13 ms·

Scale Fail (part 1)

178 points by ableal 15y ago

CountHackulus 15y ago
What I do appreciate about this article is that it doesn't just give anecdotes, but actually quantifies and explains the problems. In fact it even explains why not to use anecdotes! Seems like most of the tips here could be applied to all sort of problems, not just scalability, and I think that's probably what the author was going for. To show that scalability isn't some special problem that needs special solutions, but that if you think hard about it, and use data to back up your findings, then it's just like any other problem you want to solve. Can't wait for part 2.
- valyala 15y ago
  Part 2 is already available - see http://lwn.net/Articles/443775/ http://lwn.net/Articles/443775/
wccrawford 15y ago
The bit about metrics really rang true. At 1 company, we had a situation where 2 of the 3 webservers were out of service for various reasons. I knew from past experience that even a single web server should have a load less than 1 with all our traffic. The others were just there in case they went down. (Like they did!) In the middle of that, the 1 working server suddenly starts spiking load to 40 and misbehaving. There was a really smart new guy there that was usually right. But this time, he said there was no way 1 server could handle our traffic and that was the problem. He refused to listen to reason and eventually I gave up and started working on the problem alone. I eventually found the code that was going crazy and fixed it... And voila. 0.4 load. The point is that people get things in their head that 'have' to be true, but they aren't necessarily. He assumed that the existence of 3 webservers meant we had that much load, but it was just for redundancy. If I had had metrics to prove my case, it would have gone a lot smoother... But instead I just had my experience.
- JonLim 15y ago
  I think having numbers to prove your case is generally a good way to approach things. If you can get objective numbers that really show and prove what you are talking about, you can swing most people your way. If not, they are probably not the best of people to work with.
  - wccrawford 15y ago
    I want to reiterate that the guy was really good at his job. One of the best people I've ever worked with. He was generally pretty easy to get along with. But my lack of hard data meant I couldn't sway him on that problem, no matter how right I was. That company had a history of not having metrics to work with. Every new sysadmin or db admin came in and asked for metrics and we'd shrug and laugh nervously. (I mean, there was a reason why hired so many new ones!)
    - JonLim 15y ago
      Oh, no, heard you very loud and clear. Was just reinforcing your anecdote by saying that it's applicable to a lot more than just scaling apps. :)
  - KaeseEs 15y ago
    If you don't have numbers, you're not doing engineering, you're practicing druidism.
johngalt 15y ago
It continues to surprise me how much IT/sysadmin work is done by heuristic rather than actual measurements. Applications slow? -> Add RAM Database is slow? -> must be network/load Service failures? -> reboot It becomes very problematic in large IT organizations. Teams will play hot potato with issues, and all use excuses. Desktop support will blame the DB team, DB will blame the server team, then they all blame the network. All the while no one is actually measuring anything.
- Meai 15y ago
  Surprisingly almost no one cares about performance. When was the last time you have seen a webframework or a database that routinely does performance benchmarks at each iteration? In fact, I don't know any. I was very impressed with the continuous measurements that PyPy does.
  - shimon 15y ago
    http://django-speedcenter.djangozoom.net/ http://django-speedcenter.djangozoom.net/
    - hartror 15y ago
      The site is great, but ironically a bit slow.
  - Joakal 15y ago
    Can you name a webframework or database? There's many benchmarks available (I'm not familiar with Python): http://codeigniter.com/user_guide/libraries/benchmark.html http://codeigniter.com/user_guide/libraries/benchmark.html http://codeigniter.com/user_guide/general/profiling.html http://codeigniter.com/user_guide/general/profiling.html
- bdunbar 15y ago
  It is very frustrating when a group gathers statistics and measures everything it can. And the others do not. I administer servers. Our group tires to measure what it can, keeps historical data. Then we get a call from the application owners: 'Users are complaining about slow performance.' Who? 'A few. I didn't keep the email. It's just slow, look into it, will you?' So what can I say but 'Server x did this and such a time and measure y is normal but I don't think that's your problem because of reason z'. In other words, yes, I finger point because I _know_ it's not my problem. And as often or not 'a few' users are one or two people whose problem went away when they restarted their PC.
asymptotic 15y ago
This is a phenomenal article. After wading through bullshit "X HTTP server beats Y HTTP server at serving 40 bytes of static content!!!" articles this is a breath of fresh air. This is clearly written by someone working in the real world.
- krakensden 15y ago
  LWN is pretty great, I'm quite happy I bought a subscription. Very few other publications will have long articles with diagrams explaining a kernel memory corruption bug a day after it happens.
  - michaelchisari 15y ago
    Honestly, I'd consider getting a subscription just for this: "When I was at Amazon, we used a squid reverse proxy ..." "Dan, you were an ad sales manager at Amazon."
gregburek 15y ago
How does everyone here gather and analyze their metrics? What do you have always deployed and what do you use when shit hits the fan? [Edit for typo]
- seiji 15y ago
  <rant> The "standard" ways are all very outdated, ugly, unscalable, and brain dead in implementation. nagios, cacti, munin, ganglia, ... -- all crap. </rant> People end up writing their own [1]. They rarely open source their custom monitoring infrastructure. Sometimes a private monitoring system gets open sourced, but then you see it has complex dependencies. The complexity of monitoring blocks wide-scale deployment. People stick with 15 year old, simple, dumb, solutions. I'm working on making a new distributed monitoring/alerting/trending/stats framework/service, but it's slow going. One weekend per month of free time doesn't exactly yield the mindset to get into hardcore systems hacking flows [2]. [1]: http://www.slideshare.net/kevinweil/rainbird-realtime-analytics-at-twitter-strata-2011 http://www.slideshare.net/kevinweil/rainbird-realtime-analyt... [2]: Will develop next-gen monitoring for food.
  - gregburek 15y ago
    I'm getting the feeling that with all the unique server setups in use, monitoring and metrics systems are going to be just as unique and specific. There are some interesting process monitoring projects out there like god, monit and bluepill, as well as ec2/cloud specific stuff from ylastic, rightscale and librato silverline. Have you ever used any of those tools? Fitting all these together for my setup is trial and error, but it really does force me to think hard about my tools and assumptions even before I get hard data.
    - josephruscio 15y ago
      I hack on the aforementioned Silverline at http://librato.com http://librato.com, and we provide system-level monitoring at the process/application granularity as-a-Service. (We also have a bunch of features around active workload management controls, but that's out of scope here). It actually works on any server running one of the supported versions of Linux, not just EC2. Benefits of going with a service-based offering are the same as in any other vertical, you don't need to install and manage your own software/hardware for monitoring. Here's an example of the visualizations we provide into what's going on in your server instances: http://support.silverline.librato.com/kb/monitoring-tags/application-monitoring-versus-server-monitoring http://support.silverline.librato.com/kb/monitoring-tags/app...
armored 15y ago
Comments > the article: So to avoid these ends, let's avoid these beginnings: avoid multi-threading. Use single-threaded programs, which are easier to design, write, and debug than their shared-memory counterparts. Instead, use multiple processes to extract concurrency from the hardware. Choose a communication medium that works just as well on a single machine as it does between machines, and make sure the individual processes comprising the system are blind to the difference. This way, deployment becomes flexible and scaling becomes simpler. Because communication between processes by necessity has to be explicitly designed and specified, modularity almost happens by itself.
- jshen 15y ago
  "Use single-threaded programs, which are easier to design, write, and debug than their shared-memory counterparts." Not really. Well, only true in simple cases. Let's say I have a 16-core box and I want to crunch some data using all the cores. What's easier, clojure with a single shared memory data structure that collects that stats, or a multi-process system where we not only have to manage multiple application processes, we also have to use something like redis to hold the shared state?
  - minimax 15y ago
    Here is a link to the commenter's full comment: http://lwn.net/Articles/441867/ http://lwn.net/Articles/441867/ He's precisely considering your 16-core case. Now think about what happens when your dataset grows and you need more cores than you can reasonably fit in a single machine.
    - jshen 15y ago
      It's not when, it's if, and often you know it won't happen. Being a good engineer requires understanding when k use which model. acting as if there aren't a lot of cases where singel process, shred memory, concurrency is the only good choice for almost all cases is wrong
deleted 15y ago
[deleted]
chopsueyar 15y ago
Well written article and funny, too. Nice job.
hartror 15y ago
The talk is very amusing especially the use of Jason Fried in one of the slides. http://www.youtube.com/watch?v=nPG4sK_glls http://www.youtube.com/watch?v=nPG4sK_glls
chaostheory 15y ago
> Single-threading is the enemy of scalability. Multi-thread programming is hard. Everyone already knows about Node.js, but if you're on the Java I suggest you check out Akka (http://akka.io http://akka.io) - makes concurrency much easier
- IgorPartola 15y ago
  Is it hard? Always? If it is so hard then why is it around. How would you go about parallelizing even mildly CPU heavy work loads on node.js? Node.js and the like are awesome, but only if you have no blocking calls and your CPU usage is tiny. Otherwise you get the performance of a single threaded server. You know, because that's what it is.
  - nerd_in_rage 15y ago
    You run multiple processes. Duh.
  - chaostheory 15y ago
    >Is it hard? Yes multithread programming is difficult (for most people - like me). Hence the popularity of stuff like node.js, and also why people highlight Erlang's Actor model. > How would you go about parallelizing even mildly CPU heavy work loads on node.js? I'm not sure I understand your question, since (I believe) node.js has an event based concurrency model and not thread based (at least for its users).
selectnull 15y ago
Part 2 has been released, for those who have subscription. https://lwn.net/Articles/443775/ https://lwn.net/Articles/443775/

178 points by ableal 15y ago

What I do appreciate about this article is that it doesn't just give anecdotes, but actually quantifies and explains the problems. In fact it even explains why not to use anecdotes! Seems like most of the tips here could be applied to all sort of problems, not just scalability, and I think that's probably what the author was going for. To show that scalability isn't some special problem that needs special solutions, but that if you think hard about it, and use data to back up your findings, then it's just like any other problem you want to solve. Can't wait for part 2.

valyala 15y ago
Part 2 is already available - see http://lwn.net/Articles/443775/ http://lwn.net/Articles/443775/

wccrawford 15y ago

The bit about metrics really rang true. At 1 company, we had a situation where 2 of the 3 webservers were out of service for various reasons. I knew from past experience that even a single web server should have a load less than 1 with all our traffic. The others were just there in case they went down. (Like they did!) In the middle of that, the 1 working server suddenly starts spiking load to 40 and misbehaving. There was a really smart new guy there that was usually right. But this time, he said there was no way 1 server could handle our traffic and that was the problem. He refused to listen to reason and eventually I gave up and started working on the problem alone. I eventually found the code that was going crazy and fixed it... And voila. 0.4 load. The point is that people get things in their head that 'have' to be true, but they aren't necessarily. He assumed that the existence of 3 webservers meant we had that much load, but it was just for redundancy. If I had had metrics to prove my case, it would have gone a lot smoother... But instead I just had my experience.

JonLim 15y ago
I think having numbers to prove your case is generally a good way to approach things. If you can get objective numbers that really show and prove what you are talking about, you can swing most people your way. If not, they are probably not the best of people to work with.
- wccrawford 15y ago
  I want to reiterate that the guy was really good at his job. One of the best people I've ever worked with. He was generally pretty easy to get along with. But my lack of hard data meant I couldn't sway him on that problem, no matter how right I was. That company had a history of not having metrics to work with. Every new sysadmin or db admin came in and asked for metrics and we'd shrug and laugh nervously. (I mean, there was a reason why hired so many new ones!)
  - JonLim 15y ago
    Oh, no, heard you very loud and clear. Was just reinforcing your anecdote by saying that it's applicable to a lot more than just scaling apps. :)
- KaeseEs 15y ago
  If you don't have numbers, you're not doing engineering, you're practicing druidism.

johngalt 15y ago

It continues to surprise me how much IT/sysadmin work is done by heuristic rather than actual measurements. Applications slow? -> Add RAM Database is slow? -> must be network/load Service failures? -> reboot It becomes very problematic in large IT organizations. Teams will play hot potato with issues, and all use excuses. Desktop support will blame the DB team, DB will blame the server team, then they all blame the network. All the while no one is actually measuring anything.

Meai 15y ago
Surprisingly almost no one cares about performance. When was the last time you have seen a webframework or a database that routinely does performance benchmarks at each iteration? In fact, I don't know any. I was very impressed with the continuous measurements that PyPy does.
- shimon 15y ago
  http://django-speedcenter.djangozoom.net/ http://django-speedcenter.djangozoom.net/
  - hartror 15y ago
    The site is great, but ironically a bit slow.
- Joakal 15y ago
  Can you name a webframework or database? There's many benchmarks available (I'm not familiar with Python): http://codeigniter.com/user_guide/libraries/benchmark.html http://codeigniter.com/user_guide/libraries/benchmark.html http://codeigniter.com/user_guide/general/profiling.html http://codeigniter.com/user_guide/general/profiling.html
bdunbar 15y ago
It is very frustrating when a group gathers statistics and measures everything it can. And the others do not. I administer servers. Our group tires to measure what it can, keeps historical data. Then we get a call from the application owners: 'Users are complaining about slow performance.' Who? 'A few. I didn't keep the email. It's just slow, look into it, will you?' So what can I say but 'Server x did this and such a time and measure y is normal but I don't think that's your problem because of reason z'. In other words, yes, I finger point because I _know_ it's not my problem. And as often or not 'a few' users are one or two people whose problem went away when they restarted their PC.

asymptotic 15y ago

This is a phenomenal article. After wading through bullshit "X HTTP server beats Y HTTP server at serving 40 bytes of static content!!!" articles this is a breath of fresh air. This is clearly written by someone working in the real world.

krakensden 15y ago
LWN is pretty great, I'm quite happy I bought a subscription. Very few other publications will have long articles with diagrams explaining a kernel memory corruption bug a day after it happens.
- michaelchisari 15y ago
  Honestly, I'd consider getting a subscription just for this: "When I was at Amazon, we used a squid reverse proxy ..." "Dan, you were an ad sales manager at Amazon."

gregburek 15y ago

How does everyone here gather and analyze their metrics? What do you have always deployed and what do you use when shit hits the fan? [Edit for typo]

seiji 15y ago
<rant> The "standard" ways are all very outdated, ugly, unscalable, and brain dead in implementation. nagios, cacti, munin, ganglia, ... -- all crap. </rant> People end up writing their own [1]. They rarely open source their custom monitoring infrastructure. Sometimes a private monitoring system gets open sourced, but then you see it has complex dependencies. The complexity of monitoring blocks wide-scale deployment. People stick with 15 year old, simple, dumb, solutions. I'm working on making a new distributed monitoring/alerting/trending/stats framework/service, but it's slow going. One weekend per month of free time doesn't exactly yield the mindset to get into hardcore systems hacking flows [2]. [1]: http://www.slideshare.net/kevinweil/rainbird-realtime-analytics-at-twitter-strata-2011 http://www.slideshare.net/kevinweil/rainbird-realtime-analyt... [2]: Will develop next-gen monitoring for food.
- gregburek 15y ago
  I'm getting the feeling that with all the unique server setups in use, monitoring and metrics systems are going to be just as unique and specific. There are some interesting process monitoring projects out there like god, monit and bluepill, as well as ec2/cloud specific stuff from ylastic, rightscale and librato silverline. Have you ever used any of those tools? Fitting all these together for my setup is trial and error, but it really does force me to think hard about my tools and assumptions even before I get hard data.
  - josephruscio 15y ago
    I hack on the aforementioned Silverline at http://librato.com http://librato.com, and we provide system-level monitoring at the process/application granularity as-a-Service. (We also have a bunch of features around active workload management controls, but that's out of scope here). It actually works on any server running one of the supported versions of Linux, not just EC2. Benefits of going with a service-based offering are the same as in any other vertical, you don't need to install and manage your own software/hardware for monitoring. Here's an example of the visualizations we provide into what's going on in your server instances: http://support.silverline.librato.com/kb/monitoring-tags/application-monitoring-versus-server-monitoring http://support.silverline.librato.com/kb/monitoring-tags/app...

armored 15y ago

Comments > the article: So to avoid these ends, let's avoid these beginnings: avoid multi-threading. Use single-threaded programs, which are easier to design, write, and debug than their shared-memory counterparts. Instead, use multiple processes to extract concurrency from the hardware. Choose a communication medium that works just as well on a single machine as it does between machines, and make sure the individual processes comprising the system are blind to the difference. This way, deployment becomes flexible and scaling becomes simpler. Because communication between processes by necessity has to be explicitly designed and specified, modularity almost happens by itself.

jshen 15y ago
"Use single-threaded programs, which are easier to design, write, and debug than their shared-memory counterparts." Not really. Well, only true in simple cases. Let's say I have a 16-core box and I want to crunch some data using all the cores. What's easier, clojure with a single shared memory data structure that collects that stats, or a multi-process system where we not only have to manage multiple application processes, we also have to use something like redis to hold the shared state?
- minimax 15y ago
  Here is a link to the commenter's full comment: http://lwn.net/Articles/441867/ http://lwn.net/Articles/441867/ He's precisely considering your 16-core case. Now think about what happens when your dataset grows and you need more cores than you can reasonably fit in a single machine.
  - jshen 15y ago
    It's not when, it's if, and often you know it won't happen. Being a good engineer requires understanding when k use which model. acting as if there aren't a lot of cases where singel process, shred memory, concurrency is the only good choice for almost all cases is wrong

deleted 15y ago

[deleted]

chopsueyar 15y ago

Well written article and funny, too. Nice job.

hartror 15y ago

The talk is very amusing especially the use of Jason Fried in one of the slides. http://www.youtube.com/watch?v=nPG4sK_glls http://www.youtube.com/watch?v=nPG4sK_glls

chaostheory 15y ago

> Single-threading is the enemy of scalability. Multi-thread programming is hard. Everyone already knows about Node.js, but if you're on the Java I suggest you check out Akka (http://akka.io http://akka.io) - makes concurrency much easier

IgorPartola 15y ago
Is it hard? Always? If it is so hard then why is it around. How would you go about parallelizing even mildly CPU heavy work loads on node.js? Node.js and the like are awesome, but only if you have no blocking calls and your CPU usage is tiny. Otherwise you get the performance of a single threaded server. You know, because that's what it is.
- nerd_in_rage 15y ago
  You run multiple processes. Duh.
- chaostheory 15y ago
  >Is it hard? Yes multithread programming is difficult (for most people - like me). Hence the popularity of stuff like node.js, and also why people highlight Erlang's Actor model. > How would you go about parallelizing even mildly CPU heavy work loads on node.js? I'm not sure I understand your question, since (I believe) node.js has an event based concurrency model and not thread based (at least for its users).

selectnull 15y ago

Part 2 has been released, for those who have subscription. https://lwn.net/Articles/443775/ https://lwn.net/Articles/443775/