title

text

February 05 – 07 , 2018

PGConf.Russia 2018

PGConf.Russia is a leading Russian PostgreSQL international conference, annually taking together more than 500 PostgreSQL professionals from Russia and other countries — core and software developers, DBAs and IT-managers. The 3-day program includes training workshops presented by leading PostgreSQL experts, more than 40 talks, panel discussions and a lightning talk session.

Thems

  • PostgreSQL at the cutting edge of technology: big data, internet of things, blockchain
  • New features in PostgreSQL and around: PostgreSQL ecosystem development
  • PostgreSQL in business software applications: system architecture, migration issues and operating experience
  • Integration of PostgreSQL to 1C, GIS and other software application systems.
  • more than
    0 participants
  • 0 speakers
  • 0
    minutes of conversation
  • 54 talks
  • offline
    format

Talks

Talks archive

PGConf.Russia 2018
  • Bruce Momjian
    Bruce Momjian EnterpriseDB

    This talk explores the ways attackers with no authorized database access can steal Postgres passwords, see database queries and results, and even intercept database sessions and return false data. Postgres supports features to eliminate all of these threats, but administrators must understand the attack vulnerabilities to protect against them. This talk covers all known Postgres external attack methods.

  • Olivier Courtin
    Olivier Courtin DataPink

    PostGIS is well known and widely used since two decades, as the best OpenSource database solution for Spatial Analysis. This talk will focus on: spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available); how to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)

  • Alexander Korotkov
    Alexander Korotkov PostgresPro

    Bringing the provability and immutability of blockchain to performance and efficiency of traditional DBMS.

    Blockchain technology has several unique properties including provability and immutability. Every blockchain transaction is signed by its author, and it could be verified by any blockchain network member. Also, once data is stored in blockchain, it can't be altered in the future. Many databases operating traditional DBMS would also benefit from provability and immutability properties. However, inclusion of all the transaction data in the public blockchain is very expensive.

    Credereum is the platform, which allows creation and maintaining of databases, whose contents and history are provable and immutable without sacrifice the performance and efficiency of traditional DBMS. Thanks to Credereum, database owner can prove the validity of query results, while users can verify them. Database owner don't have to reveal the whole database contents or full history of transactions to provide the proof of database query results. Therefore, Credereum database may contain private sensitive information. Credereum utilized bleeding-edge technologies including, but not limited to decentralized cloud, public blockchain with sharding. Credereum is an emerging technology of trusted and private databases.

    We will explain why PostgreSQL is suitable database for Credereum and what we need to develop in Postgres to support signed transactions and cryptographic storage.

  • Konstantin Knignik
    Konstantin Knignik PostgresPro

    PostgreSQL looks very competitive with other mainstream databases on OLTP workload (execution of large number of simple queries). But on OLAP queries, requiring processing of larger volumes of data, DBMS-es oriented on analytic queries processing can provide an order of magnitude better speed. The following factors limit Postgres OLAP performance:

    • Unpacking tuple overhead (tuple_deform)
    • Interpretation overhead (Postgres executor has to interpret query execution plan)
    • Abstraction penalty (support of abstract data types)
    • Pull model overhead (operators are pulling tuples from heap page one-by-one, resulting numerous repeated accesses to the page)
    • MVCC overhead (extra per-tuple storage + visibility check cost)

    All this issues can be solved using vectorized executor, which proceed bulk of values at once. In this presentation I will show how vector operations can be implemented in Postgres as standard Postgres extension, not affecting Postgres core. The approach is based on introducing special types: tile types, which can be used instead of normal (scalar) types and implement vector operations. Postgres extension mechanism, such as UDT (user-defined type), FDW (foreign data wrappers), executor hooks are used to let users work with vectorized tables almost in the same way as with normal tables. But more than 10 times faster because of vector operations.

All talks

Partners

PGConf.Russia 2018

Silver

Organizational

Informational

Partner