Review: Storm’s real-time processing comes at a price

The open-source stream processing solution is proven reliable at scale, but difficult to learn and use.

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Storm, a top-level Apache project, is a Java framework designed to help programmers write real-time applications that run on Hadoop clusters. Designed at Twitter, Storm excels at processing high-volume message streams to collect metrics, detect patterns or take actions when certain conditions in the stream are detected. Typically Storm scenarios are at the intersection of real time and high volume, such as analyzing financial transactions for fraud or monitoring cell-tower traffic to maintain service level agreements.

Traditionally these sorts of systems have been constructed using a network of computers connected by a message bus (such as JMS). What makes Storm different is that it combines the message passing and processing infrastructure into a single conceptual unit known as a “topology” and runs them on a Hadoop cluster. This means that Storm clusters can take advantage of the linear scalability and fault tolerance of Hadoop, without the need to reconfigure the messaging bus when increasing capacity.

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