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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Irina Fast Bi PartnerAs an experienced Oracle DBA, I've discovered some distinctive features of PostgreSQL which I'd like to share with you. We'll review a set of essential tools for DBAs, their capabilities and usefulness in comparison to their Oracle counterparts. Also I'm going to summarize the core differences between Oracle DB and PostgreSQL in terms of administrating.
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Pavel Trukhanov okmeter.ioBrendan Gregg’s USE (Utilization, Saturation, Errors) method for monitoring is quite known. There’s also Tom Wilkie’s RED (Rate, Errors, Durations) method, which is suggested to be better suited to monitor services than USE. I want to talk about how we employ these methodologies when we develop our Postgres monitoring in okmeter.io.
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Dmitry Belyavskiy Technical Center of Internet
Teodor Sigaev PostgresProAt the end of 2018, I've got a request to extend the syntax of the ltree contrib. I'm finalizing the patch and going to speak about:
- the current state of the extension,
- the extended syntax, and
- the process of development and testing the extension.
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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)
Photos
Photo archive