Tukuk-OSAfter building an inverted index by hand I knew exactly what I needed: fast search, typo tolerance, faceted filtering, and minimal administration. Meilisearch met that list in a way nothing else I tested could, and the decision to use it turned Tukuk-OS from a personal project into a system that runs 24 hours a day.
Why Meilisearch, Not the Alternatives?
I tested several options. Heavy options required a JVM or multi-node configuration beyond my small server's ability. Lightweight options did not support faceted search or autocomplete.
Meilisearch gave me three things I needed immediately: responses in milliseconds, filtering during search (language, site, type), and autocomplete. It also runs as a single process with reasonable memory usage — perfect for a server sharing resources with a web application.
How I Tuned the Index
Tukuk-OS's main index, halaman_web, has searchableAttributes ordered by priority: title, headings, description, content, keywords, site name, and path. That order is not random — a match in the title outranks a match in body text.
filterableAttributes define what can be filtered in the UI: host, lang, type, section, siteName, and more. When I added a new siteName field the "Publisher" filter link appeared with no extra work. I learned that index design is really UI design — decisions I make in the backend immediately shape what users can do in the frontend.
Measuring Search Quality with Reference Queries
Tuning index settings without measurement is guessing in the dark. I set a list of about twenty reference queries — a mix of popular searches, long-tail queries, and deliberately challenging ones — and run it every time I change configuration.
Each query is rated on three simple criteria: is the top result genuinely relevant, are the relevant filters available, and does response time stay below the threshold. When I add a new filterableAttributes field I expect filters to appear without slowing search down. If it slows search down I have to weigh whether the filter is worth it.
This approach prevents me from getting stuck optimising numbers that do not matter. A higher average score means nothing if your most popular query gets worse. With a reference set, a bad change shows itself within seconds and I can roll it back before it reaches users.
One extra lesson: keep the list in a portable text file, not in your memory. When I need to remember why a setting was chosen six months ago, the reference list and the notes beside it save hours of guessing.
Managing a Live Index
The most important thing I learned about Meilisearch in production is how to handle updates. If I update the index live, users see half-finished results during the swap. My solution is simple: batch new documents, keep the old index until the new one is ready, then swap.
I also set displayedAttributes so internal fields are never sent to clients. This not only saves bandwidth, it prevents internal fields like internal scores from leaking into API responses.
Backups: The Part Nobody Likes to Do
A Meilisearch index can be rebuilt from source, but rebuilding takes time and can fail if the source has changed. I wrote an export script that saves the index to a gzip file every few hours to Cloudflare R2. The script is about twenty lines, but it buys me immeasurable peace of mind.
The most important test I ran was not a successful backup — it was a successful restore. I deliberately deleted an index in a testing environment and restored it. After that I knew the procedure actually worked, not just that it looked like it worked.
When Meilisearch Is Not Enough
Meilisearch solves text search, but Tukuk-OS needs more: image and video search requires external sources, autocomplete for new queries requires custom logic, and different scoring for blog posts versus link pages requires adjustment.
I decided not to force Meilisearch to do everything. Instead I built a layer around it: result scoring, media source aggregation, and filter management. The separation makes my system easy to maintain — each component does one thing well.
Advice for Users
If you are starting a search project, begin with Meilisearch and do not build your own system too early. Spend your time on index design: what can be filtered, what can be searched, and what should be displayed. Those decisions will outlive any framework choice, and they determine whether your product feels smart or ordinary.