Indexes for multidimensional data based on the R-Tree are popularly used by databases for a wide range of applications. Such index trees support point and range queries but are costly to construct over datasets of millions of points. We present the Non-Intersecting R-Tree (NIR-Tree), a novel insert-efficient, in-memory, multidimensional index that uses bounding polygons to provide efficient point and range query performance while indexing data at least an order of magnitude faster. The NIR-Tree leverages non-intersecting bounding polygons to reduce the number of nodes accessed during queries, compared to existing R-family indexes. Our experiments demonstrate that inserting into a NIR-Tree is 27 × faster than the ubiquitous R*-Tree, with point queries completing 2 × faster and range queries executing just as quickly.

The performance of data-driven, web-scale client applications is sensitive to access latency. To address this concern, enterprises strive to cache data on edge nodes that are closer to users, thereby avoiding expensive round-trips to remote data centers. However, these geo-distributed approaches are limited to caching static data. In this paper we present ChronoCache, a mid-tier caching system that exploits the presence of geo-distributed edge nodes to cache database query results closer to users. ChronoCache transparently learns and leverages client application access patterns to predictively combine query requests and cache their results ahead of time, thereby reducing costly round-trips to the remote database. We show that ChronoCache reduces query response times by up to 2/3 over prior approaches on multiple representative benchmark workloads. representative benchmark workloads.