PgConf.Russia 2019

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.

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.

So you finally have your database model for your application and you fill it in with current data. How do you keep it up to date? While INSERT may still be transparent, UPDATE and DELETE will overwrite your previous data, so you won't be able to reproduce them. Cloning the whole huge content for each minor update is not an option. For rich and complex data about hundreds of thousands of power generators in Germany and worldwide, I built a model using range data types in recent PostgreSQL which allows me to insert, update and delete data while granting the full access to the whole state of the database at any historical moment. I'll present a very simplified version of the database so the audience will be immediately able to apply it for their cases. I'll also show a few tricks in Python and Psycopg2 that will allow a whole team to prepare, review, and deploy all revisions to this database without merge conflicts. And I'll give a few ideas on how to retrieve this data efficiently.

JSONB in PostgreSQL has interesting properties for design and development of business systems with heavy domain, helping to fight against complexity and variability. We will discuss the trade-offs of the JSONB approach. Using an open source project for Health IT - fhirbase we will:

• load synthetic medical data into PostgreSQL
• learn how to search and index this data (gin, jsquery, json-knife)
• use JSON aggregation to build complex queries (GraphQL)
• see how this data can be modified and validated
• discuss architecture consequences of JSONB usage

Technical requirements:

• docker
• docker-compose

Detailed prerequisites