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class SortingStage extends PipelineStage { Comparable val = null; Comparable input = null; void firstStep() throws InterruptedException { input = (Comparable)intake(); done = (inputequals("DONE")); val = input; return; } void step() throws InterruptedException { input = (Comparable)intake(); done = (inputequals("DONE")); if (!done) { if(valcompareTo(input)<0) { output(val); val = input; } else { output(input); } } else output(val); } void lastStep() throws InterruptedException { output("DONE"); }
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The Pipeline pattern is similar to the Event Based Coordination pattern in that both patterns apply to problems where it is natural to decompose the computation into a collection of semi independent tasks The difference is that the Event Based Coordination pattern is irregular and asynchronous where the Pipeline pattern is regular and synchronous: In the Pipeline pattern, the semi independent tasks represent the stages of the pipeline, the structure of the pipeline is static, and the interaction between successive stages is regular and loosely synchronous In the Event Based Coordination pattern, however, the tasks can interact in very irregular and asynchronous ways, and there is no requirement for a static structure
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49 THE EVENT-BASED COORDINATION PATTERN
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Problem Suppose the application can be decomposed into groups of semi independent tasks interacting in an irregular fashion The interaction is determined by the flow of data between them which implies ordering constraints between the tasks How can these tasks and their interaction be implemented so they can execute concurrently Context Some problems are most naturally represented as a collection of semi independent entities interacting in an irregular way What this means is perhaps cleare st if we compare this pattern with the Pipeline pattern In the Pipeline pattern, the entities form a linear pipeline, each entity interacts only with the entities to either side, the flow of data is one way, and interaction occurs at fairly regular and predictable intervals In the Event Based Coordination pattern, in contrast, there is no restriction to a linear structure, no restriction that the flow of data be one way, and the interaction takes place at irregular and sometimes unpredictable intervals As a real world analogy, consider a newsroom, with reporters, editors, fact checkers, and other employees collaborating on stories As reporters finish stories, they send them to the appropriate editors; an editor can decide to send the story to a fact checker (who would then eventually send it back) or back to the reporter for further revision Each employee is a semi independent entity, and their interaction (for example, a reporter sending a story to an editor) is irregular Many other examples can be found in the field of discrete event simulation, that is, simulation of a physical system consisting of a collection of objects whose interaction is represented by a sequence of discrete "events" An example of such a system is the car wash facility described in [Mis86]: The facility has two car wash machines and an attendant Cars arrive at random times at the attendant Each car is directed by the attendant to a nonbusy car wash machine if one exists, or queued if both machines are busy Each car wash machine processes one car at a time The goal is to compute, for a given distribution or arrival times, the average time a car spends in the system (time being washed plus any time waiting for a nonbusy machine) and the average length of the queue that builds up at the attendant The "events" in this system include cars arriving at the attendant, cars being directed to the car wash machines, and cars leaving the machines Fig 431 sketches this example Notice that it includes "source" and "sink" objects to make it easier to model cars arriving and leaving the facility Notice also that the attendant must be notified when cars leave the car wash machines so that it knows whether the machines are busy
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