PgConf.Russia 2019
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.
Talks
Talks archive
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Andrey Fefelov Mastery.proPatroni is getting art of state standard framework for building HA clusters with postgres now.
During session we will build simple 3 node cluster using mentioned stack.
We will discuss patroni's architecture, and most interesting parameters from it's configuration. We will check how actually failover works and how could you initialise cluster.
After session you will be able to built such cluster from scratch in minutes using given ansible playbooks.
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Tatsuro Yamada NTT ComwareAs is often seen in OLAP and batch processing workloads, the more complex a query (containing many joins, filters, aggregates), the more there is a possibility of row count estimation errors, which leads to planner choosing an inefficient execution plan.
To address that problem, I developed a tool called pg_plan_advsr as a PostgreSQL extension, which corrects the estimation errors by repeatedly feeding back the information collected during query execution to the planner.
The tool has three features:
- Automatic plan tuning by repeatedly feeding execution information to planner
- Preserve all plans generated during plan tuning in a history table
- Create and store optimizer hints to be able to reproduce plans generated during tuning process
I verified the effectiveness of pg_plan_advsr by enabling it when running the join order benchmark (JOB) against PG 10.4 and observed its execution time shortening to 50% of the original. Therefore, it is useful for user who would like to do plan tuning for OLAP and batch processing.
I will talk about the following things in this presentation:
- Principles behind pg_plan_advsr and its architecture
- Detailed information about the measurements done with JOB
- Possible future enhancements
- Using aqo and pg_plan_advsr together (experimental)
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Alexander Korotkov PostgresProIt's so good when database behaves predictable. When the performance is lacking, you just add CPU cores, terabytes of RAM and millions of IOPS, and everything becomes good again. But it's rather unpleasant, when server have plenty of free resources, while database is still running slow. And it's especially sad if stress testing detects no problems, while real life workload of the same volume makes your database hang.
In this talk I will consider bottlenecks of PostgreSQL, which we met in our practice, and which causes sad behavior described above. I'll also explain what can be done at user level in order to evade these bottlenecks, and what developers are planning to do in order to eliminate those bottlenecks. I'm also planning give some recipes of stress testing, which could have to evade surprises in production.
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Vasiliy Puchkov ОООMeeting corporate standarts for information security, business continuity and software unification: Kerberos Authentification (Windows and Linux) in Active Directory Environment. 1C Enterprise specifics. Using backup and recovery software (HP Data Protector). Integration with corporate monitoring system (Solarwinds Mointor).
Photos
Photo archive