Data, Done Big
The problem is that early discussions were focused entirely on the technologies and techniques needed to manage massive data sets, and you started reading words like map reduce and Hadoop, neither of which meant anything to the average IT manager. Read more on Redmondmag.com
Google Dumps MapReduce in Favor of New Hyper-Scale Analytics System
Google has abandoned MapReduce, the system for running data analytics jobs spread across many servers the company developed and later open sourced, in favor of a new cloud analytics system it has built called Cloud Dataflow. MapReduce has been a … Read more on Data Center Knowledge
Posted by gildenshelton565 February - 14 - 2015ADD COMMENTS
Moving Hadoop beyond MapReduce
Originally designed to excel at running batch MapReduce jobs over a large static data set on clusters of commodity hardware, the combination of Apache Hadoop with a growing collection of associated projects and products is increasingly capable of far … Read more on GigaOM
MapReduce Successor Google Cloud Dataflow is a Game Changer for Hadoop …
Google CLoud logo MapReduce Successor Google Cloud Dataflow is a Game Changer for Hadoop Thunder In turn, Google decided that MapReduce, the big data analysis tool that the company initiated several years ago, was no longer an appropriate … Read more on CloudTimes
Google's Dataflow pipeline tool can now run on Spark, thanks to Cloudera
Google last year sent shockwaves through the big data world it helped create when Urs Hölzle, Google's senior vice president of technical infrastructure, announced that Googlers “don't really use MapReduce anymore.” In lieu of MapReduce, which Google … Read more on VentureBeat
MapReduce is a framework that is designed to process huge datasets. It uses a large cluster of computers which are called as nodes to perform the computations. This computational processing is done on data stored either in a file system or within a database. In MapReduce applications, there are basically two components namely, map and reduce. In Map step, the master node receives the input, partitions it into smaller sub-problems, and the finally distributes those to worker nodes. This is again repeated by the worker node leading to a multi-level tree structure. The smaller problems that are made in the worker node process each one of them and pass the answer back to its master node. On the other hand, the reduce step takes the answers and combines them in some way to get the final output.
In the MapReduce framework there is a large distributed sort which consists of hot spots as defined.
• an input reader
• a Map function
• a Reduce function
• a partition function
• a compare function
• an output writer
Here, Input reader basically divides the input into appropriate size splits. The MapReduce framework then assigns one split to each Map function. There is a distributed file system from where the input reader reads data and generates the required key/value pairs. Another component namely Map function takes a series of key/value pairs, processes them and then generates zero or more output key/value pairs. Often the input and output types of the reduce function is different from each other.
Reduce function in the MapReduce framework calls each reduce function once for each unique key in the sorted order. This Reduce function can iterate through the values which are eventually associated with that key. The output value can be 0 or some more values as well. Another important function is partition function where each Map function output is allocated to a particular reducer. This is done with the help of the application’s partition function. Then comes, a comparison function, which is used to run and sort the Map function. Then there is another very important function called as the output writer. The output writer is used to write the output of the Reduce function to the distributed file system, often called as stable storage.
Each component in the MapReduce applications is important and even if one is missing or not properly optimized, the results would not be as expected. For defining a MapReduce framework correctly you need to understand each component closely, for which you must read online tutorials. Explore the online resources and make use of this application and serve your various important purposes.
Jeniffer Thomas is a sucessful Internt Marketer and working in this area from past 5 years.Know about Mapreduce information about MapReduce applications and MapReduce.
Mod 1 of 5 part course on MapReduce. Video Rating: 4 / 5
Posted by BlairMABEL25 January - 13 - 2015ADD COMMENTS
Inside the Apache Software Foundation's newest Top-Level Project: Apache Flink
The data-processing engine, which offers APIs in Java and Scala as well as specialized APIs for graph processing, is presented as an alternative to Hadoop's MapReduce component with its own runtime. Yet the system still provides access to Hadoop's … Read more on SDTimes.com
Apache Spark 1.2.0 Supports Netty-based Implementation, High Availability and …
Spark was created initially to improve on the Map/Reduce model, so existing Map/Reduce developers should definitely give Spark a try! When compared to Map/Reduce, Spark offers a higher level, more expressive API in addition to a rich set of built-in … Read more on InfoQ.com
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also
This is official Amazon Web Services (AWS) documentation for Amazon Elastic MapReduce (Amazon EMR). This guide provides a conceptual overview of Amazon EMR, an overview of how related AWS products work with Amazon EMR, and detailed information on Amazon EMR functionality. Amazon EMR is a data analysis tool that simplifies the set-up and management of a computer cluster, the source data, and the computational tools that help you implement sophisticated data processing jobs quickly.
