PGConf.Russia 2022

Many books end with a wedding, but the reader has no idea about the future life of the heroes except that they lived happily ever after. In 2019, we successfully migrated distributed our railway application from Oracle 11g SE to vanilla PostgreSQL 11.9. But our story did not end with this successful migration - life went on, and sometimes we got startled because of "surprises". We encountered a number of problems, some of which were solved by reorganizing the data, some disappeared after we changed our stored procedures, and some got resolved after tuning the PostgreSQL parameters. Solving our problems would be impossible without the logging and profiling system built into our DB application. Our talk covers the examples of successful detection and resolving of the performance issues that occurred in our PostgreSQL-based application.

A modern database should be capable of storing big values. Storing itself is not a big deal, however, operations with big values or fields are a non-trivial task. PostgreSQL has several options for storing big values, but none of them is perfect. How do we respond to this challenge? Our presentation answers this question, let's see how to store big and complex values in Postgres properly, and how operate with them.

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 :)

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.