PGConf.Russia 2018

Patroni is a Python application to create high-availability PostgreSQL clusters based on the streaming replication. It is used by Red Hat, IBM Compose, Zalando and many other companies. This tutorial will highlight Patroni architecture, provide attendees with hands-on experience of configuring high-availability PostgreSQL clusters with Patroni, describe how to take advantage of numerous additional features and give an opportunity to learn more about common mistakes related to running Patroni and its troubleshooting.

In order to take most out of the Patroni tutorial one needs a laptop with git, vagrant and virtual box installed.

Vagrant can be obtained from https://www.vagrantup.com Virtualbox is at https://www.vagrantup.com

Alternatively, one can install your Linux distribution packages (or use homebrew on Mac).

Once Vagrant and Virtualbox are installed one can run the Patroni VM by issuing the following commands:

$ git clone https://github.com/alexeyklyukin/patroni-training
$ cd patroni-training
$ vagrant up

When the setup concludes Patroni box can be accessed via ssh using vagrant ssh command.

This report is about my PostgreSQL extension pg_profile. This extension all you need to create periodic statistics snapshots and to keep them. You can build a report on one or many serial snapshots. This report will contain statistics information about database workload in specified time period. It is very useful to start investigation on performance degragation or excessive resoure consumption in the past.

WAL-G is simple and effective disaster recovery tool for PostgreSQL using cloud storages. In its core functionality, WAL-G is the successor of WAL-E rewritten in Go. But there is one new neat feature - delate-backups. WAL-G delta-backups, whenever possible, stores only pages, changed since the previous backup. In this talk, I'm going to describe development process of this feature.

Surprisingly, most important and complicated question was the design of the interface: WAL-e is simple and comprehensive, keeping these properties was goal #1. Technical details of implementation were covering some underwater stones too. Besides these, I want to discuss the perspective of technological development and future coordination of recovery tools developers.

PostGIS is well known and widely used since two decades, as the best OpenSource database solution for Spatial Analysis. This talk will focus on: spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available); how to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)