PGConf.Russia 2022

Usage of temporary tables in PostgreSQL causes additional issues like high server resources consumption and low query performance. However, sometimes you need to rely on them especially when you need to migrate your code from MS SQL, and your initial code employed them, and you had some logic implemented at the database level. This talk covers issues related to usage of temporary tables when migrating from MS SQL, and the ways to resolve them using built-in PostgreSQL features based on the scenarios contained in the code.

This talk covers the Oracle-to-Postgres Pro migration project for the billing system of the Federal State Unitary Enterprise “State Air Traffic Management Corporation of the Russian Federation” employed to collect the air navigation charges for the use of airspace.

Previously, we have explained the internals of Avito, discussed, where and how we store your classifieds, and how they appear on the search results. In the recent 3-4 a lot has changed in Avito. We got rid of logical replication, stopped using standby servers for reads, removed nearly all stored procedures and our custom failover solution, migrated all our classifieds into a sharded DBMS, switched from the monolith app to microservices. I'll explain why we made such decisions, list some of the problems we encountered and describe the current state of our development process.

PostgreSQL has a number of peculiarities that you need to take into account not only while maintaining your database but also when designing your database schema. Experienced PostgreSQL are well aware of vacuuming process. On the web one can find tons of materials covering its internals, configurations and monitoring. Many valuable talks about vacuum were given at numerous conferences. However, we still face the common wraparound problem when the maximum possible number of transactions (xid) is reached. It happens even on databases that are relatively small in size. In my presentation, I will share a customer case that looks interesting to me. A chain of mistakes made at different stages of the database's life cycle once caused a disaster. The database fully stopped for one week, we detected a wraparound and spotted corrupted blocks. Maintenance was problematical, and we spent sleepless nights in search of a solution. We managed to achieve a local win as we finally restored the database, but it's not the end of this story, which makes it even more interesting.