PGConf.SPB 2023

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.

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.

During the years of its existence, Postgres Pro piled up a pool of problems when query execution was inappropriately slow or a query was too expensive to be executed, so it was never executed. Almost always in our practice, this was due to the choice of a non-optimal query plan. In our story, we will talk about a very unconventional attempt to solve this problem by re-planning queries. We will tell you what it is, how it works, who will find it helpful and the prospects for using this feature.

We'll talk about the tool we created, which monitors changes in the database and creates scripts for migrations of selected objects both interactively and automatically.