PgConf.Russia 2019

Workshop on Server-Side development in procedural languages PL/Perl ,PL/Python, PL / v8 inside PostgreSQL and Postgres Pro. You will not only learn what they are for but also how to use them correctly and what results can be achieved using them.

Shared_buffers = 25% – is it too much or not enough? Or it's the right value?

How can we ensure that this – pretty much outdated – recommendation suit well our needs?

It is time to start apply enterprise-level approach to tuning postgresql.conf. Not using various blind auto-tuners or advices from old articles and blog posts, but based on the following two aspects:

1. comprehensive database experiments, conducted in automated fashion, repeated multiple times in conditions as close to production as possible, and
2. deep understanding of DBMS and OS internals.

Using Nancy CLI (https://gitlab.com/postgres.ai/nancy) we will consider a concrete example: infamous shared_buffers, under various circumstances, in various projects. We will try to figure out, how to optimize this settings for given infrastructure, database, and workload.

When you are optimizing Postgres it is usually maintenance that goes by the wayside. How do we fix autovacuum? Where did all of this bloat come from? Why am I getting IO spikes? How do I get RDS to behave?! Why are commits so slow on replication? The answer to all of these questions is understanding the relationship between proper Postgres maintenance and performance. Join us for a 3 hour jaunt through the wily world of making Postgres Go!

The dark age for PostgreSQL started at 2GIS after transitioning to the microservice architecture. Every team tried to cook database on their own — by installing instances, juggling versions, trying to code deployments with numerous tools or using manual operations. It was the right time to develop a “silver bullet” — a common set of tools to solve all the problems at once. We created our own cluster solution based on well-known PostgreSQL, repmgr, pgbouncer and Barman. Despite of the complexity of our final solution, we developed a repeatable flexible deployment to accelerate postgresql cluster deployment and management. Also we deployed the our own cluster to consolidate all databases. It helped to eliminate team efforts for database management and focus on their main goals. Failover works, we tried it :-)