PgConf.Russia 2020

Odd things in query plan analysis - wasted time and "unnecessary" buffers.
Structural hints in a plan. How to help a developer with optimization without writing a single line of code. How to match plan nodes with query text and take advantage of this information.

Recently, I was working on a project where graphQL was used for sending requests to its .NET Core backend, but this was not a good idea. The point is, a graphQL query is a hierarchical structure with a dynamic set of fields. It’s difficult to perform such requests via a statically-typed programming language and a relational database as suggested by the tools available. So, I came up with the idea of using the plv8 extension and perform graphQL queries right on the database side. It took me about two hours to develop a working prototype that could perform the same queries as the software under development for more than one month! Then various improvements have been made and I want to introduce them all. If you are thinking of using graphQL instead of REST, my speech could be most useful and could help you to save a lot of time.

Moscow public transport vehicles when moving report their coordinates via GLONASS. Collected data is used for various analyses including timetable development, bottlenecks detection and planning the bus lanes. Until recently we used the PostGIS extension for this purpose but now we are switching to a new PG extension — MobilityDB — designed especially for geodata time series processing. I have compared the table size and the performance of our solution without and with MobilityDB and happy to present the results.

Postgres has always had strong support for relational storage. However, there are many cases where relational storage is either inefficient or overly restrictive. This talk shows the many ways that Postgres has expanded to support non-relational storage, specifically the ability to store and index multiple values, even unrelated ones, in a single database field. Such storage allows for greater efficiency and access simplicity, and can also avoid the negatives of entity-attribute-value (eav) storage. The talk will cover many examples of multiple-value-per-field storage, including arrays, range types, geometry, full text search, xml, json, and records.