Handling Data Skew in Map Reduce Using Hadoop Libra

There are many efficient tools significantly uses Map Reduce applications that assigns data with their corresponding tasks in parallel and distributed data processing. LIBRA symbolizes the lightweight problems of data skew with input data applications that can overlap map and reduce strategies. This is one of the innovative and accurate distribution methods for intermediate data sampling with normal steps of processing data. LIBRA has trivial overheads for output data that balances loads of computing resources. In this paper we propose the method for handling Data Skew in Map Reduce Using Hadoop in LIBRA to show the effectiveness of Hadoop on Web Crawling of Large Datasets form Web Servers. Map Reduce processes huge a set of data efficiently to establish its subsistence. The large job is divided into many small tasks and they are assigned to various nodes to perform parallel processing. Applications and Frameworks of Map Reduce. Straggler Process causes time delay. Data skew refers to the disparity for data assigned to each task, or the existence of inequality in the amount of work required to process such data. Data sets in the real world are often skewed.