PGConf.Russia 2022

Disaster-tolerant cluster implies minimal data loss in the event of the main data center disaster and switching to the backup data center. The data loss takes place due to the asynchronous replication between the main data center and the backup one. However, there's a solution that can resolve the existing problem and ensure zero data loss in the case of the main data center disaster. My talk covers this solution for zero data loss.

The more tables are involved with the query, the more difficult it is for the scheduler to choose a suitable execution plan (both time and memory usage increase). How can we "tell" the planner that it is better to connect this pair of tables first, and the rest can be connected later? What if we see that a part of our query can be improved, but the optimizer does not do this. In my presentation, I will talk about managing the order of connections. I will explain how we can influence the formation of a query plan using the standard "vanilla" PostgreSQL methods.

About 7 years ago, I moved from Windows-only development lead by an irresistible desire to add some parts to PostgreSQL. In this talk, I would like to cover things that were not obvious to me when I started working with the source code, the PG build and testing system. I'm going to talk about the simplest issues - routine IDE tasks, navigation, build and release process, and similar stuff. What I will share isn't the one and only truth. Some topics might relate to dummy-level problems :) I would be glad if other developers also shared the secrets of their development life. We can arrange a discussion of grep vs IDE :)

SPQR is a lightweight OLTP sharding solution written in Go. In this talk, I'm going to discuss design decisions that led us through FDW-based sharding, CustomNode-based sharding, C-implemented lightweight query routing, and finally to SPQR design.