Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory
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Query applications based on nested data, the most commonly used form of data representation on the web,especially precise query, is becoming more extensively used. MapReduce, a distributed architecture withparallel computing power, provides a good solution for big data processing. However, in practical application,query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem,this paper first combines a column storage structure and an inverted index to build index for nested data onMapReduce. On this basis, this paper puts forward an optimization strategy which combines query executionservice tree and frequent sub-query trajectory to reduce the response time of frequent queries and furtherimprove the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that thismethod greatly improves the efficiency of nested data query.
Abstract
Query applications based on nested data, the most commonly used form of data representation on the web,especially precise query, is becoming more extensively used. MapReduce, a distributed architecture withparallel computing power, provides a good solution for big data processing. However, in practical application,query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem,this paper first combines a column storage structure and an inverted index to build index for nested data onMapReduce. On this basis, this paper puts forward an optimization strategy which combines query executionservice tree and frequent sub-query trajectory to reduce the response time of frequent queries and furtherimprove the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that thismethod greatly improves the efficiency of nested data query.
참고문헌 (23)
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