By Jason Venner, Madhu Siddalingaiah, Sameer Wadkar
Seasoned Apache Hadoop, moment variation brings you on top of things on Hadoop – the framework of huge facts. Revised to hide Hadoop 2.0, the booklet covers the very newest advancements corresponding to YARN (aka MapReduce 2.0), new HDFS high-availability positive factors, and elevated scalability within the type of HDFS Federations. all of the previous content material has been revised too, giving the newest at the fine details of MapReduce, cluster layout, the Hadoop disbursed dossier process, and more.
This ebook covers every little thing you must construct your first Hadoop cluster and start studying and deriving worth out of your company and clinical facts. discover ways to resolve big-data difficulties the MapReduce manner, via breaking a tremendous challenge into chunks and growing small-scale options that may be flung throughout millions upon hundreds of thousands of nodes to research huge information volumes in a brief quantity of wall-clock time. find out how to allow Hadoop look after allotting and parallelizing your software—you simply specialize in the code; Hadoop looks after the rest.
* Covers all that's new in Hadoop 2.0
* Written via a qualified concerned about Hadoop considering the fact that day one
* Takes you speedy to the pro professional point at the most well-liked cloud-computing framework
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Extra resources for Pro Apache Hadoop (2nd Edition)
The idea is to have a global Resource Manager and a per–application Application Master. Note, we mentioned application, not job. x, an application can either be a single job in the sense of the classical MapReduce job or a Directed Acyclic Graph (DAG) of jobs. A DAG is a graph whose nodes are connected so that no cycles are possible. That is, regardless of how you traverse a graph, you cannot reach a node again in the process of traversal. In plain English, a DAG of jobs implies jobs with hierarchical relationships between each other.
Run() invocation in the main() method must be the same Configuration instance that is used when configuring the job in the run() method. To ensure this, the run() method consistently uses the getConf() method that is defined in the Configurable interface and implemented in the Configured class that the class extends. If the same Configuration instance is not used, the job will not be correctly configured, and the third-party JAR files will not be available for the remote Mapper and Reducer tasks.
0 for both VMware and VirtualBox. If you do not have these VM players installed, download their latest versions first. productID=F6mO278Rvo Note that the Cloudera 5 VM requires 8 GB of memory. Ensure that your machine has adequate memory to execute the VM. Alternatively, follow the steps in the subsequent section to install your own development environment. When you launch the VM, you see the screen shown in Figure 3-1. The figure points to the Eclipse icon on the desktop inside the VM. You can simply open Eclipse and begin development of the Hadoop code because the environment is configured to run jobs directly from the Eclipse environment in local mode.