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Now that we have seen the matcher coroutine we will look at how the matchers are used, and then we will look at the reporter() coroutine that receives the matchers outputs

try: for file in sysargv[1:]: print(file) html = open(file, encoding="utf8")read() for matcher in matchers: matchersend(html) finally: for matcher in matchers: matcherclose() receiverclose()

#include <stdioh> #include <stdlibh> #include <omph> #define NX 100 #define LEFTVAL 10 #define RIGHTVAL 100 #define NSTEPS 10000 void initialize(double uk[], double ukp1[]) { uk[0] = LEFTVAL; uk[NX-1] = RIGHTVAL; for (int i=1; i < NX-1; ++i) uk[i] = 00; for (int i = 0; i < NX; ++i) ukp1 [i] = uk[i]; } void printValues(double uk[], int step) { /* NOT SHOWN */ } int main(void) { /* pointers to arrays for two iterations of algorithm */ double *uk = malloc(sizeof(double) * NX); double *ukp1 = malloc(sizeof(double) * NX); double *temp; int i,k; double dx = 10/NX; double dt = 05*dx*dx; #pragma omp parallel private (k, i) { initialize(uk, ukp1); for (k = 0; k < NSTEPS; ++k) { #pragma omp for schedule(static) for (i = 1; i < NX-1; ++i) { ukp1[i]=uk[i]+ (dt/(dx*dx))*(uk[i+1]-2*uk[i]+uk[i-1]); } /* "copy" ukp1 to uk by swapping pointers */ #pragma omp single {temp = ukpl; ukpl = uk; uk = temp;} }

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The program reads the lenames listed on the command line, and for each one prints the lename and then reads the le s entire text into the html variable using the UTF-8 encoding Then the program iterates over all the matchers (three in this case), and sends the text to each of them Each matcher then proceeds independently, sending each match it makes to the reporter coroutine At the end we call close() on each matcher and on the reporter this terminates them, since otherwise they would continue (suspended) waiting for text (or matches in the case of the reporter) since they contain in nite loops

@coroutine def reporter(): ignore = frozenset({"stylecss", "faviconpng", "indexhtml"}) while True: match = (yield) if match is not None: groups = matchgroupdict() if "url" in groups and groups["url"] not in ignore: print(" URL:", groups["url"]) elif "h1" in groups: print(" H1: ", groups["h1"]) elif "h2" in groups: print(" H2: ", groups["h2"])

Index Statistics Index Maintenance Considerations Implementing Indexes in SQL Server Naming Guidelines Creating Indexes Filegroups Setting Up Index Maintenance Summary

The reporter() coroutine is used to output results It was created by the statement receiver = reporter() which we saw earlier, and passed as the receiver argument to each of the matchers The reporter() waits (is suspended) until a match is sent to it, then it prints the match s details, and then it waits again, in an endless loop stopping only if close() is called on it Using coroutines like this may produce performance bene ts, but does require us to adopt a somewhat different way of thinking about processing

} return 0;

Sometimes it is useful to create data processing pipelines A pipeline is simply the composition of one or more functions where data items are sent to the rst function, which then either discards the item ( lters it out) or passes it on to the next function (either as is or transformed in some way) The second function receives the item from the rst function and repeats the process, discarding or passing on the item (possibly transformed in a different way) to the next function, and so on Items that reach the end are then output in some way

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

We do this by first changing the initialization slightly so the initialization loop is identical to the

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