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Handling multi-dimensional complex queries in key-value data stores
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DOI：10.1016/j.is.2017.02.001.png)
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Abstract
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With the advent of the era of cloud computing and big data, in order to cope with vast amounts of data, a number of key-value databases have emerged. These systems provide the ability of large scale data storage and effective data operations based on primary keys, but they do not efficiently support the range and k-Nearest Neighbor (kNN) queries on multi-dimensional datasets. In this paper, we introduce, SPIKE, a sliced Pyramid-based index system for key-value data stores. SPIKE bridges the gap between the data scale and querying functionality for highly available, scalable distributed key-value data stores. We first present SP-Index, the kernel indexing scheme. The SP-Index is designed as a two-level index mechanism consisting of a sliced pyramid space partition index and a distributed B-Tree index. On the basis of SP-Index, we have designed and implemented SPIKE on Cassandra, which provides efficient multidimensional complex query processing. We have conducted a set of comprehensive experiments with three types of datasets including synthetic datasets, TPC-H benchmark datasets and a real-world dataset. The experiment results show that SPIKE can efficiently handle multi-dimensional complex queries on large-scale key-value datasets. Evaluation results in comparison with existing systems demonstrates that SPIKE outperforms the comparing work including the original Pyramid, MySQL Cluster and CClndex by dozens of times in complex query processing. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Big data
Key-value store
NoSQL
Range query
kNN query
Multi-attribute query
Index
Multi-dimensional dataset
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