Abstract
Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.
Highlights
With the development of data acquisition technologies, the data needed to be stored expands strikingly in both volume and velocity
Four kinds of collections, which are spatial data collections (SDC), index collections (IC), geometric metadata collection (GMC) and index metadata collection (IMC), are devised with different purposes but all contribute to our index mechanism
“CRS” specifies the Cartesian coordinate system used by the data layer and “IndexInfo” references to a document located in index metadata collection (IMC), which contains the parameters of R-tree, such as the fan factor and the oid of the root node
Summary
With the development of data acquisition technologies, the data needed to be stored expands strikingly in both volume and velocity. Constant and time-consuming data transformation between two coordinate systems (Boehm J, 2015) is a necessity when applying 2dsphere index to these applications, not to mention that sophisticated spherical computation is much more costly than Cartesian computation in spatial operations (Oracle, 2015). To solve this problem, this paper introduces a way to integrate R-tree index into MongoDB and implements corresponding accessing methods.
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