PgConf.Russia 2016

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.

More than 300.000 developers use technology platform "1C:Enterprise" as a main development tool. I'll tell you about architecture and features that made "1C:Enterprise" one of the most popular development environment in Russia and CIS and about growing popularity of PostgreSQL amongst 1C users.

Architecture features of IBM Power 8 allowing to gain high performance in comparison with x86_64. CPU, memory, IO etc. Experience of real benchmarking.

Multicorn is a generic Foreign Data Wrapper which goal is to simplify development of FDWs by writing them in Python.

We will see:

• what is an FDW what Multicorn is trying to solve how to use it, with a brief tour of the FDWs shipping with Multicorn.
• how to write your own FDW in python, including the new 9.5 IMPORT FOREIGN SCHEMA api.
• the internals: what Multicorn is doing for you behind the scenes, and what it doesn't

After a presentation of FDWs in general, and what the Multicorn extension really is, we will take a look at some of the FDWs bundled with Multicorn.

Then, a complete tour of the Multicorn API will teach you how to write a FDW in python, including the following features:

• using the table definition
• WHERE clauses push-down
• output columns restrictions
• influencing the planner
• writing to a foreign table
• IMPORT FOREIGN SCHEMA
• ORDER BY clauses pushdown
• transaction management

This will be a hands-on explanation, with code snippets allowing you to build your own FDW in python from scratch.