PgConf.Russia 2019

It's so good when database behaves predictable. When the performance is lacking, you just add CPU cores, terabytes of RAM and millions of IOPS, and everything becomes good again. But it's rather unpleasant, when server have plenty of free resources, while database is still running slow. And it's especially sad if stress testing detects no problems, while real life workload of the same volume makes your database hang.

In this talk I will consider bottlenecks of PostgreSQL, which we met in our practice, and which causes sad behavior described above. I'll also explain what can be done at user level in order to evade these bottlenecks, and what developers are planning to do in order to eliminate those bottlenecks. I'm also planning give some recipes of stress testing, which could have to evade surprises in production.

Postgres is known for it extensibility, which made it the universal database, that means it can meet the requirements of practically any project. Many extensions are well-known and widely used, for example, PostGIS extension - de-facto standard of open source GIS, hstore - an extension for storing and manipulation of arbitrary key/value pairs. I will talk about less known but useful PostgreSQL extensions, which provides a new functionaliy and/or improve the performance of PostgreSQL.

PostgreSQL was designed to be extensible, it provides an API to application developers to extend PostgreSQL functionality and/or improve the performance for specific data and workloads. It is important that there is no need of having expertise of core developers, and these new functionality could be added online without restarting of database server. Application developer can create various database objects, such as functions, data types, operators, indexes, and even new access methods.

I will present my choice of two extensions out of hundreds:

vops - greatly improves the performance of Postgres for OLAP queries using vector operations, pg_variables - provides session variables for storing scalars and relations, useful for generating reports on read-only replicas.

Python may not be the fastest programming language on the CPU, but its fast and easy development saves a lot of costs between the keyboard and the chair. Since database clients spend most of their time waiting for a response from the database server, Python's asynchronous functionality available in the recent versions (3.5+) may help to optimize the application's runtime considerably by working on something else while server's response is being prepared. The asynchronous interface between Python and PostgreSQL is called "asyncpg". In the workshop we'll explore this library and write a short application using some of its useful features.

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