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February 03 – 05 , 2016

PgConf.Russia 2016

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Talks

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PgConf.Russia 2016
  • Peter Gribanov
    Peter Gribanov 1C LLC

    More than 300.000 developers use technology platform "1C:Enterprise" as a main development tool. I'll tell you about architecture and features that made "1C:Enterprise" one of the most popular development environment in Russia and CIS and about growing popularity of PostgreSQL amongst 1C users.

  • Marco Slot
    Marco Slot Citus Data

    CitusDB is an extension for PostgreSQL that can distribute tables across a cluster of PostgreSQL servers. Data is stored in shards that can use append-partitioning for bulk-loading of time series data or hash-partitioning for real-time data ingestion. SELECT queries on distributed tables are transparently parallelised across the cluster, using all available cores. Distributed tables can also be joined in parallel, even if they are not partitioned along the same column. CitusDB is especially suitable for real-time analytics use-cases such as dashboards which require fast analytical queries over live data, and can simultaneously act as a scalable operational database. This talk will describe the internals of CitusDB and give a live demo of a large-scale CitusDB cluster.

  • Andres  Freund
    Andres Freund Citus Data

    Postgresql's buffer manager has parts where it's showing its age. We'll discuss how it currently works, what problems there are, and what attempts are in progress to rectify its weaknesses.

    • Lookups in the buffer cache are expensive
    • The buffer mapping table is organized as a hash table, which makes efficient implementations of prefetching, write coalescing, dropping of cache contents hard
    • Relation extension scales badly
    • Cache replacement is inefficient
    • Cache replacement replaces the wrong buffers

  • Heikki Linnakangas
    Heikki Linnakangas Pivotal Ltd

    PostgreSQL includes several index types: GiST, SP-GiST, GIN, and of course, the regular B-tree. DBAs are familiar with using each of these for specific use cases, GIN for full-text search, GiST for geometrical data, and so on, but how do they work internally? What makes them suitable for the cases they're typically used for?

    In this presentation, I will walk through the internal structure of each of these index types, explaining what strengths and weaknesses each one of them have.

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