PgConf.Russia 2019

I'm going to talk about emerging technologies in the area of general purpose RDBMS indexing. I will describe different approaches suitable for different workloads. We will discuss ideas from academic researches and corresponding industrial response from developers, communities, and companies. There will be the short live-coding session on creating DIY index in PostgreSQL.

Sometimes problems arise during Postgres operation, and the faster they are identified and resolved, the happier users eventually will be. pgCenter is a set of CLI powerful utilities for troubleshooting in the "here and now" mode. In the talk I will show how to use pgCenter for efficient troubleshooting, the directions in which to search, and how to respond to certain problems, in particular how to:

• check if Postgres is in the normal state;
• identify promptly the faulty clients and stop them;
• reveal too heavy queries;
• and other tips and tricks of pgCenter.

At pgconf’17 I talked about our analytics systems based on PostgreSQL. Afterwards we looked at hadoop, s3, presto, vertica, and other frights. Finally we stopped to suffer nonsense and just completed PostgreSQL with ready Greenplum and Clickhouse. As a result, we achieved amazing performance, fast migration, easy maintenance, reliability and horizontal scalability. We enabled to recover the system after fault in two commands, decreased infrastructure costs and expanded functionality due to ANSI SQL, MPP and In-memory. All within the open-source and full SQL paradigm. We called the product GreenHouseSQL, which is our inner whole cycle data platform. In the talk we will show the beauty of solution internals, explain the advantages and flaws, tips and tricks of starting with Greenplum, as well as why do we need Clickhouse, what is left to PostgreSQL, and eventually how does it all work.

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.