PGConf.Russia 2018

Many of us know that it is MVCC that provides concurrency access to data in many relational databases that guarantee transactions consistency and isolation. But only deep understanding of the implementation of this mechanism in PostgreSQL allows us to better understand the processes in the database, to design the application logic and table structures to be the most effective in a high-load world. We'll take one of the processes in our product to understand how MVCC is implemented in PostgreSQL and we'll explain one of the peculiarity when seemingly unrelated activities can affect each other.

In this talk I will share the experience of migration, NoSQL data to a relational view convert, and tell you how we managed to speed up the application in 2 times.

Initially, we used PosgtgreSQL and MongoDB to store all the data. In practice, we found out that the separation is extremely inconvenient. We spent a lot of time and attention on it.

My talk will describe how we moved the data from MongoDB to PostgreSQL using mosql. As a result, all the data can be received by one query now, and the table schema ensures data consistency.

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.

Comparison of capabilities of PostgreSQL and MS SQL to work with 1С. What does pg_restore mean for 1С user? Results of load testing for 1C database of more than 1TB size. The story of one 1C Production: 2 years, 500+ databases 1C, 4TB of data, cascading replication.