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    <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&lt;TMapInput, TMapOutput, TOutput&gt; { private int m_threads; private Mapper&lt;TMapInput, TMapOutput&gt; m_mapper; private Reducer&lt;TMapOutput, TOutput&gt; m_reducer; ... public TOutput mapReduce(Iterator&lt;TMapInput&gt; inputIterator) { ExecutorService pool = Executors.newFixedThreadPool(m_threads); Set&lt;Future&lt;TMapOutput&gt;&gt; futureSet = new HashSet&lt;Future&lt;TMapOutput&gt;&gt;(); while (inputIterator.hasNext()) { TMapInput m = inputIterator.next(); Future&lt;TMapOutput&gt; f = pool.submit(m_mapper.makeWorker(m)); futureSet.add(f); Thread.sleep(10); } while (!futureSet.isEmpty()) { Thread.sleep(5); for (Iterator&lt;Future&lt;TMapOutput&gt;&gt; fit = futureSet.iterator(); fit.hasNext();) { Future&lt;TMapOutput&gt; 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&lt;TMapInput, TMapOutput, TOutput&gt; { private int m_threads; private Mapper&lt;TMapInput, TMapOutput&gt; m_mapper; private Reducer&lt;TMapOutput, TOutput&gt; m_reducer; ... public TOutput mapReduce(Collection&lt;TMapInput&gt; input) { ExecutorService pool = Executors.newFixedThreadPool(m_threads); CompletionService&lt;TMapOutput&gt; futurePool = new ExecutorCompletionService&lt;TMapOutput&gt;(pool); Set&lt;Future&lt;TMapOutput&gt;&gt; futureSet = new HashSet&lt;Future&lt;TMapOutput&gt;&gt;(); for (TMapInput m : input) { futureSet.add(futurePool.submit(m_mapper.makeWorker(m))); } pool.shutdown(); int n = futureSet.size(); for (int i = 0; i &lt; 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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    1. POSimple Java Map/Reduce framework
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    1. USxan
    1. USxan
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      1. VTUpMod
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    1. COThere are lack of sorting reduce values by key, so reduce part is not parallelized as it done in Hadoop.
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      1. USyura
    2. CO@yura: Indeed. This is the kind of fine-tuned subtlety that I don't want to worry about.
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      1. USskaffman
    3. COThread.sleep is bad :)
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      1. USChris Dennett
 

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