title

text

March 15 – 17 , 2017

Postrelease

  • more than
    0 participants
  • 0 speakers
  • 0
    minutes of conversation
  • 63 talks
  • offline
    format

Talks

Talks archive

PgConf.Russia 2017
  • Ivan Panchenko
    Ivan Panchenko PostgresPro

    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.

  • Dmitry Cremer
    Dmitry Cremer Federal State Unitary Enterprise Rossiya Segodnya

    • 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

  • Dmitry  Lebedev
    Dmitry Lebedev BestPlace

    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

  • Dmitry Melnik
    Dmitry Melnik ISP RAS

    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.

All talks