June 2017
Beginner to intermediate
233 pages
5h 57m
English
Content preview from Data Science with JavaBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Chapter 6. Hadoop MapReduce
You write MapReduce jobs in Java when you need low-level control and want to optimize or streamline your big data pipeline. Using MapReduce is not required, but it is rewarding, because it is a beautifully designed system and API. Learning the basics can get you very far, very quickly, but before you embark on writing a customized MapReduce job, don’t overlook the fact that tools such as Apache Drill enable you to write standard SQL queries on Hadoop.
This chapter assumes you have a running Hadoop Distributed File System (HDFS) on your local machine or have access to a Hadoop cluster. To simulate how a real MapReduce job would run, we can run Hadoop in pseudodistributed mode on one node, either your localhost or a remote machine. Considering how much CPU, RAM, and storage resources we can fit on one box (laptop) these days, you can, in essence, create a mini supercomputer capable of running fairly massive distributed jobs. You can get pretty far on your localhost (on a subset of data) and then scale up to a full cluster when your application is ready.
If the Hadoop client is properly installed, you can get a complete listing of all available Hadoop operations by simply typing the following:
bash$ hadoopHadoop Distributed File System
Apache Hadoop comes with a command-line tool useful for accessing the Hadoop
filesystem and launching MapReduce jobs. The filesystem access command fs is invoked as follows:
bash$ hadoop fs <command> <args>
The command is any number ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.