PgConf.Russia 2019

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.

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.

Several real cases from those who stopped the migration to PostgreSQL.

As we all know, PostgreSQL is a classic vertically scalable database for OLTP loads. In parallel with PostgreSQL for many years there is its alternative horizontal-scalable MPP version of PostgreSQL, that is called Greenplum, sharpened for big data and OLAP workload. In my pitch I will show the internal architecture of Greenplum (distributed transactions, data sharding, partitioning with hybrid storage in external systems, column storage engines with compression, and much more), a comparison with the internal structure of PostgreSQL and the application areas of each solution are shown.