Not sure postgres (logical) databases would scale that well, but don’t see a principal reason why it couldn’t. Has anyone explored this further?
It does mean having to upgrade each schema individually, which also makes it both easier and harder. Easier because the tables are smaller, so any schema changes that require things like say a table lock, are locking for a smaller amount of time and won't affect more than the one tenant at a given time. It also means that you can get into an inconsistent state of course, where some of your tenants have all the latest DB upgrades, while it failed on another subset.
At some point Postgres's internal tables become a bit of a "problem", as you want to run as many of these updates in parallel as you can for speed, which could lead to contention on Postgres' administrative side. You'll also still need to shard across multiple actual RDS instances, because you still have many tenants running against a single physical piece of hardware that will show its limitations if too many large or active tenants happen to be on the same shard.
And then you have the problem of keeping a record of which physical RDS instance (i.e. shard) the tenant is on. Your application code will need to look that up (and cache that info for some time ;)) and you have some choice there as well. I.e. do you shard those as well and juggle load balancing as basically a 1:1 mapping to shards or do you have your application layer connect to all database shards and handle any tenants? One is more complicated I would say while the other could run out of connections depending on how you need to scale the application layer and what kind of RDS instance you have.
As it turns out, even the company the initially developed the gem ended up ditching the approach due to some of the issues you outlined above.
Managing separate schemas feels like one of those nefarious decisions that make things simple _initially_ but get you into a world of hurt when you need to scale. The company is since defunct but they have an article where they discuss why they ditched the approach [2], TL;DR it's too difficult to maintain and scale
[1] https://github.com/influitive/apartment#tenants-on-different... [2] https://web.archive.org/web/20201108191323/https://influitiv...
Migrations
Things slow down past 100 [...] No one wants friction in their deployment process, especially as we’re attempting to deploy daily or more frequently.
Database resources
"Need too large of an instance" r3.4xl
Client Memory Bloat
Ruby ActiveRecord something
Record Identification
One major drawback of schemas as tenants is that your sequence generators will reside independently in each tenant.
They complain about "what if you need to join this data somewhere else, then you need to also add the tenantId". Yeah, so? We also have a data warehouse we we do just that. Not a problem at all. We also have other services than our main one, which do use different database technologies where we use tenant as part of the key (for loads that actually benefit from being in a NoSQL type DB) and there we do not have sharding other than what the NoSQL database does by itself so to speak by dividing the keyspace.
That's it. End of article. Basically, we have none of these issues. They don't mention their actual scale. The only number they mention is the "hundreds of schemas". We have more than ten times that number per physical shard and have tens of thousands of tenants total. Again :shrug:
Sounds like this is just normal sharding. Completely different from having a totally separate schema like GP proposes (with that approach you don't use sharding keys at all).
But I do see now, searching for that part of the info specifically, they mention
Peppy Peppers’ orders can be migrated by running SELECT FROM orders WHERE shop_id = 1 FOR UPDATE on Pod 1’s database
Certainly needs a bunch of tooling, but worked well. Some apps were stateless and could connect to any physical cluster. Some others were sticky and only connected to a subset.
Similar architecture in my current company as well and we serve nearly a thousand customer instances served across 4 physical clusters.
We do have some basic tools to provision new customers on the emptiest cluster, move customers from one cluster to another etc
Shopify has pretty much mastered this https://shopify.engineering/mysql-database-shard-balancing-t...
See: https://read.engineerscodex.com/p/how-apple-built-icloud-to-...
Discussed on HN at the time: https://news.ycombinator.com/item?id=39028672
[1]: https://github.com/apple/foundationdb https://en.wikipedia.org/wiki/FoundationDB
This is sort of true and sort of false. When you think of a "database", if you're thinking of a Postgres database, you're way off the reality of what "database" means here.
FoundationDB has a concept called "layers", and essentially they have created a layer that looks like a separate database on top of a layer that is separately encrypted groups of keys. They don't have billions of FoundationDB clusters or machines, and at the infra level, i.e. the instances of the FoundationDB server software, it's unaware of individual "databases".
A closer analogy would be like having billions of tables, but even that isn't accurate because in relational databases a table is usually a notably more static concept than data in a table. The closest analogy would be that each of the billions of users has a bunch of rows with a user-id field on them, and there's a proxy that filters everything such that you can view the table as if it only had one user's data in it.
To be clear, FoundationDB is awesome and Apple have done some really cool stuff with it, but it's less crazy/impressive than it sounds.
"FoundationDB decouples its data storage technology from its data model. FoundationDB’s core ordered key-value storage technology can be efficiently adapted and remapped to a broad array of rich data models. Using indexing as an example, FoundationDB’s core provides no indexing and never will. Instead, a layer provides indexing by storing two kinds of key-values, one for the data and one for the index."
https://apple.github.io/foundationdb/layer-concept.html
Then existing standard layers like the Record layer, providing "(very) roughly equivalent to a simple relational database" providing structured types, index, complex types, queries, etc.
https://github.com/FoundationDB/fdb-record-layer
Or one for documents, which speaks the MongoDB wire protocol
However you will still have shared resource problems - some rogue query destroys IOPS in one tenant now ends up bringing down all tenants etc. There are in theory databases that solve this as well, but my experience has been that at that point what you buy into is a bad version of resource sharing - ie what an operating system does - and you’re better off using OS mechanisms
In other words: yes, but you still have noisy neighbours, and may be better off running lots of small fully separated DBMSes than a big logically separated one
It seems there is good potential for confusion unless we use the same terms consistently when discussing architecture and design across teams.
Even the term RDS (Relational Database Service) is sometimes said to be inaccurate since it is a "Relational Database SERVER as a Service"
A few related terms that cause confusion:
"Schema" could refer to a Database Schema or in some contexts, a single table's logical data structure. (Or a single data set's data structure -- or a standard for one, like JSON Schema)
Data Catalog products like "AWS Glue Data Catalog" which only store the metadata or schemas of the table they crawl ... refer to the entities they store as "Databases" and "Tables" (but no "Schemas") and documentation includes guides talk about "creating a database"[1] and "creating a table"[2] in AWS Glue. There has to be a better way to refer to all these entities without using the word schema with so many meanings -- or calling both physical tables and their metadata as "tables". Otherwise this is needlessly confusing and hard for beginners.
---
EDIT: even more madness. This StackOverflow discussion [3] has more examples of confusing usage:
> On Oracle .... A Schema is effectively a user. More specifically it's a set of tables/procs/indexes etc owned by a user. Another user has a different schema (tables he/she owns) however user can also see any schemas they have select priviliedges on.
> MySQL: database/schema :: table
> SQL Server: database :: (schema/namespace ::) table
> Oracle: database/schema/user :: (tablespace ::) table
[1]: https://docs.aws.amazon.com/glue/latest/dg/define-database.h...
[2]: https://docs.aws.amazon.com/glue/latest/dg/tables-described....
[3]: https://stackoverflow.com/questions/298739/what-is-the-diffe...
As to schema itself, yes, it’s heavily overloaded, and you just to grok it from context. I can talk about a table’s schema or a DB’s schema, or as you mentioned, JSON schema. Although the latter is helped by simply not using JSON in a relational DB.
You must remember that SQL is an ancient language, and the relational model is even older. There are going to be oddities.
I've been calling it the Lots of Little Databases model vs the Globe Spanning Gorilla.
Like the Spanner paper points out, even if your distributed database semantically appears like a single giant instance, in practice performance means developers avoid using distributed joins, etc, because these can lead to shuffling very large amounts of intermediate results across the network. So the illusion of being on a single giant machine ends up leaking through the reality, and people end up writing workarounds for distributed joins like async materialization.
If we give up the single machine illusion we get a lot of simplification, at the cost of features devs were unlikely to use anyhow. I see having consistent distributed commit but without cross shard joins as a really interesting alternative.
And besides scalability I like the extra security rope of fine grained partitioning from the start.
I'll write a blog post along these lines if I get anything worthwhile done.
You have to create each database manually using wrangler or the website. Then you have to create a binding for each database in wrangler.toml so that the database becomes accessible as a variable in your Workers code. Then you have to change your Worker source code to do something with that variable. Then you redeploy.
The issue is that Workers cannot create or list databases. There's no API for it.
In the meantime, though it's not a great solution, one can create and query databases through the REST API instead https://developers.cloudflare.com/api/operations/cloudflare-...
That's great to hear, thanks!
There is a huge long tail of companies with datasets that won't fit on a single machine but can be handled by a dozen or so, and where no single customer or service is near the limits of an individual node.
These customers are poorly served at the moment. Most of the easy to implement SaaS options for what they need would be hugely costly vs a small fleet of db servers they administer. Meanwhile, most of the open source options are cargo culting Google or Facebook style architecture, which is a huge burden for a small company. I mean do you really want to run K8S when you have 10 servers in total?
I think there's a lot of interesting stuff happening in this end of the market that's not trying to be a mini Google, like Fly.io.
As for (2), I think a middle ground is supporting cross shard transactions but not joins. This works well enough for VoltDB et all.
It's rarely an issue with the number of bytes and more an issue with hot shards. Whatever shard Google is on (that is, Google the Figma customer) is surely going to be one _hell_ of a hot shard. They have more designers/engineers/PMs actively using Figma than most startups have _users total_. You don't need more than one really really hot customer for this to become more than a hypothetical problem.
When you start to think about it that way, suddenly you need to seriously consider your iops (if you're on RDS) or how much redundancy that physical machine's SSDs have (if you're running it on your own boxes).
RDS can be absurdly limited with IOPS, granted, but a modern laptop for example ought to be up to the task. Realistically you could probably be fine even on RDS but you might need to pay through the nose for extra IOPS.
If I use Photoshop to edit a .psd file I don’t think “man that psd file should really be stored in a single planet-sized database of all psd files in existence”. It’s just a file on my computer.
Figma requires a little bit more intermingling of data than Photoshop, it has multiuser support for one, so a pure local storage based approach wouldn’t work. But, at its heart it’s still based on the document model. When you open a Figma document it’s its own isolated little universe, the connections with resources outside the document are limited, and that matches user expectations.
The first sentence from the article seems to suggest its possible?
After all, Stackoverflow is running off of a handful of machines.
There's no virtually no excuse not spinning up a pg pod (or two) for each tenant - heck even a namespace with the whole stack.
Embed your 4-phases migrations directly in your releases / deployments, slap a py script to manage progressive rollouts, and you're done.
Discovery is automated, blast / loss radius is reduced to the smallest denominator, you can now monitor / pin / adjust the stack for each customer individually as necessary, sort the release ordering / schedule based on client criticality / sensitivity, you can now easily geolocate the deployment to the tenant's location, charge by resource usage, and much more.
And you can still query & roll-up all of your databases at once for analytics with Trino/DBT with nothing more but a yaml inventory.
No magic, no proprietary garbage.
Decently sized EKS nodes can easily hold nearly 800 pods each (as documented), that'd make it 75 nodes. Each EKS cluster supports up to 13,500 nodes. Spread in a couple of regions to improve your customer experience, you're looking at 20 EKS nodes per cluster. This is a nothingburger.
Besides, it's far from being rocket science to co-locate tenant schemas on medium-sized pg instances, monitor tenant growth, and re-balance schemas as necessary. Tenants' contracts does not evolve overnight, and certainly does not grow orders of magnitude on week over week basis - a company using Figma either has 10 seats, 100 seats, 1000, or 10,000 seats. It's easy to plan ahead for. And I would MUCH rather having to think of re-balancing a heavy hitter customer's schema to another instance every now and then (can be 100% automated too), compared to facing a business-wide SPOF, and having to hire L07+ DBAs to maintain a proprietary query parser / planner / router.
Hell, OVH does tenant-based deployments of Ceph clusters, with collocated/coscheduled SSD/HDD hardware and does hot-spot resolution. And running Ceph is significantly more demanding and admin+monitoring heavy.
[0] https://blog.acolyer.org/2020/03/04/millions-of-tiny-databas...
You probably don’t even need to be able to migrate people between shards… just put everyone on one db until you hit 50% utilization, then spin up a fresh db and put all new customers on that one. Repeat whenever you run out of databases under 50%.
In practice there isn't a big difference, they just colocate several customers according to their sharding key in the same logical database.
seems like the limitation is the logical file system. Which probably will work for most users.
Having a more granular partition key makes it easier to shard the data more evenly.