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+---
+title: "Re-ranking search results on the client side"
+date: "2024-11-03"
+author: ["Daoud Clarke"]
+---
+
+By many measures, [Mwmbl](https://mwmbl.org) is doing great. We have
+indexed over half a billion pages, we have over 4,000 registered
+users, and over 30,000 curations from those users. Our volunteers are
+crawling around 5 million pages a day.
+
+But the score that I care about most right now is
+[NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain). This
+measures the quality of our search results against a "gold standard"
+which is just Bing search results for the same query. Obviously, we
+are not ultimately aiming to be like Bing, so eventually we will stop
+using Bing and start using our curated data, once we have enough and
+the quality is high enough. But we are far enough away from being good
+that moving in a Bing-like direction is great, for now.
+
+Because our NDCG score is pretty poor. A score of 1 would be "matches
+Bing exactly", while a score of 0 would be "nothing in common with
+Bing". We are scoring 0.336 on our test set. However most of that
+comes from sticking Wikipedia results at the top, by querying
+Wikipedia with their [excellent
+API](https://www.mediawiki.org/wiki/API:Search) and using their
+ranking. Without that, we were scoring 0.059. Using Wikipedia alone,
+we would score 0.297.
+
+I've experimented off and on with [learning to
+rank](https://en.wikipedia.org/wiki/Learning_to_rank). This was the
+industry standard for ranking before large language models, so seemed
+like an obvious place to start. Implementing a lot of [standard
+features](https://www.microsoft.com/en-us/research/project/mslr/)
+improved the results slightly over my original intuitively defined
+features, but still only gave us an NDCG score of 0.365.
+
+Nevertheless, after trying for a while to improve on this further, I
+decided to deploy this ranking instead of the heuristic that we were
+using before. It didn't seem to make the results worse, and it seemed
+to be fast enough, at least on my local machine.
+
+I didn't realise it at the time, but this was when things started
+breaking. You see, we only have one server. We don't do actual
+crawling on the server since that is done by volunteers. But we have
+to process the crawl data to find new links, prioritise those links
+for crawling, preprocess the data for indexing, and index it, as well
+as serving search results. All on one server.
+
+And it turned out that adding a ton of features and using XGBoost for
+every search query added enough load to the server that things started
+to slow down. Even loading the main page could take three or four
+seconds. At the time, I didn't realise that this was causing the
+problem, since a bunch of other stuff was happening. We had some
+enthusiastic volunteers that were crawling many millions of pages a
+day.
+
+Search got so slow that I decided we had to turn off the crawling. I
+made the server that sends batches to crawl return an empty list. I
+turned off the scripts that update the crawl queue and index new
+pages. And things got better.
+
+But they still weren't as good as they were before. It took me a long
+time to realise that it was the change in ranking that was the
+problem.
+
+In retrospect, we should have had better monitoring around everything
+so that we could more easily identify the cause of the slowdown. But
+this is a project that I do for fun, so I've put off doing this kind
+of thing, because I find it boring. Boring is not fun. But a broken
+website is also not fun. You live and learn.
+
+Now I've changed the ranking back to the old heuristic and turned on
+the crawling again. I still think learning to rank has potential. But
+we can't afford it with our current resources. So I've started looking
+at alternatives.
+
+What would make ranking almost infinitely scalable? If we don't do it
+ourselves, but get our users to do it, on the client side. This works
+really well for us, because we don't rank like a normal search
+engine. Our results are already ranked for each unigram and bigram
+query. We pull out the pre-ranked results for each unigram and bigram
+in a user's query, then re-rank them using our heuristic. This is
+perfectly feasible to do on the client side, since we don't have more
+than around 30 results per unigram or bigram on average.
+
+It also gave me the final push I needed to implement ranking in
+Rust. I know that Python is a bad choice for a search engine, but I
+chose it because I knew I could build something quickly, and that I
+would probably give up at some point if I tried to do it in Rust. Yes,
+I know Java and C++, which would have been fine choices, but they
+would not be fun. This project has to be fun or it will not happen.
+
+I am a beginner in Rust, and doing too hard things at the same time -
+building a search engine and learning Rust - seemed like a recipe for
+disaster. But I can build small bits in Rust, especially if I have
+already built them in Python. So over the last few days, I've [rebuilt
+our heuristic ranking in Rust](https://github.com/mwmbl/rankeval/).
+The rust compiles to web assembly which will eventually be called from
+our [excellent new front end](https://alpha.mwmbl.org/). Now all the
+back end needs to do is pull out pre-ranked results for each unigram
+and bigram. These can be easily cached, and potentially even kept in
+cloud storage. If we are ever going to serve millions of users on our
+shoe-string budget, then this is how we will have to do it.
+
+If you would like to get involved, join us on our
+[https://matrix.to/#/#mwmbl:matrix.org](Matrix server), or send a pull
+request to fix my rubbish Rust code. Thank you for reading!