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March 15 – 17 , 2017

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Talks

Talks archive

PgConf.Russia 2017
  • Dmitry Yuhtimovsky
    Dmitry Yuhtimovsky Gilev.ru

    1. 1C:Enterprise 8 and PostgreSQL 9 interoperability 1.1 Changes in new 1C platform versions 1.2 v81c_data and v81c_index schemas 1.3 Sending 1C queries to SQL 1.4 Using 1C technological log events for PostgreSQL diagnostics
    2. Analyzing queries that affect PostgreSQL performance 2.1 A free tool for automating log parsing 2.2 Pareto principle in action 2.3 Installation and configuration of the tool 2.4 A case study of query optimization 2.4.1 An issue in a PostgreSQL query 2.4.2 Finding non-optimal operations in a query 2.4.3 Resolving inefficiencies
    3. PostgreSQL statistics for performance diagnostics 3.1 Comparing Postgres with MS SQL Server 3.2 Troubleshooting locks 3.3 Operating load diagnostics 4 Case studies by the gilev.ru team

  • Dmitry Belyavskiy
    Dmitry Belyavskiy Technical Center of Internet

    Real-word data often need cryptographycal protection. The presentation describes typical problems that can be solved using cryptographycal methods and the right ways to use cryptography with RDBMS. The newest solutions suggested for usage with PostgreSQL are described.

    VIDEO

  • Yury Zhukovets
    Yury Zhukovets ЗАО Дилжитал-Дизайн

    This talk is about migrating an electronic document management system from MS SQL to PostgreSQL 9.5 or higher as part of the import phaseout initiative. We will touch upon architecture specifics, as well as describe the problems we encountered when migrating T-SQL code to pgsql, and how we resolved them.

    Learn more at https://pgconf.ru/news/94168

    VIDEO

  • 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.

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