Technically that's how you load data from Firestore specifically, but yes, that's how it's done. I started working with a team who uses Firestore, and let me tell you, I've never hated a database so much. No disrespect to anyone from the Google team, but I cannot fathom why they made the decisions they made.
1) You are severely limited in how you can query. The list of limitations is too long to recount here, but querying is nearly worthless.
2) The database design strongly pushes you towards nesting collections, making side effects cleanup a disaster, especially as a database grows in complexity.
3) You cannot sort on a field without creating an index first. I get why creating an index is a good idea, but I can't even write a simple analytics script without indexing the fields first.
4) It gears itself towards frontend developers who don't know how to write a backend, and encourages bad practices for them. One example: Firebase lets you manually edit production data very easily from within their dashboard. Like it's treated almost like it's a CSV.
5) The recommended development approach is to directly query the DB from the frontend, with no server in between. This means any data security has to be implemented in a separate DB rules document. The syntax and structure of this doc is super limited and often results in a giant, unmaintainable file.
6) Firestore cannot count. As in, you literally cannot query the number of records in a collection. If you want that value, you have to store it as a separate field in the collection, and then update that value each time you add and remove a doc. MADNESS.
Yes, these are all limitations. But the advantage that Firestore has is significant, and for some applications it's worth accepting those limitations:
* Firestore is truly a "fire and forget" datatabase that scales without effort or maintenance. If your app works for 500 users, it will work for 500 million. Without a devops staff.
Yes, Firestore (aka Cloud Datastore) feels crippled compared to running aggregations and joins on an RDBMS. If your data and load fit on a single node Postgres, by all means use it, that's a great solution! When your requirements exceed that, you're in a different world. You can look at Spanner ($$$) or its clones (operational load, maturity). Or you can do what I did, and run the firestore/datastore as a master database and replicate data to other stores (eg BigQuery) for analytics.
Firestore's sweet spots are very small (eg, you have many microservices and want simple cheap zero-maintenance persistence) or very large (where scaling and availability would give you headaches anyway). In the middle, traditional RDBMSes are great.
I also just joined a team using Firestore and I feel exactly this same way. It absolutely blew me away how bad Firestore is for any kind of conventional CRUD app use cases. I have never worked with a database less fit for purpose, and I worked with MongoDB in 2012.
Yup. One of our products was built on Firestore and I expect 90% of my time on that project is spent trying to turn Firestore into a more featureful product to meet the application's requirements. You can push it pretty far if you have the developer resources, but it often seems ludicrous that someone is willing to spend that much to build what other database solutions provide out of the box for free.
> It gears itself towards frontend developers who don't know how to write a backend, and encourages bad practices for them. One example: Firebase lets you manually edit production data very easily from within their dashboard. Like it's treated almost like it's a CSV.
This is truly spot on. I'm one of those devs who couldn't write a good backend when I first began coding out apps (as a hobby). Started out with firebase because that was the tool used by most YouTube videos and Medium blog posts introducing people to app development. In the end, I eventually had to learn other technologies such as using Elasticsearch, DynamoDB (which I would also stay the hell away from) and PostGRES.
Thankfully Supabase decided to stay away from the NoSQL format, so learning how to get started with PostGRES was made smoother.
Looking back I don't even know why I jumped into using Firebase, since I do have a pretty good footing in SQL querying. I don't know why Google isn't bothering with an Elasticsearch-like NoSQL solution for Firebase. And quite frankly, I would only use Firebase for Auth and RTDB for basic database stuff. If I had known this earlier, I might have saved months of learning Firebase Cloud Firestore crap online.
It suffers from trying to go upmarket with something that was never meant for ultra-serious, large, production apps. Look at the original Wired article [1]: every story of the founders' pitching to customers is about being able to quickly zero-to-one a concept. It seems great for internal apps, quick prototypes, and simple things and that's about it.
Yep. And what’s worse is that Firestore is notoriously difficult to migrate away from (at least from what I’ve heard, haven’t yet tried it myself). So if you take the prototype and turn it into an mvp, you’re kinda stuck with it.
If the goal is to create a prototyping DB, ideally there’d be some nice off ramping or migration tools for when the app needs to become production ready.
Great list! I think we've had similar frustrations with firestore.
#3 is not really correct. You can sort on any single field but if you have multiple fields, then yes, you must create an index.
#4 I partially agree. The web dashboard makes things I don't want to do (accidentally edit or delete a field/document) dangerously easy, and things I do want to do (copy the contents of a document, save the results of a query, copy the text of a field) exceedingly difficult. The truth is that firestore is geared toward people who want an easy way to get near real-time data synchronization. It really sacrifices almost everything else.
The number one most annoying thing to do with firestore is work with their security rules.
This has nothing to do with business. It's fundamental limitations from the technical architecture.
It does provide a lot of serverless scalability for what it offers, but it's the classic case of optimizing for a situation that won't happen for 99% of their apps.
That's a good guess, but no this is a syntax limitation. For instance, if you wanted to say "I want records where record.score >= 10 and record.score <= 100", you can't do it, because you can't filter on the same field twice.
This is not correct. You can filter on the same field twice. What you can't do is filter on different fields, eg:
I want records where record.score >= 10 and record.date <= 2022-01-01
The first form (single field) is a simple range query on a single index - that's what Firestore is optimized for. The second form (with different fields) potentially requires walking the near-entirety of both indexes looking for matches, and therefore has unbounded time and computational requirements.
Firestore is designed so that you can't do things that don't scale. Sometimes that sucks, especially when you know that the data volume for that query will always be "reasonable". But the limitations are not arbitrary.
I never assumed they were arbitrary, it's more that they don't fit the product. This DB architecture fits some use cases, I'm sure, but the decision should be made by an experienced person. In the real world, what happens is that early stage startups get hooked on the whole "fast and scalable prototype" marketing copy, then hire some FE devs that build the entire app on Firestore because they don't know any better. 99 times out of 100, a startup's use case is not going to be a good fit for Firestore, but Firebase has no vested interest in informing the customer about this little detail. I've only worked with Firebase for 1 year and I've now seen this exact situation happen 3-5 times.
1) You are severely limited in how you can query. The list of limitations is too long to recount here, but querying is nearly worthless.
2) The database design strongly pushes you towards nesting collections, making side effects cleanup a disaster, especially as a database grows in complexity.
3) You cannot sort on a field without creating an index first. I get why creating an index is a good idea, but I can't even write a simple analytics script without indexing the fields first.
4) It gears itself towards frontend developers who don't know how to write a backend, and encourages bad practices for them. One example: Firebase lets you manually edit production data very easily from within their dashboard. Like it's treated almost like it's a CSV.
5) The recommended development approach is to directly query the DB from the frontend, with no server in between. This means any data security has to be implemented in a separate DB rules document. The syntax and structure of this doc is super limited and often results in a giant, unmaintainable file.
6) Firestore cannot count. As in, you literally cannot query the number of records in a collection. If you want that value, you have to store it as a separate field in the collection, and then update that value each time you add and remove a doc. MADNESS.
I could go on, and on, and on.