PgConf.Russia 2019

This case study walks participants through a case where we decided to embark on a data recovery effort. This talk is applicable to all users, from novices to advanced PostgreSQL database administrators. Beginners will get an understanding of what data recovery is and is not, what expectations to have going into it, and how to work with contracted experts in order to ensure the best possible outcome, while more advanced users and experts will also get a fair bit out of the technical aspects of the case study.

While the talk will emphasize non-technical operational aspects of data recovery, it will also include discussions of the internals of PostgreSQL we had to work with, as well as how we went about approaching difficulties so that we could retrieve the data we hoped to.

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.

• Migration of the standard 1C cluster to MPI environment - "machine to machine migration of services".
• PostgreSQL migration to GPU powered machine.

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.