> A single database server cannot handle such demand, so we must spread the queries and data out across many servers with database sharding
Did you max out the capacity of the best server you can buy?
Such a database can serve millions of customers (the numbers given).
You always want to scale up the other parts first, request handlers, caching, etc. The day you can no longer inspect the essential state of your system is the day your company better be included in NASDAQ and ready to pay a few hundred engineers 300k salaries.
I found the article very helpful from a technical perspective, and didn't focus on the number too much, as it could easily be swapped and the decision-making process for when to shard is kinda out of scope.
But I hadn't considered this, so thanks for pushing back. Good to keep in mind their incentives.
I will say, since their product is a proxy whose interface is a single SQL connection, you should in theory be able to do dev queries through that black box, much the same as application queries? What is so scary here that it would require a hundred engineers?
> you should in theory be able to do dev queries through that black box
Because it’s a leaky abstraction which is trying to make guarantees over network connections which are extremely difficult to make within the same kernel.
A few questions I would start with:
- is the system even ACID compliant?
In my reading of this article, no.
- is my sql feature set limited? Will it enforce all constraints? Or are their cross-shard limitations?
For example the article doesn’t discuss transactions or how they would roll back, or how they guarantee a consistent view of data.
Next:
- how does it respond to error?
- how does it respond to load?
This is a complex system and complexity breeds bugs. But now you don’t have the tools or procedures to investigate those bugs because you can’t poke the system at desk with tools. You can’t run experiments; you can’t even see all the data.
What about sequences? The example shows an auto-incrementing user ID. How’s that possible without contention between all shards? Is the proxy responsible for sequences?
What about foreign keys? Do they all have to live on the same shard? How do you do distributed transactions?
On cross-shard reads: how do you do sorting? And cross-shard joins?
I’d love to be proven wrong, but I suspect the 768 servers look like 1 only on the very surface, and you’ll get wildly different characteristics from cross-shard and single-shard queries.
I personally would prefer if they _didn’t_ look like 1 if they can’t behave like 1.
Looks like it's not drop-in, you need to heavily modify your data and indexes structurally to make sharding performant. Cross-shard joins are possible but need to be designed for explicitly (to be fair, indexes also need to be designed explicitly in regular SQL). IDK if a drop-in solution is possible or if this is an information-theoretical limitation.
Of course 768 servers NEVER behave as 1. This is physically impossible.
Global services using relational dbs typically severely restrict queries that run against the cluster. So no joins, no intervals, no grouping, etc.
Transactional queries are usually limited to something like "get a single record, preferably from cache". For many typical web services this can go VERY FAR. Only a handful of global services needs more than a few dozen database servers and a caching cluster. In fact, i have seen major businesses running off a pair of very big postgres instances.
Analytical stuff is extracted into dedicated storages optimized for throughput, like Snowflake or Redshift or BigQuery.
This seems like the important distinction: making the infrastructure look like one database to the application is different from making it behave like one unrestricted relational database.
At what point does hiding the sharding become counterproductive? I imagine teams still need a fairly deep understanding of shard keys, query routing, and failure modes to avoid accidentally expensive cross-shard operations.
Distributed systems introduce severe restrictions on what can be reasonably done at scale. Having a single connection string is one thing, being able to do a massive JOIN is another (and should only be ever done in analytical databases).
The question is not "when sharding becomes counter-productive" but "when it starts making sense".
With sharding something somewhere has to know how to route queries to subsets of data. So it is a complexity price paid for being able to scale. If one can avoid paying this cost then he should.
And USUALLY cross-shard queries are not just expensive but simply impossible in operational clusters. Like, if you do COUNT on a table, you only count within a single db shard table.
i do wonder how something like this can be generally implemented. i presume this must only support a subset of SQL/plpgsql, as some things would be.. utterly insane to manage manually. e.g., if i have a table with a btree-gist overlap constraint, or some inclusion-exclusion check-constraint (or literally any constraint that requires multiple rows to be fully determined - there are quite a lot of them), how on earth does this work?
there's a reason why postgres writing is (mostly) serialised (asterisk) to a single writer (asterisk asterisk). something something ACID, but in short by having multiple writers improves availability, but weakens integrity.
Looks like the GIF is fully built out in code. It's really nice to look at, well made, and easy to understand too.
I wonder what program or code they used. I'd love to know.
p.sI thought it was a GIF, but it's an iframe. That was a nice little surprise.
Yeah, I'm looking at it in developer mode. It's using a GSAP timeline approach to update SVG properties. I'm curious how they handle security and caching for something like this. It looks like they're using Tailwind, at least. but this approach is really clean and nice.
It really feels like the best way to learn is by studying other people's code.
I disagree with the opening premise:
> A single database server cannot handle such demand, so we must spread the queries and data out across many servers with database sharding
Did you max out the capacity of the best server you can buy?
Such a database can serve millions of customers (the numbers given).
You always want to scale up the other parts first, request handlers, caching, etc. The day you can no longer inspect the essential state of your system is the day your company better be included in NASDAQ and ready to pay a few hundred engineers 300k salaries.
I found the article very helpful from a technical perspective, and didn't focus on the number too much, as it could easily be swapped and the decision-making process for when to shard is kinda out of scope.
But I hadn't considered this, so thanks for pushing back. Good to keep in mind their incentives.
I will say, since their product is a proxy whose interface is a single SQL connection, you should in theory be able to do dev queries through that black box, much the same as application queries? What is so scary here that it would require a hundred engineers?
> you should in theory be able to do dev queries through that black box
Because it’s a leaky abstraction which is trying to make guarantees over network connections which are extremely difficult to make within the same kernel.
A few questions I would start with:
- is the system even ACID compliant?
In my reading of this article, no.
- is my sql feature set limited? Will it enforce all constraints? Or are their cross-shard limitations?
For example the article doesn’t discuss transactions or how they would roll back, or how they guarantee a consistent view of data.
Next:
- how does it respond to error?
- how does it respond to load?
This is a complex system and complexity breeds bugs. But now you don’t have the tools or procedures to investigate those bugs because you can’t poke the system at desk with tools. You can’t run experiments; you can’t even see all the data.
Well, they are selling this thing so they don't want you to buy a big server (with a read replica) as that's much cheaper.
Surely the cost of running sql server on premium hardware with replication would be more than running on commodity hardware with sharding?
Only if your engineering resources and free and unbounded.
Even then I would probably use those resources to optimize software instead.
In the physical world of trucks and cranes no company would make that mistake to try to save 30-80k.
Most non FAANG orgs could probably serve all their customers from postgres on a laptop.
What about sequences? The example shows an auto-incrementing user ID. How’s that possible without contention between all shards? Is the proxy responsible for sequences?
What about foreign keys? Do they all have to live on the same shard? How do you do distributed transactions?
On cross-shard reads: how do you do sorting? And cross-shard joins?
I’d love to be proven wrong, but I suspect the 768 servers look like 1 only on the very surface, and you’ll get wildly different characteristics from cross-shard and single-shard queries.
I personally would prefer if they _didn’t_ look like 1 if they can’t behave like 1.
low-traffic auto increment like user creation can be relatively performant by reserving batches up front, then using those in each shard.
But the use-case for this is mostly if you do not need, or have very limited use for anything cross-shard.
I had this question too, even their "Database Scaling" course doesn't get to it.
https://vitess.io/docs/faq/sharding/advanced/
Looks like it's not drop-in, you need to heavily modify your data and indexes structurally to make sharding performant. Cross-shard joins are possible but need to be designed for explicitly (to be fair, indexes also need to be designed explicitly in regular SQL). IDK if a drop-in solution is possible or if this is an information-theoretical limitation.
> The example shows an auto-incrementing user ID. How’s that possible without contention between all shards?
Why not divide ID range (63bit?) by the maximum planned number of shards and then set on each shard it's min/max value so ranges will not overlap?
Of course 768 servers NEVER behave as 1. This is physically impossible.
Global services using relational dbs typically severely restrict queries that run against the cluster. So no joins, no intervals, no grouping, etc.
Transactional queries are usually limited to something like "get a single record, preferably from cache". For many typical web services this can go VERY FAR. Only a handful of global services needs more than a few dozen database servers and a caching cluster. In fact, i have seen major businesses running off a pair of very big postgres instances.
Analytical stuff is extracted into dedicated storages optimized for throughput, like Snowflake or Redshift or BigQuery.
This seems like the important distinction: making the infrastructure look like one database to the application is different from making it behave like one unrestricted relational database.
At what point does hiding the sharding become counterproductive? I imagine teams still need a fairly deep understanding of shard keys, query routing, and failure modes to avoid accidentally expensive cross-shard operations.
Yes!
Distributed systems introduce severe restrictions on what can be reasonably done at scale. Having a single connection string is one thing, being able to do a massive JOIN is another (and should only be ever done in analytical databases).
The question is not "when sharding becomes counter-productive" but "when it starts making sense".
With sharding something somewhere has to know how to route queries to subsets of data. So it is a complexity price paid for being able to scale. If one can avoid paying this cost then he should.
And USUALLY cross-shard queries are not just expensive but simply impossible in operational clusters. Like, if you do COUNT on a table, you only count within a single db shard table.
A 767 servers KV store should be enough for everyone
Sibling post has author answering questions in comments: https://news.ycombinator.com/item?id=48925420
Took me an embarrassing amount of time to realize this is an ad.
i do wonder how something like this can be generally implemented. i presume this must only support a subset of SQL/plpgsql, as some things would be.. utterly insane to manage manually. e.g., if i have a table with a btree-gist overlap constraint, or some inclusion-exclusion check-constraint (or literally any constraint that requires multiple rows to be fully determined - there are quite a lot of them), how on earth does this work?
there's a reason why postgres writing is (mostly) serialised (asterisk) to a single writer (asterisk asterisk). something something ACID, but in short by having multiple writers improves availability, but weakens integrity.
Looks like the GIF is fully built out in code. It's really nice to look at, well made, and easy to understand too. I wonder what program or code they used. I'd love to know.
p.sI thought it was a GIF, but it's an iframe. That was a nice little surprise.
Specifically, it's an JS-controlled/animated SVG embedded in an iframe.
Yeah, I'm looking at it in developer mode. It's using a GSAP timeline approach to update SVG properties. I'm curious how they handle security and caching for something like this. It looks like they're using Tailwind, at least. but this approach is really clean and nice.
It really feels like the best way to learn is by studying other people's code.
What kind of security and caching concerns do they need to handle to animate an SVG?
Load balancers, microservices and horizontal scaling?
Previously: https://news.ycombinator.com/item?id=48925420