I've built a static web app called IMDb SQL Best Movie Finder that lets you query a database of 1.5 million IMDb titles using SQL directly in your browser. It’s entirely client-side, so all the data processing happens locally on your machine — no server involved.
It's so nice that they make this data available in such a fashion.
That said, it would be amazing to have (anonymized) data about raw ratings per person, and be able to build your own personal recommenders based on that... One can dream!
It always seems more like an forgotten artifact on their end that is used for universities and research. They are not really a competitor to the open platforms as contributing on IMDb is very limited.
Wikidata.org the database behind Wikipedia is a good alternative as a general database including films and related crew
for instance with Avatar (2009)
https://www.wikidata.org/wiki/Q24871
Interesting! But it seems these datasets don't contain the description of each movie? Do you know where tools such as Jellyfin or Plex get them? Do they simply scrape the website?
SELECT * EXCLUDE (titleType, primaryTitle, language)
FROM 'imdb01-11-2024.parquet'
WHERE
(region is null and
titleType = 'movie')
ORDER BY
( (numVotes * averageRating)+(1000007))/ (numVotes+1000000) desc
LIMIT 100
Ah 7 might have been a typo. May be it was meant to be *7? Then that's exactly the Laplace estimator. We start with a prior of 1_000_000 votes of 7.0 and then adjust as we accumulate real votes.
SELECT * EXCLUDE (titleType, primaryTitle, language) FROM 'imdb01-11-2024.parquet' WHERE (region is null and titleType = 'movie') ORDER BY ( (numVotes * averageRating)+(100000 * 7))/ (numVotes+100000) desc LIMIT 100
This is cool. I don't watch so many movies anymore, so there must be many good ones i've missed. Is there a functioning natural language query somewhere?
"I want to watch a science fiction movie that is not a super hero movie. Movies I love are Dark City and Daybreakers. Prioritize movies of short length, nothing more than 2h30. Filter out movies I've already rated, here's my Watchlist"
The movie has countless things we know from much later productions and countless things not seen elsewhere.
It takes place in the distant future, 1980, a place where everyone has a flying car, people have numbers in stead of names, the government decides who you can marry and babies come out of vending machines.
One IMDB comment described it as: I sat on the tip of my chair wondering if it could get any stranger and then it did!
It'd be trivial to ask LLM to generate the query; but since it's a client-side app there's nowhere to store the api key - so each user would have to supply one, which is a bit of an awkward experience.
Love seeing things like this! I think static databases are underrated and we're going to see more of that soon. I'm working on a similar project - https://github.com/bjesus/teatime - for book reading. Any Github repo can be a database source, as long as it contains some JSON configuration file and has the matching repo tags.
If you gave the SQL a light gray border and perhaps overflow-y:scroll, it would make it much more obvious that it can be edited directly without needing any other adjustments.
Yeah, I also struggled with the purple box - I assumed I'd change the options, click run and it'd reload the data accordingly. Almost quit before I realised you have to build and then run. If the changes in the purple box were reflected in the SQL code preview that would be much better.
That being said it's an impressive demo of what the browser can do without breaking a sweat! Will try and discover some new stuff to watch now!
It's slow on the first load as you download a 100mb parquet file with all the IMDb data. It'll be cached on the second load though (using browser's indexeddb to cache).
Groan... Are you hosting this 100MB (and not mb)? If yes, I hope the bill will teach a lesson. If not, 100 visitors is already 10GB of bandwidth, that's quite abusive.
Maybe I'm just an old fart, and blowing 10GB of data is cheap nowadays?
OVH and Hetzner offer cheap unmetered VPS instances, Cloudflare CDN doesn't have any bandwidth restrictions... Yes 10GB of data is cheap. If they were serving off EC2, then after the first 100 GB of the month it's 9 cents/GB or 90 cents per 10GB, which is absurd, but still not terribly expensive in the grand scheme of things: what would be a surprise lesson-teaching bill, $90 after a TB / 10,000 visitors?
I'm surprised some users think it's a lot on an individual scale. Consider an ideal persona for this submission: query some movies, then go stream several GBs for one. The 100 MB payload isn't much in comparison. I admit it is kind of bad form for mobile users who might be on metered data plans, and a warning and trigger for manual action for those devices would be kinder.
One thing that might help reset your notion of what a lot of bandwidth is would be to browse around with your network tab in the developer console open for a day. nytimes home page is 14 MB and they get a ton of traffic. Even a corporate blog on the HN front page right now that could just be the tiny compressed text is 2 MB. Single image loads on many pages can be 1 MB or more. Glancing at the submission, the response headers indicate it seems they're serving from S3 Cloudfront, which is free for the first TB per month, though after that it gets back to absurd pricing. AWS is not price competitive.
I have never imagined that simply by using your app it would download and cache a 100 MB file on my phone. If I hadn’t read the discussion here, I wouldn’t even know about it.
That’s an irresponsible waste of both bandwidth and storage. You should have really made that clear in the website.
I initially loved looking for obscure stuff, e.g. setting region to soviet union. It surely is the case that 99% of the users want 10% of the data at most. I'll have to work ability to select the file and download & cache it only if the relevant query is asking for it.
a) I wrote a jupyter notebook ages ago that'd join the raw data into a queryable form.
b) I committed it and forgot about it after the initial viewing of top movies/series.
c) For the Halloween I wanted to find a good-rated horror movie, a genre I don't watch much.
d) I found my notebook but it was a drag to get it running again, first pandas would keep throwing OOM errors so I had to migrate to polars. Secondly I had to find a spare working laptop since iPad is my primary off-work computing device as of late. Finally the schema is not so intuitive - I need to look up things like region/language codes.
I want something I can just leave unattended but still be able to use a few months later without going through a dev-env set-up. A static website with all the data is one potential way of avoiding maintaining it as it should keep running, just like an exe would keep running on Windows in the past.
I avoid maintaining personal projects as my software job requires me to maintain software and I don't find it enjoyable to do the same in my spare time.
One neat trick I discovered for finding good movies on IMDb is to delve into the review history of users who share my unique tastes, especially when they diverge from mainstream opinions.
For example, I found "Paris, Texas" to be pretty disappointing, yet most reviews were overwhelmingly positive. So, I sought out others who also didn't enjoy it and explored their review history to find movies we both agreed on.
Occasionally, you'll hit the jackpot and find an avid reviewer whose taste aligns perfectly with yours, providing a treasure trove of excellent movie recommendations.
I like to call these users my "IMDb doppelgangers."
This is the premise of Criticker.com. You rate movies, and it will show you recommendations from similar movie watchers. They call it a "Taste Compatibility Index".
