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.

Logical decoding and replication slots introduced in PostgreSQL 9.4 (released in 2014) created a solid foundation for implementing built-in core logical replication in version 10 (released in 2017). Unfortunately, there are a few limitations that make logical replication not very useful in real-world scenarios. Logical decoding currently isn’t supported on the standby server, and PostgreSQL allows creating logical replication slots only on the primary server. Or in other words, logical slots are lost on failover/switchover.

Postgres hackers made many attempts to address the problem, and most of them resulted in not too much success. Although, there is one little function introduced in PostgreSQL 11 that made it possible to implement failover of logical replication slots externally.

In my talk I will tell a story of how Patroni solves the problem of logical replication slots failover without using invasive third-party extensions, dig down into some of the Postgres internals in order to prove why this approach is safe, and finally, we will discuss limitations and potential downsides of this solution.

This talk is about our work on detailed vacuum workload statistics collection by the statistics collector and about problem solutions and benefits it can provide.

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.