# Thoughts on Hanukkah of Data 2022

This blog post contains spoilers for Hanukkah of Data 2022.

This holiday season I’ve been doing the Hanukkah of Data, which is a puzzle suite by a group of hackers called the Devottys. It’s a sequence of programming puzzles, with one puzzle dropping for every day of Hanukkah. If you’re familiar with Advent of Code, it’s very similar to that, except (a) it only lasts 8 days instead of 25, and (b) it’s more data-oriented, instead of coding or algorithms-oriented.

I did it in VisiData, which is a tool I’ve been using a lot recently (both at work and for my side projects) that I really wanted to develop expert proficiency with.

Here were my solve statistics:

 Puzzle | Solve Time | # Attempts ║
0 |  3 minutes |          2 ║
1 | 82 minutes |          2 ║
2 | 20 minutes |          1 ║
3 | 20 minutes |          5 ║
4 | 37 minutes |          1 ║
5 |  7 minutes |          1 ║
6 |  6 minutes |          1 ║
7 | 24 minutes |          1 ║
8 |  5 minutes |          1 ║


## Overall Impressions

Hanukkah of Data is much shorter than Advent of Code, which I think is a hugely underrated benefit — in previous years, Advent of Code sometimes felt more like homework than a puzzle suite.

It’s also more of a puzzle than Advent of Code — for example, Puzzle 2 required very non-trivial reading comprehension and logical inference to realize that you were looking for (a) a customer with the initials JD (b) who had, in the same order, bought coffee and bagels at Noah’s market (c) in 2017. I found this much more enjoyable than Advent of Code, where the solution is usually straightforward, and the implementation is the meat of the challenge.

On VisiData: for those comfortable with command line interfaces, Vim-style key bindings, or simply willing to put in the time to learn a mini-language of keyboard shortcuts, I think VisiData is the best tool for doing well-scoped, one-off data explorations or analyses.

Towards the end of Hanukkah, the puzzles became less conceptually ambiguous and more technically difficult (in terms of the sophistication of the data wrangling required). As someone already experienced with querying data, I was pleased to finish these puzzles in single-digit minutes — an achievement that I credit almost entirely to VisiData, which makes visualizing, filtering and aggregating data seamlessly interactive.

I only see two downsides of VisiData: the sparse documentation of advanced features (more on that below) and performance. Performance is most obviously an issue when you’re doing joins — joining two tables with a few thousand rows each takes a noticeably long time. I’m looking forward to vdsql, which is VisiData’s sibling project that skins various databases with a VisiData interface (via Ibis), and should therefore be as performant as the underlying database.

## Some Miscellaneous Thoughts

• Puzzle 1 required a non-trivial function (basically a “phonespell” to convert words to numbers, as if you were dialling on a phone). I struggled a lot with making this custom function available to me in VisiData — I spent around an hour figuring out how to make a custom plugin (this is what really blew up my solve time on the first day). I later learnt that adding a Python function to your .visidatarc is a much simpler way to achieve the same thing.

While I think the basics of VisiData are exceptionally well documented, the advanced features are not — I still don’t really understand how to extend VisiData with its API. Nevertheless, this won’t be an issue for most users, since 90% of VisiData’s value is in its interactivity and interoperability, not in its extensibility.

• For me, the most challenging puzzle was Puzzle 4, which asked to find someone who buys pastries. When you have a dataset with over a thousand products, how do find all the pastries?

sku     | desc                                    | wholesale_cost ║
DLI0002 | Smoked Whitefish Sandwich               |           9.33 ║
PET0005 | Vegan Cat Food, Turkey & Chicken        |           4.35 ║
HOM0018 | Power Radio (red)                       |          21.81 ║
KIT0034 | Azure Ladle                             |           2.81 ║
PET0041 | Gluten-free Cat Food, Pumpkin & Pumpkin |           4.60 ║


What I ended up doing was to split out the “suffix” of each product description (with some special handling for parenthetical modifiers), like so:

sku     | desc                                    | descsuffix | wholesale_cost ║
DLI0002 | Smoked Whitefish Sandwich               | Sandwich   |           9.33 ║
PET0005 | Vegan Cat Food, Turkey & Chicken        | Chicken    |           4.35 ║
PET0041 | Gluten-free Cat Food, Pumpkin & Pumpkin | Pumpkin    |           4.60 ║


This number of kinds of products is drastically fewer than the number of products, to the point where it’s feasible to look through them all manually and pick out the pastries.

This obviously won’t work in general: for example, Vegan Cat Food, Turkey & Chicken isn’t a kind of Chicken, and you could imagine that this would really let you down for a product called Rugelach, Raspberry instead of Raspberry Rugelach. Still, I thought this was a neat trick, and I managed to eke out the correct solution.

Later in the week I realized that all pastries had an sku that started with BKY, which would’ve helped considerably — similarly, cat foods start with PET and collectibles start with COL. Sometimes it pays to actually read random-looking alphanumeric codes!

• I was surprised that the puzzles didn’t seem to be monotonically increasing in difficulty — as you’ll see from my times, and as you might expect from Advent of Code. Saul Pwanson (the creator of Hanukkah of Data) had this to say:

There is a ramp in difficulty, but it is not very steep, and for people who are already familiar with data queries, it might feel like not much has been added between puzzles. But if you look at each puzzle compared with the previous one, there is always something new. Sometimes it’s structural (now you need to do a join), sometimes it’s worldly (what is a pastry?), and sometimes it’s technical (it’s surprisingly difficult in most tools to filter based on a date range that doesn’t include the year).

It’s a really good observation — I suppose I shouldn’t be surprised that Saul’s thought about the puzzle design a lot more than I have! 😅

• The text art is just stunning! Each solved puzzle reveals a new animal, until the whole tapestry is illuminated: