PgConf.Russia 2019

Magic tricks followed by exposure (1C+PG):

• Focus number one. How to convince the accounting department to buy a new server.
• Focus number two. How to show that MS SQL is faster than PostgreSQL.
• Focus number three. How to show that PostgreSQL is faster than MS SQL Server.

At the end of 2018, I've got a request to extend the syntax of the ltree contrib. I'm finalizing the patch and going to speak about:

• the current state of the extension,
• the extended syntax, and
• the process of development and testing the extension.

A major responsibility of a database engine is to convert a declarative SQL query to an efficient execution plan, employing various methods to scan and join the relations. There is always a development effort to improve this area. What clever execution plans can PostgreSQL generate, what's new in version 11 and what is in development? To name a few things, the joins are optimized by removing unneeded outer and inner joins, and reducing joins from outer and semi to inner. There is work to enable merge joins on inequality and range overlap, and to improve join selectivity estimates with multi-column statistics. When it comes to scanning a single relation, covering indexes allow to use index-only scans more often. Incremental sort and more precise estimation of sorting costs help generate better paths when sorted output is required, e.g. when using GROUP BY and ORDER BY or performing merge joins. This talk aims to give an overview of such optimizations that already exist and that are being developed now.

I would like to share my experience in optimization queries in PostgreSQL of RMIS (Regional Medical Information System).