PgConf.Russia 2016

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.

PostgreSQL includes several index types: GiST, SP-GiST, GIN, and of course, the regular B-tree. DBAs are familiar with using each of these for specific use cases, GIN for full-text search, GiST for geometrical data, and so on, but how do they work internally? What makes them suitable for the cases they're typically used for?

In this presentation, I will walk through the internal structure of each of these index types, explaining what strengths and weaknesses each one of them have.

The talk describes performance benchmarking results of PostgreSQL on modern Hi-End servers. The main attention was paid to the locks for shared data access and associated bottlenecks. The testing propose was to test the linear read scalability limits with an increase of cores number allocated for PostgreSQL. Testing was performed for different postgres versions (9.4, 9.5, 9.6) to check new features designed to increase performance on multiprocessing architectures.

Challenges and solutions found in documents dematerialization and bank cheque processing system used in the Bank of Brazil.