Comment by cogman10
3 years ago
> Use One Big Database.
> Seriously. If you are a backend engineer, nothing is worse than breaking up your data into self contained service databases, where everything is passed over Rest/RPC. Your product asks will consistently want to combine these data sources (they don't know how your distributed databases look, and oftentimes they really do not care).
This works until it doesn't and then you land in the position my company finds itself in where our databases can't handle the load we generate. We can't get bigger or faster hardware because we are using the biggest and fastest hardware you can buy.
Distributed systems suck, sure, and they make querying cross systems a nightmare. However, by giving those aspects up, what you gain is the ability to add new services, features, etc without running into scotty yelling "She can't take much more of it!"
Once you get to that point, it becomes SUPER hard to start splitting things out. All the sudden you have 10000 "just a one off" queries against several domains that are broken by trying carve out a domain into a single owner.
I don't know what's the complexity of your project, but more often than not the feeling of doom coming from hitting that wall is bigger than the actual effort it takes to solve it.
People often feel they should have anticipated and avoid the scaling issues altogether, but moving from a single DB to master/replica model, and/or shards or other solutions is fairly doable, and it doesn't come with worse tradeoffs than if you sharded/split services from the start. It always feels fragile and bolt on compared to the elegance of the single DB, but you'd also have many dirty hacks to have a multi DB setup work properly.
Also, you do that from a position where you usually have money, resources and a good knowledge of your core parts, which is not true when you're still growing full speed.
I can't speak for cogman10, but in my experience when you start to encounter issues of hitting the limit of "one big database" you are generally dealing with some really complicated shit and refactoring to dedicated read instances, shards, and other DB hacks are just short term solutions to buy time.
The long term solutions end up being difficult to implement and can be high risk because now you have real customers (maybe not so happy because now slow db) and probably not much in house experience for dealing with such large scale data; and an absolute lack of ability to hire existing talent as the few people that really can solve for it are up to their ears in job offers.
The other side of this is once you actually can’t scale a single DB the project has proved it’s value and you have a solid idea what you actually want.
Designing let alone building something scaleable on the other hand is a great way to waste extreme effort up front when it’s completely superfluous. That’s vastly more likely to actually kill a project than some growing pains especially when most projects never scale past a single reasonably optimized database.
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> The long term solutions end up being difficult to implement and can be high risk because now you have real customers (maybe not so happy because now slow db) and probably not much in house experience for dealing with such large scale data; and an absolute lack of ability to hire existing talent as the few people that really can solve for it are up to their ears in job offers.
This is a problem of having succeeded beyond your expectations, which is a problem only unicorns have.
At that point you have all this income from having fully saturated the One Big Server (which, TBH, has unimaginably large capacity when everything is local with no network requests), so you can use that money to expand your capacity.
Any reason why the following won't work:
Step 1: Move the DB onto it's own DBOneBigServer[1]. Warn your customers of the downtime in advance. Keep the monolith as-is on the current OriginalOneBigServer.
Step 2: OriginalOneBigServer still saturated? Put copies of the monolith on separate machines behind a load-balancer.
Step 3: DBOneBigServer is still saturated, in spite of being the biggest Oxide rack there is? Okay, now go ahead and make RO instances, shards, etc. Monolith needs to connect to RO instances for RO operations, and business as usual for everything else.
Okay, so Step 3 is not as easy as you'd like, but until you get to the point that your DBOneBigServer cannot handle the loads, there's no point in spending the dev effort on sharding. Replication doesn't usually require a team of engineers f/time, like a distributed DB would.
If, after Step 3, you're still saturated, then it might be time to hire the f/time team of engineers to break up everything into microservices. While they get up to speed you're making more money than god.
Competitors who went the distributed route from day one have long since gone out of business because while they were still bugfixing in month 6, and solving operational issues for half of each workday (all at a higher salary) in month 12, and blowing their runway cash on AWS for the first 24 months, you had already deployed in month 2, spending less than they did.
I guess the TLDR is "don't architect your system as if you're gonna be a unicorn". It's the equivalent of you, personally, setting your two-year budget to include the revenue from winning a significant lottery.
You don't plan your personal life "just in case I win the lottery", so why do it with a company?
[1] backedup/failover as needed
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> I don't know what's the complexity of your project, but more often than not the feeling of doom coming from hitting that wall is bigger than the actual effort it takes to solve it.
We've spent and failed at multiple multi year projects to "solve" the problem. I'm sure there are more simple problems that are easier to disentangle. But not in our case.
Without details it's hard to really get anything from this. Could you share some?
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One nice compromise is to migrate to using read-only database connections for read tasks from the moment you upgrade from medium sized DB hardware to big hardware. Keep talking to the one big DB with both connections.
Then when you are looking at the cost of upgrading from big DB hardware to huge DB hardware, you've got another option available to compare cost-wise: a RW main instance and one more read-only replicas, where your monolith talks to both: read/write to the master and read-only to the replicas via a load balancer.
I don’t see anyone mentioning memcached which can reduce the load on the db tremendously.
I've basically been building CRUD backends for websites and later apps since about 1996.
I've fortunately/unfortunately never yet been involved in a project that we couldn't comfortably host using one big write master and a handful of read slaves.
Maybe one day a project I'm involved with will approach "FAANG scale" where that stops working, but you can 100% run 10s of millions of dollars a month in revenue with that setup, at least in a bunch of typical web/app business models.
Early on I did hit the "OMG, we're cooking our database" where we needed to add read cacheing. When I first did that memcached was still written in Perl. So that joined my toolbox very early on (sometime in the late 90s).
Once read cacheing started to not keep up, it was easy enough to make the read cache/memcached layer understand and distribute reads across read slaves. I remember talking to Monty Widenius at The Open Source Conference, I think in Sad Jose around 2001 or so, about getting MySQL replication to use SSL so I could safely replicate to read slaves in Sydney and London from our write master in PAIX.
I have twice committed the sin of premature optimisation and sharded databases "because this one was _for sure_ going to get too big for our usual database setup". It only ever brought unneeded grief and never actually proved necessary.
Many databases can be distributed horizontally if you put in the extra work, would that not solve the problems you're describing? MariaDB supports at least two forms of replication (one master/replica and one multi-master), for example, and if you're willing to shell out for a MaxScale license it's a breeze to load balance it and have automatic failover.
I worked at a mobile game company for years and years, and our #1 biggest scaling concern was DB write throughput. We used Percona's MySQL fork/patch/whatever, we tuned as best we could, but when it comes down to it, gaming is a write-heavy application rather than the read-heavy applications I'm used to from ecommerce etc.
Sharding things out and replicating worked for us, but only because we were microservices-y and we were able to split our schemas up between different services. Still, there was one service that required the most disk space, the most write throughput, the most everything.
(IIRC it was the 'property' service, which recorded everything anyone owned in our games and was updated every time someone gained, lost, or used any item, building, ally, etc).
We did have two read replicas and the service didn't do reads from the primary so that it could focus on writes, but it was still a heavy load that was only solved by adding hardware, improving disks, adding RAM, and so on.
Not without big compromises and a lot of extra work. If you want a truly horizontally scaling database, and not just multi-master for the purpose of availability, a good example solution is Spanner. You have to lay your data out differently, you're very restricted in what kinds of queries you can make, etc.
Clarification, you can make unoptimized queries on Spanner with a great degree of freedom when you're doing offline analysis, but even then it's easy to hit something that's too slow to work at all, whereas in Postgres I know it'd not be a problem.
For what it's worth, I think distributing horizontally is also much easier if you're already limited your database to specific concerns by splitting it up in different ways. Sharding a very large database with lots of data deeply linked sounds like much more of a pain than something with a limited scope that isn't too deeply linked with data because it's already in other databases.
To some degree, sharding brings in a lot of the same complexities as different microservices with their own data store, in that you sometimes have to query across multiple sources and combine in the client.
We've done that (MSSQL Always on) and that's what keeps the lights on today. It's not, however, something that'll remain sustainable.
Shouldn't your company have started to split things out and plan for hitting the limit of hardware a couple box sizes back? I feel there is a happy middle ground between "spend months making everything a service for our 10 users" and "welp i looks like we cant upsize the DB anymore, guess we should split things off now?"
Isn’t this easily solved with sharding ?
That is, one huge table keyed by (for instance) alphabet and when the load gets too big you split it into a-m and n-z tables, each on either their own disk or their own machine.
Then just keep splitting it like that. All of your application logic stays the same … everything stays very flat and simple … you just point different queries to different shards.
I like this because the shards can evolve from their own disk IO to their own machines… and later you can reassemble them if you acquire faster hardware, etc.
> Once you get to that point, it becomes SUPER hard to start splitting things out.
Maybe, but if you split it from the start you die by a thousand cuts, and likely pay the cost up front, even if you’d never get to the volumes that’d require a split.
>Once you get to that point, it becomes SUPER hard to start splitting things out. All the sudden you have 10000 "just a one off" queries against several domains that are broken by trying carve out a domain into a single owner.
But that's survivorship bias and looking back at things from current problems perspective.
You know what's the least future proof and scalable project ? The one that gets canceled because they failed to deliver any value in reasonable time in the early phase. Once you get to "huge project status" you can afford glacial pace. Most of the time you can't afford that early on - so even if by some miracle you knew what scaling issues you're going to have long term and invested in fixing them early on - it's rarely been a good tradeoff in my experience.
I've seen more projects fail because they tangle themselves up in unnecessary complexity early on and fail to execute on core value proposition, than I've seen fail from being unable to manage the tech debt 10 years in. Developers like to complain about the second, but they get fired on the first kind. Unfortunately in todays job market they just resume pad their failures as "relevant experience" and move on to the next project - so there is not correcting feedback.
I'd be curious to know what your company does which generates this volume of data (if you can disclose), what database you are using and how you are planning to solve this issue.
Finance. MSSQL.
There are multiple plans on how to fix this problem but they all end up boiling down to carving out domains and their owners and trying to pull apart the data from the database.
What's been keeping the lights on is "Always On" and read only replicas. New projects aren't adding load to the db and it's simply been a slow going getting stuff split apart.
What we've tried (and failed at) is sharding the data. The main issue we have is a bunch of systems reading directly from the db for common records rather than hitting other services. That means any change in structure requires a bunch of system wide updates.
You can get a machine with multiple terabytes of ram and hundreds of CPU cores easily. If you can afford that, you can afford a live replica to switch to during maintenance.
FastComments runs on one big DB in each region, with a hot backup... no issues yet.
Before you go to microservices you can also shard, as others have mentioned.
Do you have Spectre countermeasures active in the kernel of that machine?
What does it matter, in this context?
If it's about bare metal vs. virtual machines, know that Spectre affects virtual machines, too.
I think they are implying disabling them (if on) could squeeze you out a bit more performance.
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Why can’t the databases handle the load? That is to say, did you see this coming from a while away or was it a surprise?
Wonder if this would be a good use case for Tidalscale?