PgConf.Russia 2017

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

Sets are apparently a useful data type for many kinds of applications. While PostgreSQL offers no built-in set data type, sets may be emulated to some degree with its built-in array and JSONB data types. Also, acceleration of respective containment (subset) queries is readily available as a built-in feature of the GIN index type.

Starting with the above, we will then explore the performance gains enabled by custom set data types, and especially by customisation code in C ("operator classes") for the GIN and GiST index types.

When working with a complex business logic, you often have to implement a workflow - a sequence of several processing steps, with each step implementing a separate part of the business logic. This is usually done with specialized queues, but if there are high reliability demands, it makes sense to do everything on PostgreSQL.

I will describe the tasks that require a workflow implementation, offer a solution, compare it with other options, and tell you about the implementation traps and pitfalls.