PgConf.Russia 2020

Today no one is surprised by cloud infrastructure anymore, but not all its components are easy to deploy in cloud. For example, the database is always very demanding in terms of performance and resources. Scaling and fault tolerance are the most acute problems, that's why we have been observing rapid development of alternative DBMS in the recent years. However, traditional relational DBMS have already accumulated a lot of various features, so they often remain the first choice. Besides, they are constantly evolving and offer a wide variety of scaling tools. I will mainly speak about PostgreSQL, when you should consider scaling, and how to do it right.

We will touch upon the following topics:
- Streaming replication and balancing read/write workloads
- Logical replication and data sharding
- High availability and fault tolerance

This talk should be interesting to DBAs, system administrators, team leads, infrastructure architects, as well as wider audience dealing with PostgreSQL.

In Zabbix-server 4.4, a new Zabbix-agent has become available. It is written in Golang, has a plug-in structure, keeps a permanent connection and makes it possible to control metrics in runtime. We will make an overview of the Zabbix-agent 2 and talk about how to develop plugins for it, in particular, the PostgreSQL monitoring plugin developed by Postgres Professional in collaboration with Zabbix.

Patroni and Stolon are two of the most popular and advanced solutions for orchestrating PostgreSQL and ensuring auto-failover for clusters with the Leader-Followers configuration. However, engineers who migrate from good old tools like Corosync&Pacemaker or built-in solutions of other DBMS often run into issues when installing these tools and figuring out the purpose of each component.
In this tutorial, we will go through a typical installation procedure of Patroni and Stolon clusters on virtual machines (not in containers), as well as review their behavior in case of various infrastructure failures. The whole process will be demonstrated on three vagrant virtual machines with pre-built images. You can follow along on your own system if you set up the required environment.

To build a decent query plan, the optimizer has to understand statistical characteristics of underlying data. It is interesting to observe how the structure of the collected information became more complicated over time: what the optimizer relied on back in its early days and what is at his disposal now with the release of the 12th version. We will also talk about how and when statistics are collected, how to manage this process and whether it is necessary to think about it at all.