PgConf.Russia 2016

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.

Postgresql's buffer manager has parts where it's showing its age. We'll discuss how it currently works, what problems there are, and what attempts are in progress to rectify its weaknesses.

• Lookups in the buffer cache are expensive
• The buffer mapping table is organized as a hash table, which makes efficient implementations of prefetching, write coalescing, dropping of cache contents hard
• Relation extension scales badly
• Cache replacement is inefficient
• Cache replacement replaces the wrong buffers

CitusDB is an extension for PostgreSQL that can distribute tables across a cluster of PostgreSQL servers. Data is stored in shards that can use append-partitioning for bulk-loading of time series data or hash-partitioning for real-time data ingestion. SELECT queries on distributed tables are transparently parallelised across the cluster, using all available cores. Distributed tables can also be joined in parallel, even if they are not partitioned along the same column. CitusDB is especially suitable for real-time analytics use-cases such as dashboards which require fast analytical queries over live data, and can simultaneously act as a scalable operational database. This talk will describe the internals of CitusDB and give a live demo of a large-scale CitusDB cluster.

We will consider the advantages and disadvantages of solutions based on JSONB compared to traditional relational approach on real projects, including: 1. Performance 2. Data Versioning 3. Scalability 4. Reliability 5. Report building