PgConf.Russia 2019

You just set up your first PostgreSQL cluster, created a database schema, loaded some data, did some fine tuning of configuration. Now you want to make your cluster highly available. Unfortunately, PostgreSQL doesn't offer built-in automatic failover, but luckily for us, there are plenty of external tools for that. As a next logical step you start choosing a tool, and... you already doing it wrong, because first you have to define SLA, RTO, and RPO. In this talk I am going to cover most of the common mistakes people do when setting up a highly available cluster.

JSONB in PostgreSQL has interesting properties for design and development of business systems with heavy domain, helping to fight against complexity and variability. We will discuss the trade-offs of the JSONB approach. Using an open source project for Health IT - fhirbase we will:

• load synthetic medical data into PostgreSQL
• learn how to search and index this data (gin, jsquery, json-knife)
• use JSON aggregation to build complex queries (GraphQL)
• see how this data can be modified and validated
• discuss architecture consequences of JSONB usage

Technical requirements:

• docker
• docker-compose

Detailed prerequisites

A major responsibility of a database engine is to convert a declarative SQL query to an efficient execution plan, employing various methods to scan and join the relations. There is always a development effort to improve this area. What clever execution plans can PostgreSQL generate, what's new in version 11 and what is in development? To name a few things, the joins are optimized by removing unneeded outer and inner joins, and reducing joins from outer and semi to inner. There is work to enable merge joins on inequality and range overlap, and to improve join selectivity estimates with multi-column statistics. When it comes to scanning a single relation, covering indexes allow to use index-only scans more often. Incremental sort and more precise estimation of sorting costs help generate better paths when sorted output is required, e.g. when using GROUP BY and ORDER BY or performing merge joins. This talk aims to give an overview of such optimizations that already exist and that are being developed now.

Postgres is known for it extensibility, which made it the universal database, that means it can meet the requirements of practically any project. Many extensions are well-known and widely used, for example, PostGIS extension - de-facto standard of open source GIS, hstore - an extension for storing and manipulation of arbitrary key/value pairs. I will talk about less known but useful PostgreSQL extensions, which provides a new functionaliy and/or improve the performance of PostgreSQL.

PostgreSQL was designed to be extensible, it provides an API to application developers to extend PostgreSQL functionality and/or improve the performance for specific data and workloads. It is important that there is no need of having expertise of core developers, and these new functionality could be added online without restarting of database server. Application developer can create various database objects, such as functions, data types, operators, indexes, and even new access methods.

I will present my choice of two extensions out of hundreds:

vops - greatly improves the performance of Postgres for OLAP queries using vector operations, pg_variables - provides session variables for storing scalars and relations, useful for generating reports on read-only replicas.