title

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March 15 – 17 , 2017

Postrelease

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  • 63 talks
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Talks

Talks archive

PgConf.Russia 2017
  • Peter  van Hardenberg
    Peter van Hardenberg Heroku

    The PostgreSQL community is over 20 years old, but the history of PostgreSQL dates back even farther. In this talk, we'll learn about the roots of the Postgres project, learn about some of the people who contribute to it, study how it has changed over time, and pay special attention to the many contributions of Russian people.

  • Andrey Fefelov
    Andrey Fefelov Mastery.pro

    I will tell you about why Postgres is first-choice product as a foundation for your BI system with classical OLAP workload. Briefly it will be said about existing open source BI solutions.

    I will also describe specific of our architecture, why we chose snowflake scheme and how we are doing extract, transformation and load procedures. It will be mentioned about special Postgres tuning for OLAP and massive data bulkload workloads. Also I will let you know about Postgres usage as a column database with cstore_fdw by Citus and results achieved. Cons and problems of our approach will be described in the end of the talk.

    VIDEO

  • Dmitry Vagin
    Dmitry Vagin Avito

    A short talk about collecting data and monitoring database workload in Avito. Exporting metrics from stored procedures to Graphite. Collecting and visualizing pg_stat* metrics in Grafana. Case studies.

    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.

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