By Arun C. Murthy, Vinod Kumar Vavilapalli, Doug Eadline, Joseph Niemiec, Jeff Markham
Read or Download Apache Hadoop YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 (Addison-Wesley Data & Analytics Series) PDF
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Extra resources for Apache Hadoop YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 (Addison-Wesley Data & Analytics Series)
This strategy, although the best option for individual users, leads to bad scenarios from the overall cluster utilization point of view. Specifically, sometimes all of the map tasks are finished (resulting in idle nodes in the cluster) while a few reduce tasks simply chug along for a long while. Hadoop on Demand did not have the ability to grow and shrink the MapReduce clusters on demand for a variety of reasons. Most importantly, elasticity wasn’t a firstclass feature in the underlying ResourceManager itself.
By default, HDFS keeps three copies of each file in the file system for redundancy. replication will be set to 1. xml, we specify the NameNode, Secondary NameNode, and DataNode data directories that we created in Step 4. These are the directories used by the various components of HDFS to store data. xml and remove the original empty
Agility By conf lating the platform responsible for arbitrating resource usage with the framework expressing that program, one is forced to evolve both structures simultaneously. While cluster administrators try to improve the allocation efficiency of the platform, it is the users’ responsibility to help incorporate framework changes into the new structure. Thus, upgrading a cluster should not require users to halt, validate, and restore their pipelines. But the exact opposite thing happened with shared MapReduce clusters: While updates typically required no more than recompilation, users’ assumptions about internal framework details or developers’ assumptions about users’ programs occasionally created incompatibilities, wasting more software development cycles.