Multi-dimensional Index on Hadoop Distributed File System

Experimental evaluation demonstrates that the built-in index structure can efficiently improve query performance, and serve as cornerstones for structured or semi-structured data management.

Abstract

In this paper, we present an approach to construct a built-in block-based hierarchical index structures, like R-tree, to organize data sets in one, two, or higher dimensional space and improve the query performance towards the common query types (e.g., point query, range query) on Hadoop distributed file system (HDFS). The query response time for data sets that are stored in HDFS can be significantly reduced by avoiding exhaustive search on the corresponding data sets in the presence of index structures. The basic idea is to adopt the conventional hierarchical structure to HDFS, and several issues, including index organization, index node size, buffer management, and data transfer protocol, are considered to reduce the query response time and data transfer overhead through network. Experimental evaluation demonstrates that the built-in index structure can efficiently improve query performance, and serve as cornerstones for structured or semi-structured data management.