The filters in a pipeline can be implemented as separate hosted tasks running close to the data that they maintain. A distributed transaction can be broken down into separate, compensable tasks, each of which can be implemented by using a filter that also implements the Compensating Transaction pattern. Using the Pipes and Filters pattern in conjunction with the Compensating Transaction pattern is an alternative approach to implementing distributed transactions. Failure of a single filter doesn't necessarily result in failure of the entire pipeline. If a filter fails or the machine it's running on is no longer available, the pipeline can reschedule the work that the filter was performing and direct this work to another instance of the component. The first filter in the pipeline can start its work and output its results, which are passed directly on to the next filter in the sequence before the first filter has completed its work.Īnother benefit is the resiliency that this model can provide.
If the input and output of a filter are structured as a stream, it's possible to perform the processing for each filter in parallel. The next figure shows an example applied to the pipeline for the data from Source 1. The filters don't even have to be in the same datacenter or geographic location, which allows each element in a pipeline to run in an environment close to the resources it requires. A filter that is computationally intensive can run on high-performance hardware, while other less demanding filters can be hosted on less expensive commodity hardware. The filters that make up a pipeline can run on different machines, enabling them to be scaled independently and take advantage of the elasticity that many cloud environments provide. A key advantage of the pipeline structure is that it provides opportunities for running parallel instances of slow filters, enabling the system to spread the load and improve throughput. One or more filters could be a bottleneck, especially if a large number of requests appear in a stream from a particular data source. The time it takes to process a single request depends on the speed of the slowest filter in the pipeline. The next figure shows a solution implemented using pipes and filters. This helps to avoid duplicating code, and makes it easy to remove, replace, or integrate additional components if the processing requirements change. By standardizing the format of the data that each component receives and sends, these filters can be combined together into a pipeline. Solutionīreak down the processing required for each stream into a set of separate components (or filters), each performing a single task.
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A solution is required that addresses these issues, and increases the possibilities for code reuse. Also, additional processing might be required in the future, or the order in which the tasks performed by the processing could change. Some tasks might be compute intensive and could benefit from running on powerful hardware, while others might not require such expensive resources. However, the processing tasks performed by each module, or the deployment requirements for each task, could change as business requirements are updated. The code that implements the tasks is closely coupled in a module, and has been developed with little or no thought given to reuse or scalability. Some of the tasks that the monolithic modules perform are functionally very similar, but the modules have been designed separately.
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The data from each source is processed by a separate module that performs a series of tasks to transform this data, before passing the result to the business logic of the application.
An application receives and processes data from two sources. The figure illustrates the issues with processing data using the monolithic approach. However, this approach is likely to reduce the opportunities for refactoring the code, optimizing it, or reusing it if parts of the same processing are required elsewhere within the application. A straightforward but inflexible approach to implementing an application is to perform this processing as a monolithic module.
Context and problemĪn application is required to perform a variety of tasks of varying complexity on the information that it processes. This can improve performance, scalability, and reusability by allowing task elements that perform the processing to be deployed and scaled independently. Decompose a task that performs complex processing into a series of separate elements that can be reused.
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