Event Series Intelligence
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 Processing Language (EPL) 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. It enables rapid development of applications that process large volumes of incoming messages or 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.
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.
In Esper, a tailored EPL allows registering queries in the engine. A listener class--which is basically a POJO--will then be called by the engine when the EPL condition is matched as events flow in. The EPL enables to express complex matching conditions that include temporal windows, joining of different event streams, as well as filtering, aggregation, and sorting. Esper statements can also be combined together with "followed by" conditions thus deriving complex events from more simple events. Events can be represented as POJO or Bean or other classes, java.util.Map (a generic tuple), Object-Array, XML documents, or Avro, which promotes reuse of existing systems acting as messages publishers or subscribers and allows for flexibility. It is not necessary for create a class for every event type.
A trivial yet meaningful example is as follow: assume a trader wants to buy Google stock as soon as the price goes below some floor value-- not when looking at each tick but when the computation is done over a sliding time window--say of 30 seconds. Given a StockTick event bean with a price and symbol property and the EPL "select avg(price) from StockTick.win:time(30 sec) where symbol='GOOG'", a listener POJO would get notified as ticks come in to trigger the buy order.
Developing event-driven application is not hard using Esper. You may want to roughly follow these steps:
- Define the mission of your application by analyzing your business domain and defining the situations to be detected or information to be reported
- Define your performance requirements, specifically throughput and latency
- Identify where events are coming from
- Identify lower level event formats and event content that is applicable to your domain
- Design the event relationships leading to complex events
- Instrument your sources of events
- Design how you want to represent events: as Java classes, as Maps, or as XML events
- Define EPL statements for patterns and stream processing
- Use the CSV adapter as an event simulation tool, to test situations to be detected, or to generate to load
- Test against your performance requirements: throughput and latency in your target environment
With "Instrument your sources of events" we mean to plan for, design and implement hooks in the event source systems so that they can generate the events in the format defined in 4. Instrumentation is roughly placing hooks that don't change the nominal execution flow. There are several techniques for that, from custom code to aspect-oriented technology with in the middle a whole range of component-dependant and framework-dependant technology (servlet filter, proxy objects, decorators, topic-based architecture etc.). This can also be implemented at a more coarse grained level (pipeline derivation in an enterprise service bus or a BPEL/BPM process for example).
Java classes are a simple, rich and versatile way to represent events in Esper. Java classes offer inheritance and polymorphism via interfaces and super-classes, and can represent a complex business domain via an object graph. Maps and XML are an alternative way of representing events.
EPL statements derive and aggregate information from one or more streams of events, to join or merge event streams, and to feed results from one event stream to subsequent statements.
EPL is similar to SQL in it's use of the select clause and the where clause. However EPL statements instead of tables use event streams and a concept called views. Similar to tables in an SQL statement, views define the data available for querying and filtering. Views can represent windows over a stream of events. Views can also sort events, derive statistics from event properties, group events or handle unique event property values.
This is a sample EPL statement that computes the average price for the last 30 seconds of stock tick events:
select avg(price) from StockTickEvent.win:time(30 sec)
A sample EPL that returns the average price per symbol for the last 100 stock ticks.
select symbol, avg(price) as averagePrice from StockTickEvent.win:length(100) group by symbol
This example joins 2 event streams. The first event stream consists of fraud warning events for which we keep the last 30 minutes (1800 seconds). The second stream is withdrawal events for which we consider the last 30 seconds. The streams are joined on account number.
select fraud.accountNumber as accntNum, fraud.warning as warn, withdraw.amount as amount, MAX(fraud.timestamp, withdraw.timestamp) as timestamp, 'withdrawlFraud' as desc from FraudWarningEvent.win:time(30 min) as fraud, WithdrawalEvent.win:time(30 sec) as withdraw where fraud.accountNumber = withdraw.accountNumber
Event patterns match when an event or multiple events occur that match the pattern's definition. Patterns can also be temporal (time-based). Pattern matching is implemented via state machines.
Pattern expressions can consist of filter expressions combined with pattern operators. Expressions can contain further nested pattern expressions by including the nested expression(s) in round brackets.
There are 5 types of operators:
- Operators that control pattern finder creation and termination: every
- Logical operators: and, or, not
- Temporal operators that operate on event order: -> (followed-by)
- Guards are where-conditions that filter out events and cause termination of the pattern finder, such as timer:within
- Observers observe time events as well as other events, such as timer:interval, timer:at
A sample pattern that alerts on each IBM stock tick with a price greater then 80 and within the next 60 seconds:
every StockTickEvent(symbol="IBM", price>80) where timer:within(60 seconds)
A sample pattern that alerts every 5 minutes past the hour:
every timer:at(5, *, *, *, *)
A sample pattern that alerts when event A occurs, followed by either event B or event C:
A -> ( B or C )
An event pattern where a property of a following event must match a property from the first event:
every a=EventX -> every b=EventY(objectID=a.objectID)
Patterns match when a sequence (or absence) of events is detected. Pattern match results are available for further analysis and processing.
The pattern below detects a situation where a Status event is not followed by another Status event with the same id within 10 seconds. The statement further counts all such occurrences grouped per id.
select a.id, count(*) from pattern [ every a=Status -> (timer:interval(10 sec) and not Status(id=a.id) ] group by id
A named window is a global data window that can take part in many statement queries, and that can be selected-from, inserted- into and deleted-from by multiple statements. Named windows are similar to a table in a relational database system.
One can create a named window for example as follows:
create window AlertNamedWindow as (origin string, priority string, alarmNumber long)
One can trigger a select, update or delete when an event arrives. Here we show a select that simply counts the number of rows:
on TriggerEvent select count(*) from AlertNamedWindow
Named windows can as well be queried with fire-and-forget queries through the API and inward-facing JDBC driver.
A match-recognize pattern is a regular-expression-based pattern-matching syntax proposed for inclusion in SQL standards.
The below query is a sample match-recognize pattern. It detects a pattern that may be present in the events held by the named window as declared above. It looks for two immediately-followed events, i.e. with no events in-between for the same origin. The first of the two events must have high priority and the second of the two events must have medium priority.
select * from AlertNamedWindow match_recognize ( partition by origin measures a1.origin as origin, a1.alarmNumber as alarmNumber1, a2.alarmNumber as alarmNumber2 pattern (a1 a2) define a1 as a1.priority = 'high', a2 as a2.priority = 'medium' )
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