On the anatomy of event processing agents

Weekends is catch-up times for any workaholic. In my case, I dedicate the spare time in weekend to advance in the EPIA book, which, as expected, is somewhat behind the original schedule. We are now cleaning up the main chapters of the books that deal with : event processing network, contexts and patterns. I'll dedicate some of the coming postings to book related stuff.

In chapter 6 we get a deep dive into the notion of EPN, and its various components: event processing agents, channels and global states. Consumers and producer are discussed earlier in the book. Event Processing Agent (EPA) is a major abstraction we are using. In recent posting I've discussed that the conceptual EPN may be mapped in various ways to implementations, EPA is part of the EPN, as such it is conceptual creature, and can be mapped to implementation in various ways.

The illustration on the top of this page illustrates the (logical) anatomy of an EPA. It illustrates a kind of SCA like component (SCA has now a proposed events extension, I'll discuss it another time), with input terminals and output terminals. In this cases, the EPA gets events from input terminals, each of them has a distinct type of event. Let's take a simple three event conjunction example. A virtual car dealer matches used car sellers and buyers, once there is an agreement on the transaction, it supervises that it is carried out. The used car buying transaction is a conjunction of three events (the order of them may not be important) :

Event1: The seller physical hands over the car to the buyer
Event2: The buyer transfers to the seller the agreed upon amount for the car
Event3: The department of transportation approved and records the ownership transfer -- in Israel it is a service given in post offices, the buyer and seller identify and sign, and the post office clerk has access to the transportation ministry vehicle registration system and can verify who is the registered owner, and if the car has any incumbrance

The three logical steps of EPA are: filtering, matching (if the EPA is looking for a pattern), and deriving. The filtering part may be done outside the EPA, e.g. if the EPA instance deals with a certain transaction, then the channel should route only events related to this transaction to the EPA, I'll discuss more of the notion of channel in the near future. However, there are cases that the events should be filtered by the EPA, e.g. when it involves a condition on more than one event. After the filtering part some subset of the input events survive to be the players in the EPA. If the EPA is doing also pattern matching, it creates matching sets according to the requested pattern. In the case of the used car selling, the pattern is a conjunction of these three events that relate to the same transaction, the order is up to the buyer and seller. When the pattern is matched, it means, in this case, that three events have been detected, which satisfy together the pattern. These three events create a matching set, for each EPA instance there may be zero , one or multiple matching sets during the lifespan of this EPA instance. After the matching set is created, the third phase - the derivation phase is aimed to determine what should be reported out of the results of this EPA. It can just report the composite event that contains the three events in the matching set, and it can contain any other raw or calculated value that is a function of the attributes of these three events. Furthermore, for different purposes, different derived events can be required - consumed by other EPAs or by consumers. This is the way we describe EPA, note that this description is general enough to cover EPAs from different types. The input terminals can take either individual events or sets. I'll discuss more about EPA types, channels, contexts and patterns later