Examples with KeyedReduceTLink edu.iu.dsc.tws.tset.links.batch.KeyedReduceTLink used on opensource projects

Example 1 with KeyedReduceTLink

use of edu.iu.dsc.tws.tset.links.batch.KeyedReduceTLink in project twister2 by DSC-SPIDAL.

the class KReduceExample method execute.

@Override
public void execute(WorkerEnvironment workerEnv) {
    BatchEnvironment env = TSetEnvironment.initBatch(workerEnv);
    int start = env.getWorkerID() * 100;
    SourceTSet<Integer> src = dummySource(env, start, COUNT, PARALLELISM);
    KeyedReduceTLink<Integer, Integer> kreduce = src.mapToTuple(i -> new Tuple<>(i % 10, i)).keyedReduce(Integer::sum);
    LOG.info("test foreach");
    kreduce.forEach(t -> LOG.info("sum by key=" + t.getKey() + ", " + t.getValue()));
    LOG.info("test map");
    kreduce.map(i -> i.toString() + "$$").direct().forEach(s -> LOG.info("map: " + s));
    LOG.info("test compute");
    kreduce.compute((ComputeFunc<Iterator<Tuple<Integer, Integer>>, String>) input -> {
        StringBuilder s = new StringBuilder();
        while (input.hasNext()) {
            s.append(input.next().toString()).append(" ");
        }
        return s.toString();
    }).direct().forEach(s -> LOG.info("compute: concat " + s));
    LOG.info("test computec");
    kreduce.compute((ComputeCollectorFunc<Iterator<Tuple<Integer, Integer>>, String>) (input, output) -> {
        while (input.hasNext()) {
            output.collect(input.next().toString());
        }
    }).direct().forEach(s -> LOG.info("computec: " + s));
}

Example 2 with KeyedReduceTLink

use of edu.iu.dsc.tws.tset.links.batch.KeyedReduceTLink in project twister2 by DSC-SPIDAL.

the class FileBasedWordCount method execute.

@Override
public void execute(WorkerEnvironment workerEnv) {
    BatchEnvironment env = TSetEnvironment.initBatch(workerEnv);
    int sourcePar = (int) env.getConfig().get("PAR");
    // read the file line by line by using a single worker
    SourceTSet<String> lines = env.createSource(new WordCountFileSource(), 1);
    // distribute the lines among the workers and performs a flatmap operation to extract words
    ComputeTSet<String> words = lines.partition(new HashingPartitioner<>(), sourcePar).flatmap((FlatMapFunc<String, String>) (l, collector) -> {
        StringTokenizer itr = new StringTokenizer(l);
        while (itr.hasMoreTokens()) {
            collector.collect(itr.nextToken());
        }
    });
    // attach count as 1 for each word
    KeyedTSet<String, Integer> groupedWords = words.mapToTuple(w -> new Tuple<>(w, 1));
    // performs reduce by key at each worker
    KeyedReduceTLink<String, Integer> keyedReduce = groupedWords.keyedReduce(Integer::sum);
    // gather the results to worker0 (there is a dummy map op here to pass the values to edges)
    // and write to a file
    keyedReduce.map(i -> i).gather().forEach(new WordcountFileWriter());
}