PgConf.Russia 2017

• why we build PostgreSQL from sources?
• choice build options
• dependencies
• creation system environment
• how to customise Linux for work with PostgreSQL
• extra soft for PostgreSQL DBA

VIDEO

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.

Java is one of the most used languages when programming with PostgreSQL databases. Join this tutorial to learn or review the techniques to connect to postgres, best programming practices with JDBC, and to explore jOOQ, a mapper software that allows you to use the full power of SQL and postgres advanced query features while avoiding all the boilerplate code.

This tutorial is very practical: most of the time will be dedicated to iterate through code samples. It will cover:

• Introduction to Java and PostgreSQL
• Ways of connecting to PostgreSQL from Java (not only JDBC!)
• Introduction to JDBC. JDBC types. PostgreSQL JDBC
• Code demo: JDBC with PostgreSQL. From Java 1.4 to Java 8, best practices and code samples
• Code demo: jOOQ, a great mapper for PostgreSQL
• Java inside PostgreSQL
• The future of Java and PostgreSQL

About two-thirds of the tutorial will be dedicated to iterate over code samples and demos. All the code would be available from public open-source repositories and built with maven, so that any attendee may download it and build easily to play with it during the tutorial (although not required).

VIDEO

Part 1

Part 2

Part 3

Part 4

Citus allows you to distribute postgres tables across many servers. It extends postgres to transparently delegate or parallelise work across a set of worker nodes, enabling you to scale out the CPU and memory available for queries.

One year ago, we began a long journey to allow Citus to scale out another dimension: write throughput. With writes being routed through a single postgres node, write throughput in Citus was ultimately bottlenecked on the CPUs of a single node. Citus MX is a new edition of Citus which allows distributed tables to be used from from any of the nodes, enabling NoSQL-like write-scalability.