Download Apache Hadoop YARN: Moving beyond MapReduce and Batch by Arun C. Murthy, Vinod Kumar Vavilapalli, Doug Eadline, PDF

By Arun C. Murthy, Vinod Kumar Vavilapalli, Doug Eadline, Joseph Niemiec, Jeff Markham

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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)

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Yahoo! Hadoop administrators would install and manage a shared HDFS instance, and load commonly used and interesting data sets into the shared cluster, attracting scientists interested in deriving insights from them. HDFS also acquired a POSIX-like permissions model for supporting multiuser environments, file and namespace quotas, and other features to improve its multitenant operation. Tracing the evolution of HDFS is in itself an interesting endeavor, but we will focus on the compute platform in the remainder of this chapter.

Formatting destroys everything in the directory and sets up a new file system. Format the NameNode directory as the HDFS superuser, which is typically the “hdfs” user account. Storage: Storage directory /var/data/hadoop/hdfs/nn has been ➥successfully formatted. Steps to Configure a Single-Node YARN Cluster Step 11: Start the HDFS Services Once formatting is successful, the HDFS services must be started. There is one service for the NameNode (metadata server), a single DataNode (where the actual data is stored), and the SecondaryNameNode (checkpoint data for the NameNode).

For those familiar with Hadoop version 1, there are two core components; the Hadoop Distributed File System and the integrated MapReduce distributed processing engine. Hadoop YARN is the new replacement for the monolithic MapReduce component found in version 1. The scheduling and resource management have been separated from the management of MapReduce pipelines. While Hadoop version 2 with YARN still provides full MapReduce capability and backwards compatibility with version 1, it also opens the door to many other “application frameworks” that are not based on MapReduce processing.

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