PgConf.Russia 2019

In my talk I'll tell you about practices of working with Postgres in the Go-services. I’ll describe general advantages and disadvantages of the basic tools that are commonly used when working with Postgres using Go. Of course, we will touch on the nuances that need to be taken into account when your services are running inside the Kubernetes. I will also talk about Avito’s experience in providing a database of product’s developers. This presentation will be of interest to developers who want to avoid problems when working with Postgres, and will be useful to DBA who want to know what difficulties customers face in their database.

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.

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.

Brendan Gregg’s USE (Utilization, Saturation, Errors) method for monitoring is quite known. There’s also Tom Wilkie’s RED (Rate, Errors, Durations) method, which is suggested to be better suited to monitor services than USE. I want to talk about how we employ these methodologies when we develop our Postgres monitoring in okmeter.io.