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.

Whenever multiple users, processes, or threads are concurrently modifying data which is shared among them, problems can occur if race conditions are not handled somehow. These problems are particularly acute in a database which provide ACID semantics. A set of changes grouped into a database transaction must appear atomically, both to concurrent transactions and in terms of crash recovery. Each transaction must move the database from one consistent state (with regard to business rules) to another. For programming efficiency, each transaction must be able to be coded independently of what other transactions may happen to be running at the same time. In the event of a crash, all modifications made by transactions for which the application was notified of successful completion, and all modifications which had become visible to other transactions, must still be completed upon crash recovery. Over the years, various strategies have been employed to provide these guarantees, and sometimes the guarantees have been compromised in one way or another. This talk will cover the approaches taken to provide these guarantees or compromised variations of them, with an emphasis on the Serializable Snapshot Isolation (SSI) technique available in PostgreSQL (and so far not in any other production product). While SSI already performs faster and with higher concurrency than any other technique for managing race conditions with most common workloads, there are many opportunities for further enhancing performance, some of which would require the assistance of people expert in the various index access methods; these issues will be discussed. The talk will also present some rough ideas about how SSI techniques might be used with XTM in a distributed system.

Time will be reserved at the end of the talk for group discussion of optimizations and possible application in distributed environments.

Currently, PostgreSQL uses the interpreter to execute SQL-queries. This yields an overhead caused by indirect calls to handler functions and runtime checks, which could be avoided if the query were compiled into the native code "on-the-fly" (i.e. JIT-compiled): at a run time the specific table structure is known as well as data types used in the query. This is especially important for complex queries, which performance is CPU-bound. At the moment there are two major projects that implement JIT-compilation in PostgreSQL: a commercial database Vitesse DB and an open-source project PGStorm. The former uses LLVM JIT to achieve up to 8x speedup on selected TPC-H benchmarks, while the latter JIT-compiles the query using CUDA and executes it on GPU, which allows to speed up execution of specific query types by an order.

Our work is dedicated to adding support for SQL query JIT-compilation to PostgreSQL using LLVM compiler infrastructure. In the presentation we'll discuss how JIT-compilation can be used to speed up various stages of query execution in PostgreSQL, and the specifics of translating an SQL query into LLVM bitcode to achieve good performing native code. Also we'll present preliminary results for our JIT-compiler on TPC-H benchmark.

We'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.