Postgres Performance and Maintenance

When you are optimizing Postgres it is usually maintenance that goes by the wayside. How do we fix autovacuum? Where did all of this bloat come from? Why am I getting IO spikes? How do I get RDS to behave?! Why are commits so slow on replication? The answer to all of these questions is understanding the relationship between proper Postgres maintenance and performance. Join us for a 3 hour jaunt through the wily world of making Postgres Go!

Differences between Oracle DB and PostgreSQL in terms of administrating

As an experienced Oracle DBA, I've discovered some distinctive features of PostgreSQL which I'd like to share with you. We'll review a set of essential tools for DBAs, their capabilities and usefulness in comparison to their Oracle counterparts. Also I'm going to summarize the core differences between Oracle DB and PostgreSQL in terms of administrating.

CTE Queries Usage for Business Logic

Wide usage of Common Table Expression queries considered as a core paradigm for implementing the Business Logic for high loaded web applications development based on PostgreSQL functions.

Auto plan tuning using feedback loop

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)