Postgres monitoring with USE and RED

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.

Technical features of porting T-SQL code to plpgsql and data from MS SQL to PG on the example of transition ECM "Priority" to Postgres

This report focuses on the continuation of transferring our ECM “Priority” from MS SQL to Postgres. Technical solutions, issues of rewriting from T-SQL to plpgsql, optimization of the effective code and moving data will be covered. Additionally, there will be considered aspects of pgplsql performance testing to find the “bad code” of pgplsql as a candidate for optimization. The main objective of the presentation is to answer the question: "We have it in T-SQL - how to transfer it in PG?". The report is intended for junior Postgres developers and is a continuation of the previous report made at the conference in 2017(https://youtu.be/v6_4Szr8t14).

PostgreSQL bottlenecks

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.

How we cook PostgreSQL at 2GIS in the DevOps age

The dark age for PostgreSQL started at 2GIS after transitioning to the microservice architecture. Every team tried to cook database on their own — by installing instances, juggling versions, trying to code deployments with numerous tools or using manual operations. It was the right time to develop a “silver bullet” — a common set of tools to solve all the problems at once. We created our own cluster solution based on well-known PostgreSQL, repmgr, pgbouncer and Barman. Despite of the complexity of our final solution, we developed a repeatable flexible deployment to accelerate postgresql cluster deployment and management. Also we deployed the our own cluster to consolidate all databases. It helped to eliminate team efforts for database management and focus on their main goals. Failover works, we tried it :-)