PgConf.Russia 2017

Experience we've got after 5 years of developing, deploying and improving BI system http://colibri365.ru used in government. I would talk about government IT reality and our way over it. Postgres performance improvements, using of latest features, overwriting of user generated queries to help query optimizer and other tweaks and hacks to tackle limited hardware problems. These lead us to number of computer science papers and (now committed) patches to Postgres (see Andrey Borodin talks for details).

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Citus allows you to distribute postgres tables across many servers. It extends postgres to transparently delegate or parallelise work across a set of worker nodes, enabling you to scale out the CPU and memory available for queries.

One year ago, we began a long journey to allow Citus to scale out another dimension: write throughput. With writes being routed through a single postgres node, write throughput in Citus was ultimately bottlenecked on the CPUs of a single node. Citus MX is a new edition of Citus which allows distributed tables to be used from from any of the nodes, enabling NoSQL-like write-scalability.

This report overviews some ideas and implementations to speedup different parts of generalized search trees (GiST): 1. Intrapage indexing 2. Fractal tree technology 3. Modern algorithms for spatial indexing (RR*-tree) 4.. Possible advancements of GiST API

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Practical experience of carrying out import substitution with using PostgreSQL in government information system including not only the free software, but also the Russian hardware (Elbrus servers and other).

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