Improving the performance of Hadoop/Hive by sharing scan and computation tasks

Özal, Serkan
MapReduce is a popular model of executing time-consuming analytical queries as a batch of tasks on large scale data. During simultaneous execution of multiple queries, many oppor- tunities can arise for sharing scan and/or computation tasks. Executing common tasks only once can reduce the total execution time of all queries remarkably. Therefore, we propose to use Multiple Query Optimization (MQO) techniques to improve the overall performance of Hadoop Hive, an open source SQL-based distributed warehouse system based on MapReduce. Our framework, SharedHive, transforms a set of correlated HiveQL queries into new global queries that can produce the same results in remarkably smaller total execution times. It is ex- perimentally shown that SharedHive outperforms the conventional Hive by %20-90 reduction, depending on the number of queries and percentage of shared tasks, in the total execution time of correlated TPC-H queries.


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Search engines and large scale IR systems need to cache query results for efficiency and scalability purposes. In this study, we propose to explicitly incorporate the query costs in the static caching policy. To this end, a query’s cost is represented by its execution time, which involves CPU time to decompress the postings and compute the query-document similarities to obtain the final top-N answers. Simulation results using a large Web crawl data and a real query log reveal that the proposed strategy impr...
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Selective search with traditional partitioning have advantages over exhaustive search in terms of total query cost. However, it can suffer from query latency and load imbalance for most of the time due to its nature. To overcome these issues, we proposed a new partitioning method in this thesis, namely Hybrid partitioning. Our studies shows that it is possible to obtain significant savings in query latency with this new partitioning methodology. In addition to that, query processing with Hybrid partitioning...
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Citation Formats
S. Özal, “Improving the performance of Hadoop/Hive by sharing scan and computation tasks,” M.S. - Master of Science, Middle East Technical University, 2013.