PgConf.Russia 2020

From the very beginning, PostgreSQL in Avito has been solving very important tasks. All the main architecture components are built around DBMS. For over 10 years, the project has been actively evolving, and the infrastructure and architecture have changed a lot.

The talk will start with an overview of how PostgreSQL infrastructure and architecture have advanced in Avito over time and which challenges have been successfully resolved.

Then we'll discuss PostgreSQL usage scenarios in Avito in 2020: microservice architecture, sharding, hosting multiple databases on a single server instance, DBaaS (Database discovery, access control, failover, backup, archive, resource sharing, etc.), integration, and team evolution.
And finally, I'll share our backlog and wishlist.

Patroni and Stolon are two of the most popular and advanced solutions for orchestrating PostgreSQL and ensuring auto-failover for clusters with the Leader-Followers configuration. However, engineers who migrate from good old tools like Corosync&Pacemaker or built-in solutions of other DBMS often run into issues when installing these tools and figuring out the purpose of each component.
In this tutorial, we will go through a typical installation procedure of Patroni and Stolon clusters on virtual machines (not in containers), as well as review their behavior in case of various infrastructure failures. The whole process will be demonstrated on three vagrant virtual machines with pre-built images. You can follow along on your own system if you set up the required environment.

I'll show an example of solving the problem of searching "similar" texts for one given text in big array using GiST index. The problem itself is not much important, but it is easy to understand. Using this problem as example, I'll show one of many methods of adapting GiST index for custom search problems. Maybe this talk will help you to solve other search problems.

A brief story how MySQL → PG migration could increase company efficiency tenfold times:

1. Program code has been reduced 50 times, with optimization of backend team (from 15 to 3 engineers)
2. Software development of new features has become measuring in days, not in months
3. Infrastructure costs per 1M users have been reduced 20 times
4. Database structure and technical documentation were simplified significantly, from 100K high-dependent tables to just 20 simple tables
5. New security level because of total forbidden on external SQL commands to the database
6. Quick analytics aggregation on multiple parameters, without external analytics systems
7. The last, but not the least: the main business was keeping alive during migration