model is relevant when a workload needs multiple connected processing stages, while local and remote modes support in-process development and cluster deployment.

Its self-hosted deployment model and security settings deserve consideration: authentication and authorization are disabled by default, so operators need to account for configuring access controls. The benchmark figure can help frame the project's stated throughput, but actual performance depends on a particular workload and deployment.

Pros and cons

• Pros: Supports a broad range of continuous processing use cases and multi-stage topologies.
• Pros: Includes integrations for Kafka, HDFS, JDBC databases, JMS and Redis, plus local and remote deployment modes.
• Pros: Designed for fault tolerance and reliable processing, with TLS options and signed, checksummed releases.
• Cons: Self-hosted deployment means the operating team is responsible for running the system.
• Cons: Authentication and authorization are disabled by default and need configuration when required.

Alternatives

For other options, compare Feldera, AFL++, Apache Beam, Kafka Streams, RisingWave, Timeplus, Apache Spark and Confluent Cloud.

Verdict

Apache Storm is a free, self-hosted choice for teams that need continuous, distributed stream computation with composable topologies and established integrations. Its core strengths are the breadth of workloads it supports, its reliability goals and its deployment flexibility. The trade-off is operational responsibility: teams must run it themselves and configure security controls that are off by default.