Towards Serverless Processing of Spatiotemporal Big Data Queries
This work addresses scalability challenges for big spatiotemporal data processing, which is relevant for applications relying on rapid analysis of such data.
The paper proposes a serverless approach for processing spatiotemporal big data queries by breaking them into small subqueries executed in parallel on Function-as-a-Service platforms, aiming to address scalability limitations of existing systems like PostGIS and MobilityDB.
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.