PgConf.Russia 2020

An enterprise-grade PostgreSQL requires many complementary technologies to the database core: high availability and automated failover, monitoring and alerting, centralized logging, connection pooling, etc. That is, a stack of components around PostgreSQL. Kubernetes has enabled a new model to deploy software abstracting away the infrastructure. However, containers are not lightweight VMs, and the packing of software paradigms that work on VMs are not valid on containers/Kubernetes. How should be PostgreSQL and its stack be deployed on Kubernetes? Enter StackGres. An open source software that is the result of re-engineering PostgreSQL to become cloud native. Join this talk to learn and see demos of how to generate PostgreSQL minimal containers; how the sidecar pattern is used (abused) to integrate PostgreSQL’s stack components, and how the networking and storage are handled. More info: stackgres.io.

Any DBA needs some kind of tool for historical workload analyse. Assume once at morning your monitoring team will report of sudden performance degradation at 2-3 a.m., and now you need to investigate this issue. What activities was most resource consuming within that hour? There are several tools for solving this problem, and I'll talk about one very easy and convenient tool - pg_profile. It need only a postgres database and a cron-like tool to run, and it will generate a workload profile report for your database as you need it. Ths report will be a good start point for further investigation.

Locking is critical for providing high concurrency for any database — you cannot fully utilize your hardware if locking is throttling its use. This talk explores all aspects of locking in Postgres by showing queries and their locks; covered lock types include row, table, shared, exclusive, and advisory lock types. The high concurrency provided by Multiversion Concurrency Control (MVCC) is also covered.

Slides are at https://momjian.us/main/writings/pgsql/locking.pdf

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.