Persisting data can be a subtle art. Today I am taking a look at how F#’s Discriminated Unions can interact with Dapper. I’ve discussed Dapper before, but never really discussed its facilities for interacting with Discriminated Unions. It is a useful bit of knowledge when determining project data structures.
Data in Motion - Precipitation Map
Read Time: 4 minutesToday is again a lighter post playing with visualizations. The data focus is on the Standardized Precipitation Index data for the U.S. over that last one-hundred years. Static images and data can be useful, but visualizing data over time can be a welcome addition for analysis. So I’ll be converting data ultimately into a video of the data over time using primarily F#.
Leveraging RocksDB with F#
Read Time: 14 minutesExamining Boyer-Moore String Search with F#
Read Time: 19 minutesText search is something we do everyday. Fast and reliable search is such a staple, it is easy to forget there can be elegance to those underlying mechanisms. It is time to pull back the curtain and dig into one of the foundational search algorithms, the Boyer-Moore fast string search. It is a good algorithm to demonstrate some of the methods used to achieve fast search results. As typical, I’ll reach for F# to show implementation details.
Data in Motion - Drought Map
Read Time: 5 minutesVisualizations like charts and graphs can be powerful tools, but they are often static. An even more powerful story can be told over time with animations and videos. Using F#, along with a couple tools, I’ll do just that. Today’s focus is on the Palmer Drought Severity data for the U.S. over that last one-hundred years. This is a lighter post, so hopefully the video is mesmerizing enough to compensate for any lack of depth.