PgConf.Russia 2019

This report focuses on the continuation of transferring our ECM “Priority” from MS SQL to Postgres. Technical solutions, issues of rewriting from T-SQL to plpgsql, optimization of the effective code and moving data will be covered. Additionally, there will be considered aspects of pgplsql performance testing to find the “bad code” of pgplsql as a candidate for optimization. The main objective of the presentation is to answer the question: "We have it in T-SQL - how to transfer it in PG?". The report is intended for junior Postgres developers and is a continuation of the previous report made at the conference in 2017(https://youtu.be/v6_4Szr8t14).

As we all know, PostgreSQL is a classic vertically scalable database for OLTP loads. In parallel with PostgreSQL for many years there is its alternative horizontal-scalable MPP version of PostgreSQL, that is called Greenplum, sharpened for big data and OLAP workload. In my pitch I will show the internal architecture of Greenplum (distributed transactions, data sharding, partitioning with hybrid storage in external systems, column storage engines with compression, and much more), a comparison with the internal structure of PostgreSQL and the application areas of each solution are shown.

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How can we ensure that this – pretty much outdated – recommendation suit well our needs?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

Software applications working on PostgreSQL is a very typical case in my practice. Some of them manage to work well, some of them do not. In the talk I will focus on errors and problems of the last ones.

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