Historically the use of User Defined Functions aka UDF has represented a great option to encapsulate logic into Database reusable objects, nonetheless, during the implementation of scalar UDF (a variation based on the function’s output) for MS SQL Server we have been suffering painful performance penalties until the arrival of version 2019, so in this article, we are going to address how the new Scalar UDF Inlining feature helps us to avoid performance problems and get the maximum value in our Database code inside SQL Server.

First at all, I would like to clarify something that maybe sounds evident, but sometimes we forget, the path is not a string, it is a fact that we must keep in mind to avoid headaches in our journey in Python.  Since Python 3.4 in advance, we count on a powerful and versatile module called pathlib.

Sometimes, the only thing that we want is to drop a database(not in PRODUCTION) and recreate it from scratch, especially when we are in the process of automating the deployment of our MySQL databases, for instance using liquibase. Therefore, it is not uncommon in an initial testing process to wish to drop and recreate our tested database.

The aim of this post is to show you a specific and useful tip in Python for replacing strings with matched values contained in a dictionary, for this task that could sound trivial but in the practice may represent an interesting challenge.