PgConf.Russia 2019

Sometimes there is a great desire to return the database to the past, for a day or two or more days. The reasons are diverse, but most often one is to see what has changed. Or to see if the application behaved incorrectly after the update. Or it was just a command from the boss. The classic way everyone knows is to keep full backups and sets of WAL-logs to be able to recover to an arbitrary moment. This method is a real headache for DBAs/administrators, and it will not work quickly. Sure, there are some ways to optimize this process, but downtime is inevitable. PostgresPro offers a new way — database snapshots and the ability to return to them.

I would like to share my experience in optimization queries in PostgreSQL of RMIS (Regional Medical Information System).

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.

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.