PgConf.Russia 2017

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.

Full text search in PostgreSQL is probably the most advanced one among relational DBMS. This tutorial will explain how to setup full text search configurations and dictionaries and how to build a ful text search system using an example of a simple popular science web site, with demonstration of various ranking functions. Also I will tell about new RUM index, which allows to accelerate execution of some kinds of full text queries and implements a new improved ranking function.

Sets are apparently a useful data type for many kinds of applications. While PostgreSQL offers no built-in set data type, sets may be emulated to some degree with its built-in array and JSONB data types. Also, acceleration of respective containment (subset) queries is readily available as a built-in feature of the GIN index type.

Starting with the above, we will then explore the performance gains enabled by custom set data types, and especially by customisation code in C ("operator classes") for the GIN and GiST index types.

It often happens that you already have PosgtreSQL in production, but you don’t have a DBA. To demystify the PostgreSQL database, I’ll tell you how to troubleshoot various problems while working with PosgtreSQL. We will try to understand how to answer "routine" questions of a typical system administrator: - Is everything OK with the database? - What consumes DB server resources? - What to optimize first to reduce resource consumption?

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