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Spark Release 0.5.1 | Apache Spark
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<li><a href="/news/spark-2-0-0-released.html">Spark 2.0.0 released</a>
<span class="small">(Jul 26, 2016)</span></li>
<li><a href="/news/spark-1-6-2-released.html">Spark 1.6.2 released</a>
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<span class="small">(Jun 16, 2016)</span></li>
<li><a href="/news/spark-2.0.0-preview.html">Preview release of Spark 2.0</a>
<span class="small">(May 26, 2016)</span></li>
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<h2>Spark Release 0.5.1</h2>
<p>Spark 0.5.1 is a maintenance release that adds several important bug fixes and usability features. You can download it as a <a href="http://github.com/downloads/mesos/spark/spark-0.5.1.tgz">tar.gz file</a>.</p>
<h3>Maven Publishing</h3>
<p>Spark is now available in Maven Central, making it easier to link into your programs without having to build it as a JAR. Use the following Maven identifiers to add it to a project:</p>
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<li>groupId: org.spark-project</li>
<li>artifactId: spark-core_2.9.2</li>
<li>version: 0.5.1</li>
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<h3>Scala 2.9.2</h3>
<p>Spark now builds against Scala 2.9.2 by default.</p>
<h3>Improved Accumulators</h3>
<p>The new Accumulable class generalizes Accumulators for the case when the type being accumulated is not the same as the types of elements being added (e.g. you wish to accumulate a collection, such as a Set, by adding individual elements). This interface is also more efficient in avoiding the creation of temporary objects. (Contributed by Imran Rashid.)</p>
<h3>Bug Fixes</h3>
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<li>Spark's algorithm for estimating the sizes of objects (in order to manage memory correctly) has been improved
to handle JVMs with 32- vs 64-bit pointers and to measure objects more accurately. (Contributed by Shivaram Venkataraman.)</li>
<li>Improved algorithms for taking random samples out of datasets to avoid biases that could occur in the previous ones. (Suggested by Henry Milner.)</li>
<li>Improved load balancing across nodes in sort operations.</li>
<li>Fixed a shuffle bug that could cause reduce tasks to fail to receive a map task's full output.</li>
<li>Fixed a bug with locating custom KryoSerializers.</li>
<li>Reduced memory consumption of <tt>saveAsObjectFile</tt> when objects are large.</li>
</ul>
<h3>EC2 Improvements</h3>
<p>Spark’s EC2 launch script now configures Spark’s memory limit automatically based on the machine’s available RAM.</p>
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