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Apache Hive is an Apache open-source project built on top of Hadoop for querying, summarizing and analyzing large data sets using a SQL-like interface. Apache Pig and Apache Hive, both are commonly used on Hadoop cluster.

Apache Pig vs Apache Hive – Top 12 Useful Differences

Apache Pig and Apache Hive are mostly used in the production environment. A user needs to select a tool based on data types and expected output. Both tools provide a unique way of analyzing Big Data on Hadoop cluster. Based on above discussion user can choose between Apache Pig and Apache Hive for their requirement. You may also look at the following articles to learn more —. Verifiable Certificate of Completion. Lifetime Access. Your email address will not be published.

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Forgot Password? The Job Tracker and TaskTracker status and information is exposed by Jetty and can be viewed from a web browser. If the JobTracker failed on Hadoop 0. Hadoop version 0. The JobTracker records what it is up to in the file system. When a JobTracker starts up, it looks for any such data, so that it can restart work from where it left off.


The allocation of work to TaskTrackers is very simple. Every TaskTracker has a number of available slots such as "4 slots".

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Every active map or reduce task takes up one slot. The Job Tracker allocates work to the tracker nearest to the data with an available slot. There is no consideration of the current system load of the allocated machine, and hence its actual availability. If one TaskTracker is very slow, it can delay the entire MapReduce job—especially towards the end of a job, where everything can end up waiting for the slowest task.

With speculative execution enabled, however, a single task can be executed on multiple slave nodes. MapReduce has undergone a complete overhaul in hadoop The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.

Beginning Apache Pig PDF | Big Data | Big data, Data processing, App development

The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager s to execute and monitor the tasks. As part of Hadoop 2. This also streamlines MapReduce to do what it does best, process data. With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management. When enterprise data is made available in HDFS, it is important to have multiple ways to process that data.

With Hadoop 2.

The ResourceManager and the NodeManager form the new, and generic, system for managing applications in a distributed manner. The per-application ApplicationMaster is a framework-specific entity and is tasked with negotiating resources from the ResourceManager and working with the NodeManager s to execute and monitor the component tasks. The ResourceManager has a scheduler, which is responsible for allocating resources to the various running applications, according to constraints such as queue capacities, user-limits etc.

The scheduler performs its scheduling function based on the resource requirements of the applications. The NodeManager is the per-machine slave, which is responsible for launching the applications' containers, monitoring their resource usage cpu, memory, disk, network and reporting the same to the ResourceManager. Each ApplicationMaster has the responsibility of negotiating appropriate resource containers from the scheduler, tracking their status, and monitoring their progress.

  1. Learn to process your data by using Apache Pig and Hadoop platform?
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  4. From the system perspective, the ApplicationMaster runs as a normal container. Originally posted on Sachin Puttur's Big Data blog.

    Revisions made under Creative Commons. Excellent and very Informative presentation,It really gives detailed information We would except more presentation on it from you in future Thank you Really a nice introduction about Hadoop! Apache Hadoop has been the driving force behind the growth of the big data industry. Hadoop brings the ability to cheaply process large amounts of data, regardless of its structure.

    Hadoop is a highly scalable storage platform, because it can store and distribute very large data sets across hundreds of inexpensive servers that operate in parallel.

    More at www. Image by :. Get the highlights in your inbox every week. Hadoop distributed file system The Hadoop distributed file system HDFS is a distributed, scalable, and portable file-system written in Java for the Hadoop framework. Known limitations of this approach in Hadoop 1. Share your thoughts with other customers. Write a customer review. Discover the best of shopping and entertainment with Amazon Prime. Prime members enjoy FREE Delivery on millions of eligible domestic and international items, in addition to exclusive access to movies, TV shows, and more.

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