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    <title>Apache Ignite - Faster In-Memory MapReduce</title>
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                    <h2 class="first">Faster In-Memory MapReduce</h2>
                    <p>
                        Apache Ignite In-Memory MapReduce allows to effectively parallelize the processing of data stored in
                        any Hadoop file system, including the In-Memory File System provided by Ignite.
                    </p>
                    <p>
                        It eliminates the overhead associated with name-node, job tracker, and task trackers in a
                        standard Hadoop architecture while providing low-latency distributed processing.
                    </p>

                    <div class="page-heading">Faster Scheduling</div>
                    <p>
                        Ignite MapReduce performs much better than Hadoop due to push-based resource allocation
                        (vs. pull-based in Hadoop), as well as in-process collocation of computations with data.
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            <div class="page-heading">Data Locality</div>
            <p>
                In HDFS, <i>NameNode</i> stores all the metadata and can be a single point of failure. In Ignite, every client
                can determine which node a key belongs to by plugging it into a hashing function, without a need for any special mapping servers or name nodes.
            </p>

            <div class="page-heading">Reduced Network Overhead</div>
            <p>
                Since <a href="/features/igfs.html">Ignite File System (IGFS)</a> does not need a
                <i>NameNode</i>, when used with <i>IGFS</i>, Ignite MapReduce jobs go directly to the
                IGFS data nodes in a single <span style="white-space: nowrap">round-trip</span>.
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            <p>
                <a href="https://apacheignite-fs.readme.io/docs/map-reduce" target="docs">
                    <b>Configure In-Memory MapReduce <i class="fa fa-angle-double-right"></i></b>
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