Complex Event Processing (CEP)

Esper: Event Stream Processing and Correlation
Esper is an Event Stream Processing (ESP) and event correlation engine (CEP, Complex Event Processing). Targeted to real-time Event Driven Architectures (EDA), Esper is capable of triggering custom actions written as Plain Old Java Objects (POJO) when event conditions occur among event streams. It is designed for high-volume event correlation where millions of events coming in would make it impossible to store them all to later query them using classical database architecture. A tailored Event Query Language (EQL) allows expressing rich event conditions, correlation, possibly spanning time windows, thus minimizing the development effort required to set up a system that can react to complex situations.

Esper is a lightweight kernel written in Java which is fully embeddable into any Java process, JEE application server or Java-based Enterprise Service Bus/span>. It enables rapid development of applications that process large volumes of incoming messages or events.
asr: main take is Events (existing software components )can be Java Beans, java.util.Map, or XML documents .
- Listeners are actions you want to take once those events happens
- This mean this can be applied to existing java based application, so main benefit is with out writing a special application for real-time intelligence gathering you can use this out of box solution
- See below Examples which comes with ready to test application with Download

Introduction to event streams and complex events
Information is critical to make wise decisions. This is true in real life but also in computing, and especially critical in several areas, such as finance, fraud detection, business intelligence or battlefield operation. Information flows in from different sources in the form of messages or events, giving a hint on the state at a given time such as stock price. That said, looking at those discrete events is most of the time meaningless. A trader needs to look at the stock trend over a period, possibly combined with other information to make the best deal at the right time.

While discrete events when looked one by one might be meaningless, event streams--that is an infinite set of events--considered over a sliding window and further correlated, are highly meaningful, and reacting to them with the minimal latency is critical for effective action and competitive advantage.

Introduction to Esper
Relational databases or message-based systems such as JMS make it really hard to deal with temporal data and real-time queries. Indeed, databases require explicit querying to return meaningful data and are not suited to push data as it changes. JMS systems are stateless and require the developer to implement the temporal and aggregation logic himself. By contrast, the Esper engine provides a higher abstraction and intelligence and can be thought of as a database turned upside-down: instead of storing the data and running queries against stored data, Esper allows applications to store queries and run the data through. Response from the Esper engine is real-time when conditions occur that match user defined queries. The execution model is thus continuous rather than only when a query is submitted.

Such concepts are a key foundation of EDA, and have been under active research in more than the last 10 years. Awareness of the importance of such systems in real-world architectures has started to emerge only recently.

Events as JavaBeans
Esper is able to handle events as JavaBeans, arbitrary java classes, java.util.Map, or XML documents. In this case study we assume we decided to use the JavaBeans representation for simplicity. Each self-service terminal publishes any of the six events kind below.

Additional Examples
Transaction Case Study
Solution Patterns
Past Presentation Material

Performance key figures
Esper exceeds over 500 000 event/s on a dual CPU 2GHz Intel based hardware, with engine latency below 3 microseconds average (below 10us with more than 99% predictability) on a VWAP benchmark with 1000 statements registered in the system - this tops at 70 Mbit/s at 85% CPU usage. Esper also demonstrates linear scalability from 100 000 to 500 000 event/s on this hardware, with consistent results accross different statements.


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Introduction to Complex Event Processing (CEP)
Event processing has been at the heart of any computing system for more then a decade. A common challenge across industries is to be able to extract actionable intelligence from disparate event sources in the most real-time possible way.

Introduction to Esper
Esper is an open-source CEP engine written entirely in Java and fully embeddable into any Java process - custom, JEE, ESB, BPM, etc. It recently has reached version 2.0, is backed by EsperTech under a professional open source / dual license business model, and triggers a growing interest in the Java and .Net community as well with NEsper, its full .Net/C# implementation.

Esper:Event Stream Intelligence: Continuous Event Processing for the Right Time Enterprise