PgConf.Russia 2019

So you finally have your database model for your application and you fill it in with current data. How do you keep it up to date? While INSERT may still be transparent, UPDATE and DELETE will overwrite your previous data, so you won't be able to reproduce them. Cloning the whole huge content for each minor update is not an option. For rich and complex data about hundreds of thousands of power generators in Germany and worldwide, I built a model using range data types in recent PostgreSQL which allows me to insert, update and delete data while granting the full access to the whole state of the database at any historical moment. I'll present a very simplified version of the database so the audience will be immediately able to apply it for their cases. I'll also show a few tricks in Python and Psycopg2 that will allow a whole team to prepare, review, and deploy all revisions to this database without merge conflicts. And I'll give a few ideas on how to retrieve this data efficiently.

My presentation is about writing extensions in Postgres. I have written pg_threads that implements simplified POSIX thread API inside Postgres database. It adds a new powerful abstraction giving database developers new opportunities for writing parallel code thus taking advantage of multicore CPUs. There is an extra API for transactional and non-transactional IPC between threads. I also have an example application that takes advantage of this new API that scales linearly even across 2 nodes. The presentation is with live working demo using vagrant project with 2 VMs running Ubuntu and 2 Postgres 11 databases.

DBMS inside container is a nightmare for DBA. I will describe how we use containerized Postgres in Juno. What problems we faced with and how did we solve them.

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How can we ensure that this – pretty much outdated – recommendation suit well our needs?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