Book Image

Apache Hadoop 3 Quick Start Guide

By : Hrishikesh Vijay Karambelkar
Book Image

Apache Hadoop 3 Quick Start Guide

By: Hrishikesh Vijay Karambelkar

Overview of this book

Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster.
Table of Contents (10 chapters)

Compiling and running MapReduce jobs

In this section, we will cover compiling and running MapReduce jobs. We have already seen examples of how jobs can be run on standalone, pseudo-development, and cluster environments. You need to remember that, when you compile the classes, you must do it with same versions of your libraries and Java that you will otherwise run in production, otherwise you may get major-minor version mismatch errors in your run-time (read the description here). In almost all cases, the JAR for programs is created and run directly through the following command:

Hadoop jar <jarfile> <parameters>

Now let's look at different alternatives available for running the jobs.

Triggering the job remotely