PgConf.Russia 2019

As any ordinary software developers, we just pursued a goal to develop a system robust for high loads, and even succeeded. The system architecture was fine, but the data volume was keeping increased and revealed the painful issues and errors that nobody had expected. We faced very strange queries seemed to be unbelievable. In my short talk I would like to share sad experience of arised-from-nothing high loads in DBMS and solving the challenge.

Python may not be the fastest programming language on the CPU, but its fast and easy development saves a lot of costs between the keyboard and the chair. In this workshop we'll explore the most popular library for the access to the PostgreSQL server named "psycopg2" and write a short application using some of its useful features.

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How can we ensure that this – pretty much outdated – recommendation suit well our needs?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

Wide usage of Common Table Expression queries considered as a core paradigm for implementing the Business Logic for high loaded web applications development based on PostgreSQL functions.