Towards Serverless Processing of Spatiotemporal Big Data Queries

TL;DR

This paper proposes a serverless approach for processing large-scale spatiotemporal data by breaking down queries into subqueries that leverage Function-as-a-Service platforms for parallel execution, enhancing scalability.

Contribution

It introduces a novel native serverless processing paradigm for spatiotemporal data that improves scalability by parallelizing query execution on FaaS platforms.

Findings

Partial solution to scalability of big spatiotemporal data processing

Breaks down queries into small subqueries for parallel execution

Leverages near-instant scaling of FaaS platforms

Abstract

Spatiotemporal data are being produced in continuously growing volumes by a variety of data sources and a variety of application fields rely on rapid analysis of such data. Existing systems such as PostGIS or MobilityDB usually build on relational database systems, thus, inheriting their scale-out characteristics. As a consequence, big spatiotemporal data scenarios still have limited support even though many query types can easily be parallelized. In this paper, we propose our vision of a native serverless data processing approach for spatiotemporal data: We break down queries into small subqueries which then leverage the near-instant scaling of Function-as-a-Service platforms to execute them in parallel. With this, we partially solve the scalability needs of big spatiotemporal data processing.