{'fraction_non_alphanumeric': 0.006346328195829556, 'fraction_numerical': 0.0, 'mean_word_length': 5.6909090909090905, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A NOVEL APPROACH FOR PARTITIONING IN HADOOP USING ROUND ROBIN TECHNIQUE

The Hadoop Distributed File System is constructed to store profoundly and immensely colossal data sets accurately and to send those data sets at huge bandwidth to end user applications. Hadoop gives a distributed file system and a structure for the analysis and conversion of profoundly and astronomically immense data sets utilizing the MapReduce paradigm. A paramount characteristic of Hadoop is the partitioning of data and computation across many of hosts and the execution of application computations in parallel proximate to their data. This paper recommends an enhanced partitioning algorithm utilizing round robin partitioning that advances load balancing and recollection utilization. A sequence of experimentations have exposed that given a skewed data sample, the Round Robin architecture was capable to reduce skew by distributing records on average when compared with subsisting Hash Partitioning. Experimentations demonstrate that the proposed method is efficient and more precise than the subsisting implementation.