We use it a lot for a specific use-case and it works great. Mongo has come a long long way since the release over a decade ago, and if you keep it in Majority Read and Write, it's very reliable.
Also, on some things, it allows us to pivot much faster.
And now with the help of LLMs, writing "Aggregation Pipelines" are very fast.
I've been using Mongo while developing some analysis / retrieval systems around video, and this is the correct answer. Aggregation pipelines allow me to do really powerful search around amorphous / changing data. Adding a way to automatically update / recalculate embeddings to your database makes even more sense.
Do you have any tricks for writing and debugging pipelines? I feel like there are so many little hiccups that I spend ages figuring out if that one field name needs a $ or not.
Pretty sure they achieved fiscal nirvana by exploiting enterprise brain rot. You hook em, they accumulate tech debt for years, all their devs leave, now they can't move away & you can start increasing prices. Eventually the empty husk will topple over but that's still years away.
They do have a good product, but "they accumulate tech debt for years, all their devs leave, now they can't move away" is the story of the place I worked at a few years ago. The database was such a disorganized, inconsistent mess that no-one had the stomach (or budget) to try and get off it.
I never understood this argument, there are many great products running on Java, PHP, Ruby, JavaScript... All of these languages have a "crowd" that hates them for historic and/or esoteric reasons.
Great products are in my opinion a function of skill and care. The only benefit a "popular" tool or language gets you is a greater developer pool for hiring.
that’s what I thought, but every single candidate I interviewed mentioned MongoDB as their recent reference document database, I asked the last candidate if they were self-hosting, the answer is no, they used MongoDB cloud.
I self host a handful of mongodb deployments for personal projects and manage self hosted mongo deployments of almost a hundred nodes for some companies. Atlas can get very expensive if you need good IO.
You cant use the embeddings/vector search stuff this refers to in self hosted anyway, it’s only implemented in their Atlas Cloud product. It makes it a real PITA to test locally. The Atlas Dev local container didn’t work the same when I tried it earlier in the year.
Precisely, and if you are enterprise, you want to have an option to request priority support and have a lot of features out of the box. Also some of the search features are only available in Atlas unfortunately.
MongoDB is a public company. Its quarterly financial reports will give you a much more accurate picture of the company's health than "everyone you know".
There are a lot of people still on it, including the place I worked at last.
It was starting to get expensive though, so we were experimenting with other document stores (dynamodb was being trialled, since we were already AWS for most things, just around the time I left)
Are they profitable, and at which point in time? How good of an investment was it? Sorry, my eyes were swimming in their financial report hosted in their domain.
This may be a shock to many HN readers, but MongoDB's revenue has been growing quite fast in the last few years (from 400M in 2020 to 1.7B in 2024). They've been pushing Atlas pretty hard in the Enterprise world. Have no experience with it myself, but I've heard some decently positive things about it (ease of set up and maintenance, reliability).
