PgConf.Russia 2017

Debugging, profiling, and tracing of the executed commands play an important role in development of any applications. This is also true for developing stored procedures in DBMS.

PostgreSQL offers various tools (both built-in and external) for these purposes.

In this talk, we will provide an overview of the available tools and their advantages and disadvantages, as well as a detailed demo of their use cases.

This talk is a part of a basic course for backend application developers (DEV1), which “Postgres Professional” company plans to announce in the near future.

Currently, to execute SQL queries PostgreSQL uses interpreter, which implements Volcano-style iteration model. At the same time it’s possible to get significant speedup by dynamically JIT-compiling query “on-the-fly”. In this case it’s possible to generate code that is specialized for given SQL query, and perform compiler optimizations using the information about table structure and data types that is already known at run time. This approach is especially important for complex queries, which performance is CPU-bound.

Query optimization is an important problem, which solution has a great influence on DBMS performance, especially for complex queries. In this talk we consider PostgreSQL query optimizer and specifically cardinality estimation problem for correlated clauses, which is one of the most well-known drawbacks of query optimizers in general. In the talk we propose our solution for this problem which involves machine learning methods and is available for PostgreSQL 9.6 as an extension with a patch. We discuss the experimental evaluation, advantages, disadvantages, and fields of application of the proposed approach as well.

VIDEO

Overview of the architecture of Greenplum MPP (Massively Parallel Processing) database. Explain the internals of GPDB. Show how to configure and setup GPDB. How to distribute data effectively for MPP

VIDEO