SQLMR complies SQL-like queries to a sequence of MapReduce jobs. We develop a data management system for cloud, named SQLMR. In this paper, we propose a hybrid approach to fill the gap between SQL-based and MapReduce data processing. On the other hand, traditional SQL-based data processing is familiar to user but is limited in scalability. However, it has higher learning curve than SQL-like language and the codes are hard to maintain and reuse. MapReduce provides a framework for large data processing and is shown to be scalable and fault-tolerant on commondity machines. During this case, to handle this downside, ancient RDBMS are complemented by specifically designed a chic set of other DBMS like-NoSQL, NewSQL and Search-based systemms.Īs the size of data set in cloud increases rapidly, how to process large amount of data efficiently has become a critical issue. Particularly in massive scale and high concurrency applications, like search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. At the side of the event of the web and cloud computing, there would like knowledge bases to be able to store and method massive data effectively, demand for top performance once reading and writing, therefore the ancient computer database is facing several new challenges. This can be usually thought-about to be information from a knowledge of an information assortment that has fully grown thus massive it can't be effectively managed or exploited victimization standard data management tools: e.g., classic relational database management systems (RDBMS) or standard search engines. This refers to as 'Big Data' that's a world development. Digital world is growing in no time and become a lot of complicated within the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), speed (high speed in growth) in nature.
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