Scale Fail (part 1)

by ableal • 15 years ago

178 37 15 years 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• 15 years ago

Part 2 is already available - see http://lwn.net/Articles/443775/

wccrawford2• 15 years 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.

JonLim2• 15 years 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.

wccrawford2• 15 years 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!)

KaeseEs• 15 years ago

If you don't have numbers, you're not doing engineering, you're practicing druidism.

johngalt• 15 years 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• 15 years 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• 15 years ago

http://django-speedcenter.djangozoom.net/

Joakal3• 15 years 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/general/profiling.html

bdunbar• 15 years 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• 15 years 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• 15 years 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• 15 years 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."

gregburek2• 15 years 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]

seiji2• 15 years 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-analyt...

[2]: Will develop next-gen monitoring for food.

gregburek2• 15 years 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.

Joakal3• 15 years ago

Sounds like Zabbix, Pandora FMS, Osmius, NetXMS, AccelOps and those are the ones that match your requirements.

Within each, if you search for templates or cookbooks or config scripts, you'll find ways of configuring it easily enough.

https://secure.wikimedia.org/wikipedia/en/wiki/Comparison_of...

fictorial• 15 years ago

Etsy's statsd and Graphite. http://codeascraft.etsy.com/2011/02/15/measure-anything-meas...

mnutt• 15 years ago

I tried out statsd + graphite and it's flexible, but it requires a ton of dependencies (python, django, apache, cairo, sqlite, and node.js) and needs a lot of configuration across a bunch of apps to get up and running. It might not be too bad if you have a django app, but there was very little overlap with my stack so I decided against it.

It would be nice if the whole thing could be rolled up into a single config file and one node.js app that forked two processes for web UI and stats collection. And cairo could be replaced with javascript+canvas or svg on the client side.

armored• 15 years 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.

jshen2• 15 years 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• 15 years ago

Here is a link to the commenter's full comment: 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.

chopsueyar• 15 years ago

Well written article and funny, too. Nice job.

hartror2• 15 years 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

chaostheory2• 15 years 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) - makes concurrency much easier

IgorPartola• 15 years 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• 15 years ago

You run multiple processes. Duh.

chaostheory2• 15 years 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• 15 years ago

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

Scale Fail (part 1)

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