Citus Blog

GPS has become part of our daily life. GPS is in cars for navigation, in smartphones helping us to find places, and more recently GPS has been helping us to avoid getting infected by COVID-19. Managing and analyzing mobility tracks is the core of my work. My group in Université libre de Bruxelles specializes in mobility data management. We build an open source database system for spatiotemporal trajectories, called MobilityDB. MobilityDB adds support for temporal and spatiotemporal objects to the Postgres database and its spatial extension, PostGIS. If you're not yet familiar with spatiotemporal trajectories, not to worry, we'll walk through some movement trajectories for a public transport bus in just a bit.

One of my team's projects is to develop a distributed version of MobilityDB. This is where we came in touch with the Citus extension to Postgres and the Citus engineering team. This post presents issues and solutions for distributed query processing of movement trajectory data. GPS is the most common source of trajectory data, but the ideas in this post also apply to movement trajectories collected by other location tracking sensors, such as radar systems for aircraft, and AIS systems for sea vessels.

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Custom types—called user-defined types in the PostgreSQL docs—are a powerful Postgres capability that, just like Postgres extensions, were envisioned from Day One in the original design of Postgres. Published in 1985, the Design of Postgres paper stated the 2nd design goal as: “provide user extendibility for data types, operators and access methods.”

It’s kind of cool that the creators of Postgres laid the foundation for the powerful Postgres extensions of today (like PostGIS for geospatial use cases, Citus for scaling out Postgres horizontally, pg_partman for time-based partitioning, and so many more Postgres extensions) way back in 1985 when the design of Postgres paper was first published.

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