Deep Dive into the RDS PostgreSQL Universe

In this session we will deep dive into the exciting features of Amazon RDS for PostgreSQL, including new versions of PostgreSQL releases, new extensions, larger instances. We will also show benchmarks of new RDS instance types, and their value proposition. We will also look at how high availability and read scaling works on RDS PostgreSQL. We will also explore lessons we have learned managing a large fleet of PostgreSQL instances, including important tunables and possible gotchas around pg_upgrade.

Forward to the past

Sometimes there is a great desire to return the database to the past, for a day or two or more days. The reasons are diverse, but most often one is to see what has changed. Or to see if the application behaved incorrectly after the update. Or it was just a command from the boss. The classic way everyone knows is to keep full backups and sets of WAL-logs to be able to recover to an arbitrary moment. This method is a real headache for DBAs/administrators, and it will not work quickly. Sure, there are some ways to optimize this process, but downtime is inevitable. PostgresPro offers a new way — database snapshots and the ability to return to them.

1С:Enterprise and PostgreSQL

• 1С: as a cross-platform business application development environment
• 1С and PostgreSQL together since 2006
• 1C How to work with 1С on PostgreSQL in 1cFresh cloud service
• What major improvements in 1С:platform make work with PostgreSQL more efficient.

Enterprise-level approach to PostgreSQL tuning: database experiments

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How can we ensure that this – pretty much outdated – recommendation suit well our needs?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