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.

1. Principles of searching for troublesome queries in PostgreSQL.
2. Evaluation of hypothetical indexes and their impact on query plans.
3. The most common errors in 1C-programming.
4. Basic methods of code refactoring, taking into account the features of PostgreSQL.
5. Storing analytical information from the PostgreSQL log to assess the quality of refactoring

This talk will contain 3 parts: 1. Express PITR setup to the Cloud 2. Latest advancements in WAL-G for backups 3. Why you may need or should avoid this new features, depending on your specifications and workload.

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.