Many use cases don’t require Kafka (2019)

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.

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...

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.

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.

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.

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.

this. I'm working on a project which will need to be deployed in a local network without internet access. deploying kafka is a nightmare, more so in docker or k8s. I ended up switching to other tech rather than undergo this

that's correct - we actually don't manually write the kafka api, but we codegenerate it: https://github.com/vectorizedio/redpanda/blob/dev/src/v/kafk...

the tl;dr is that we wrote a pretty fast fragmented buffer parser (due to our thread-per-core design), we consume some json files and codegen it.... which basically means, if you connect an existing app that uses the kafka driver and it doesn't work it's a bug, and it's a matter of teaching the codegen to fix it most of the time. Transactions should land in the next month or so, which is the only thing really missing today.

we have a ton of solo devs typically that come from the Go, Rust, JS & Python seem to like it quite a bit because of what you said.

100% agree on redpanda

The problem I see with kafka is that it was built before cloud architectures were commonly adopted (with distributed systems everywhere). Confluent has put a lot of effort dragging kafka's architecture to the present, but some major features are missing:

  - Auto-scaling:  Confluent finally introduced "elastic scaling" a few months ago but it only allows you to scale up and must be triggered by the admin (no threshold-based auto-scaling).

  - Multi-tenancy:  Planning for a multi-tenant kafka cluster is not for the faint of heart.  Achieving isolation tends toward liberal usage of topics of which starts to become unmanageable in the low thousands.  This isn't crazy when you've got a few hundred microservices and several tenants to keep isolated.

  - Decoupled brokers and storage:  Any broker scaling or failure can lead to downtime while event storage is redistributed.

Confluent's Cloud service reduces operational overhead but isn't always feasible due to cost, resource limits (like service accounts or schemas for instance), data controls, etc.

Yep. WhatsApp scaled their software with over 10m users per engineer on their team.

I read an article a few years ago about how StackOverflow ran (still?) their entire site off just 6 physical computers or something. And before you claim its because they have hand written assembly everywhere - nope. Their code is just clean C#.

I adore Kafka - I think its a lovely way to build scalable software. But almost every website out there will never need to scale past what a simple postgres + rails/nodejs/whatever + nginx setup can provide, assuming each of those pieces is set up correctly. And you personally probably aren't working on software at that scale.

I personally also suspect we can make the experience of building on top of event sourcing better than the experience of building on top of a traditional database, because decoupling reads from writes gives you more architecture options. But I don't think we're there yet.

> I've been through some interviews where I'm the oldest at the table and they tell me their stack and I try to keep a straight face. > Well, yeah, with every acronyms AWS could come up with, using them all for no reasons at all (except S-C-A-L-I-N-G), you could build it all rock solid on a mid-size linode and do it with 5 dudes

I've been through some interviews where I was the youngest person at the table, but it otherwise sounds like I had a very similar experience to you (they had ~1000 total users - their "scaling issues" were entirely due to poorly architected code). In my case, I politely questioned it during the interview and they actually ended up letting me take over and simplify things.

Why do you think it’s more cost effective to build a service on linode than just use AWS?

Planning for scale that will probably never happen is a specific instance of the YAGNI principle, and no doubt a particularly expensive one.

> To which the retort can be many: they might be building for the future - to integrate more systems, they might have fan-out use cases, they might want to have a log to replay in case of disasters - so on and so forth. All well documented and very valid use cases of Kafka.

WeWork has very predictable scale because its data ingestion needs are tied to expensive investments in physical capital. They don't have to worry about getting slashdotted. Same thing at my company, where data needs are tied to expensive enterprise contracts. In fact, we're considering Kafka for something we do, but it didn't sneak up on us--we knew for years that a particular use case involved a lot of data and wasn't ideal for a relational database.

> It's, frankly, elitist

Why not see the coin from the other side. Developers giving their experience when they are working sane ?

Many of these articles, I have seen authors quoting from their experience building large systems.

Moreover, as a system architect, you often have to analyze the requirements and choose a good stack for implementation. A young dev in a startup is biased against the architecture in one way, because they might be seeing Kafka on their resume as a shining star to greater riches.

> Giving direct access to your database is fast but leads to very strong data coupling.

Or you could just give "direct" access to (a DB role that only has been granted SELECT on) a single schema within your database, entirely consisting of views. Then you've got a stable data-access API that you can iterate your data model behind, with nothing more than your DB itself.

(And you don't need to put much thought into setting it up, if you don't want to; your stable schema-API-layer can just start off as a bunch of views with 1:1 relationships to your real tables, which each just "SELECT * FROM their_underlying_table".)

I once looked into authen/authorization with Kafka topics and whether one can only be allowed to subscribe to specific ones and came up short with any solution to how to do it with Kafka. From my POV, points: 1 to 4 listed above now have been moved to one single point (Kafka) and its capabilities are a lot limited than those other tools/entrypoints (of data access) mentioned. Have there been new development to Kafka's IAM?

Wouldn’t apps relying on CDC events be strongly coupled as well? They’re just not synchronously coupled.

To save some time for others.

CDC -> Change Data Capture

Honestly, I've only skimmed the article, but this comment rubbed me the wrong way right off the bat. The article (discussing use of Kafka) doesn't seem to have any direct relation to WeWork's failure.

There is a lot that you can learn especially from businesses that failed.

Just because the company wasn't successful does not mean you should discount everything related to that company, especially when the content and lessons are technical, and the business failed for non-technical reasons (shady business practices and self-dealing).

Just getting in here to say someone should edit the HN title to have a date. Hearing about success at WeWork now is akin to being in accounting and finance in the late 90s doling out praise for Arthur Anderson.

Maybe it should have been an article on: "You Don't Need WeWork"

Has Substack emerged as "new Medium" for ambitious but not-as-seasoned-as-they-would-appear tech diarists?

Yeah, I found the article to be a little annoying in that it's mostly attacking WeWork.

My recommendation for anyone new to the area, is the streaming systems book: http://streamingbook.net/, which came after two excellent blog posts: https://www.oreilly.com/radar/the-world-beyond-batch-streami..., and https://www.oreilly.com/radar/the-world-beyond-batch-streami...

Basically, if you have an unbounded data problem, and you might have something that is _practically_ unbounded, that book really helped me gain insight to describe what we used Kinesis for (which is really similar to Kafka in a lot of ways).

Thinking about event vs processing time, ordering and windowing requirements, etc, were really helpful in thinking about my own data problems.

In the end, the author of this article claimed that WeWork just simply didn't have much of an unbounded data problem. Which I generally agree with. But simply saying "you can do this in PostgreSQL" isn't really a great takeaway, and, it really feels that was the message being reinforced here.

In case you haven't already read it, do read the classic LinkedIn "Log" article: https://engineering.linkedin.com/distributed-systems/log-wha...

I just learned Kafka this year to augment an existing system at work. One thing I have found is, there's a good amount of smarts in the Kafka clients. Look carefully at the Kafka clients available for the runtimes you work with, and evaluate them for robustness, features, and development activity (sadly, for example, kafka-node seems to have lost all momentum).

This could be achieved by good architecture and system design.

You should have various aspects of your application configurable and independent enough to enable you to develop an alternative system and make a small downtime migration.

Many of scaling issues I see in startups, and stories told by devs in startups, seem to be because corners are cut to make the MVP and push it to the market as quickly as possible, and then suffer scaling woes due to poor design and architecture choices.

LMAO

Can you make your point without the appeal to a (missing) authority? There is no standards document I've ever seen which names kafka explicitly.

Kafka has some benefits (lovely API) and some tradeoffs (hard to maintain, zookeeper is a bit of a mess). But there are obviously a lot of other ways to build successful, scalable software. WhatsApp was implemented on top of Erlang, and scaled to over 500m users when they had only 35 employees.

I want to disagree with your claim that kafka should be considered the default option - but you haven't dignified your (controversial) claim with any actual arguments. There's nothing here to even disagree with.