PgConf.Russia 2020

Open-source maintainers face many challenges as projects grow. How to write more required features, fix more issues and have time to watch more pull requests? On the example of WAL-G(backup tool for PostgreSQL) I will tell you about how we solved these problems by launching a course of Open-source development at Ural Federal University, what we achieved and what will we do next.

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.

In the report, we want to talk about the experience of migrating one large system from Oracle to PostgreSQL. The system itself was built on the PHP + Oracle stack; its distinguishing feature was that all business logic was implemented in PL / SQL code. In a DBMS, there are more than 3000 packages with 4-10 functions (procedures) in each. In PHP - more than 10,000 forms with inserts of anonymous blocks used to receive data, process and save results in Oracle.

To solve this extremely voluminous work, we took ANTLR4 (a powerful parser generator for reading, processing, executing, or translating structured text), PL/SQL grammar, and created an automatic converter that allows you to convert all objects in the schema and our system from Oracle to working code for PostgreSQL.

I'll show an example of solving the problem of searching "similar" texts for one given text in big array using GiST index. The problem itself is not much important, but it is easy to understand. Using this problem as example, I'll show one of many methods of adapting GiST index for custom search problems. Maybe this talk will help you to solve other search problems.