PGConf.Russia 2022

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.

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.

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.

The volume of data stored in Postgres Pro databases is growing rapidly. In some organizations, such databases have already exceeded tens of terabytes in size. The existing approaches to backups don't ensure timely backup and restore of data in such cases. We have supplemented the well-known and well-tested Postgres Pro backup tools with functionality that will allow users to backup and restore large databases. In addition to that, we have created our solution with the convenience of DBAs in mind.