PgConf.Russia 2016

B-tree is the most widely used index type in PostgreSQL. This data structure and concerned algorithms are developed about forty years ago. But there is still an area for optimisations. In this presentation I'm going to talk about B-tree data structure, and its features important for the optimal index usage. Furthermore, I'll present a couple of new features which are expected to be included in PostgreSQL 9.6 release.

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.

Currently, PostgreSQL uses the interpreter to execute SQL-queries. This yields an overhead caused by indirect calls to handler functions and runtime checks, which could be avoided if the query were compiled into the native code "on-the-fly" (i.e. JIT-compiled): at a run time the specific table structure is known as well as data types used in the query. This is especially important for complex queries, which performance is CPU-bound. At the moment there are two major projects that implement JIT-compilation in PostgreSQL: a commercial database Vitesse DB and an open-source project PGStorm. The former uses LLVM JIT to achieve up to 8x speedup on selected TPC-H benchmarks, while the latter JIT-compiles the query using CUDA and executes it on GPU, which allows to speed up execution of specific query types by an order.

Our work is dedicated to adding support for SQL query JIT-compilation to PostgreSQL using LLVM compiler infrastructure. In the presentation we'll discuss how JIT-compilation can be used to speed up various stages of query execution in PostgreSQL, and the specifics of translating an SQL query into LLVM bitcode to achieve good performing native code. Also we'll present preliminary results for our JIT-compiler on TPC-H benchmark.

A backup is something that no Postgres deployments should go without as it gives the insurance to get back a deployment on its feet should a disaster strike.

In this talk we will discuss why backups are essential in any sane PostgreSQL deployments (this seems obvious) and what are the different options available to define and set up a good backup strategy. On top of that is discussed how the future of backups would need to be handled, particularly regarding differential backups that gain in popularity among users with large deployments.