PGConf.Russia 2022

As opposed to in-memory DBMSs, traditional disc database management systems have an inherent physical limitation. It's their storage system which is by several orders of magnitude slower than CPU operation memory, even in the case of All-Flash NVM. It becomes especially critical for the development of business analytics apps and OLAP scenarios on disc DBMS. We will consider a number how-tos for improving the performance of traditional DBMSs, and then offer a new storage-centric approach to hardware-based speed-up for disc DBMSs.

In this presentation, we'll talk about the newest SQL/JSON features committed to PostgreSQL 15. We'll explain how to use them, where they can help and why they make a difference. Is PostgreSQL JSON implementation fully compliant with SQL:2016? In the meantime, the next generation of the SQL standard is being developed. What will the new standard include, and how will Postgres respond to it?

During my career, I've seen a lot of code, and very often I faced inaccuracies in handling dates and times. Sometimes the parties got discrepancies in the monthly reports due to such inaccuracies; or daily reports were different for Moscow and SFO, etc. I wouldn't call it a serious problem, but it's annoying and time-consuming. In all cases, such issues occur because of neglectful handling of dates and times. In my presentation, I will discuss how we can avoid it.

A modern database should be capable of storing big values. Storing itself is not a big deal, however, operations with big values or fields are a non-trivial task. PostgreSQL has several options for storing big values, but none of them is perfect. How do we respond to this challenge? Our presentation answers this question, let's see how to store big and complex values in Postgres properly, and how operate with them.