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copied!<p>I created a one-off for myself a couple years ago when I got an 8-core machine, but I wasn't terribly happy with it. I never got it to be as simple to used as I had hoped, and memory-intensive tasks didn't scale well.</p> <p>If you don't get any <em>real</em> answers I can share more, but the core of it is:</p> <pre><code>public class LocalMapReduce<TMapInput, TMapOutput, TOutput> { private int m_threads; private Mapper<TMapInput, TMapOutput> m_mapper; private Reducer<TMapOutput, TOutput> m_reducer; ... public TOutput mapReduce(Iterator<TMapInput> inputIterator) { ExecutorService pool = Executors.newFixedThreadPool(m_threads); Set<Future<TMapOutput>> futureSet = new HashSet<Future<TMapOutput>>(); while (inputIterator.hasNext()) { TMapInput m = inputIterator.next(); Future<TMapOutput> f = pool.submit(m_mapper.makeWorker(m)); futureSet.add(f); Thread.sleep(10); } while (!futureSet.isEmpty()) { Thread.sleep(5); for (Iterator<Future<TMapOutput>> fit = futureSet.iterator(); fit.hasNext();) { Future<TMapOutput> f = fit.next(); if (f.isDone()) { fit.remove(); TMapOutput x = f.get(); m_reducer.reduce(x); } } } return m_reducer.getResult(); } } </code></pre> <p>EDIT: Based on a comment, below is a version without <code>sleep</code>. The trick is to use <code>CompletionService</code> which essentially provides a blocking queue of completed <code>Future</code>s.</p> <pre><code> public class LocalMapReduce<TMapInput, TMapOutput, TOutput> { private int m_threads; private Mapper<TMapInput, TMapOutput> m_mapper; private Reducer<TMapOutput, TOutput> m_reducer; ... public TOutput mapReduce(Collection<TMapInput> input) { ExecutorService pool = Executors.newFixedThreadPool(m_threads); CompletionService<TMapOutput> futurePool = new ExecutorCompletionService<TMapOutput>(pool); Set<Future<TMapOutput>> futureSet = new HashSet<Future<TMapOutput>>(); for (TMapInput m : input) { futureSet.add(futurePool.submit(m_mapper.makeWorker(m))); } pool.shutdown(); int n = futureSet.size(); for (int i = 0; i < n; i++) { m_reducer.reduce(futurePool.take().get()); } return m_reducer.getResult(); } </code></pre> <p>I'll also note this is a very distilled map-reduce algorithm, including a single reduce worker which does both the reduce and merge operation.</p>

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