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.

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.

We will answer the questions about the importance of compression in backup process. Will show a demo how to use the following available algorithms: pglz, zlib, zstd, lz4 in different editions of pg_probackup. We'll show you how to make incremental backups and restore even faster with PTRACK and CFS (tablespace compression) in Postgres Pro. The plans for further development will also be announced.

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.