PgConf.Russia 2019

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.

To migrate to a PostgreSQL/Postgres Pro we need multi-core servers to be carefully tuned for correct parallelism. What settings make multi-terabyte installations work fast and correctly?

We will share our PostgreSQL/Postgres Pro on BullSequana S and Bullion S servers testing experience.

The features of this hardware platform which are crucial for high-loaded configurations
• Multi-core Scale-up servers and PostgreSQL/Postgres Pro
• Results of stress testing of PostgreSQL/Postgres Pro running on the equipment.

As is often seen in OLAP and batch processing workloads, the more complex a query (containing many joins, filters, aggregates), the more there is a possibility of row count estimation errors, which leads to planner choosing an inefficient execution plan.

To address that problem, I developed a tool called pg_plan_advsr as a PostgreSQL extension, which corrects the estimation errors by repeatedly feeding back the information collected during query execution to the planner.

The tool has three features:

1. Automatic plan tuning by repeatedly feeding execution information to planner
2. Preserve all plans generated during plan tuning in a history table
3. Create and store optimizer hints to be able to reproduce plans generated during tuning process

I verified the effectiveness of pg_plan_advsr by enabling it when running the join order benchmark (JOB) against PG 10.4 and observed its execution time shortening to 50% of the original. Therefore, it is useful for user who would like to do plan tuning for OLAP and batch processing.

I will talk about the following things in this presentation:

• Principles behind pg_plan_advsr and its architecture
• Detailed information about the measurements done with JOB
• Possible future enhancements
• Using aqo and pg_plan_advsr together (experimental)

PostGIS is spatial extension for PostgreSQL.

This talk will go in depth on using PostGIS for disaster management: which functions can be used and for what.