PgConf.Russia 2020

We would like to share our test results (including performance testing) of PostgreSQL/Citrus and MongoDB for our company's data. It has been a very exciting process with unexpected turns and a somewhat controversial outcome.

To build a decent query plan, the optimizer has to understand statistical characteristics of underlying data. It is interesting to observe how the structure of the collected information became more complicated over time: what the optimizer relied on back in its early days and what is at his disposal now with the release of the 12th version. We will also talk about how and when statistics are collected, how to manage this process and whether it is necessary to think about it at all.

Open-source maintainers face many challenges as projects grow. How to write more required features, fix more issues and have time to watch more pull requests? On the example of WAL-G(backup tool for PostgreSQL) I will tell you about how we solved these problems by launching a course of Open-source development at Ural Federal University, what we achieved and what will we do next.

MobilityDB is an open source moving object database system (https://github.com/ULB-CoDE-WIT/MobilityDB). Its core function is to efficiently store and query mobility tracks, such as vehicle GPS trajectories. It implements the Moving Features specification from the Open Geospatial Consortium (OGC). MobiltyDB is engineered up from PostgreSQL and PostGIS, providing spatiotemporal data management via SQL. It thus integrates with the postgreSQL eco-system allowing for complex architectures such as mobility stream processing and cloud deployments.

The presentation will explain the architecture of MobilityDB, its database types, indexes, and operations. We will highlight the PostgreSQL features that enable this extension, and the would like to have features. This presentation will be of special interest to the PostgreSQL community, and to professionals in the transportation domain.