# Day: November 19, 2021

Linear optimisation (often referred to as linear programming) is not cutting edge or new. It has been around for a very long time. It was first introduced within the field of operational research during World War II, where it was used to help minimise costings. The method proposed for solving these problems is known as the simplex method, and it hasn’t changed much today. Although this method hasn’t changed significantly, what has changed significantly is the computing power and accessibility of this technique, allowing these methods to be used on very complex scenarios with almost a click of a button. Convenient libraries have allowed the intricate complexities of setting these problems up on a computer to be simplified.

Read on for an example of linear programming. This is something I’ve always enjoyed, but haven’t had many places to use this technique in my professional career. That said, shout out to everyone who’s ever used LINGO.

In part 1 of the article we learned about dynamic typing, Pydantic and decorators.

In this part we will learn how to combine these concepts for Pandas DataFrame validation in our codebase.

1. Combining Decorators, Pydantic and Pandas – Combine section 2. and 3. of Part 1 to showcase how to use them for output validation.

2. Let’s define ourselves a proper spaceship!

3. Summary

Check out both parts of the article.

When sharing a makeover, typically I’ll show a side-by-side “before” and “after” view. This is a powerful moment for many audiences as they witness the dramatic impact of an effective graph. I share this with you, because until recently if a combination chart was found within my makeovers it represented the “before” state. That’s because most combo charts are hard to read, so I tend to revise them into something simpler—like this makeover.

Today’s article shows the inverse of that process, where, in order to make a visual more informative and easier to understand, I chose to transform the original, a “simple” line chart, into a more “complicated” combination chart.

This is a good reminder that visuals themselves aren’t necessarily bad (except for the pie chart, which is inherently evil and don’t try to convince me otherwise); it’s all about whether the specific chart makes sense given the story you are trying to tell.

The direction for software as a service providers is to provide APIs to access their data instead of structured file exports. Which is a pity, as every SaaS system requires a bespoke data extract solution. I inheireted a solution that had an adverse pattern I’d like to talk about.

The next level of complexity, at least in the space Bill covers in the example, is what to do when the upstream provider changes their data, some with changes even a week later.

IO Completion Ports are a set of Windows APIs which allow for efficient, fast, multithreaded asynchronous IO. Great, that pretty much tells you nothing.

SQL Server uses IO Completion Ports not for disk-based IO but for general network IO when it comes into SQL Server for TDS level items. This means it’s used for things such as connecting to an instance of SQL Server, sending batch and rpc information, etc., and is used to properly take actions on the incoming items. These actions should be extremely short and quick, the name of the game is low latency and high throughput which means not doing things like reading or writing from disk, allocating memory, calling functions that may block, etc., to keep things flowing.

Read on to see what happens when there is a problem and what might cause that problem.

This month’s T-SQL Tuesday attracted some great responses! Thank you to everyone who participated!

My invitation for this month’s #tsql2sday was 3 fold on sharing your experiences on data governance

– The current cost of data governance versus its benefits

– The amazing things data governance has enabled you to achieve or will enable you to achieve in the future

– The potential uses for Azure Purview within your estates and the automated deployment options for that