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.

I love complex knowledge domains, strong typing in applications, and 3NF, but I hate ORM. That is why I’ve been actively storing serialized structures in JSON fields (even before the JSON type was introduced). In this talk, I will tell you about some specifics of storing complex structures within DBMS fields, what problems can arise, and how to cushion the blow.

VIDEO

In the data persistence layer, using PostgreSQL from the very start of development, we went all the way from a small cluster on a virtual machine to a multi-host system that provides near real-time processing of mixed OLTP/OLAP load. In this talk, I’m going to tell you about the main development stages of our analytical solution at the application and infrastructure levels, and describe the specifics of using PG that we encountered.

VIDEO

Tencent, based in China, is one of the world's largest companies in the social networking space. This talk discusses how Tencent modified the code of Postgres-XC to meet their internal payment system requirements