Composing functions 395 in Java

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Pipelines typically have several components, one that acquires data, one or more that lter or transform data, and one that outputs results This is exactly the functional-style approach to programming that we discussed earlier in the section when we looked at composing some of Python s built-in functions, such as filter() and map() One bene t of using pipelines is that we can read data items incrementally, often one at a time, and have to give the pipeline only enough data items to ll it (usually one or a few items per component) This can lead to signi cant memory savings compared with, say, reading an entire data set into memory and then processing it all in one go
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Step 1 2 3 4 5 6 7 8 9 10 11 Close coroutines Action pipeline = get_data( process(reporter())) pipelinesend("a") pipelinesend("b") pipelinesend("c") pipelinesend("d") pipelinesend("e") pipelinesend("f") get_data() Waiting Read "a" Read "b" Read "c" Read "d" Read "e" Read "f" Waiting Waiting Waiting Finished process() Waiting Waiting Process "a" Process "b" Process "c" Drop "d" Process "e" Process "f" Waiting Waiting Finished reporter() Waiting Waiting Waiting Output "a" Output "b" Output "c" Waiting Output "e" Output "f" Waiting Finished
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Figure 83 A three-step coroutine pipeline processing six items of data
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Figure 83 illustrates a simple three component pipeline The rst component of the pipeline (get_data()) acquires each data item to be processed in turn The second component (process()) processes the data and may drop unwanted data items there could be any number of other processing/ ltering components, of course The last component (reporter()) outputs results In the gure,
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items "a", "b", "c", "e", and "f" are processed and produce output, while item "d" is dropped The pipeline shown in Figure 83 is a lter, since each data item is passed through unchanged and is either dropped or output in its original form The end points of pipelines tend to perform the same roles: acquiring data items and outputting results But between these we can have as many components as necessary, each ltering or transforming or both And in some cases, composing the components in different orders can produce pipelines that do different things We will start out by looking at a theoretical example to get a better idea of how coroutine-based pipelines work, and then we will look at a real example Suppose we have a sequence of oating-point numbers and we want to process them in a multicomponent pipeline such that we transform each number into an integer (by rounding), but drop any numbers that are out of range (< 0 or >= 10) If we had the four coroutine components, acquire() (get a number), to_int() (transform a number by rounding and converting to an integer), check() (pass on a number that is in range; drop a number that is out of range), and output() (output a number), we could create the pipeline like this:
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pipe = acquire(to_int(check(output())))
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We would then send numbers into the pipeline by calling pipesend() We ll look at the progress of the numbers 43 and 96 as they go through the pipeline, using a different visualization from the step-by-step gures used earlier:
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pipesend(43) acquire(43) to_int(43) check(4) output(4) pipesend(96) acquire(96) to_int(96) check(10)
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Notice that for 96 there is no output This is because the check() coroutine received 10, which is out of range (>= 10), and so it was ltered out Let s see what would happen if we created a different pipeline, but using the same components:
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pipe = acquire(check(to_int(output())))
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This simply performs the ltering (check()) before the transforming (to_int()) Here is how it would work for 43 and 96:
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pipesend(43) acquire(43) check(43) to_int(43) output(4) pipesend(96) acquire(96) check(96) to_int(96) output(10)
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Here we have incorrectly output 10, even though it is out of range This is because we applied the check() component rst, and since this received an in-range value of 96, it simply passed it on But the to_int() component rounds the numbers it gets
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