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Comment by cortesoft | Hacker News Reader

I always find these posts ironic, because I work at a company where we are often times dealing with the opposite problem; trying to redesign systems that worked fine when we were a small startup but have hit scaling issues now that we are many times the size.

People are always so quick to comment, “don’t worry about scaling... if you are successful, you can redesign at that point.” However, it isn’t always easy to redesign later. Now people are relying on your systems, and you can’t have any down time. All redesigns have to be able to be implemented in a way that can transition from the previous system without breaking anything. As we often say, “we are changing the tires while driving 90mph down the road”.

I wish we had spent more time on thinking about scale when we were smaller. Our new mantra is that everything we make now has to be designed to horizontally scale, even if it is something small right now.

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Everybody at a company experiencing scaling issues feels the way you do.

The argument however is if the past went the way you wish it did, arguably the company you currently work for would not exist, because they would have lacked the knowledge that you have now to know which areas were the ones that would matter. Absent that knowledge, they would have built “scalable” systems that still had bottlenecks they could not yet see and also would have adopted extraneous technology that ultimately would be thrown out as the product discarded the parts that ended up not being critical to the value proposition uncovered. And of course, this whole process would have burned time, energy, and cash, not to mention shifted the nature of hires and the composition of the team itself, such that in sum it may have been a sufficient opportunity cost to jeopardize the whole business.

Yeah, I know this is true, and we always try to remember to understand that the engineering decisions that were made at the time were made for good reasons given the circumstances we were facing.

It helps that I was one of the people who made those decisions at the time, so I know we were making what we thought were the best decisions at the time.

Still wish we had made some different ones, though!

Hear hear!

I worked at a startup a handful of years ago which decided early on that mongodb was good enough. We figured we'd move to a proper database down the road. Spoilers: That never happened - the longer we used mongo, the more expensive migrating became. We never did manage to justify the migration. As far as I know the team is still stuck with it, and paying the price.

(If you're reading this and you know who you are, I'm sorry!)

Never use MongoDB. Ever. It's a rule of thumb that hasn't led me astray yet.

http://www.sarahmei.com/blog/2013/11/11/why-you-should-never...

> It's a rule of thumb that hasn't led me astray yet.

How can you know if your decision is correct if you never use it in the first place. Isn't it like only implementing A version of A/B and concluding that not implementing B version was a good decision.

Meh. I read the article, and I agree with it. But that is not what you are saying.

Don’t use it for relational data. Don’t use it as your primary data store.

At the enterprise I work at, I’ve had success delivering features where data science would have some job that aggregates production data and dumps it to kafka. Then we’ll map it and dump it in mongo. Stick a .net core microservice on top for retrieval. Works great, has none of the problems mentioned in that article. Of course, this might not make sense if you’re not big enough to where you need kafka, kubernetes, etc

But “Never use it” is certainly not good advice.

This part is totally misguided.

> What’s missing from MongoDB is a SQL-style join operation, which is the ability to write one query that mashes together the activity stream and all the users that the stream references. Because MongoDB doesn’t have this ability, you end up manually doing that mashup in your application code, instead.

Even with a normalized relational database one wouldn't make a single query to return rows from a join of tables T1, T2, T3, etc when there are repeating relations such as the users in the social graph. This might be done sometimes paying for the duplication to save a network round trip, that's all and no different.

A list of activities joined to users wouldn't be a problem for an RDBMS. I'm not sure what you mean by "repeating relations".

The deep relations (7-table join) example in the link is something I wouldn't do (including the repeated 'user' entities) with an RDBMS, given that I expect some elements of some tables to appear multiple times, that's a waste of I/O and hard to cache.

> Users have friends, friends have posts, posts have comments and likes, each comment has one commenter and each like has one liker.

I would likely gather all the instances of User id in each context (user, friend, commenter, and liker) and query it separately from the JOIN query. Depends on your data which way works out better on average/worst-cases.

> I worked at a startup a handful of years ago which decided early on that mongodb was good enough. We figured we'd move to a proper database down the road.

Yeah, that can't end well. It's a bit ironic from parent poster's viewpoint but "boring", well-understood technology is generally also the easiest to migrate away from. That's one key reason to choose it at the outset even over more "innovative" and perhaps more "ergonomic" choices like mongo, that in reality aren't nearly as simple to deal with.

The key part you highlight is that of hiring. If you want to be scale ready from day 1, you may not find the scale out talent because they either are working on large scale problems or would want to work on large scale problems. In your early days, you are mostly hunting for a product market fit.So your problem statement will not even be relevant to the hires.

This is the best case, the worst case is that the scale out thinking hampers your product market fit progress. That's the risk you run.

So, while it seems to have gone scale out on day 1 in hindsight, it's always always better to start small and scale out as needs arise, even if that means re-architecting the whole stack. It's painful, but doable. It's also a reasonable challenge to entice new hires. You have scaled out a system from degree 1 to degree m. Now scale that out from m to r++. Something like that.

On balance it's probably more important to create (a) stable public APIs backed by tests and (b) an automated deployment process than to get the internal implementation right from the start.

These two things help any system at any stage. They also make it easier to handle migration if that's necessary.

My last 3 consulting gigs have all had the same general issue - they went with the "quick to start up", scalable solutions and immediately smacked into a brick wall of complexity. These are 1 to 3 person eng teams that started with serverless functions, NoSQL databases, and event-based architecture and not even 6 months down the line had to hire an expensive consultant to shake out their architecture and point them at something less scalable but more practical to build on at their size.

If they didn't have VC, they'd likely die on the spot, drowned before they even had a shot at real product-market fit under the weight of their own accidental complexity.

Now, is there a set of common patterns that works well enough for startups to iterate quickly with, that _also_ scales? Probably. Can you be successful with NoSQL et al out of the gate? Almost definitely. Does it make it _much harder_? I have three anecdotal data points that point to "yes".

From my work over the last year, I'd suggest "choose boring technology" over "worry about scaling early" 10 out of 10 times. You need something that scales _conceptually_ before it can scale in the cloud, if you don't know what your core business value is or how your product actually works it's really damn hard to build a cloud-native app that anyone can actually iterate on. You're fighting incidental complexity every step of the way.

Maybe in 5 or 10 years, when the average developer has a wealth of experience in scaling technologies, when serverless functions have fewer dramatic downsides, when we start seeing out-of-the-box event driven full stack frameworks, when the bleeding sharp edges are sanded off NoSQL and we've figured out a good story around schemas and validation, then it starts to look more attractive for young companies. Until then, though? Stick with the boring stuff.

EDIT: Though, re-reading this, I suppose I should acknowledge my own survivorship bias - the companies that figured out their scalable, cloud-based tech stack early have no need for my consulting services...

"Choose Boring Technology": http://boringtechnology.club/

Great comment. This mirrors the ridiculous 'big data' fad of a few years ago, where startups would deploy Hadoop clusters to crunch through Terabytes (or even Gigabytes!!) of data ... a task that could have been done much better by a well resourced monolithic system that was easy to implement, and easy to maintain and understand and just worked.

I am the only FTE in engineering. Have 10ish cloud-run services plus a full K8Ss cluster, all in GC.

and it is pretty good. So even, in a one person situation, i decided to split services sometimes into two.

Main reason for having separate services is - a different privacy/security context or deployment time:

So one service had so many native libs that it needed that i didnt want to wait 3 minutes to build to deploy when independent code had changed.

So far I would say the setup totally practical for our use-case. A monolithic setup would have been slower. Mainly, with regard to quickly iterating.

That's a bit of survivor bias: Most startups don't go from small to large, or even really medium. Most startups are strapped for resources. Paying an extra engineer, or taking valuable time away from an existing one to implement an extremely scalable infrastructure that might not be used. They should focus on shipping a good product that people want, and getting people to buy it. I can't think of startups that failed because their stack needed to be upgraded for scalability. It's generally not why startups fail. I can find a whole lot who burned through their cash before they had a chance to need something orders of magnitude larger in scale than currently necessary. If it's a minimal cost in time & money? Sure, go for high scalability. But it it's going to reduce your runway significantly then worry about the bigger problem of becoming successful enough to need it in the first place.

If a startup is really concerned about avoiding the problems you're encountering at your company they should just take an extra week when planning their infrastructure to map out details of how they would transition to a more scalable infrastructure. Then save that file somewhere and dust it off if they ever need it. And if they need it, they'll have ample investment money coming their way to pay for the transition.

There is no startup in the scaling phase that doesn’t have stuff that needs to be rewritten.

If your startup’s product is constantly iterating to find product market fit, it’s a fool’s errand to keep making sure everything is perfectly horizontally scalable in the first place. It’s very rare that the things you expect to be important early on actually are in the product that gets popular.

You’re absolutely right to change your mantra now that you’re scaling, but you were right to not do it early on. The difference between life an death in an early startup is quick iteration.

>People are always so quick to comment, “don’t worry about scaling... if you are successful, you can redesign at that point.” However, it isn’t always easy to redesign later.

Well ... you're going to have to. You don't know what bottlenecks you're going to hit as you scale up, so why spend precious development time trying to guess which systems to optimize? And to be clear, 'cloud-scale' architectures are complicated and bring their own set of issues - why subject yourself to that?

Plus a well designed (monolithic) system should get you very far with vertical (resource) scaling and/or data sharding.

At my work we’re one step past where it sounds you’re at. I’ve been around since the very early days so I’m both part of the original problem and the solution. Now our biggest problem is we don’t like to work with the things we built to handle scale. I’m not sure how common that is but I don’t believe we’re alone. A lot of the problem from my point of view was trying to chase trends.

We have Kafka in places the article’s author would suggest are inappropriate. We have a lot of “one true systems” that hid horizontally scaling instead of making it easy to build clusters of micro-services. Now we’re riding micro-services pretty hard and that isn’t going well since we have too many mixed architectures in use.

I think it’s correct to push out worrying about scale while you seek product market fit. I think that once you do need to scale you really need to put thought into the process. This is difficult if everything is on fire and failing. If it’s not, burn some time to plan. It’ll pay off. Be sure you don’t reinvent things you can cheaply buy. Make sure developers still like their day jobs after the changes are made before making more changes with the same underlying design theories.

As companies grow there will be "pain points" that come up due to scaling (performance, architecture, development, etc). When you get to that point it's normal to wish the pain didn't exist by "doing it right" the first time, but I think it's still better to wait until you have the pain. Because that pain tells you unequivocally what you need to value in the scaling.

So, Let's Encrypt just uses a very big piece of iron for their database server, they don't scale horizontally.

Stack Overflow also famously buys the biggest hardware they can get and upgrade their database server accordingly.

I always wonder if horizontal scaling is really always the right answer, when you can just buy a bigger box (of bigger VM) and spare yourself a lot of issues.

I am not a proponent of scaling out horizontally as the best solution to all things, but being able to do so comes as a result of design that helps to minimize coupling of services and single points of failure, in general, which are other benefits.

But I also tend to think "you can always get a bigger box" can take you pretty far, and much further than most people that only think in terms of AWS realize.

Up to a certain point, sure, you can vertically scale.

I work for a large CDN, with many thousands of servers pushing near line-rate out of our NICs... we certainly can't vertically scale that.

There is a limit to how much you can vertically scale.

Even for the things we CAN vertically scale, we need to be able to handle hardware failure and OS upgrades without taking any downtime, so you are going to need to be able to horizontally scale enough to handle that.

> I work for a large CDN, with many thousands of servers pushing near line-rate out of our NICs... we certainly can't vertically scale that.

A CDN is essentially "horizontal scale as a service". The vast majority of products are very different.

And the products that aren’t should hire out CDN networks to do that work for them instead of trying to roll their own.

I agree with Jorams, this is not a common service. Most apps are things with an app server and db and most of all: they have a load that is easily handled by modest gear.

I know, and I get that we aren’t the average tech company.

I don’t think we are unique, though, and there are many engineers on here who work at all sorts of interesting places with non-standard workloads. I was just pointing out the reasons we can’t vertically scale.

StackOverflow seems to be doing well keeping the number of servers small, and having one dedicated hot failover for each machine.

Today you can without issues have 128 cores and 1TB RAM in one server (or 48 cores and 1.5TB memory if you prefer Intel). Of course not everything scales perfectly, but you can get pretty far before you are really forced to scale horizontally.

Right, I know you can get really beefy servers these days. We use thousands of them.

Oh, you can get 6TB+ of memory without problem.

https://yourdatafitsinram.net/

Databases are notoriously better scaled with more iron. Web servers on the other hand are a great fit for horizontal.

Horizontally scaling your database is a last resort used at truly mindboggling scales.

I bet that you can handle a tremendous amount of traffic with a single machine (2 in ha for availability behind an LB).

Especially with today’s hardware or simple cloud vms you’ll come a long way.

I don’t think the size where you need to horizontally scale your database is as ‘mindboggling’ly large as you make it out to be. There are a lot of engineers working at a lot of companies who are at and above that scale.

Most websites (probably 99.9% of websites) never reach that scale. And I suspect most working engineers work on products that aren't at that scale. I mean, how many websites, anywhere in the world are bigger than stackoverflow? 100?

There's interesting tech out there, but I think adding "interesting" technology to your stack without a very good reason is usually a mistake.

Sure, but not everyone is making websites.

True. And likewise, not everyone needs an ETL pipeline.

"Keep your architecture as simple as possible, but no simpler". There are important problems kafka solves. But its worth checking if you have one of those problems before using it.

I think many engineers underestimate a well-tuned postgres with a good memcache layer. Most of your traffic likely won’t even hit the database. And a good ORM supports this out of the box.

This assumes that everyone is making websites.

No? It assumes most people using databases have more reads than writes and find relations useful.

Scaling the writeable DB horizontally is the hard part. You can get pretty far with tuning, bare metal (if load isn't too lumpy), many read only copies, caches, and other boring solutions.

Under-scaling and over-scaling are both suboptimal for all the obvious reasons.

Middle-scaling is also suboptimal - you’re paying too much upfront and you don’t get enough later on anyway.

Stage-appropriate scaling is the smart way. Scale as you can see the need. This requires willpower - you will be pressured to kick things down the road just one more time, every single time.

The path of the wise is generational design. Build every component such that it can be easily destroyed and replaced. Anything that mustn’t be destroyed should be scrutinized and planned for in advance. Store your data in most mundane formats so it’s easier read. Keep interactions simple and need-to-know. These are platitudes, you will need to fill the planned obsolesce roadmap for yourself. This unites your hands make the quickest crappiest implementation at the moment secure in knowledge you can fix it or scale it as needed later.

There's two very different ways to interpret the "don't worry about scaling" stance and I think that's where things go sideways.

On the one, people take it as literally "don't worry about it, just make it work" and don't think through simple decisions.

On the other, with some basic analysis you can identify where your likely bottlenecks are going to be and make some preparations to dramatically simplify scaling when you need to do it. Sometimes minor architectural decisions, that don't take much time, can lead to a happier long term life.

But I 100% agree with designing for horizontally scaling.

It's one of the reasons I always try to steer people in the direction of Heroku for new projects, because while it reduces your ops burden and makes your life easier...it also forces you to design for horizontal scalability from day 1 via 12 Factor.

That’s how I take this, with a related point of thinking about how you’d replace components when the time comes. Maybe you have a monolith-leaning design but you have tests, a clean API with some idea of ownership, etc. rather than a bunch of code directly making SQL calls, copying the same logic instead of using a function, or touching the shared files so when you do split things out and redesign them the process is safe.

(Having flashbacks to the same code in 3 PHP repos, maintained by one person, no two copies identical)

Spending 5x the effort to redesign when you have 10x the money/resources isn't an unreasonable tradeoff. Since the vast majority of early stage startups fail spending money on things which don't matter until they succeed is not a good business decision.

Of course, WeWork at this point had 1500 engineers so calling it a small startup or using it as a case study (either way) for small startups seems a bit silly.

There's horizontal scaling, and then there's horizontal scaling.

A lot of software assumes a single server is fine and as a consequence doesn't even bother trying to abstract that away; the singleton nature of the software then percolates virally through it all.

On the flip side, some software actually isolates their scaling limitations and pushes the bottleneck into a single part of the system.

With the former, you're screwed if you hit your vertical scaling limits (to say nothing of what your system availability probably looks like, if scaling is actually a concern). With the latter, you can play games with your scaling, perhaps by shoving a hierarchical or replicated cache in front of your scaling bottleneck, and effectively buy time to build a solution with better scaling. (Or perhaps never bother with that, and just be acutely aware of your system's scaling dimensions, like StackOverflow.)

There's no such thing as a system that truly scales infinitely, just systems whose scale limits exceed our imagination.

(Admittedly, most bugs of the form "INT64_MAX overflow" are generally a leak and not the result of growth :).)

People always assume either there would be high-velocity development or high-scalability architecture. In theory, this would be correct at the end of the day. But in fact, we're far from that equilibrium. For example, here we have these two major players:

Ruby: High-velocity development; Decisions have been made so you don't need to make them; Suffer from scalability issues both performance-wise and architecturally in the endgame.

Java: Much slower development; Could be fast if correctly architected, but doomed as well if done wrong; Require a hell of infrastructures to work well.

But you can also find some superstars among the minor players without many trade-offs, for example:

Elixir: Development velocity and decision-wise are very close to Ruby; Performance is on-par or faster than Java without decisions; Messaging and cache already baked-in the language and tech stack though you don't need them in the beginning; Hire Ruby programmers and just start rollin'

I wonder what the ratio of failed startups that didn't engineer for scaling early vs those that did is. Having to rewrite parts of your application to match scaling issues seems like a great problem to have.

Yea, I don't think it's always a waste of time to think about scale at the start. You don't have to do all the work up front, but you can definitely put thought into doing the early work in a way that will make scaling easier later. There is no sense in committing to an architecture early that limits your options later. Also, avoiding sloppiness and ensuring simplicity up front usually pays dividends later.

Like suggested by many people that the number of companies will not reach that scale, so starting small and simple is the most efficient. There are few things in mind that may help :

1. Define the domain segregation and be discipline on the implementation. It will not only help ease the process when the system need to be segregated into multiple service, but also help on organizing the team.

2. After segregating the domain, even if all of them live in the same database dont use `JOIN` as much as possible. Define the API for each domain and use it as the main communication.

3. As boring as it sounds, use boring technologies. Pick framework that has been used by many like Jango, Rails and Laravel. They offer not only tools and libraries but also programming pattern that will help us to implement domain segregation. The framework I mentioned have good established (way to work with if not integrated) ORM, event driven method, front end development and means to scale.

4. Usually postgreSQL is enough and can bring long way, just remember point 1 when designing the DB.

I think those points are quite straightforward and doesn't make the development more difficult, while trying to be future proof.

I have had the same issue, and wishing we thought about that up the road rather than down the road is exactly the feeling when we huddle up for team meetings on optimisation. We could not have any down time for most days of the year.

This. The very fact that you might think your use case does not require Kafka is actually proof that your use case requires Kafka.

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