An LSM-Tree Index for Spatial Data

Abstract

An LSM-tree (log-structured merge-tree) is a hierarchical, orderly and disk-oriented data storage structure which makes full use of the characteristics of disk sequential writing, which are much better than those of random writing. However, an LSM-tree can only be queried by a key and cannot meet the needs of a spatial query. To improve the query efficiency of spatial data stored in LSM-trees, the traditional method is to introduce stand-alone tree-like secondary indexes, the problem with which is the read amplification brought about by dual index queries. Moreover, when more spatial data are stored, the index tree becomes increasingly large, bringing the problems of a lower query efficiency and a higher index update cost. To address the above problems, this paper proposes an ER-tree(embedded R-tree) index structure based on the orderliness of LSM-tree data. By building an SER-tree(embedded R-tree on an SSTable) index structure for each storage component, we optimised dual index queries into single and organised SER-tree indexes into an ER-tree index with a binary linked list. The experiments showed that the query performance of the ER-tree index was effectively improved compared to that of stand-alone R-tree indexes.

Hierarchical Quadrant Spatial LSM Tree for Indexing Blockchain-Based Geospatial Point Data

“…Large overhead in insertion by computing Hilbert values. ER-tree [13] Hilbert space filling curves. Secondary index approach.…”

“…Using a secondary index outside the LSM-tree was an another solution with a few drawbacks. The embedded Rtree(ER-tree) [13] was proposed as an efficient method for indexing spatial data while using a secondary index by improving the drawbacks. The ER-tree is in the form of a LSM-tree, built from a SER-tree index(embedded R-tree on a SSTable).…”

“…Second, the supplementary index tree also needs to be merged when the components of LSM tree are flushed and merged causing a large update cost. Recently, an embedded R-tree(ER-tree) [13] was proposed which improved the two weaknesses of using a supplementary index tree. However, the ER-tree is not suitable for practical applications due to the fact that it provides a decent performance only on a read-intensive workload.…”