Postrelease
Talks
Talks archive
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Alvaro Hernandez 8KdataJava 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.
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Tatsuo IshiiThe talk is about PostgreSQL clusters using streaming replication and pgpool-II, which are quite popular in Japan. Plus, the next version of pgpool-II will be released this winter, so the talk will be about what's new in the version.
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Alexander Krizhanovsky NatSys LabWe'll discuss how does Linux work with virtual memory. The following topics will be covered: * x86-64 page table, context switch and page fault; * internals of virtual memory management (VMM) in Linux; * page eviction methods in Linux, page cache and anonymous pages; * huge and gigantic pages, transparent huge pages; * how mmap(2) works and what madvise(2), msync(2) etc. provide; * why large databases don't use mmap(2), but rather implement buffer pool on their own; * ans surely how to tune Linux VMM using sysctl.
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Oleg Ivanov PostgresProIn the speech we consider the current PostgreSQL planner model, then the possibilities of applying machine learning methods for planner improvement and the obtained results.
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