PGConf.SPB 2023

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.

We will present adaptive query optimization techniques and key capabilities of the new version of AQO and SR_PLAN extensions.

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.

Postgres Pro Enterprise is based on open source PostgreSQL. However, the difference is significant: the current version of Postgres Pro Enterprise has 40 more features that are not a part of PostgreSQL. 20 more mechanisms are being developed either as built-in features or separate products. This includes BiHA, DBaaS, pg_probackup, etc. In this presentation, we'll briefly discuss some of them.