PgConf.Russia 2016

Java is the most used programming language in the world. Yet how is it supported in PostgreSQL? What are the gotchas and the best practices? Now that Java is evolving significantly, how will PostgreSQL follow?

Despite Java's age, language is stronger than ever. It's the de facto programming language in the enterprise world. And since Java 8, it is having a come back in the startup and open source world. PostgreSQL is accessed more from Java than any other interface but, how's Java supported in PostgreSQL?

This talk will analyze how it has been in the past, but more importantly how can you use it and what can you do today. JDBC drivers, best practices, pl/java and other less frequently used tools will be presented and discussed.

And then we will look into the future, to see what is currently under development. Like Phoebe, a new Java Reactive Driver for PostgreSQL that targets clusters, pipelined queries and non-JDBC interface for fully asynchronous operation. And also what needs to be done in areas like server-side Java, to bring Java to a fully advanced first-level language within PostgreSQL.

When new versions of Postgres are released most of the attention is focused on new features. Inevitably a release note claiming speed improvements seems relatively mundane and doesn't provide the compelling argument for upgrading. However the reality is that these speed improvements represent pain points that have been identified and solved.

Reviewing the changes to the sort code in Postgres over the last 10 years clearly shows the kinds of problems users have run into. As usage patterns changed over years, databases scaled up, and hardware changed new problems arose and drove further development to solve them.

Upcoming changes in 9.5 and 9.6 will dramatically change the experience further. Making sorting UTF8 and other encodings less of a problem and handling scaling to larger machines with many processors and memory cache more effectively.

Schema-less is definitely a trend in the data storage nowadays, and it's not only about NoSQL, but also about traditional RDBMS. Many relational databases (e.g. PostgreSQL, Oracle, db2, Mysql) allow to storing data in the schema-less json format and use their own more or less unique way to do that.

This talk contains two parts:

• Comparison of the json support in PostgreSQL and different relational databases, namely Mysql, Oracle, db2, MSSql in terms of supported features, functions and so on.
• Performance benchmarks for databases with the advanced json support, namely PostgreSQL and Mysql, and the MongoDB on different workload types and configurations.

Common Java wisdom is to use PreparedStatements and Batch DML in order to achieve top performance. It turns out one cannot just blindly follow the best practices. In order to get high throughput, you need to understand the specifics of the database in question, and the content of the data.

In the talk we will see how proper usage of PostgreSQL protocol enables high performance operation while fetching and storing the data. We will see how trivial application and/or JDBC driver code changes can result in dramatic performance improvements. We will examine how server-side prepared statements should be activated, and discuss pitfalls of using server-prepared statements.