Ingestion, Indexing and Retrieval of High-Velocity Multidimensional Sensor Data on a Single Node

TL;DR

This paper evaluates a single-node multidimensional data store optimized for high-velocity sensor data, demonstrating significantly improved ingestion rates and competitive query performance using a two-level indexing structure.

Contribution

It introduces a novel single-node storage architecture with a two-level index combining an in-memory R*-tree and serialized kd-trees, enabling high ingestion throughput for multidimensional data.

Findings

Ingestion rates are two orders of magnitude higher than existing systems.

Query response times are comparable or better than state-of-the-art systems.

Local indices significantly improve query performance.

Abstract

Multidimensional data are becoming more prevalent, partly due to the rise of the Internet of Things (IoT), and with that the need to ingest and analyze data streams at rates higher than before. Some industrial IoT applications require ingesting millions of records per second, while processing queries on recently ingested and historical data. Unfortunately, existing database systems suited to multidimensional data exhibit low per-node ingestion performance, and even if they can scale horizontally in distributed settings, they require large number of nodes to meet such ingest demands. For this reason, in this paper we evaluate a single-node multidimensional data store for high-velocity sensor data. Its design centers around a two-level indexing structure, wherein the global index is an in-memory R*-tree and the local indices are serialized kd-trees. This study is confined to records with numerical indexing fields and range queries, and covers ingest throughput, query response time, and storage footprint. We show that the adopted design streamlines data ingestion and offers ingress rates two orders of magnitude higher than those of Percona Server, SQLite, and Druid. Our prototype also reports query response times comparable to or better than those of Percona Server and Druid, and compares favorably in terms of storage footprint. In addition, we evaluate a kd-tree partitioning based scheme for grouping incoming streamed data records. Compared to a random scheme, this scheme produces less overlap between groups of streamed records, but contrary to what we expected, such reduced overlap does not translate into better query performance. By contrast, the local indices prove much more beneficial to query performance. We believe the experience reported in this paper is valuable to practitioners and researchers alike interested in building database systems for high-velocity multidimensional data.