PgConf.Russia 2017

This tutorial is about various applied JSON usage patterns and the related PostgreSQL functionality. We will discuss data storage in the JSON format, retrieving, changing, and searching such data, JSON features for simple SQL queries, as well as using JSON in stored procedures in different languages. You’ll get hands-on experience with some of the discussed problems on the provided virtual machines.

• why we build PostgreSQL from sources?
• choice build options
• dependencies
• creation system environment
• how to customise Linux for work with PostgreSQL
• extra soft for PostgreSQL DBA

VIDEO

Nowadays one can make a decent urban research based simply on public datasets, making interesting and unexpected insights. In the presentation, I'll show examples of these calculations in PostGIS, the industry standard de-facto.

But just PostGIS is not enough. You need tools to import, verify and visualize the data. It's critically important to visualize the data live, to debug your calculations and shorten iterations. I'll describe all these steps:

1. Collecting the data: public API, OpenStreetMap; direct user input.
2. 3rd party APIs for calculations.
3. Visualization of GIS and other sorts of data: QGIS, Matplotlib, Zeppelin integrated with PostGIS.
4. Debugging the calculations: live visualization (Arc, QGIS, NextGIS Web)
5. Scripting and minimizing the chores: Makefile, Gulp

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.