PGConf.Russia 2018

A database is one of the key components in any information system, requiring the monitoring of multiple metrics. The talk highlights examples and approaches of monitoring and analysis of PostgreSQL performance that allow to minimize the load on the database server from the monitoring and data collection system for the subsequent analysis of problem situations.

• Quantum effects or as an observer affect the observed system
• Features of collecting metrics while monitoring the database with Zabbix
• Data collection for analytics and visualization PostgreSQL queries with rsyslog + kafka + clickhouse + grafana
• Operational Analysis Tools for DB loglile

Storing binary data in database tables is sometimes a good solution for a particular project. But sometimes, due to changes in conditions or insufficient consideration of decisions, such storage is becoming a real nightmare. If there is an understanding of how and where to place these data, the transition to the new solutions are often very hard, often require modification in the application code and downtime the system for migration. The presentation is a particular solution of such problems. Our extension allows to move binary data from database to the storage Ceph and not only. And does it seamless for the applications.

When one faces the issues with PostgreSQL, the main suspicion falls on vacuum. Experience of Data Egret team proves how many DBAs are attacking this rake. While there are tons of information, documentation and discussions on vacuum itself, the topic is still associated with a lot of myths, tales, horror stories and misconceptions. In my talk I will try to reveal the key points concerning the inner structure of vacuum, basic approaches to its adjustment and tuning, performance monitoring, and so on.

The speaker will share his experience of transferring high-loaded game projects that were originally developed to work with MySQL into PostgreSQL. Problems that had been expected and ones that were actually faced. Tricky bugs that were found in production servers after the migration. Solutions that were used to deal with problems. PostgreSQL features that were very useful and desirable ones that were missing.