GreenHouseSQL as a scalable analytics system for postgresql, greenplum and clickhouse

At pgconf’17 I talked about our analytics systems based on PostgreSQL. Afterwards we looked at hadoop, s3, presto, vertica, and other frights. Finally we stopped to suffer nonsense and just completed PostgreSQL with ready Greenplum and Clickhouse. As a result, we achieved amazing performance, fast migration, easy maintenance, reliability and horizontal scalability. We enabled to recover the system after fault in two commands, decreased infrastructure costs and expanded functionality due to ANSI SQL, MPP and In-memory. All within the open-source and full SQL paradigm. We called the product GreenHouseSQL, which is our inner whole cycle data platform. In the talk we will show the beauty of solution internals, explain the advantages and flaws, tips and tricks of starting with Greenplum, as well as why do we need Clickhouse, what is left to PostgreSQL, and eventually how does it all work.

HypoPG 2: Hypothetical Partitioning support for PostgreSQL

Declarative partitioning was a long-awaited feature and has been enhanced since its introduction in PostgreSQL 10. However, for many users, finding optimal partitioning schemes to have the best benefits from partitioning is not an easy task. Therefore, we added in HypoPG a new hypothetical partitioning feature which helps users to design partitioning. In this presentation, I will provide a brief introduction of HypoPG and explain declarative partitioning, and then I'll show the usage of hypothetical partitioning feature and explain how the extension is working.

Python and PostgreSQL Using psycopg2

Python may not be the fastest programming language on the CPU, but its fast and easy development saves a lot of costs between the keyboard and the chair. In this workshop we'll explore the most popular library for the access to the PostgreSQL server named "psycopg2" and write a short application using some of its useful features.

Analysis of troublesome queries as a means of recurrent refactoring of 1C code

1. Principles of searching for troublesome queries in PostgreSQL.
2. Evaluation of hypothetical indexes and their impact on query plans.
3. The most common errors in 1C-programming.
4. Basic methods of code refactoring, taking into account the features of PostgreSQL.
5. Storing analytical information from the PostgreSQL log to assess the quality of refactoring