PGConf.Russia 2022

Every DBMS stores its data on a hard disc, so you may face a situation when your data on disc gets corrupted. This can happen due to a controller failure, logical or physical data corruption; there are also other reasons. If it is just the index file that gets corrupted, the index recreation command will enable you to restore data consistency in your DBMS. If a table file or a file of a system section gets corrupted, data restore is impossible. In this case, you need to invent workarounds. You can try to restore this data from backup files, copy it from the corrupted table, or find another way to solve this problem. In this talk, we'll tackle several cases of data corruption on disc and describe the options for restoring the data from the corrupted tables.

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 release cycle for version 15 is nearing completion. At the time of the presentation, the complete list of changes will not be known. But for sure there will be something to talk about.

PostgreSQL has a number of peculiarities that you need to take into account not only while maintaining your database but also when designing your database schema. Experienced PostgreSQL are well aware of vacuuming process. On the web one can find tons of materials covering its internals, configurations and monitoring. Many valuable talks about vacuum were given at numerous conferences. However, we still face the common wraparound problem when the maximum possible number of transactions (xid) is reached. It happens even on databases that are relatively small in size. In my presentation, I will share a customer case that looks interesting to me. A chain of mistakes made at different stages of the database's life cycle once caused a disaster. The database fully stopped for one week, we detected a wraparound and spotted corrupted blocks. Maintenance was problematical, and we spent sleepless nights in search of a solution. We managed to achieve a local win as we finally restored the database, but it's not the end of this story, which makes it even more interesting.