PgConf.Russia 2017

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).

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Part 1

Part 2

Part 3

Part 4

Experience we've got after 5 years of developing, deploying and improving BI system http://colibri365.ru used in government. I would talk about government IT reality and our way over it. Postgres performance improvements, using of latest features, overwriting of user generated queries to help query optimizer and other tweaks and hacks to tackle limited hardware problems. These lead us to number of computer science papers and (now committed) patches to Postgres (see Andrey Borodin talks for details).

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Query optimization is an important problem, which solution has a great influence on DBMS performance, especially for complex queries. In this talk we consider PostgreSQL query optimizer and specifically cardinality estimation problem for correlated clauses, which is one of the most well-known drawbacks of query optimizers in general. In the talk we propose our solution for this problem which involves machine learning methods and is available for PostgreSQL 9.6 as an extension with a patch. We discuss the experimental evaluation, advantages, disadvantages, and fields of application of the proposed approach as well.

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Modern high-performance and scale-out Database solutions need to move huge amounts of data between the system components. They need to perform it quickly and without delay for the end-user. While developing interconnect technologies Mellanox focuses on highest bandwidth, ultra-low latency, and advanced CPU offload capabilities. In this session Michael will tell about fast interconnect value for Database and Big Data solutions and will share Mellanox experience in building such systems in cooperation with leading global vendors.

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