PGConf.SPB 2023

As you probably know, PostgreSQL has a number of distinct features compared to other DBMSs. For example, Postgres can process and store many different types of data. However, you need to know something about them before using them. In this talk, we will find the reason why queries to the table begin to slow down (and autovacuum / vacuum has nothing to do with it) and try to speed up such queries. I will tell you how integer data types work in PostgreSQL and touch on the topic of speeding up such queries. And finally, let's talk about how to make your data in tables take up less space while increasing the speed of queries to this data.

Unfortunately, ideal computer systems exist only in science fiction books. PostgreSQL is not exception and sometimes problems may occur. I would like to discuss how to correctly (and incorrectly) try to solve a problem, which way to choose, which tool to use.

The talk is of interest to both beginners and experienced users and database administrators.

In modern data analysis, machine learning models are used as often as databases. Such IT giants as Google and Amazon have already combined them. Microsoft and Yandex are not far behind. Isn't it time to implement a machine learning model in PostgreSQL? In the report you will hear about the basics of machine learning, its implementation in databases and an example of realization as postres extension.

One of the requirements for the operation of the Corosync/Pacemaker failover cluster is the fencing of the failed node. In virtual environments, fencing is implemented by disabling the virtual machine through a hypervisor, in a cluster on physical servers - through IPMI/ILO. But what if it is impossible to organize fencing, for example, in the cloud environment? During this presentation, I will list and explain alternative methods of fencing a failed node.