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March 15 – 17 , 2017

Postrelease

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Talks

Talks archive

PgConf.Russia 2017
  • Ildar Musin
    Ildar Musin PostgresPro
    Dmitry Ivanov
    Dmitry Ivanov PostgresPro

    Partitioning is a long-awaited feature in PostgreSQL. Although Postgres supports partitioning via inheritance, this approach has some disadvantages, such as the need to manually create partitions and support triggers, significant planning overhead, and no query execution optimizations. In this talk, we’ll tell you about the pg_pathman extension we are developing. pg_pathman supports HASH and RANGE partitioning, performs planning and execution optimizations, supports fast insert by using Custom Node instead of triggers, provides functions for partition management (add, split, merge, etc.), supports FDW, non-blocking data migration, and more. We'll also speak about pg_pathman integration with Postgres Pro Enterprise Edition and Oracle-like syntax support for partitioning. Finally, we'll discuss new partitioning capabilities in PostgreSQL 10, the already implemented features and further development plans.

    VIDEO

  • Oleg Ivanov
    Oleg Ivanov PostgresPro

    Query optimization is an important problem, which solution has a great influence on DBMS performance, especially for complex queries. In this talk we consider PostgreSQL query optimizer and specifically cardinality estimation problem for correlated clauses, which is one of the most well-known drawbacks of query optimizers in general. In the talk we propose our solution for this problem which involves machine learning methods and is available for PostgreSQL 9.6 as an extension with a patch. We discuss the experimental evaluation, advantages, disadvantages, and fields of application of the proposed approach as well.

    VIDEO

  • Arthur Zakirov
    Arthur Zakirov PostgresPro
    Teodor Sigaev
    Teodor Sigaev PostgresPro

    Full text search in PostgreSQL is probably the most advanced one among relational DBMS. This tutorial will explain how to setup full text search configurations and dictionaries and how to build a ful text search system using an example of a simple popular science web site, with demonstration of various ranking functions. Also I will tell about new RUM index, which allows to accelerate execution of some kinds of full text queries and implements a new improved ranking function.

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