PGConf.Russia 2021

TimescaleDB extension allows to turn good old Postgres into a real distributed cluster for storing time series data while maintaining the relational model, convenient SQL and a time-tested ecosystem. And additional features such as continuous materialized views and data compression allow to build truly powerful telematic hubs.

PostgreSQL could easily be named the master of indexes - there is no other database that could compete with Postgres for the variety of indexes. How developers can use this advantage effectively in their day to day work? How do you know which index to create? When the created index might slow our application’s performance? We will try to get our head around this topic with some use cases and live examples. This talk would be of interest for any developer who wants to expand their knowledge of indexes in Postgres and secure the best performance for their application.

Postgres has been a vibrant project for decades, and probably will be popular for decades to come. However, as with any complex endeavor, challenges are always lurking. This talk explores the many project, competitive, and technical challenges in the future that could derail its success. By exploring these challenges, we will be better able to avoid them.

I will not teach you database design or query optimization. This talk will cover Python drivers, PostgreSQL usage in Python, and various tools from sync and async world enabling such usage, and how this ecosystem evolved. We'll discuss Python libraries including aiopg, psycopg2, asyncpg, pgbouncer and how they interact with Postgres.