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

    A summary of what the Postgres Professional company has achieved in its two-year history:

    • Our achievements in PostgreSQL development.
    • What is the Russian Postgres Pro DBMS, and how is it related to PostgreSQL?
    • What is Postgres Pro Enterprise, and why Enterprise?
    • What about trainings and certification?

    VIDEO

  • Markus Nullmeier
    Markus Nullmeier University of Heidelberg

    Sets are apparently a useful data type for many kinds of applications. While PostgreSQL offers no built-in set data type, sets may be emulated to some degree with its built-in array and JSONB data types. Also, acceleration of respective containment (subset) queries is readily available as a built-in feature of the GIN index type.

    Starting with the above, we will then explore the performance gains enabled by custom set data types, and especially by customisation code in C ("operator classes") for the GIN and GiST index types.

  • Sergey Mirvoda
    Sergey Mirvoda Octonica, UrFU

    Experience we've got after 5 years of developing, deploying and improving BI system http://colibri365.ru used in government. I would talk about government IT reality and our way over it. Postgres performance improvements, using of latest features, overwriting of user generated queries to help query optimizer and other tweaks and hacks to tackle limited hardware problems. These lead us to number of computer science papers and (now committed) patches to Postgres (see Andrey Borodin talks for details).

    VIDEO

  • Radoslav Glinsky
    Radoslav Glinsky Skype (Microsoft)

    Do you test your PostgreSQL releases prior to Production in a dedicated test environment? Are you sure that your test environment (shortly Test) is equal to Production and in an appropriate state?

    In Skype we were facing multiple challenges associated with database testing:
    - Simplifying complex Production architecture of thousands of PostgreSQL instances, interconnected with RPCs and replications, infrastructure servers and external DB scripts, into their Test counterparts.
    - Constantly growing hardware requirements, insufficient cleanup of data generated in Test.
    - Differences between Test and Production were appearing and accumulating. Recognizing and fixing them required lots of effort.

All talks