PgConf.Russia 2017

Currently, to execute SQL queries PostgreSQL uses interpreter, which implements Volcano-style iteration model. At the same time it’s possible to get significant speedup by dynamically JIT-compiling query “on-the-fly”. In this case it’s possible to generate code that is specialized for given SQL query, and perform compiler optimizations using the information about table structure and data types that is already known at run time. This approach is especially important for complex queries, which performance is CPU-bound.

Modern state of backup tools for PostgreSQL certainly leaves some room for improvements. Standard utilities provides only basic functionality, third-party tools solve some, but not all, problems. To take backups reliably, fast, and incrementally on page level, one needs not only a backup tool, but also some support from the database server. On this tutorial we will talk about our new backup and recovery manager ProBackup, and will show it in action.

Demonstration script:

• pg_probackup_script.txt

Query optimization is an important problem, which solution has a great influence on DBMS performance, especially for complex queries. In this talk we consider PostgreSQL query optimizer and specifically cardinality estimation problem for correlated clauses, which is one of the most well-known drawbacks of query optimizers in general. In the talk we propose our solution for this problem which involves machine learning methods and is available for PostgreSQL 9.6 as an extension with a patch. We discuss the experimental evaluation, advantages, disadvantages, and fields of application of the proposed approach as well.

VIDEO

PostGIS enables spatial processing in Postgres. Usually PostGIS is used for planar euclidean calculations, but real world adds time, ambiguity and uncertainty. We'll have a look on making a thousand of cars move smoothly in real time on OpenStreetMap base map.

VIDEO