PgConf.Russia 2016

Java is the most used programming language in the world. Yet how is it supported in PostgreSQL? What are the gotchas and the best practices? Now that Java is evolving significantly, how will PostgreSQL follow?

Despite Java's age, language is stronger than ever. It's the de facto programming language in the enterprise world. And since Java 8, it is having a come back in the startup and open source world. PostgreSQL is accessed more from Java than any other interface but, how's Java supported in PostgreSQL?

This talk will analyze how it has been in the past, but more importantly how can you use it and what can you do today. JDBC drivers, best practices, pl/java and other less frequently used tools will be presented and discussed.

And then we will look into the future, to see what is currently under development. Like Phoebe, a new Java Reactive Driver for PostgreSQL that targets clusters, pipelined queries and non-JDBC interface for fully asynchronous operation. And also what needs to be done in areas like server-side Java, to bring Java to a fully advanced first-level language within PostgreSQL.

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.

When new versions of Postgres are released most of the attention is focused on new features. Inevitably a release note claiming speed improvements seems relatively mundane and doesn't provide the compelling argument for upgrading. However the reality is that these speed improvements represent pain points that have been identified and solved.

Reviewing the changes to the sort code in Postgres over the last 10 years clearly shows the kinds of problems users have run into. As usage patterns changed over years, databases scaled up, and hardware changed new problems arose and drove further development to solve them.

Upcoming changes in 9.5 and 9.6 will dramatically change the experience further. Making sorting UTF8 and other encodings less of a problem and handling scaling to larger machines with many processors and memory cache more effectively.

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.