I disagree. Even on hacker news, people rarely express such absurd things with so little confidence. You fail to take into account many of the following:
* Extremely high volume. Uber has indicated elsewhere that they receive upwards of a few hundred thousand requests per second on just one service. Please show me the logistics stack that did this in the 70s.
* Yes, building the first version of something is extremely cheap and easy. But being able to improve it becomes harder and harder. Especially given high volume, modern companies need sophisticated analytical tools that provides reliable data to both technical and non technical staff. Please show me the analytics stack that was able to ingest, store, and analyze terabytes of business data in realtime from the 70s.
* Reliability. Modern web applications need to fail gracefully and be debugged quickly. Please show me the logistics and routing stack that was capable of extremely high uptime while being deployed constantly and serving hundreds of thousands of requests per second from the 70s.
* Extensibility. Businesses need to extend to new markets. Moves like this often invalidate past assumptions. In order to support business flexibility, modern engineers deliberately invests considerable time into building decoupled components that can be reused as platforms instead of stuffed into a monolithic codebase. Please show me the operations and routing stack that could easily be reconfigured to enable such products as Amazon Web Services, Uber's external API, the google maps API, or Uber EATS—from the 70s.
To make this more concrete, I worked on a routing stack at another company which probably works similarly to Uber's ETA systems. When considering these things, it's important to keep in mind the dependency tree of each new problem set and the work required to make those dependencies work reliably at big scale.
To give you an idea of what this area alone entails:
1. Machine learning.
- wiring together of and improving algorithms: linear regression to begin with, then random forest, then neural networks.
- ensuring data required for learning is reliably available and correctly computed.
- tools to launch, deploy, test these models.
2. Working with map data in memory many times larger than what fits onto the smallest consumer laptop.
- how do you handle updates of data?
- what if you want to use different data sets in different places, because they're more accurate?
- how do you debug errors in the data without visual tools (hint: it's really hard and time consuming)?
- how do you optimize loading this data into memory without requiring hours to deploy your application?
- where do you even store this data?
3. Requests per second in the hundreds of thousands and latency requirements (in order to ensure the app responds quickly) hovering around 10ms.
- how do you profile complex distributed applications?
- what optimizations are available to make graph search faster (hint: A* isn't fast enough)?
- how hard is it to implement these optimizations?
4. Data science and data science tools
- Visualizations!
- again, reliable data pipelines
That's about what one team works on over the course of a year. Note the dependencies we have here:
1. We assume access to cloud infrastructure that doesn't require us to do all of our own devops.
2. We assume mature and automatically scaling data infrastructure: that kafka and storm have been set up and tuned to a degree that we don't have to worry about it. In reality, kafka alone requires a team of at least a dozen at linkedin to keep up with the maintenance, operations, and optimization burden of keeping up with scale.
3. We assume mature and scalable service oriented architecture tooling—if a call to another service is slow, I should be able to see on a dashboard what service is slow, how frequently, it's slow, why it's slow (if it depends on another service) etc.
and countless other things I could spend days enumerating for you but I guess it'd be wasted on you because you're pretty convinced you already solved these problems in the 70s, so why am i wasting my breath
It's important to counter the trivializing sort of dismissal that people often post to HN (the old "I could build Twitter in a weekend" and whatnot). We want the culture to move more toward thoughtful, substantive critique. So your detailed argument here, based on experience, is valuable. Please don't spoil it by becoming uncivil like this:
> I guess it'd be wasted on you because you're pretty convinced you already solved these problems in the 70s, so why am i wasting my breath
With that your comment does more harm than good: it poisons the atmosphere and detracts from your substantive contribution.
You're definitely not "wasting your breath" even if you fail to persuade the other person not to be snarkily dismissive, because the real audience for a comment like yours is everybody else: i.e. the rest of us who are curious about how (in this case) Uber operates and why things might be the way they are. That audience needs to see both good information about the challenges involved (as opposed to this-has-been-trivial-since-the-70s) and a good example of how to patiently respond to a trivializing comment with a thoughtful one. It's bad if, instead, you give us a reason to wince and an example of replying to a dismissive comment with a rude one.
How are you going to discuss all that in a substantive manner? All anyone can do in the limited time frame of a HN discussion is draw parallels to previous job experiences or previous user experiences.
My own experiences are more aligned with the sentiment expressed in the "dismissal" comment.
Mine too, but that doesn't make it a good comment. In fact its first paragraph is almost a parody of the know-it-all internet comment.
There are a zillion ways to make the same kind of argument thoughtfully. Talking about one's own concrete experiences helps. So does not acting like you know everything about somebody else's situation.
Bloat is a problem, and so (in my view) is the kitchen-sink software culture of hauling in libraries and frameworks without thought for overall complexity. But we need to be able to talk about this at a higher level than other-people-are-idiots-compared-to-me. A much higher level.
Point taken. I'll try to do better next time but it does get old after a while of seeing the same set of mistakes and articles parroted over and over again. Trivial problems blown out of proportions because people don't know the proper science, theory, and history and have opted to re-invent things badly. Uber is especially known for this since they re-invented/re-wrote basic geospatial algorithms in Go and hailed it as innovation.
The dismissal comes from years of reading such articles and then chipping away at the veneer to see what's really underneath and being disappointed every time and then working on such things and experiencing first hand how the bloat comes about.
I understand, but we need you to give us the experience and omit the dismissal. The former can dramatically improve the quality of this site; the latter only degrades it. And the former will actually be persuasive while the latter merely gets people's backs up (or makes them cheer if they happen to hate the same thing) without teaching the reader.
I get irritated at having to repeat the same things over and over, too, but the internet is basically stateless and so (sadly) is the software business. And like everyone, I get peevish when people say/do wrong things and act like they know what they don't. The longer one has been around, the more occasions one has to secrete bile. But it's a humor one must metabolize internally and not release into the community—hard work and not fun at first, but far more rewarding in its effects, and maybe our only chance at creating an actually functional culture.
I've built things that handle less than 1 tps and things that handles more than a few thousand without any significant memory or CPU load. All of these things have had uptime that has been unmatched with other systems that it has had to interface with all the while degrading gracefully and handling everything else in between. So lets just say I understand a thing or two about designing fault-tolerant systems that need to operate under high loads and degrade gracefully.
* Re: workload in the 70s. You are missing the point about logistics stacks than handle 1000s of transactions per second. The point is that Uber's problem is self-imposed. Stepping back and thinking about the problem a little will let them handle the same amount of work with 1/10 the hardware costs.
* Re: first version. The first version and the n-th version when properly designed requires the same set of gradual steps. If you build the first version to throw away then whose problem is it that you built it that way and need 10x the hardware to handle the workload because of shitty architecture? Again, stepping back and taking a holistic view and thinking a little bit is the trick.
* Re: extensibility. Same deal. Design your architecture properly and you can extend it as far as any business requirement forces it without spending 10x on hardware and software. How do you do this? Same as above. Thinking.
* Re: reliability. See above. Thousands of transactions a second with unmatched uptime. It is more likely the systems I interface with will go down or even for AWS to have an outage than for a properly designed system to fail.
1. Machine learning - already doing it wrong. You've failed to learn from history and instead are following fads and trends. When properly framed routing/allocation is a linear program and there are solvers than will solve such problems with millions of variables. Instead you have opted to complicate the problems with latest fads and trends that are not even suited to the problem you are solving. In essence you've made my point.
2. Consumer laptop? I'd hope the software runs on server grade hardware. Bringing up a consumer laptop as a restriction on memory is a non-sequitur.
3. Hundreds of thousands. Great. I can handle several thousand connections per second on a dinky c4.2xlarge instance with 10-20ms guarantee with a ruby stack. There are plenty of ways to optimize it further but I've never needed to. The literature is full of optimized and distributed graph search algorithms. Operationalizing any one of them wouldn't be much work. How do I know? Because I've done it before.
4. Reliable data pipelines have been a solved problem since hadoop and friends. This is a solved problem. Again making my point about bloat.
Re: one team over a year. Seems like you need better engineers or better designed systems. If you're developing software with more than 100 engineers and the boundaries between teams are so ill-defined that you need more than 10 per team then that's an organizational problem and highly inefficient way to do things. How do I know? Worked on teams that gelled and those that didn't. The determining factor was always reducing communication overhead by proper architectural design. The amount of communication overhead was almost directly correlated with software bloat and sprawl.
1. Devops: Solved problem. Chef, ansible, puppet. Pick one they're all the same.
2. Kafka is not good software. Pick something else for your event management pipeline. Heck, build it from scratch. Neither Kafka nor Storm are novel or required. Chances are you've over-engineered it if you are reaching for those and need to step back and think.
3. Simplify your call graph. There is no magic bullet here. No amount of dashboards, logs, and metrics will let you get around an ill-designed and bloated service architecture. Again you've made my point.
Same reason any other software is not good software. Chances are you don't need it and are reaching for a shiny tool. Kafka requires zookeeper and in my experience zookeeper is an operational nightmare. If you need an event bus then there are many out there that are much simpler and easier to maintain operationally with much simpler failure modes.
Don't just reach for something because it has been the most common thing posted on programming forums. The behavioral psychologists and economists consider this a well known cognitive bug.
Rabbitmq, perfectly fine message bus in pretty much all use cases. Easier to operate and maintain without any extra dependencies and much simpler failure modes. A few more: zeromq, sqs, hornetq, nats, nsq, etc. Any one of those will most certainly fulfill whatever use case you have.
The point being kafka has a very heavy operational overhead and you better understand what you are getting into and what bargain you're making for the scalability you mention.
I've used Rabbitmq, it most certainly does not fulfill the volume requirements I have.
The fact that you are comparing zeromq to Kafka is pretty good evidence that you have no idea what you are talking about, and are just tossing out names from google. I'm a little disappointed, honestly, I hoped you were aware of something I hadn't heard of.
There are two ways to solve problems in engineering. You either bring the problem closer to your existing solutions by redefining the problem or you keep the problem the same and bring your solutions closer to the problem.
Sounds like you are unwilling to redefine your problem so that it is amenable to solutions that are not kafka.
Yeah, you recommended a sockets library as an alternative to a distributed durable circular buffer. Not obviously clueful. Might as well recommend Nginx as an alternative to JavaScript.
I need to durably handle billions of events per day. No amount of redefining changes the underlying business problem.
Kafka, on the other hand, has been helping me solve that problem for years.
Let's see. I can handle a few million on a single instance and I have yet to hit any memory or CPU limits indicating I can handle 10x of what I'm currently handling. Oh and it's about 100k or more transactions per hour at peak load. Just from basic operational observation and logs. Also, have yet to see any durability issues and I've managed to do it without kafka. So pretty basic math says the entire thing can be scaled to a "few billion" transactions in a pretty straightforward way. Then again I'm more willing to redefine my problems to come up with simpler solutions.
But this discussion has devolved into personal insults at this point. We have nothing to teach each other it seems.
If nothing else, I could teach you that zeromq has nothing to do with queing or durability.
It's certainly possible that Rabbit has improved in the years since I used it, if it works for your use cases, great. But don't assume that everyone using a popular technology is doing so because of a fad or without understanding the tradeoffs.
* Extremely high volume. Uber has indicated elsewhere that they receive upwards of a few hundred thousand requests per second on just one service. Please show me the logistics stack that did this in the 70s.
* Yes, building the first version of something is extremely cheap and easy. But being able to improve it becomes harder and harder. Especially given high volume, modern companies need sophisticated analytical tools that provides reliable data to both technical and non technical staff. Please show me the analytics stack that was able to ingest, store, and analyze terabytes of business data in realtime from the 70s.
* Reliability. Modern web applications need to fail gracefully and be debugged quickly. Please show me the logistics and routing stack that was capable of extremely high uptime while being deployed constantly and serving hundreds of thousands of requests per second from the 70s.
* Extensibility. Businesses need to extend to new markets. Moves like this often invalidate past assumptions. In order to support business flexibility, modern engineers deliberately invests considerable time into building decoupled components that can be reused as platforms instead of stuffed into a monolithic codebase. Please show me the operations and routing stack that could easily be reconfigured to enable such products as Amazon Web Services, Uber's external API, the google maps API, or Uber EATS—from the 70s.
To make this more concrete, I worked on a routing stack at another company which probably works similarly to Uber's ETA systems. When considering these things, it's important to keep in mind the dependency tree of each new problem set and the work required to make those dependencies work reliably at big scale.
To give you an idea of what this area alone entails:
1. Machine learning. - wiring together of and improving algorithms: linear regression to begin with, then random forest, then neural networks. - ensuring data required for learning is reliably available and correctly computed. - tools to launch, deploy, test these models.
2. Working with map data in memory many times larger than what fits onto the smallest consumer laptop. - how do you handle updates of data? - what if you want to use different data sets in different places, because they're more accurate? - how do you debug errors in the data without visual tools (hint: it's really hard and time consuming)? - how do you optimize loading this data into memory without requiring hours to deploy your application? - where do you even store this data?
3. Requests per second in the hundreds of thousands and latency requirements (in order to ensure the app responds quickly) hovering around 10ms. - how do you profile complex distributed applications? - what optimizations are available to make graph search faster (hint: A* isn't fast enough)? - how hard is it to implement these optimizations?
4. Data science and data science tools - Visualizations! - again, reliable data pipelines
That's about what one team works on over the course of a year. Note the dependencies we have here:
1. We assume access to cloud infrastructure that doesn't require us to do all of our own devops.
2. We assume mature and automatically scaling data infrastructure: that kafka and storm have been set up and tuned to a degree that we don't have to worry about it. In reality, kafka alone requires a team of at least a dozen at linkedin to keep up with the maintenance, operations, and optimization burden of keeping up with scale.
3. We assume mature and scalable service oriented architecture tooling—if a call to another service is slow, I should be able to see on a dashboard what service is slow, how frequently, it's slow, why it's slow (if it depends on another service) etc.
and countless other things I could spend days enumerating for you but I guess it'd be wasted on you because you're pretty convinced you already solved these problems in the 70s, so why am i wasting my breath