Comment by makeitdouble
3 years ago
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.
You're not wrong. Probably more than 95% of applications will never outgrow one large relational database. I just think that this leads to an unfortunate, but mostly inevitable issue of complexity for the few that do hit such a level of success and scale.
Alex DeVrie (author of 'The DynamoDB Book') discusses that his approach is to essentially start all new projects with DynamoDB.
Now I don't really agree with him, yet I can't fully say he's wrong either. While we won't need it most of the time, reaching for a tool like this before we need it provides more time to really understand it when/if we reach that point.
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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
^ This. Not so long ago, I had worked in the finance department of a $350M company as one of the five IT guys and we had just begun implementing Step 2, after OriginalOneBigServer had shown its limits. DBOneBigServer was really big though, 256 GB RAM and 128 cores if I remember correctly. So big in fact that I implemented some of my ETL tasks as stored SQL procedures to be run directly on the server. The result? A task that would easily take a big fraction of OneBigServer memory and 15 hours (expected to increase correlatedly with the revenue) is run in 30 minutes.
It's worth noting that when I left we still were nowhere close to saturate DBOneBigServer.
> This is a problem of having succeeded beyond your expectations, which is a problem only unicorns have.
Nope. I've worked on a few projects that are not "unicorns" yet have legitimately hit that wall. Particularly around on line gaming and gambling.
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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?
I can share some. Had a similar experience as the parent comment. I do support "one big database" but it requires a dedicated db admin team to solve the tragedy of the commons problem.
Say you have one big database. You have 300 engineers and 30-50 product managers shipping new features every day accountable to the C-Suite. They are all writing queries to retrieve the data they want. One more join, one more N+1 query. Tons of indexes to support all the different queries, to the point where your indexes exceed the size of your tables in many cases. Database maintenance is always someone else's problem, because hey, it's one big shared database. You keep scaling up the instance size cause "hardware is cheap". Eventually you hit the m6g.16xlarge. You add read replicas. Congratulations, Now you have an eventually consistent system. You have to start figuring out which queries can hit the replica and which ones always need the fresh data. You start getting long replication lag, but it varies and you don't know why. If you decide to try to optimize a single table, you find dozens or 100+ queries that access it. You didn't write them. The engineers who did don't work here anymore....
I could go on, and all these problems are certainly solvable and could have been avoided with a little foresight, but you don't always have good engineers at a startup doing the "right thing" before you show up.
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I did in the original comment.
We have over 200 monolith applications each accessing overlapping schemas of data with their own sets of stored procedures, views, and direct queries. To migrate a portion of that data out into it's own database requires, generally, refactoring a large subset of the 200 monolith apps to no longer get all the data in one query, but rather a portion of the data with the query and the rest of the data with a new service.
Sharding the data is equally difficult because even tracing who is writing the data is spread from one side of the system to the next. We've tried to do that trough an elaborate system of views, but as you can imagine, those are too slow and cover too much data for some critical applications so they end up breaking the shard. That, in and of itself, introduces additional complexity with the evolution of the products.
Couple that with the fact that even with these solutions, getting a large portion of the organization is not on board with these solutions (why can't we JUST buy more hardware? Get JUST bigger databases?) and these efforts end up being sabotaged from the beginning because not everyone thinks it's a good idea (And if you think you are different, I suggest just looking at the rest of the comments here in HN that provide 20 different solutions to the problem some of which are "why can't you just buy more hardware?")
But, to add to all of this, we also just have organizational deficiencies that have really harmed these efforts. Including things like a bunch of random scripts checked into who knows where that are apparently mission critical and reading/writing across the entire database. General for things like "the application isn't doing the right thing, so this cron job run every Wednesday will go in and fix things up" Quiet literally 1000s of those scripts have been written.
This isn't to say we've been 100% unsuccessful at splitting some of the data into it's own server. But, it's a long and hard slog.
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Just trust me, I can refute anything with vague explanations.
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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.