Amazon Elastic MapReduce
Based on Hadoop, MapReduce equips users with potent distributed data-processing tools
- Doesn't take long to get the hang of
- Currently available in the US region only
You'll want to be familiar with the Apache Hadoop framework before you jump into Elastic MapReduce. It doesn't take long to get the hang of it, though. Most developers can have a MapReduce application running within a few hours.
These two steps, the map function and the reduce function, comprise what Amazon MapReduce refers to as a "job flow." Admittedly, this is an oversimplification, because job flows involve other configuration parameters (such as where you get the input data and where you put the output), and you can define additional steps in the process, but that's the basic idea.
As a result, a programmer building a Hadoop-powered MapReduce system can focus on the comparatively simple job of crafting the individual functions that process single key/value pairs at a time. Hadoop does the legwork of carving the input data into initial key/value pairs; starting multiple map function instances; feeding them input data; gathering, sorting, and ordering the intermediate key/value pairs; launching reduce instances; feeding them the properly arranged intermediate data; and -- finally -- delivering the output. And all the while, Hadoop monitors the progress map and reduce tasks, as well as restarts "dead" ones automatically. Whuf.
Hadoop in the cloud
To access Amazon's Elastic MapReduce, your first stop is your Amazon Web Services account page (assuming you have an account with AWS), where you must sign up for the Elastic MapReduce service. Then, head on over to the AWS Management Console and log in. You'll find that the AWS Console -- which had been a control panel for Amazon's EC2 only -- displays a new Amazon Elastic MapReduce tab. Click the tab, and you are transferred to the Job Flows page, from which you can monitor the status of current job flows, as well as examine details of previous (terminated) job flows.
To define a new job flow, click the Create New Job Flow button. This sends you through a series of windows in step-by-step fashion. You fill in textboxes to define the location of your input data, where you want your output data, and paths to your map and reduce function. All of these locations must exist in Amazon S3 buckets. In the case of the output data, the location will exist when the job flow concludes. Consequently, it's a good idea to have a utility for transferring data to and from S3 on hand. I recommend the excellent S3Fox Organizer.
Amazon Elastic MapReduce allows for two kinds of job flows: custom jar and streaming. A custom jar-style job flow expects your map and reduce functions to be in compiled Java classes stored in Java JAR files. The Hadoop framework is Java-based, so a custom jar job flow provides the better performance. On the other hand, a streaming-type job flow lets you write your map and reduce functions in non-Java languages such as Python, Ruby, Perl, and others. The functions of a streaming job flow read the input data from stdin, and send the output to stdout. So, data flows in and out of the functions as strings, and -- by convention -- a tab separates the key and value of each input line. Once you've specified the whereabouts of your job flow's components, you identify the quantity and processing power of the EC2 instances on which the job will execute. You can select up to 20 EC2 instances; any more than that, and you have to fill out a special request form. Your choice of compute instances ranges from Small to Extra Large High CPU. Check the Amazon documentation for a complete description of the power of a CPU instance.
Join the PC World newsletter!
Most Popular Reviews
- 1 Samsung Galaxy Note 7 review
- 2 Portable power: Venom Blackbook 13 Zero review
- 3 Alcatel Idol 4S review: King of the mid-range?
- 4 Witness a 241% Australian price hike: Dell Latitude 7370 review
- 5 Is this the best value phone on the market? Moto G4 Plus review
Latest News Articles
- Apple to replace defective USB-C cables that shipped with some 12-inch MacBooks
- Like Chromebooks, thumb-size PCs will bloom
- Apple's Q1: Record $US18.4 billion profit, but iPhone sales are slowing
- Chromebooks are siphoning market share from Windows PCs
- Microsoft beefs up its Surface Book and Surface Pro 4
GGG Evaluation Team
First impression on unpacking the Q702 test unit was the solid feel and clean, minimalist styling.
For work use, Microsoft Word and Excel programs pre-installed on the device are adequate for preparing short documents.
The Fujitsu LifeBook UH574 allowed for great mobility without being obnoxiously heavy or clunky. Its twelve hours of battery life did not disappoint.
The screen was particularly good. It is bright and visible from most angles, however heat is an issue, particularly around the Windows button on the front, and on the back where the battery housing is located.
My first impression after unboxing the Q702 is that it is a nice looking unit. Styling is somewhat minimalist but very effective. The tablet part, once detached, has a nice weight, and no buttons or switches are located in awkward or intrusive positions.
- CCProject Manager - PCI DSS / IMACSQLD
- FTDefence Network EngineerACT
- FTSenior Test Analyst | End to End TestingNSW
- CCContract Analyst Programmer (Crystal Report/JAVA) 160816/AP/vhsAsia
- FTMicrosoft Solution ArchitectACT
- CCActive Directory Consultant/ArchitectWA
- CCSolution Architect - POSVIC
- FTProject ManagerNSW
- CCiOS DeveloperVIC
- FTDigital Product Owner | Advertising Technology | SearchNSW
- CCChange Communications ManagerNSW
- CCSenior Security Specialist - McAfeeVIC
- FTIT Release CoordinatorWA
- CCSr. Project Manager- Infrastructure- Data Centre,VirtualizationNSW
- CCData Centre Solutions Architect - Red Hat, Wintel & VMwareACT
- FTCapacity PlannerNSW
- FTJava Tech Lead - Full StackNSW
- FTPrincipal Business Consultant- Wealth ManagementNSW
- FTSenior IoT / M2M .Net DeveloperVIC
- CCBI-Business Intelligence Technical LeadNSW
- CCSenior Java DeveloperVIC
- CCService Desk AnalystNSW
- CCProject Resource SpecialistVIC
- FTBack End Developer - Java, Spring, RESTNSW
- FTSAP FI/CO module- Tester/ Quality AnalystNSW