MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program`s execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google`s clusters every day. Due to the exponential growth of information technology, tremendous amount of data is generated. It is important to sort meaningful information from the scattered big data. So Data mining plays a vital role in many of these applications [1]. In order to speed up the mining process we go for parallel and distributed processing. However, sorting distributedly is not an easy task since in distributed environment, irregular and imbalanced computation loads may cause the overall performance to be greatly degraded. Load balance among processors is thus very important to parallel and distributed mining. In this work, a new sorting is proposed using Mapreduce technique over the hadoop framework for distributed processing. A popular free implementation is Apache Hadoop. The Hadoop stack is a data processing platform. It combines elements of databases, data integration tools and parallel coding environments into a new and interesting mix. One advantage Hadoop has over data integration tools is that it�s accessible to a variety of programming languages, which means it can be used for any arbitrary parallel coding, like complex analytics.

Big data, Structured Big data, Hadoop, HDFS MapReduce, PI

[1] Mahesh Maurya, Sunita Mahajan, " Performance analysis of mapreduce Programs on Hadoop cluster", World Congress on Information and Communication Technologies pp. 506-510, March, 2012.

[2] Jeffrey Dean and Sanjay Ghemawat �mapreduce: Simplified Data Processing On Large Clusters�, Google, Inc., Usenix Association OSDI �04: 6th Symposium on Operating Systems Design and Implementation, 2009.
[3]Http://En.Wikipedia.Org/Wiki/Mapreduce
[4] Http://en.wikipedia.org/wiki/Apache_Hadoop
[5]Aditya B. Patel, Manashvi Birla, Ushma Nair, �Addressing Big Data Problem Using Hadoop and Map Reduce�, 2012 NIRMA University International Conference On Engineering, Nuicone-2012, 06-08december, 2012.