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Siddhi: a second look at complex event processing architectures

Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing. Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We present a performance study that exhibits that the resulting CEP Engine--Siddhi--has significantly improved performance. Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying suggestions for improvements, implementing those improvements through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.