In the early of this September, the Chief Strategy Offer of Cloudera Mike Olson has announced that the next important initiatives for Couldera - One Platform to advance their investment on Apache Spark. The Spark is originally invented by few guys who started up the Databrick. Later, Spark catches most attention from big data communities and companies by its high-performance in-memory computing framework, which can run on top of Hadoop Yarn.
1. Constructor With Parameters Java Code public class Foo() { public Bar bar; public Foo(Bar bar) { this.bar = bar; } } Scala Code class Foo(val bar:Bar) 2. Constructor With Private Attribute Java Code public class Foo() { private final Bar bar; public Foo(Bar bar) { this.bar = bar; } } Scala Code class Foo(private val bar:Bar) 3. Call Super Constructor Java Code public class Foo() extends SuperFoo { public Foo(Bar bar) { super(bar); } } Scala Code
Summary Before new year, DataFibers has completed the winter release of 2016, which has more than 20+ changes requests applied. In this release, DataFibers is featured with new api document and landing pages. In addition, the preview version of stream processing (by flink) is ready. Details Below is the list of key changes in this release. Integrated REST API Document to the DF Application Added landing welcome page
Backgrounds This is the link from WikiMedia about what’s Speculative Execution. In Hadoop, the following parameters string are for this settings. And, they are true by default. mapred.map.tasks.speculative.execution mapred.reduce.tasks.speculative.execution When to Disable Most time, it helps. However, I am here to collect some scenario when we do not need it. Of course, when ever your cluster really in shortage of resource or for the purpose of experiment, we can disable them by setting them to false since “SE” really a big resource consumer It is generally advisable to turn off ”SE” for mapred jobs that use HBase as a source.
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