PgConf.Russia 2019

In this talk we will have a look at the details of autovacuum's implementation and see what kind of practical implications they have. The talk will also provide a short overview of patches for autovacuum that are currently being considered by the developer community and that may be included in newer versions of PostgreSQL.

In this master class, I will take you step-by step through a major upgrade of PostgreSQL. Through our practice we see a lot of different PostgreSQL servers in production environment and often, almost too often teams who once made PostgreSQL their database of choice never update it following the initial installation. The are many different reasons for it, the result, however, is the same - they all miss out on the new useful features of the newer releases and reduced database performance.
The goal of my masterclass is to equip attendees with necessary tools for performing PostgreSQL upgrade. I will take you through each step of the major upgrade and will dive into each executed command. I will also explain the particular order in which I perform an upgrade and explain the consequences of not following this order or missing a particular step. We will perform an upgrade of PostgreSQL 9.0 to 11. My hope is that following this masterclass number of outdated PostgreSQL database will reduce since participants will then go back to their databases and make sure that they are running the most recent version.

Shared_buffers = 25% – is it too much or not enough? Or it's the right value?

How can we ensure that this – pretty much outdated – recommendation suit well our needs?

It is time to start apply enterprise-level approach to tuning postgresql.conf. Not using various blind auto-tuners or advices from old articles and blog posts, but based on the following two aspects:

1. comprehensive database experiments, conducted in automated fashion, repeated multiple times in conditions as close to production as possible, and
2. deep understanding of DBMS and OS internals.

Using Nancy CLI (https://gitlab.com/postgres.ai/nancy) we will consider a concrete example: infamous shared_buffers, under various circumstances, in various projects. We will try to figure out, how to optimize this settings for given infrastructure, database, and workload.

Our company has developed a software product Live Universal Interface (LUI) is a tool to quickly create and modify standardized on-screen forms to WEB browsers without compiling code, know just enough SQL. LUI is aimed at B2B, B2G, G2C and B2C segments and intended for use in billing systems, financial management, accounting and control of production, where you must decide first and foremost functional tasks, rather than demonstrate unnecessary graphics elements.

Collective elaboration is provided by storing all elements of program code in the total database, which can be located on the corporate server or in the "cloud" on a third-party site.