I am experimenting with Lidar datasets lately. In lidar you work with huge point clouds. Millions to billions. For some algorithms - convex hull - you need to sort the points for example by x-cooridinate. I did some quick tests with quick sort, merge sort, and timsort. In my experiments quick sort was the slowest with 5.5 seconds on the chosen dataset. Mergesort was 5 seconds and timsort was around 4 seconds.

When sorting coordinates, radix sort with an appropriately chose radix (often but not always 256) is usually fastest.

Hi there, thanks. Need to try this

Yeah seeing more datasets would definitely be nice. I linked my GitHub repo where I made the changes necessary for this example in the YouTube video. Using the last commit (the only changes from upstream) as a guide, other datasets cold be supported similarly.

That said, I don't think my choice of data is that bad actually. Apparently starting with a sorted dataset, appending some new items, and then sorting the result again is very commonly done by programmers. That is essentially what's represented in my example.

Maybe the UK Postcode lookup database?

It's about 650MB (CSV):

https://data.gov.uk/dataset/7ec10db7-c8f4-4a40-8d82-8921935b...

In theory, it's supposed to be updated every now and then, but doesn't seem to have happened since 2015. :(

For a direct SQLite version of it, we have it on DBHub:

https://dbhub.io/justinclift/National%20Statistics%20Postcod...

That site is still in (slow) development though. YMMV. :)