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Uses of Stream Processing

Stream processing has long been used for monitoring purposes, where an organization wants to be alerted if certain things happen. For example:

  • • Fraud detection systems need to determine if the usage patterns of a credit card have unexpectedly changed, and block the card if it is likely to have been stolen.
  • • Trading systems need to examine price changes in a financial market and execute trades according to specified rules.
  • • Manufacturing systems need to monitor the status of machines in a factory, and quickly identify the problem if there is a malfunction.
  • • Military and intelligence systems need to track the activities of a potential aggressor, and raise the alarm if there are signs of an attack.

These kinds of applications require quite sophisticated pattern matching and correlations. However, other uses of stream processing have also emerged over time. In this section we will briefly compare and contrast some of these applications.

Complex event processing

Complex event processing (CEP) is an approach developed in the 1990s for analyzing event streams, especially geared toward the kind of application that requires searching for certain event patterns [65, 66]. Similarly to the way that a regular expression allows you to search for certain patterns of characters in a string, CEP allows you to specify rules to search for certain patterns of events in a stream.

CEP systems often use a high-level declarative query language like SQL, or a graphical user interface, to describe the patterns of events that should be detected. These queries are submitted to a processing engine that consumes the input streams and internally maintains a state machine that performs the required matching. When a match is found, the engine emits a complex event (hence the name) with the details of the event pattern that was detected [67].

In these systems, the relationship between queries and data is reversed compared to normal databases. Usually, a database stores data persistently and treats queries as transient: when a query comes in, the database searches for data matching the query, and then forgets about the query when it has finished. CEP engines reverse these roles: queries are stored long-term, and events from the input streams continuously flow past them in search of a query that matches an event pattern [68].

Implementations of CEP include Esper [69], IBM InfoSphere Streams [70], Apama, TIBCO StreamBase, and SQLstream. Distributed stream processors like Samza are also gaining SQL support for declarative queries on streams [71].

 
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