PgConf.Russia 2020

In my talk, I will tell how we built a geographically distributed system of personal data storage based on Open Source software and PostgreSQL. The concept of the inCountry business is to provide customers with a ready-to-use infrastructure for personal data storage. Our business customers are ensured that their customer’s personal data is securely stored within their country’s borders. We wrote an API and SDK and built a variety of services. Our system complies with generally accepted security standards (SOC Type 1, Type 2, PCI DSS, etc.). We built our infrastructure with Consul, Nomad, and Vault, used PostgreSQL, ElasticSearch as a storage system, Nginx, Jenkins, Artifactory, other tools to automate management and deployment. We have assembled our development and management teams - DevOps, Security, Monitoring, and DBA. We use both cloud providers and bare-metal servers located in different regions of the world. Development of the system architecture and ensuring the stability of the infrastructure, consistent and secure operation of all its components is the main task facing our teams.

Migrating engineering software from Oracle to PostgreSQL
Dmitry will talk about using an automated migrator developed by "Diasoft" company for migrating Russian systems to PostgreSQL.
Taking the LOOTSMAN engineering software (developed by the ASKON company), we will speak about the key aspects of migration, review possible issues and ways to resolve them, as well as discuss performance optimization methods for migrated stored procedures.

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.

Moscow public transport vehicles when moving report their coordinates via GLONASS. Collected data is used for various analyses including timetable development, bottlenecks detection and planning the bus lanes. Until recently we used the PostGIS extension for this purpose but now we are switching to a new PG extension — MobilityDB — designed especially for geodata time series processing. I have compared the table size and the performance of our solution without and with MobilityDB and happy to present the results.