PgConf.Russia 2017

Using PostgreSQL since 2003, we went all the way from a database of a couple of GB to a cluster of more than 5TB. At the moment, we have more than 700 tables and about 1500 stored procedures. We are ready to share with you the following: - Problems encountered at different development stages and how we resolved them. - Best practices in database administration. - Our own extension to work with several closely related databases. - Best known methods and tools that enable our several teams to work together without interference. - How we set up test equipment of different types. And, of course, we'll talk about optimization, and how we identify bottlenecks and high-load use cases.

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1. 1C:Enterprise 8 and PostgreSQL 9 interoperability 1.1 Changes in new 1C platform versions 1.2 v81c_data and v81c_index schemas 1.3 Sending 1C queries to SQL 1.4 Using 1C technological log events for PostgreSQL diagnostics
2. Analyzing queries that affect PostgreSQL performance 2.1 A free tool for automating log parsing 2.2 Pareto principle in action 2.3 Installation and configuration of the tool 2.4 A case study of query optimization 2.4.1 An issue in a PostgreSQL query 2.4.2 Finding non-optimal operations in a query 2.4.3 Resolving inefficiencies
3. PostgreSQL statistics for performance diagnostics 3.1 Comparing Postgres with MS SQL Server 3.2 Troubleshooting locks 3.3 Operating load diagnostics 4 Case studies by the gilev.ru team

Input-output (IO) performance issues have been on DBAs’ agenda since the beginning of databases. The volume of data grows rapidly and time is of an essence when one needs to get necessary data fast from the disk and, more importantly, to the disk.

For most databases it is relatively easy to find checklist of recommended Linux settings to maximize IO throughput and, in most cases, this checklist is indeed good enough. It is however essential always to understand how the optimisation of those settings actually works, especially, if you run into corner cases.

The new ISO/IEC 9075-2:2016 standard specifies JSON data and operations syntax and semantics specifics for SQL. This talk overviews the requirements of this standard, and focuses on the differences between them and the actual implementation of JSON/JSONB in PostgreSQL. Special attention will be paid to JSON Path (XPath analogue), SQL/JSON functions and our plans to make Postgres compliant with this standard.

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