PgConf.Russia 2019

While doing development of one of our project we were asked to build HA database using Postgres, geographically distributed.

First our choice was obvious, we started to work with big 3 cloud providers, but soon it was quite understand that everything costs big enough for us. Also there were a bunch of incompatibilities with unsupported extensions as well as londiste replication we were heavily used.

I will talk about why we chose patroni, what types of problem we faced with and patroni's special features can dramatically simplify deploy and everyday usage.

A major responsibility of a database engine is to convert a declarative SQL query to an efficient execution plan, employing various methods to scan and join the relations. There is always a development effort to improve this area. What clever execution plans can PostgreSQL generate, what's new in version 11 and what is in development? To name a few things, the joins are optimized by removing unneeded outer and inner joins, and reducing joins from outer and semi to inner. There is work to enable merge joins on inequality and range overlap, and to improve join selectivity estimates with multi-column statistics. When it comes to scanning a single relation, covering indexes allow to use index-only scans more often. Incremental sort and more precise estimation of sorting costs help generate better paths when sorted output is required, e.g. when using GROUP BY and ORDER BY or performing merge joins. This talk aims to give an overview of such optimizations that already exist and that are being developed now.

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.

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.