Над пропастью WAL-G

What do we expect from a backup system? What makes a good backup system stand out? And, most importantly, how do you select the right tools for this process? When preparing backups, many pressing questions arise. In my talk, I will tell you a story of building a system for taking and verifying backups in one company. We will discuss the issues of choosing a tool for working with backups, adapting to changing realities, the problems of cloud storage, and the limitless possibilities of open-source collaboration. This talk will comprise the experience I gained in two years. You'll take the same journey, but much faster. Welcome aboard!

Анализатор исторической нагрузки pg_profile/pgpro_pwr и его новые возможности

This talk is about postgres extension pg_profile - simple historic database workload profiler. I'll describe it's architecture, features and use cases. There is a new branch of pg_profile called pgpro_pwr, designed to run in PostgresPro Enterprise Edition and PostgresPro Standard Edition databases. It is using extended performance statistics of those databases providing some valuable benefits.

GPU-версия PostGIS и индекса GiST

This talk introduces GPU version of PostGIS and GiST-Index that we have developed as a new feature of PG-Strom.

Nowadays, our devices (like mobile phones) generate geolocational data time-by-time, and it is often utilized for area-marketing, push-delivery, disaster notification, and so on. People often use GIS technology to pick up users based on their current location. Even if area definitions are complicated polygons, PostGIS functions can generate right intersections, however, it is often highly computing intensive workloads.

GPU is designed for massive parallel computing workloads, with more than thousands cores per chip. And, we have developed PG-Strom extension to run a part of SQL workloads on GPU devices. At the upcoming PG-Strom v3.0, it newly supports several PostGIS functions and GiST-index for the computing intensive geolocational workloads.

In this talk, we will introduce the technology background, usage, implementation and benchmark result of GPU version of PostGIS and GiST-Index.

Multicorn Foreign Data Wrapper против plpython

Multicorn technology allows you to develop FDWs in Python, which is much easier and faster than creating FDWs in C. However, there is a downside, Multicorn FDWs work well with primitive WHERE conditions, but more complex cases cause difficulties, which I will talk about. Cases will be considered on the example of my Multicorn FDW for getting OpenStreetMap data. I will also show examples of using the same code in Multicorn FDW and plpython functions, including performance comparison. In conclusion, I will share my findings on when it is better to use plpython, and when Multicorn FDW is more preferable.