Efficient way of searching data in MapReduce paradigm

Cloud computing has emerged as an effective solution in the computing world. When the cloud is used for large amounts of data storage, searching for any required data takes lots of time. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in Hadoop Distributed File System is scattered and needs lots of time to retrieve. MapReduce function on data sets of key & value pair is the programming paradigm of large distributed operation. The proposed work aims to minimize the data retrieval time taken by the MapReduce program in the cloud. The major idea is to design a web server in the map phase using the jetty web server which shall give a fast and efficient way of searching data in MapReduce paradigm. For real time processing on Hadoop, a search mechanism is implemented in HDFS. The load balancer is used to balance the workload across servers to improve its availability, performance and scalability.