PgConf.Russia 2019

During the report, I would like to share the experience of implementing BlockChain in a real business task based on 1C+PostgreSQL. Where did this task come from? From whom do we protect data with the help of technology? How to get a chain integrity report of tens of millions of records in seconds?

In this master class, I will take you step-by step through a major upgrade of PostgreSQL. Through our practice we see a lot of different PostgreSQL servers in production environment and often, almost too often teams who once made PostgreSQL their database of choice never update it following the initial installation. The are many different reasons for it, the result, however, is the same - they all miss out on the new useful features of the newer releases and reduced database performance.
The goal of my masterclass is to equip attendees with necessary tools for performing PostgreSQL upgrade. I will take you through each step of the major upgrade and will dive into each executed command. I will also explain the particular order in which I perform an upgrade and explain the consequences of not following this order or missing a particular step. We will perform an upgrade of PostgreSQL 9.0 to 11. My hope is that following this masterclass number of outdated PostgreSQL database will reduce since participants will then go back to their databases and make sure that they are running the most recent version.

PostgreSQL provides possibility to create temporary tables. Though a temporary table is accessible only to a single session and is removed at the end of the session, all information about it is stored in the system catalogs of PostgreSQL. This is related to several issues, which make it difficult or impossible to use temporary tables in some cases. There are attempts to solve this feature, including in our company. But they have not yet succeeded, mainly because of the PostgreSQL engine. In the talk I want to tell about simple and small pg_variables extension. It allows you to create table variables along with scalar ones. I will tell how it can replace temporary tables, what advantages and disadvantages it has.

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)