I just want to take a class about [X] but searching the online catalog is so slow, and my results are largely irrelevant. WTF?
Harvard has many course search websites, but none of them are good. This project is an attempt to take the problem more seriously: write high-performance software and set great defaults so that people can get better, more useful suggestions, 100x faster.
Classes.wtf is a custom, distributed search engine written in Go that focuses on speed and quality of results. It's built on an in-memory Redis database that runs as a subprocess of the application. This index supports full-text fuzzy and prefix search on all fields, along with a rich query syntax.
The frontend is a static website built with Svelte, and it processes search queries immediately after every keystroke. The goal is for the entire {request, computation, response, and render} pipeline to take under 30 milliseconds.
"Now hang on just a second," I hear you saying. The speed of light is not fast enough for data to travel around the world at this latency! But don't worry, this is fine. We run multiple replicas at geographically distributed locations using Fly.io and route requests to the nearest one. Each replica runs its own full-text query engine, so they are completely independent.
(The nearest server replica to Cambridge, MA lives in Secaucus, NJ, only 200 miles away.)
Why did you make this? I was frustrated by how annoying it was to search for classes. And I'm a systems software engineer, which pretty much makes it my mandate to make things faster.
Why is it written in Go? Because I wrote this in a weekend and needed a really fast systems language to iterate on while also having low latency. Go's simplicity and compile times helped with this. I might rewrite it in Rust if I decide to spend a couple more weeks on it.
Why are you using Redis? It's really fast, it stores data in memory, the API is simple and robust, and it has a best-in-class full-text search module. For this size of dataset, embedding Redis gives you unmatched performance with a fraction of the cost and effort of alternatives.
Can you make this for my school? The code is all open-source, and you're welcome to take a look or port it! If you're doing this please also consider reaching out on Twitter @ekzhang1 or by email, since I'd love to hear about your work.
Where is the data sourced? The course catalog was indexed from publicly available course titles and descriptions online. See the code in the datasource/ folder.
You need Go 1.20 and Docker to work on the backend and Node.js v18 for the frontend.
This loads data from Curricle for academic terms before Spring 2022 (AY 2022) and from My.Harvard starting in Fall 2022 (AY 2023). You can customize the data loading script if you'd like to index a different set of courses.
go run . download -year 2019 # -> data/courses-2019.json
go run . download -year 2020 # -> data/courses-2020.json
# ... and so on
go run . download -year 2025 # -> data/courses-2025.jsonUnfortunately, My.Harvard does not allow you to view courses from previous academic years, so years between 2023 and the current one will probably not return any data. For those, you can download the appropriate preloaded datasets from our public S3 bucket.
Historical notes about preloaded data:
- We don't have data on divisional distributions for AY 2023, from Fall 2022 to Spring 2023.
- Some of the courses in Spring 2023 are missing because they were added after we initially indexed them from the public data source, and we have no way of backfilling.
Once you have the year-by-year course data, you can combine them to form a single courses.json file with all of the courses, which can be searched by the webapp.
go run . combineThis looks for all files named data/courses-{year}.json and merges them.
You can also do the inverse, splitting a single data/courses.json into multiple data/courses-{year}.json.
go run . splitThe server listens for web requests on port 7500. (It also spawns a Redis instance, using Docker, on port 7501.)
go run . server -local -data data/courses.jsonYou can also run it with other data files. For example, if you pass data/courses-2021.json, you'll only get search results for the academic year from Fall 2020 to Spring 2021.
Now you can develop on the frontend, which automatically proxies API requests to the server port.
npm install
npm run dev
Visit localhost:5173 to see the website.
docker build -t classes.wtf .
docker run -it --rm -p 7500:7500 classes.wtfaws s3 cp data/courses-$YEAR.json s3://classes.wtf
aws s3 cp data/courses.json s3://classes.wtffly deploySee the contributors page. Current maintainers can be reached by email at [email protected]. Licensed under the MIT license.
Thanks to numerous students who helped advertise the site in college communities.