Maximizing Spark Performance: Minimizing Shuffle Overhead
<p><strong>Shuffling</strong> is a procedure used to <a href="https://en.wikipedia.org/wiki/Randomization" rel="noopener ugc nofollow" target="_blank">randomize</a> a deck of <a href="https://en.wikipedia.org/wiki/Playing_card" rel="noopener ugc nofollow" target="_blank">playing cards</a> to provide an element of chance in <a href="https://en.wikipedia.org/wiki/Card_game" rel="noopener ugc nofollow" target="_blank">card games</a></p>
<h2>But what is Shuffling in the Spark world ??</h2>
<p><strong>Apache Spark processes queries by distributing data over multiple nodes and calculating the values separately on every node.</strong> However, occasionally, <strong>the nodes need to exchange the data</strong>. After all, that’s the purpose of Spark — processing data that doesn’t fit on a single machine.</p>
<p><strong>Shuffling is the process of exchanging data between partitions</strong>. As a result, data rows can move between worker nodes when their source partition and the target partition reside on a different machine.</p>
<p>Spark doesn’t move data between nodes randomly. Shuffling is a time-consuming operation, so it happens only when there is no other option.</p>
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