I am only going to suggest a small edit -> s/Postgres can’t/Heroku's Postgres can't/
PG can scale up pretty well on a single box, but scaling PG on AWS can be problematic due to the disk io issue, so I suspect they just don't do it. I'd love to be corrected :)
It has to deal with the number of open connections to postgresql, not disk IO. You don't want to have 4,000 connections open at once (unless you were using a connection balancer, but that would only work in transaction mode).
Postgres has a limitation on number of open connections. This is because 1 connection = 1 process. MySQL uses threads, which scales better but has other downsides. The thread-based approach is also possible with Postgres using 3rd party connection pooling apps, e.g. pgbouncer.
Data Dep't here. Postgres scales great on Heroku, AWS, and in general. We've got users doing many thousands of query per second, and terabytes of data. Not a problem.
The issue with the number of connections is that each connection creates a process on the server. We cap the connections at 500, because at that point you start to see problems with O(n^2) data structures in the Postgres internals that start to make all kinds of mischief. This has been improved over the last few releases, but in general, it's still a good idea to try and keep the number of concurrent connections down.
Given how bold the claims are, I'd say it'd be best if Heroku reacted quickly, indeed. Cause right now, it seems that the author is either not understanding how Heroku works or ignoring important specifics/nuances inherent to their "secret-sauce" algorithms. Whether the analysis done in the article is sound or the author is actually pushing an agenda remains to be discussed and the sooner the better.
I'm also quite wary of the incentive a 20K monthly bill would give you to try and shake Heroku down for a rebate. By the way, the figure in itself seems very high, but out of context it's impossible for me, the reader, to evaluate if that's actually good money or not. Maybe other solutions (handling everything yourself) would actually be WAY more costly, maybe Heroku actually provides a service that is well-worth the money or maybe the author is right and it's actually swindling on Heroku's part, no way to know.
But then again, those two links don't address the core of the problem:
Heroku is used by tons of people around the world. Some of them are paying good money for the service. Given the amount of scrutiny under which they operate, what is the incentive for them to turn an algorithm into a less effective one and still charge the same amount of money in a growing "cloud economy" where companies providing the same kind of service are a dime a dozen (AWS, Linode, Engine Yard, etc)?
How does that benefit their business if "calling their BS" is as easy as firing Apache Benchmark, collecting results, drawing a few charts and flat out "prove" that they're lying about the service they provide??
I mean, I doubt Heroku is that stupid, they know how their audience doesn't give them much room for mistakes. So as nice as the story sounds on paper, I'd really like another take on all this, either from other users of Heroku, independent dev ops, researchers, routing algorithms specialists or even Heroku themselves before we all too hastily jump to sensationalist conclusions.
I didn't jump to any conclusion that they are doing this simply for the money.
The only conclusion I jumped to was that they ditched the routing they originally said they had (without telling anyone) and that their routing is worse than what you get as a default from passenger.
My bad if that wasn't clear in my comment: the sensationalist bit wasn't directed at you but at the general tone of the thread. In fact, I actually upvoted your comment for providing two links highly relevant to the article.
This has a simple explanation. Heroku was targeting the ruby-on-rail crowd, which more or less belongs to the "Stupid Rich" quadrant when it comes to hosting / deployment.
Meanwhile, AWS has been a dominant presence in the "Stupid Poor" market segment.
This should be more prominent. I want to love Heroku, and am sure that I could.
But really, throwing in the towel at intelligent routing and replacing it with "random routing" is horrific, if true.
It's arguable that the routing mesh and scaling dynamics of Heroku are a large part, if not -the- defining reason for someone to choose Heroku over AWS directly.
Is it a "hard" problem? I'm absolutely sure it is. That's one reason customers are throwing money at you to solve it, Heroku.
Even their own docs were wrong on this for a long time. It bit me in the ass back in 2011 and I got them to clarify and update the documentation just a little.
Thanks for the blog post, by the way. When we were struggling with our own Heroku scaling issues last year (we eventually moved to AWS), I came across it and it was good vindication that somebody else was facing the same issue.
> But really, throwing in the towel at intelligent routing and replacing it with "random routing" is horrific, if true.
The thing is, their old "intelligent routing" was really just "we will only route one request at a time to a dyno." In other words, what changed is that they now allow dynos to serve multiple requests at a time. When you put it that way, it doesn't sound as horrific, does it?
The requests will not be served at the same time, that's the whole point. If a request is routed to a busy dyno, you will have to wait that the previous job finish before being able to start yours.
I believe the article is saying that this wait will only occur with Rails due to it being single threaded? Reactor pattern frameworks like node don't suffer the same issue.
At some point you hit memory limits, disk IO limits, or simply a connection limit. It doesn't matter what limit:
If you have some requests that are longer running than others, random load balancing will make them start piling up once you reach some traffic threshold.
You can increase the threshold by adding more backends or increasing the capacity of each backend (by optimizing, or picking beefier hardware if you're on a platform that will let you), and maybe you can increase it enough that it won't affect you.
But no matter what you do, you end up having to allocate more spare resources at it than what you would need with more intelligent routing.
If you're lucky, the effect might be small enough to not cost you much money, and you might be able to ignore it, but it's still there.
> But no matter what you do, you end up having to allocate more spare resources at it than what you would need with more intelligent routing.
I think we have to remember that the "intelligent routing" in question here is actually marketing-speak for "one request per server." Are you saying that when your servers can only receive one request at a time, you will necessarily need fewer than if your servers can handle three requests at a time but are assigned requests randomly?
No it's not. "Intelligent routing" means giving each server exactly as many requests as it can handle, and no more. If your servers can handle 3 requests each, using intelligent routing to make sure they never get more than 3 will blow the latency of the random method out of the water.
You are exactly right, but I would hope that someone wouldn't be using Node at all if CPU bound requests are even remotely commoneplace on their app. But of course, if they built their app to spawn processes when this happens, you end up with a case of a need for intelligent routing.
Thing is though, it says that a Dyno is a Ubuntu virtual machine. In what sort of horrendous configuration can an ENTIRE VM serve only a SINGLE REQUEST AT A TIME?!
That is utter madness, and the validity of the argument depends on whether it's the Heroku or this dude's fault that the VM is serving only a single request at a time (and it taking >1sec to handle a request).
But one VM can host multiple Rails instances, each on different port. That's what Passenger or Unicorn do, acting as proxy of group of locally spawn Rails instances.
Yes it does sound as horrific. It should never route more than N requests to a dyno, where N is the number of requests it can handle simultaneously. It doesn't matter if N is 1, 2, or 10. And it should always send new requests to the emptiest dyno.
While I agree that routing to the emptiest dyno would be a good thing, I think it's a little melodramatic to describe giving every dyno at least three times and up to several thousand times as much power as "horrific."
The problem is when you send a dyno that has all its threads stuck on long-running computations a new request, because it won't be able to even start processing it. The power is orthogonal to the problem.
The only mitigation is that if a dyno can handle a large number of threads, it probably won't get clogged. But if it can only handle 3 and gets new requests at random, you're in a bad place.
> That's one reason customers are throwing money at you to solve it, Heroku.
People are throwing money at Heroku because it's really easy to use, not because it's the best long-term technology choice. Seriously - what percentage of Heroku paying users do you think actually read up on the finest technical details like routing algorithms before they put in their credit card? Heroku knows. They know you can't even build a highly-available service on top of it, since it's singly-homed, and they're still making tons of money.
> So heroku doesn't want these $20,000/mo accounts, just technically understaffed startups paying $1000/mo?
> I think heroku does want to be a long-term technology choice.
Oh, I'm sure Heroku wants to be a long-term technology choice. That doesn't mean they're trying to be one with their current product offerings.
Consider their product choices since launch: they've added dozens of small value-added technology integrations. Features for a few bucks a month like New Relic to upsell their smallest customers. The price drop was also a big move to reduce barriers to using their platform - which also targets smaller customers. They launched Clojure as their third supported language! Meanwhile, they're singly-homed and have had several protracted outages, and have no announced plans to build a multi-homed product. Scalability has gotten worse with this random routing development.
I think Heroku has known for a long time that they don't have a long-term platform product and that they can't keep big accounts until they build one.
As others here have pointed out in different discussions: Ideas aren't worth much by themselves. It's the subset that's implementable of which it's the subset that has gotten implemented that's worth anything. If we just love ideas we should better become philosophers.
Sorry to be a bit harsh, but I find it a bit shocking how even in this field where we can basically play god and do whatever we want and what we think is best on increasingly powerful bit-cruncher-and-storer-machines, so many here seem to behave like a herd of sheep and just do what 'everyone else' does. Just sit down for a moment and think! What are my requirements right now? What could be a requirement in the near future? What technologies are there which can help me? Am I sure about these feature? Better read up on it first! How difficult is it to get it to behave in ways that are or can be important for me?
Now list that stuff down. If it's puzzling sleep over it, forget it for a few days. Then suddenly, for example under a hot shower you get an idea - that requirement I had isn't really one, I can solve it differently! Come back, take the now fitting piece of the puzzle and do your job in 20% of the time that would have been needed if you would just have blindly followed some path. That's how it usually works for me. Be picky, be exact, but be lazy.
Now about that routing dispatcher problem: Couldn't we solve that in one to two weeks on a generic plattform, but specifically for a certain use case? Let's say you want to have a worker queue of rails request handlers that work in parallel. Just write that damn router! Maybe I'd be lazy, learn Erlang for a week and think about it afterwards.
I guess the problem I have is with the word 'love' itself. It's such a strong feeling for something that's just a tool that can in some situations make you turn around bits more efficiently. Can't we just look at that stuff rationally and reserve that kind of feelings for family and friends?
Because the premise is a good one, abstracting away some of the lower level details so people can just worry about building their apps. It has always been a tough abstraction to pull off though. It is possible the answer is simply that rap genius has outgrown heroku, in the same way as twitter outgrowing rails it doesn't make it a bad tool for everyone.
Precisely. I'm definitely not a startup worshipper; in fact, in terms of the HN spectrum, I'm probably at the more cynical end of the spectrum. But I like the ease of integration, and the fact that, for a smaller application, it certainly helps with my administration and infrastructure overhead.
This is something that I have been struggling with the past long while. Very troublesome when a dyno cycles itself (like they always will at least every 24 hours), as the routing layer continues to send it requests, resulting in router level "Request Timeouts" if it takes too long to restart.
Especially difficult to diagnose when the queue and wait time in your logs are 0. What is the point of these in the logs if it never waits or queues?
We ran into this exact same problem at Impact Dialing. When we hit scale, we optimized the crap out of our app; our New Relic stats looked insanely fast, but Twilio logs told us that we were taking over 15 seconds to respond to many of their callbacks. After spending a few weeks working with Heroku support (and paying for a dedicated support engineer), we moved to raw AWS and our performance problems disappeared. I want to love Heroku, but it doesn't scale for Rails apps.
We moved our Twilio app off Heroku for the same reasons. Extensive optimizations and we would still get timeouts on Twilio callbacks.
The routing dynamics should be explained better in Heroku's documentation. From an engineering perspective, they're a very important piece of information to understand.
i don't think some details of the argument hold. it alleges that you need more dynos to get the same throughput. but that's not true once you have sufficient demand to keep a queue of about sqrt(n) (i think - someone who knows more theory than me can correct me) in size on the dyno (where you have n dynos). because at that point all dynos will be running continuously, and the throughput will be the same with either routing.
the average latency will be higher, though (and the spread in latency larger).
But you never want to have a queue on any of your dynos! A queued request means that a user is waiting with no response. If your goal is to have 0 (or less than epsilon) requests queued, it takes far fewer dynos if the requests are routed intelligently
If you have 10 dynos and 1000 simultaneous requests, the difference between naive and intelligent might well be reduced, but that's also a scenario in which your end user response times would be horrendously slow and so you'd need more dynos either way
Personally - I prefer Linode to Heroku, sure there is more of my time consumed with sys admin, but I like having full control over my platform & setup, rather than having it virtually dictated to me. I'm always open to change but this strategy has served me very well for almost 3 years now.
So the issue here is two-fold:
- It's very hard to do 'intelligent routing' at scale.
- Random routing plays poorly with request times with a really bad tail (median is 50ms, 99th is 3 seconds)
The solution here is to figure out why your 99th is 3 seconds. Once you solve that, randomized routing won't hurt you anymore. You hit this exact same problem in a non-preemptive multi-tasking system (like gevent or golang).
I do perf work at Facebook, and over time I've become more and more convinced that the most crucial metric is the width of the latency histogram. Narrowing your latency band --even if it makes the average case worse-- makes so many systems problems better (top of the list: load balancing) it's not even funny.
I can chime in here that I have had similar experiences at another large scale place :). Some requests would take a second or more to complete with the vast majority finishing in under 100MS. A solution was put in place that added about 5 MS to the average request, but also crushed the long tail(it just doesn't even exist anymore) and everything is hugely more stable and responsive.
I think you misunderstood: they optimized the long running requests and the optimization incurred 5ms performance loss for short requests. It is not that the additional 5ms solved the problem.
Let's assume that it is unacceptable to have each dyno tell the router each time it finishes a request ( http://news.ycombinator.com/item?id=5217157 ). And also that the goal is to reduce worst-case latency. And also that we don't know a priori how many requests each dyno should queue before it is 'full' and rejects further requests ( http://news.ycombinator.com/item?id=5216771 ).
Proposal:
1) Once per minute (or less often if you have a zillion dynos), each dyno tells the router the maximum number of requests it had queued at any time over the past minute.
2) Using that information, the router recalculates a threshold once a minute that defines how many queued requests is "too many" (e.g. maybe if you have n dynos, you take the log(n)th-busiest-dyno's load as the threshold -- you want the threshold to only catch the tail).
3) When each request is sent to a dyno, a header fields is added that tells the dyno the current 'too many' threshold.
4) If the receiving dyno has too many, it passes the request back to the router, telling the router that it's busy ( http://news.ycombinator.com/item?id=5217157 ). The 'busy' dyno remembers that the router thinks it is 'busy'. The next time its queue is empty, it tells the router "i'm not busy anymore" (and repeats this message once per minute until it receives another request, at which point it assumes the router 'heard').
5) When a receiving dyno tells the router that it is busy, the router remembers this and stops giving requests to that dyno until the dyno tells it that it is not busy anymore.
I haven't worked on stuff like this myself, do you think that would work?
I seem to recall Google mentioning on some blog several years ago that high variance in response latency degrades user experience much more than slightly higher average request times. I can't find the link though; if anyone has it, I'd be grateful.
There's a relatively easy fix for Heroku. They should do random routing with a backup second request sent if the first request times fails to respond after a relatively short period of time (say, 95th percentile latency), killing any outstanding requests when the first response comes back in. The amount of bookkeeping required for this is a lot less than full-on intelligent routing, but it can reduce tail latency dramatically since it's very unlikely that the second request will hit the same overloaded server.
A relatively easy fix, for read-only or idempotent requests. Also, if long-tail latency requests wind up being run twice, this technique might accelerate tip-over saturation. Still, this 'hedged request' idea is good to keep in mind, thanks for the pointer.
The 'tied request' idea from the Dean paper is neat, too, and Heroku could possibly implement that, and give dyno request-handlers the ability to check, "did I win the race to handle this, or can this request be dropped?"
> There's a relatively easy fix for Heroku. They should do random routing with a backup second request sent if the first request times fails to respond after a relatively short period of time (say, 95th percentile latency), killing any outstanding requests when the first response comes back in. The amount of bookkeeping required for this is a lot less than full-on intelligent routing, but it can reduce tail latency dramatically since it's very unlikely that the second request will hit the same overloaded server.
Your solution doesn't work if requests aren't idempotent.
Yeah, I know. I figure that for incoming HTTP traffic it's relatively easy to balance the GET requests, and if they're doing anything remotely sane with HTTP those ought to be idempotent (if they're not, Googlebot will come along and delete their site ;-)).
For mutating requests, there's a solution as well, but it involves checksumming the request and passing the checksum along so that the database layer knows to discard duplicate requests that it's already handled. You need this anyway if there's any sort of retry logic in your application, though.
From experience, this is an incredibly effective way to DoS yourself. It was the default behaviour of nginx LB ages ago. Maybe only on EngineYard. Doesn't really matter as nobody uses nginx LB anymore.
Even ignoring the POST requests problem (yup, it tried to replay those) properly cancelling a request on all levels of a multi-level rails stack is very hard/not possible in practice. So you end up DOSing the hard to scale lower levels of the stack (e.g. database) at the expense of the easy to scale LB.
It's a capacity/latency tradeoff. Perhaps I'm biased by working at Google, where capacity is cheap and latency will kill you, but IIUC Heroku runs off of AWS and their database tier needs to scale horizontally anyway, so reserving sufficient overflow capacity should simply be a matter of throwing money at the problem.
Right now, heroku has one inbound load-balancer that's out of their control (probably ELB(s)). This load balancer hits another layer of mesh routers that heroku does control, and that perform all of herokus magic. In order for "intelligent routing," which is more commonly known as "least-conn" routing to work amongst the mesh layer, all of the mesh routers would have to share state with each other in real-time, which makes this a hard problem.
Alternately, heroku can introduce a third layer between the mesh routers and the inbound random load balancer. This layer consistently hashes (http://en.wikipedia.org/wiki/Consistent_hashing) the api-key/primary key of your app, and sends you to a single mesh router for all of your requests. Mesh routers are/should be blazing fast relative to rails dynos, so that this isn't really a bottleneck for your app. Since the one mesh router can maintain connection state for your app, heroku can implement a least-conn strategy. If the mesh router dies, another router can be automatically chosen.
This is something I've read in networked game literature: players react far better to consistent and high latency than to inconsistent and low latency, even if the averages are lower in the latter case. (It might even have been a John Carmack article).
Correct, this matches my observations as well. I'd trade an increase in mean latency for a decrease in worst-case latency anytime. It makes it so much easier to reason about how many resources are needed for a given workload when your latency is bounded.
That's probably true, but the value that Heroku is selling (and they charge a lot for it!) is that you _don't_ need to deal with this - that they will balance that out for you.
> Narrowing your latency band --even if it makes the average case worse-- makes so many systems problems better (top of the list: load balancing) it's not even funny.
Yeah, it's a lot more practical than implementing QoS, isn't it?
Wouldn't the bad tails of random routing be an unpredictably long length of time since long running requests times are unpredictable?
Even if you work on narrowing the fat tails, shouldn't you still need to be upfront and clear about how adding a new dyno only gives you an increased chance of better request handling times as you scale?
Re the distribution, absolutely. That "FIFTY TIMES" is totally due to the width of the distribution. Although, you know, even if their app was written such that every single request took exactly 100ms of dyno time, this random routing would create the problem all over again, to some degree.
As for the intelligent routing, could you explain the problem? The goal isn't to predict which request will take a long time, the goal is to not give more work to dynos that already have work. Remember that in the "intelligent" model it's okay to have requests spend a little time in the global queue, a few ms mean across all requests, even when there are free dynos.
Isn't it as simple as just having the dynos pull jobs from the queue? The dynos waste a little time idle-spinning until the central queue hands them their next job, but that tax would be pretty small, right? Factor of two, tops? (Supposing that the time for the dyno-initiated give-me-work request is equal to the mean handling time of a request.) And if your central queue can only handle distributing to say 100 dynos, I can think of relatively simple workarounds that add another 10ms of lag every factor-of-100 growth, which would be a hell of a lot better than this naive routing.
I think the problem is that any servers which can handle concurrent requests now need to decide how many requests they can handle. Since most application servers seem to have concurrency values of "1, ever" or "I dunno, lots" this is a hard problem.
Your solution would likely work if you had some higher level (application level? not real up on Heroku) at which you could specify a push vs. pull mechanism for request routing.
Given that, according to TFA (and it's consistent with some other things I've read) Heroku's bread and butter is Rails apps, and given that, according to TFA, Rails is single-threaded, that (valid) point about concurrency in a single dyno is perhaps not that relevant? You'd think that Heroku would continue to support the routing model that almost all of their marketing and documentation advertises, right? Even if it's a configurable option, and it only works usefully with single-threaded servers?
And if you did do it pull-based, it wouldn't be Heroku's problem to decide how many concurrent requests to send. Leave it to the application (or whatever you call the thing you run on a dyno).
And it doesn't need to be pull-based, if the router can detect HTTP connections closing in dynos, or whatever.
But the idea of pull-based work distribution is pretty straightforward. It's called a message queue.
Simulation author here with some additional analysis using a faster distribution of request times. If you use a distribution with median 50 ms, 90th percentile 225 ms, and 99.9th percentile 898 ms, then you need 30 intelligent dynos to handle 9000 requests/minute without queueing. In the same scenario with 30 naive dynos, 44% of requests get queued.
> The solution here is to figure out why your 99th is 3 seconds. Once you solve that, randomized routing won't hurt you anymore. You hit this exact same problem in a non-preemptive multi-tasking system (like gevent or golang).
The Golang runtime uses non-blocking I/O to get around this problem.
That isn't the only problem with random routing - the problems aren't as pronounced with uniform response speeds, but you still get a significant difference in net effective queue time, especially if you're operating close to your throughput limit.
Yes, it is very hard to do it at scale, but so what?
I mean, isn't the whole premise of their company to do intelligent things at scale so you don't have to?
It's not an insurmountable problem by any measure, and it's definitely worth it.
The solution here is to figure out why your 99th is 3 seconds.
I'm not sure this applies to the OP. His in-app measurements were showing all requests being handled very fast by the app itself; the variability in total response time was entirely due to the random routing.
Randomized routing isn't all bad. In fact, if Heroku were to switch from purely random routing to minimum-of-two random routing, they'd perform asymptotically better [1].
I'm not familiar with minimum-of-two-random routing, but it does seem like assigning request to dynos in sequence would perform much better than assigning randomly (ie in a scenario with n dyno capacity, request 1 => dyno 1, request 2 => dyno 2, ... request n => dyno n, request n+1 => dyno 1, ..., repeat)
That'd be probably significantly better than the case of (request i => dyno picked out of hat) for all i
If Heroku had the data needed to do minimum-of-two random routing, they'd have the data needed to do intelligent routing. The problem is not the algorithm itself: "decrement and reheap" isn't going to be a performance bottleneck. The problem is tracking the number of requests queued on the dyno.
If Heroku had the data needed to do minimum-of-two random routing, they'd have the data needed to do intelligent routing.
Not strictly true; imagine that they can query the load state of a dyno, but at some non-zero cost. (For example, that it requires contacting the dyno, because the load-balancer itself is distributed and doesn't have a global view.)
Then, contacting 2, and picking the better of the 2, remains a possible win compared to contacting more/all.
See for example the 'hedged request' strategy, referenced in a sibling thread by nostradaemons from a Jeff Dean Google paper, where 2 redundant requests are issued and the slower-to-respond is discarded (or even actively cancelled, in the 'tiered request' variant).
Setting up SSL on Elastic Beanstalk was very easy for us. The documentation explained the entire process. It is easier if you get a wildcard SSL cert, so then you can use the same SSL cert for your various deployments under the same domain.
I found Elastic Beanstalk had extremely serious latency and performance problems for PHP5, that didn't occur when setting up manual EC2/Load balancer. I intended to investigate it, but never had time.
I spent a lot of time trying to come up with a stable installation of EB for a Rails 3.2 app using RDS and just couldn't get it to a state where I'd ever deploy it as production. Here's where I decided to pull the plug:
Quite a few people still report a myriad of issues with Rails applications. I really want EB to work well with Rails, but just didn't have confidence in it.
This explains why some of my benchmarking tools gave very different and sometimes weird results when figuring out how many dynos our application needed.
As this blogpost also states Heroku really need to keep their documentation up to date. I sometimes stumble across something old referring to an old stack, or something contradicting.
Wow. I suspect Rap Genius has the dollars now where it's totally feasible for them to go beyond Heroku, but it still might not be the best use of their time. But if they have to do it, they have to do it.
OTOH, having a customer have a serious problem like this AND still say "we love your product! We want to remain on your platform", just asking you to fix something, is a pretty ringing endorsement. If you had a marginal product with a problem this severe, people would just silently leave.
There's also the outage hell. It's been ok for a month or two, but getting killed whenever AWS has a blip in US-East (there's no cross-region redundancy, and minimal resilience with an AZ or Region-wide service has serious problems) isn't great.
It probably doesn't hurt RG as much as lower overall performance during normal operations does, though.
Engine Yard is more like opinionated configuration management. It allocates and configures EC2 instances that you can log into like normal. The software stack is HAProxy, nginx, unicorn, etc, and customizable through the web interface and/or chef.
HAproxy is a more efficient load balancer for really high scale apps. That said, only about 5% of people would see a difference in load/memory usage. Source: I work for EY.
we just switched 1/3rd of our infrastructure off our existing host (engineyard, which uses AWS) onto raw AWS and saved about $2500/month. You can do it too!
I've been around a few companies migrating from EY/Heroku -> AWS and the cost effectiveness is always astonishing. In addition you gain full control over your architecture, which is a major plus.
I don't know, $20k/mo strikes me as an awful lot of money to avoid engaging in work that a scrappy internet startup really ought to be competent at. If you don't know how to run the pipes and they get clogged and your plumber's not picking up the phone, you're screwed.
That amount buys a whole lotta dedicated servers and the talent to run them. (Sidenote: Every time I price AWS or one of its competitors for a reasonably busy site, my eyes seem to pop out at the cost when compared to dedicated hardware and the corresponding sysadmin salary.)
The larger issue is: Invest in your own sysadmin skills, it'll pay off in spades, especially when your back's up against the wall and you figure out that the vendor-which-solves-all-your-problems won't.
1. Employees are expensive. A good ops guy who believes in your cause and wants to work at an early stage startup can be had for $100k. (Maybe NYC is much cheaper than the bay area, but I'll use bay area numbers for now because it's what I know). That's base. Now add benefits, payroll taxes, 401k match, and the cost of his options. So what... $133k?. That's one guy who can then never go on vacation or get hit by the proverbial bus. Now buy/lease your web cluster, database cluster, worker(s), load balancers, dev and staging environments, etc. Spend engineering time building out Cap and Chef/Puppet scripts and myriad other sysops tools. (You'd need some of that on AWS for sure, but less on Heroku which is certainly much much more expensive than AWS)
2. When you price-out these AWS systems are you using the retail rates or are you factoring in the generous discount larger customers are getting from Amazon? You realize large savings first by going for reserved instances and spot pricing and stack on top of that a hefty discount you negotiate with your Amazon account rep.
3. I've worked at 2 successful, talent Bay Area startups in the last few years: One that was built entirely on AWS, and now, currently, one that owns all of their own hardware. Here's what I think: It's a wash. There isn't a huge difference in cost. You should go with whatever your natural talents lead you towards. You have a founding team with solid devops experience? Great, start on the cloud and then transition early to your own hardware. If not, focus on where your value-add is and outsource the ops.
Your last sentence hides another option which is the route we took: outsource devops. It cost us about $2000 in consultant fees for a fully setup system, easy to expand and add hardware to, and much more cost effective long term than AWS or Heroku. Our guy runs a small ops company who have 24/7 on-call. It's really the perfect solution.
This is a knee-jerk reply. I know, because my knee jerked as well. Think about the problem a little more: if you have the data necessary to pick the min-of-two, then you have the data you need to do intelligent routing.
Not necessarily. Heroku claims that a global request queue is hard to scale, and therefore they switched to random load balancing. The comment above shows that a global request queue is not necessary. Lets say that minimum-of-n scales up to 10 dynos. If your application requires 40 dynos, you can have one front load balancer which dispatches the requests to 4 back load balancers, each of which has 10 dynos assigned to them on which they perform min-of-10 routing. This gives you routing that's almost as good as min-of-40 but it scales up nonetheless.
> Heroku claims that a global request queue is hard to scale, and therefore they switched to random load balancing.
I wish Heroku would tell us more about what they tried. I can imagine a few cock-a-mimie schemes off the top of my head; it would be good to know whether they thought of those.
Assuming by intelligent, you mean minimally loaded, choice of 2 requires less bookkeeping than this. (choice of 2 requires only local (per-node level) info, where as most other intelligent load-balancing requires global info ie. the min, etc.)
Taking the case of minimally loaded, you need to keep track of how many active requests each node/replica is serving, as well as globally keeping track of the min. (which past a certain load, will suffer a lot of contention to update)
To do choice of 2, all you need is to keep track of active requests per node/replica.
Under spiky workloads, there is also a problem with choosing minimally loaded. The counter for numRequests of a node might not update fast enough, so that a bunch of requests will go to that node, quickly saturating its capacity.
Choice of 2 doesn't suffer this problem bec of its inherent randomization.
Heroku implements this change in mid-2010, then sells to Salesforce six months later. Hmm...wondering how this impacted revenue numbers as customers had to scale up dynos following the change...
We've always been of the opinion that queues were happening on the router, not on the dyno.
We consistently see performance problems that, whilst we could tie down to a particular user request (file uploads for example, now moved to S3 direct), we could never figure out why this would result in queuing requests given Heroku's advertised "intelligent routing". We mistakenly thought the occasion slow request couldn't create a queue....although evidence pointed to the contrary.
Now that it's apparent that requests are queuing on the dyno (although we have no way to tell from what I can gather) it makes the occasional "slow requests" we have all the more fatal. e.g. data exports, reporting and any other non-paged data request.
I can confirm this. We experimented with Unicorn as a way to get some of the benefits of availability-based routing despite Heroku's random routing. Our medium-sized app (occupying ~230 MB on boot) would quickly exceed Heroku's 512 MB memory limit when forking just 2 unicorn workers, so we had to revert to thin and a greater number of dynos.
That makes more efficient use of the nodes, because otherwise idle cpu time and memory allocation within a node has a chance of getting used, but it doesn't stop the queueing problem (it mearly gives you more virtual nodes to hand tasks to unintelligently).
Also as the nodes are virtual machines anyway and may be contending with each other for IO, and for most apps these days you spend more time waiting for IO than you do spinning the CPU (unless you have a lot of static content so don't need to hit the db for many requests - but such requests are better handled by a caching layer above that which handles the fancier stuff), so the benefit of running multiple processes per node is going to be a lot less noticable than if you are talking about the nodes being physical machines with dedicated storage channels.
Several Rails apps I develop have been suffering from similar issues. Perhaps 2-3% of requests take 0.4-2s in just processing. If the allocation is a little intelligent, it'll not perform too badly and is less work than much harder optimization. Yet if it's random, it'll queue up horribly.
I'm pissed. Spent way too much time unable to explain it to coworkers, thinking I just didn't understand Heroku's platform and that it was my fault.
Turns out, I didn't understand it, because Heroku never thought to clearly mention something that's pretty important.
Easiest fix: moving to EC2 next week. I've wanted to ever since our issues became evident but it's hard to make a good argument from handwaving about 'problems'.
> Easiest fix: moving to EC2 next week. I've wanted to ever since these issues became evident but it's hard to make a good argument from handwaving about 'problems'.
Of course, then you need to solve all these problems yourself. That sounds pretty easy, you'll have it done next week no problem!
That was sarcastic, but this isn't: good luck, let us know how it goes.
> Of course, then you need to solve all these problems yourself. That sounds pretty easy, you'll have it done next week no problem!
I agree with this, actually. I know it's not simple to do your own servers when you're growing. Yet I'd rather improve my existing ops skills a bit than have to setup everything as async APIs (on EC2 anyway). That's the only way I can see that I can solve this.
"Yet I'd rather improve my existing ops skills a bit than have to setup everything as async APIs (on EC2 anyway). That's the only way I can see that I can solve this."
You're going to discover that a lot of "ops skills" boils down to "do things asynchronously whenever possible". And while nearly any smart engineer can think of the "right" way of doing something, finding the time to do it all is a huge opportunity cost.
That's what the parent is trying to say. It's not that you can't do it; it's that it's a really bad idea to do it, at first.
It's a lot easier than you think when you aren't limited by artificial restrictions on the number of concurrent requests you can serve. Taking out what could be considered here to be a hostile intermediary will free up tech resources to fix problems that actually exist.
I can only imagine how these guys must have been beating their heads against the wall. Heroku charges a premium price and should be providing a premium service.
I've done dev-ops in the ads industry before, it's really not that hard if you're a competent programmer. You just have to take a more studious approach than most non-dev-ops programmers and read up on things before deploying them.
But if you want to let the various PAAS providers put the fear into you, that's your cowardice.
Let the others learn as they may.
Edit:
To clarify, Heroku is making the problem harder on themselves than it would be for an individual to serve their own needs because of the complexity managing so many customers and apps.
You don't have to be Heroku to do for yourself what they offer.
Thanks for this codewright. I've consistently found that learning things others are afraid of is a good business decision - and I reckon this is a big one.
Solving this problem is easy: Run haproxy, and he'll have detailed control over the balancing algorithm, including balancing by least connections and a number of other measures (if he, for example, wants to segregate the long running requests on a specific set of backends, it's trivial).
This solves the issue of the application seeing out-of-whack queue times if there's clock skew between the front-end routing framework and the actual dyno box, but misses all the queued time spent in the dyno-queue per rap genius's post.
I work on New Relic's ruby agent, and you're right (hey Justin). In fact we support both methods (i.e. passing a timestamp or a duration). We rely on the front end server (e.g. nginx, apache) to set a timestamp in an HTTP header and forward that to the ruby application. In the case of heroku there is a special header that they pass which describes the queuing duration. Because we're in the ruby application we don't have control over whether this timestamp is accurate but I'm very interested in ideas on how we could do a better job in this situation.
We do provide javascript based browser instrumentation ("Real User Monitoring") which measures request time from the browser's perspective. This might give you a more accurate idea of what real users are experiencing in this case.
My thoughts before I left the project were to add increased granularity of queue times via having headers added at each passing server and show a rainbow chart for the 'depth' of queue at each layer, not sure if that ever got added.
There's facility for that in the Agent, to allow multiple copies of the header and use whichever came first (for the beginning) and whichever came last (for the end ), it'd be relatively easy to hook metrics into each of those.
Best thing we can do is follow through on the article's call-to-action for emailing support@heroku.com:
"After reading the following RapGenius article (http://rapgenius.com/James-somers-herokus-ugly-secret-lyrics), we are reevaluating the decision to use Heroku. I understand that using a webserver like unicorn_rails will alleviate the symptoms of the dyno queuing problem, but as a cash-strapped startup, cost-efficiency is of high importance.
I look forward to hearing you address the concerns raised by the article, and hope that the issue can be resolved in a cost-effective manner for your customers."
Normally when I read "X is screwing Y!!!" posts on Hacker News I generally consider them to be an overreaction or I can't relate. In this case, I think this was a reasonable reaction and I am immediately convinced never to rely on Heroku again.
Does anyone have a reasonably easy to follow guide on moving from Heroku to AWS? Let's keep it simple and say I'm just looking to move an app with 2 web Dynos and 1 worker. I realize this is not the type of app that will be hurt by Heroku's new routing scheme but I might as well learn to get out before it's too late.
My company switched off of Heroku for our high-load app because of these same problems, but I still really like Heroku for apps with smaller loads, or ones in which I'm will to let a very small percentage of requests take a long time.
I think this analysis and simulation does not account for one important thing: random routing is stateless and thus easy to be distributed. Routing to the least loaded Dyno needs to be stateful. It is quite easy to implement when you have one centralized router, but for 75 dynos this router would likely become a bottleneck. With many routers, intelligent routing has its own performance cost, the routers need to somehow synchronize state, and the simulation ignores this cost.
> With many routers, intelligent routing has its own performance cost, the routers need to somehow synchronize state, and the simulation ignores this cost.
Which is why we pay companies like Heroku to engineer clouds in which to run our applications. Because they're supposed to be better at this than us and spend the time and money building this difficult infrastructure well. That includes a scalable, stateful intelligent routing service.
Does anyone else think that RapGenius makes a great blogging platform? I'd love a plugin that enabled similar annotations on any blog, even if they're just by the original author and not crowdsourced.
It's been tried before (Apture and a crowd-sourced proof-reading plug-in I can't remember the name of). It needs critical mass to work, but it might very well on a very community-focused platform.
436 comments
[ 5.2 ms ] story [ 293 ms ] threadPG can scale up pretty well on a single box, but scaling PG on AWS can be problematic due to the disk io issue, so I suspect they just don't do it. I'd love to be corrected :)
In theory this is horrible, since PG connections are so expensive. In practice the cost of establishing a connection is negligible for a Rails app.
I do suspect this will make performance "fall-off-the cliff" as you get close to capacity.
The issue with the number of connections is that each connection creates a process on the server. We cap the connections at 500, because at that point you start to see problems with O(n^2) data structures in the Postgres internals that start to make all kinds of mischief. This has been improved over the last few releases, but in general, it's still a good idea to try and keep the number of concurrent connections down.
*EDIT: thanks. not a thread. :)
I'm also quite wary of the incentive a 20K monthly bill would give you to try and shake Heroku down for a rebate. By the way, the figure in itself seems very high, but out of context it's impossible for me, the reader, to evaluate if that's actually good money or not. Maybe other solutions (handling everything yourself) would actually be WAY more costly, maybe Heroku actually provides a service that is well-worth the money or maybe the author is right and it's actually swindling on Heroku's part, no way to know.
http://tiwatson.com/blog/2011-2-17-heroku-no-longer-using-a-...
and specifically:
https://groups.google.com/forum/?fromgroups#!msg/heroku/8eOo...
Heroku is used by tons of people around the world. Some of them are paying good money for the service. Given the amount of scrutiny under which they operate, what is the incentive for them to turn an algorithm into a less effective one and still charge the same amount of money in a growing "cloud economy" where companies providing the same kind of service are a dime a dozen (AWS, Linode, Engine Yard, etc)?
How does that benefit their business if "calling their BS" is as easy as firing Apache Benchmark, collecting results, drawing a few charts and flat out "prove" that they're lying about the service they provide??
I mean, I doubt Heroku is that stupid, they know how their audience doesn't give them much room for mistakes. So as nice as the story sounds on paper, I'd really like another take on all this, either from other users of Heroku, independent dev ops, researchers, routing algorithms specialists or even Heroku themselves before we all too hastily jump to sensationalist conclusions.
The only conclusion I jumped to was that they ditched the routing they originally said they had (without telling anyone) and that their routing is worse than what you get as a default from passenger.
Meanwhile, AWS has been a dominant presence in the "Stupid Poor" market segment.
The other two quadrants do not exist.
But really, throwing in the towel at intelligent routing and replacing it with "random routing" is horrific, if true.
It's arguable that the routing mesh and scaling dynamics of Heroku are a large part, if not -the- defining reason for someone to choose Heroku over AWS directly.
Is it a "hard" problem? I'm absolutely sure it is. That's one reason customers are throwing money at you to solve it, Heroku.
http://tiwatson.com/blog/2011-2-17-heroku-no-longer-using-a-...
The thing is, their old "intelligent routing" was really just "we will only route one request at a time to a dyno." In other words, what changed is that they now allow dynos to serve multiple requests at a time. When you put it that way, it doesn't sound as horrific, does it?
At some point you hit memory limits, disk IO limits, or simply a connection limit. It doesn't matter what limit:
If you have some requests that are longer running than others, random load balancing will make them start piling up once you reach some traffic threshold.
You can increase the threshold by adding more backends or increasing the capacity of each backend (by optimizing, or picking beefier hardware if you're on a platform that will let you), and maybe you can increase it enough that it won't affect you.
But no matter what you do, you end up having to allocate more spare resources at it than what you would need with more intelligent routing.
If you're lucky, the effect might be small enough to not cost you much money, and you might be able to ignore it, but it's still there.
I think we have to remember that the "intelligent routing" in question here is actually marketing-speak for "one request per server." Are you saying that when your servers can only receive one request at a time, you will necessarily need fewer than if your servers can handle three requests at a time but are assigned requests randomly?
That is utter madness, and the validity of the argument depends on whether it's the Heroku or this dude's fault that the VM is serving only a single request at a time (and it taking >1sec to handle a request).
The problem is when you send a dyno that has all its threads stuck on long-running computations a new request, because it won't be able to even start processing it. The power is orthogonal to the problem.
The only mitigation is that if a dyno can handle a large number of threads, it probably won't get clogged. But if it can only handle 3 and gets new requests at random, you're in a bad place.
People are throwing money at Heroku because it's really easy to use, not because it's the best long-term technology choice. Seriously - what percentage of Heroku paying users do you think actually read up on the finest technical details like routing algorithms before they put in their credit card? Heroku knows. They know you can't even build a highly-available service on top of it, since it's singly-homed, and they're still making tons of money.
I think heroku does want to be a long-term technology choice.
> I think heroku does want to be a long-term technology choice.
Oh, I'm sure Heroku wants to be a long-term technology choice. That doesn't mean they're trying to be one with their current product offerings.
Consider their product choices since launch: they've added dozens of small value-added technology integrations. Features for a few bucks a month like New Relic to upsell their smallest customers. The price drop was also a big move to reduce barriers to using their platform - which also targets smaller customers. They launched Clojure as their third supported language! Meanwhile, they're singly-homed and have had several protracted outages, and have no announced plans to build a multi-homed product. Scalability has gotten worse with this random routing development.
I think Heroku has known for a long time that they don't have a long-term platform product and that they can't keep big accounts until they build one.
Sorry to be a bit harsh, but I find it a bit shocking how even in this field where we can basically play god and do whatever we want and what we think is best on increasingly powerful bit-cruncher-and-storer-machines, so many here seem to behave like a herd of sheep and just do what 'everyone else' does. Just sit down for a moment and think! What are my requirements right now? What could be a requirement in the near future? What technologies are there which can help me? Am I sure about these feature? Better read up on it first! How difficult is it to get it to behave in ways that are or can be important for me?
Now list that stuff down. If it's puzzling sleep over it, forget it for a few days. Then suddenly, for example under a hot shower you get an idea - that requirement I had isn't really one, I can solve it differently! Come back, take the now fitting piece of the puzzle and do your job in 20% of the time that would have been needed if you would just have blindly followed some path. That's how it usually works for me. Be picky, be exact, but be lazy.
Now about that routing dispatcher problem: Couldn't we solve that in one to two weeks on a generic plattform, but specifically for a certain use case? Let's say you want to have a worker queue of rails request handlers that work in parallel. Just write that damn router! Maybe I'd be lazy, learn Erlang for a week and think about it afterwards.
Java is almost done from "Love".
Rails is done from "Love" of those who do not "love" java.
Hype.
I don't "love" java and Rails.
Especially difficult to diagnose when the queue and wait time in your logs are 0. What is the point of these in the logs if it never waits or queues?
The routing dynamics should be explained better in Heroku's documentation. From an engineering perspective, they're a very important piece of information to understand.
We're with https://bluebox.net now and are very happy.
the average latency will be higher, though (and the spread in latency larger).
If you have 10 dynos and 1000 simultaneous requests, the difference between naive and intelligent might well be reduced, but that's also a scenario in which your end user response times would be horrendously slow and so you'd need more dynos either way
You don't need 50x as many dynos to get the same throughput, you need 50x as many dynos to get the same latency characteristics at that throughput.
cap deploy:setup
cap deploy:cold
cap deploy
And now my app is running on my server, I then add routing and I am good to go.
It is less fancy than Heroku if you want to play with some new technology, you need to install it, and get it configured, and get it to run properly.
The solution here is to figure out why your 99th is 3 seconds. Once you solve that, randomized routing won't hurt you anymore. You hit this exact same problem in a non-preemptive multi-tasking system (like gevent or golang).
I imagine the long tail disappears in a similar way that a traffic jam is prevented by lowering the speed limit.
Proposal:
1) Once per minute (or less often if you have a zillion dynos), each dyno tells the router the maximum number of requests it had queued at any time over the past minute.
2) Using that information, the router recalculates a threshold once a minute that defines how many queued requests is "too many" (e.g. maybe if you have n dynos, you take the log(n)th-busiest-dyno's load as the threshold -- you want the threshold to only catch the tail).
3) When each request is sent to a dyno, a header fields is added that tells the dyno the current 'too many' threshold.
4) If the receiving dyno has too many, it passes the request back to the router, telling the router that it's busy ( http://news.ycombinator.com/item?id=5217157 ). The 'busy' dyno remembers that the router thinks it is 'busy'. The next time its queue is empty, it tells the router "i'm not busy anymore" (and repeats this message once per minute until it receives another request, at which point it assumes the router 'heard').
5) When a receiving dyno tells the router that it is busy, the router remembers this and stops giving requests to that dyno until the dyno tells it that it is not busy anymore.
I haven't worked on stuff like this myself, do you think that would work?
http://cacm.acm.org/magazines/2013/2/160173-the-tail-at-scal...
http://cacm.acm.org/magazines/2013/2/160173-the-tail-at-scal...
There's a relatively easy fix for Heroku. They should do random routing with a backup second request sent if the first request times fails to respond after a relatively short period of time (say, 95th percentile latency), killing any outstanding requests when the first response comes back in. The amount of bookkeeping required for this is a lot less than full-on intelligent routing, but it can reduce tail latency dramatically since it's very unlikely that the second request will hit the same overloaded server.
The 'tied request' idea from the Dean paper is neat, too, and Heroku could possibly implement that, and give dyno request-handlers the ability to check, "did I win the race to handle this, or can this request be dropped?"
Your solution doesn't work if requests aren't idempotent.
For mutating requests, there's a solution as well, but it involves checksumming the request and passing the checksum along so that the database layer knows to discard duplicate requests that it's already handled. You need this anyway if there's any sort of retry logic in your application, though.
Even ignoring the POST requests problem (yup, it tried to replay those) properly cancelling a request on all levels of a multi-level rails stack is very hard/not possible in practice. So you end up DOSing the hard to scale lower levels of the stack (e.g. database) at the expense of the easy to scale LB.
ha-proxy is a lot better than nginx + more flexible if you want to introduce non-http to your stack.
Shouldn't the request be canceled on all levels if you cut the HTTP connection to the frontend?
Alternately, heroku can introduce a third layer between the mesh routers and the inbound random load balancer. This layer consistently hashes (http://en.wikipedia.org/wiki/Consistent_hashing) the api-key/primary key of your app, and sends you to a single mesh router for all of your requests. Mesh routers are/should be blazing fast relative to rails dynos, so that this isn't really a bottleneck for your app. Since the one mesh router can maintain connection state for your app, heroku can implement a least-conn strategy. If the mesh router dies, another router can be automatically chosen.
Yeah, it's a lot more practical than implementing QoS, isn't it?
Even if you work on narrowing the fat tails, shouldn't you still need to be upfront and clear about how adding a new dyno only gives you an increased chance of better request handling times as you scale?
As for the intelligent routing, could you explain the problem? The goal isn't to predict which request will take a long time, the goal is to not give more work to dynos that already have work. Remember that in the "intelligent" model it's okay to have requests spend a little time in the global queue, a few ms mean across all requests, even when there are free dynos.
Isn't it as simple as just having the dynos pull jobs from the queue? The dynos waste a little time idle-spinning until the central queue hands them their next job, but that tax would be pretty small, right? Factor of two, tops? (Supposing that the time for the dyno-initiated give-me-work request is equal to the mean handling time of a request.) And if your central queue can only handle distributing to say 100 dynos, I can think of relatively simple workarounds that add another 10ms of lag every factor-of-100 growth, which would be a hell of a lot better than this naive routing.
What am I missing?
Your solution would likely work if you had some higher level (application level? not real up on Heroku) at which you could specify a push vs. pull mechanism for request routing.
Given that, according to TFA (and it's consistent with some other things I've read) Heroku's bread and butter is Rails apps, and given that, according to TFA, Rails is single-threaded, that (valid) point about concurrency in a single dyno is perhaps not that relevant? You'd think that Heroku would continue to support the routing model that almost all of their marketing and documentation advertises, right? Even if it's a configurable option, and it only works usefully with single-threaded servers?
And if you did do it pull-based, it wouldn't be Heroku's problem to decide how many concurrent requests to send. Leave it to the application (or whatever you call the thing you run on a dyno).
And it doesn't need to be pull-based, if the router can detect HTTP connections closing in dynos, or whatever.
But the idea of pull-based work distribution is pretty straightforward. It's called a message queue.
Animations and results are in the explanation at http://rapgenius.com/1502046
The Golang runtime uses non-blocking I/O to get around this problem.
You could write a pthreads-compliant threading library without using threads at all, just epoll.
It's not an insurmountable problem by any measure, and it's definitely worth it.
I'm not sure this applies to the OP. His in-app measurements were showing all requests being handled very fast by the app itself; the variability in total response time was entirely due to the random routing.
[1]: http://www.eecs.harvard.edu/~michaelm/postscripts/mythesis.p...
That'd be probably significantly better than the case of (request i => dyno picked out of hat) for all i
Not strictly true; imagine that they can query the load state of a dyno, but at some non-zero cost. (For example, that it requires contacting the dyno, because the load-balancer itself is distributed and doesn't have a global view.)
Then, contacting 2, and picking the better of the 2, remains a possible win compared to contacting more/all.
See for example the 'hedged request' strategy, referenced in a sibling thread by nostradaemons from a Jeff Dean Google paper, where 2 redundant requests are issued and the slower-to-respond is discarded (or even actively cancelled, in the 'tiered request' variant).
https://forums.aws.amazon.com/thread.jspa?messageID=410445&#...
Quite a few people still report a myriad of issues with Rails applications. I really want EB to work well with Rails, but just didn't have confidence in it.
As this blogpost also states Heroku really need to keep their documentation up to date. I sometimes stumble across something old referring to an old stack, or something contradicting.
No procfile? No Unicorn or Puma? No worker process or threads defined
Maybe this is the start of Off the Rails Rap.
OTOH, having a customer have a serious problem like this AND still say "we love your product! We want to remain on your platform", just asking you to fix something, is a pretty ringing endorsement. If you had a marginal product with a problem this severe, people would just silently leave.
It probably doesn't hurt RG as much as lower overall performance during normal operations does, though.
http://success.heroku.com/rapgenius
This reminds me of the excellent 5 stages of hosting story shared on here from a while back:
http://blog.pinboard.in/2012/01/the_five_stages_of_hosting/
They should go dedicated for now, it's too early to colo IMO.
That amount buys a whole lotta dedicated servers and the talent to run them. (Sidenote: Every time I price AWS or one of its competitors for a reasonably busy site, my eyes seem to pop out at the cost when compared to dedicated hardware and the corresponding sysadmin salary.)
The larger issue is: Invest in your own sysadmin skills, it'll pay off in spades, especially when your back's up against the wall and you figure out that the vendor-which-solves-all-your-problems won't.
1. Employees are expensive. A good ops guy who believes in your cause and wants to work at an early stage startup can be had for $100k. (Maybe NYC is much cheaper than the bay area, but I'll use bay area numbers for now because it's what I know). That's base. Now add benefits, payroll taxes, 401k match, and the cost of his options. So what... $133k?. That's one guy who can then never go on vacation or get hit by the proverbial bus. Now buy/lease your web cluster, database cluster, worker(s), load balancers, dev and staging environments, etc. Spend engineering time building out Cap and Chef/Puppet scripts and myriad other sysops tools. (You'd need some of that on AWS for sure, but less on Heroku which is certainly much much more expensive than AWS)
2. When you price-out these AWS systems are you using the retail rates or are you factoring in the generous discount larger customers are getting from Amazon? You realize large savings first by going for reserved instances and spot pricing and stack on top of that a hefty discount you negotiate with your Amazon account rep.
3. I've worked at 2 successful, talent Bay Area startups in the last few years: One that was built entirely on AWS, and now, currently, one that owns all of their own hardware. Here's what I think: It's a wash. There isn't a huge difference in cost. You should go with whatever your natural talents lead you towards. You have a founding team with solid devops experience? Great, start on the cloud and then transition early to your own hardware. If not, focus on where your value-add is and outsource the ops.
I wish Heroku would tell us more about what they tried. I can imagine a few cock-a-mimie schemes off the top of my head; it would be good to know whether they thought of those.
Taking the case of minimally loaded, you need to keep track of how many active requests each node/replica is serving, as well as globally keeping track of the min. (which past a certain load, will suffer a lot of contention to update)
To do choice of 2, all you need is to keep track of active requests per node/replica.
Under spiky workloads, there is also a problem with choosing minimally loaded. The counter for numRequests of a node might not update fast enough, so that a bunch of requests will go to that node, quickly saturating its capacity.
Choice of 2 doesn't suffer this problem bec of its inherent randomization.
We've always been of the opinion that queues were happening on the router, not on the dyno.
We consistently see performance problems that, whilst we could tie down to a particular user request (file uploads for example, now moved to S3 direct), we could never figure out why this would result in queuing requests given Heroku's advertised "intelligent routing". We mistakenly thought the occasion slow request couldn't create a queue....although evidence pointed to the contrary.
Now that it's apparent that requests are queuing on the dyno (although we have no way to tell from what I can gather) it makes the occasional "slow requests" we have all the more fatal. e.g. data exports, reporting and any other non-paged data request.
Why dos it not use some kind of scheduling system to handle other task while one task is waiting on i/o?
EDIT: will need to look into our memory perf though, looks like we'll need to do some work to get more than a couple of workers.
Also as the nodes are virtual machines anyway and may be contending with each other for IO, and for most apps these days you spend more time waiting for IO than you do spinning the CPU (unless you have a lot of static content so don't need to hit the db for many requests - but such requests are better handled by a caching layer above that which handles the fancier stuff), so the benefit of running multiple processes per node is going to be a lot less noticable than if you are talking about the nodes being physical machines with dedicated storage channels.
I'm pissed. Spent way too much time unable to explain it to coworkers, thinking I just didn't understand Heroku's platform and that it was my fault.
Turns out, I didn't understand it, because Heroku never thought to clearly mention something that's pretty important.
Easiest fix: moving to EC2 next week. I've wanted to ever since our issues became evident but it's hard to make a good argument from handwaving about 'problems'.
Of course, then you need to solve all these problems yourself. That sounds pretty easy, you'll have it done next week no problem!
That was sarcastic, but this isn't: good luck, let us know how it goes.
I agree with this, actually. I know it's not simple to do your own servers when you're growing. Yet I'd rather improve my existing ops skills a bit than have to setup everything as async APIs (on EC2 anyway). That's the only way I can see that I can solve this.
You're going to discover that a lot of "ops skills" boils down to "do things asynchronously whenever possible". And while nearly any smart engineer can think of the "right" way of doing something, finding the time to do it all is a huge opportunity cost.
That's what the parent is trying to say. It's not that you can't do it; it's that it's a really bad idea to do it, at first.
This "bad idea" is how 90% of the web works...
I can only imagine how these guys must have been beating their heads against the wall. Heroku charges a premium price and should be providing a premium service.
But if you want to let the various PAAS providers put the fear into you, that's your cowardice.
Let the others learn as they may.
Edit:
To clarify, Heroku is making the problem harder on themselves than it would be for an individual to serve their own needs because of the complexity managing so many customers and apps.
You don't have to be Heroku to do for yourself what they offer.
Depending on the complexity of their setup, they COULD have it done next week no problem.
After all tens of thousands of other sites have. It's not like everybody except Google and Facebook is using Heroku.
Currently, though, I believe it's just fed as a number of milliseconds: https://github.com/newrelic/rpm/blame/master/lib/new_relic/a...
This solves the issue of the application seeing out-of-whack queue times if there's clock skew between the front-end routing framework and the actual dyno box, but misses all the queued time spent in the dyno-queue per rap genius's post.
We do provide javascript based browser instrumentation ("Real User Monitoring") which measures request time from the browser's perspective. This might give you a more accurate idea of what real users are experiencing in this case.
There's facility for that in the Agent, to allow multiple copies of the header and use whichever came first (for the beginning) and whichever came last (for the end ), it'd be relatively easy to hook metrics into each of those.
"After reading the following RapGenius article (http://rapgenius.com/James-somers-herokus-ugly-secret-lyrics), we are reevaluating the decision to use Heroku. I understand that using a webserver like unicorn_rails will alleviate the symptoms of the dyno queuing problem, but as a cash-strapped startup, cost-efficiency is of high importance.
I look forward to hearing you address the concerns raised by the article, and hope that the issue can be resolved in a cost-effective manner for your customers."
Normally when I read "X is screwing Y!!!" posts on Hacker News I generally consider them to be an overreaction or I can't relate. In this case, I think this was a reasonable reaction and I am immediately convinced never to rely on Heroku again.
Does anyone have a reasonably easy to follow guide on moving from Heroku to AWS? Let's keep it simple and say I'm just looking to move an app with 2 web Dynos and 1 worker. I realize this is not the type of app that will be hurt by Heroku's new routing scheme but I might as well learn to get out before it's too late.
Which is why we pay companies like Heroku to engineer clouds in which to run our applications. Because they're supposed to be better at this than us and spend the time and money building this difficult infrastructure well. That includes a scalable, stateful intelligent routing service.
Does anyone else think that RapGenius makes a great blogging platform? I'd love a plugin that enabled similar annotations on any blog, even if they're just by the original author and not crowdsourced.