Title: A Comparative Study of Log-Structured Merge-Tree-Based Spatial Indexes for Big Data
Abstract: The proliferation of GPS-enabled mobile devices has generated geo-tagged data at an unprecedented rate over the past decade. Data-processing systems that aim to ingest, store, index, and analyze Big Data must deal with such geo-tagged data efficiently. In this paper, among representative, disk-resident spatial indexing methods that have been adopted by major SQL and NoSQL systems, we implement five variants of these methods in the form of Log-Structured Merge-tree-based (LSM) spatial indexes in order to evaluate their pros and cons for dynamic geo-tagged Big Data. We have implemented the alternatives, including LSM-based B-tree, R-tree, and inverted index variants, in Apache AsterixDB, an open source Big Data management system. This implementation enabled comparison in terms of real end-to-end performance, including logging and locking overheads, in a full-function, query-based system setting. Our evaluation includes both static and dynamic workloads, ranging from a "load once, query many" case to a case where continuous concurrent incremental inserts are mixed with concurrent queries. Based on the results, we discuss the pros and cons of the five index variants.
Publication Year: 2017
Publication Date: 2017-04-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 16
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