PgConf.Russia 2016

We love to stress test software, since we are a performance engineering company. Our friends from a hosting company servers.com provided us with a modern dedicated server so we immediately started to test PostgreSQL in different environments, including SmartOS, DragonFly and Windows. We would like to present our results (and all the gory details) to community.

In the speech we consider the current PostgreSQL planner model, then the possibilities of applying machine learning methods for planner improvement and the obtained results.

PostgreSQL includes several index types: GiST, SP-GiST, GIN, and of course, the regular B-tree. DBAs are familiar with using each of these for specific use cases, GIN for full-text search, GiST for geometrical data, and so on, but how do they work internally? What makes them suitable for the cases they're typically used for?

In this presentation, I will walk through the internal structure of each of these index types, explaining what strengths and weaknesses each one of them have.

Common Java wisdom is to use PreparedStatements and Batch DML in order to achieve top performance. It turns out one cannot just blindly follow the best practices. In order to get high throughput, you need to understand the specifics of the database in question, and the content of the data.

In the talk we will see how proper usage of PostgreSQL protocol enables high performance operation while fetching and storing the data. We will see how trivial application and/or JDBC driver code changes can result in dramatic performance improvements. We will examine how server-side prepared statements should be activated, and discuss pitfalls of using server-prepared statements.