PGConf.SPB 2023

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.

Fuzzing research is feeding random input data to a program (or a part of it) (in fact, randomness is very conditional) and seeing what we get out of it. And we repeat it many times on many processors.

Fuzzing a large monolithic program complex is never a simple task. It requires extraordinary solutions. In this talk, I will tell you what and how we searched with the help of fuzzing and what results it led to.

• Investigation of data type parsing functions (input-functions): for warming up;
• Investigation of functions implementing operations between types (op-functions): it is better to consider the structure here;
• Network subsystem fuzzing: let's pretend we are POSIX calls, it's cheaper that way;
• Recovering disk context: we need Groundhog Day.

A story about funny bugs and ridiculous hand gestures will be included.

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.

Recently, one of the most common migration methods has become migration using the free ora2pg tool. However, many note the extremely low performance of this tool, especially when it comes to databases of 1 Terabyte and above. This presentation describes how to significantly speed up database migration using oracle_fdw alone or in combination with ora2pg.