By Balaswamy Vaddeman
Learn to exploit Apache Pig to improve light-weight titanic info functions simply and fast. This publication exhibits you several optimization innovations and covers each context the place Pig is utilized in enormous information analytics. Beginning Apache Pig exhibits you ways Pig is straightforward to benefit and calls for fairly little time to increase monstrous information applications.The publication is split into 4 components: the whole beneficial properties of Apache Pig; integration with different instruments; tips to remedy complicated enterprise difficulties; and optimization of tools.You'll notice subject matters reminiscent of MapReduce and why it can't meet each company want; the positive factors of Pig Latin reminiscent of facts forms for every load, shop, joins, teams, and ordering; how Pig workflows may be created; filing Pig jobs utilizing Hue; and dealing with Oozie. you will additionally see tips to expand the framework via writing UDFs and customized load, shop, and clear out services. eventually you are going to hide varied optimization strategies resembling amassing information a couple of Pig script, becoming a member of techniques, parallelism, and the position of information codecs in sturdy performance.
What you are going to Learn• Use all of the positive factors of Apache Pig• combine Apache Pig with different instruments• expand Apache Pig• Optimize Pig Latin code• clear up varied use situations for Pig LatinWho This booklet Is ForAll degrees of IT pros: architects, monstrous information fanatics, engineers, builders, and large info administrators
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Additional resources for Beginning Apache Pig: Big Data Processing Made Easy
This section shows Pig properties for which you can set values. The set default_parallel command specifies the default number of reducers; in this example, it sets the default number of reducers to 20: Grunt>set default_parallel 20; The set debug command enables and disables debugging in a Pig Latin script. It is set to disable, by default. The following command enables debugging: Grunt>set debug 'on'; To then disable it, use the off option as follows: Grunt> set debug off; This set command allows you to set a name for a job.
If you want to specify key and value data types, you can control them from this program for both the mapper and the reducer. jar file with the previous three programs. jar file using the Eclipse export option. jar file to one of the nodes in the Hadoop cluster using FTP software such as FileZilla or WinScp. jar Mainclass InputDir OutputDir Most grid computing technologies send data to code for processing. Hadoop works in the opposite way; it sends code to data. Once the previous command is submitted, the Java code is sent to all data nodes, and they will start processing data in a parallel manner.
They are the resource manager and node manager. The resource manager is responsible for providing resources to all applications in the system. The node manager is the per-machine framework agent that is responsible for containers, monitoring their resource usage (CPU, memory, disk, network), and reporting the same to the resource manager. The per-application 11 Chapter 1 ■ MapReduce and Its Abstractions application master is, in effect, a framework-specific library and is tasked with negotiating resources from the resource manager and working with the node manager to execute and monitor the tasks.