Archive for the ‘haproxy’ Category

Dogfood: making our systems more resilient

Сентябрь 18th, 2009

This is a “dogfood” type story (see below for explanation of the term)… Open Query has ideas on resilient architecture which it teaches (training) and recommends (consulting, support) to clients and the general public (blog, conferences, user group talks). Like many other businesses, when we first started we set up our infrastructure quickly and on the cheap, and it’s grown since. That’s how things grow naturally, and is as always a trade-off between keeping your business running and developing while also improving infrastructure (business processes and technical).

Quite a few months ago we also started investing (mostly time) in the technical infrastructure, and slowly moving the various systems across to new servers and splitting things up along the way. Around the same time, the main webserver frequently became unresponsive. I’ll spare you the details, we know what the problem was and it was predictable, but since it wasn’t our system there was only so much we could do. However, systems get dependencies over time and thus it was actually quite complicated to move. In fact, apart from our mail, the public website was the last thing we moved, and that was through necessity not desire.

Of course it’s best for a company when their public website works, it’s quite likely you have noticed some glitches in ours over time. Now running on the new infra, I happened to take a quick peek at our Google Analytics data, and noticed an increase in average traffic numbers of about 40%. Great big auch.

And I’m telling this, because I think it’s educational and the world is generally not served by companies keeping problems and mishaps secret. Nasties grow organically and without malicious intent, improvements are a step-wise process, all that… but in the end, the net results of improvements can be more amazing than just general peace of mind! And of course it’s very important to not just see things happen, but to actively work on those incremental improvements, ongoing.

Our new infra has dual master MySQL servers (no surprise there ;-) but based in separate data centres so that makes the setup a bit more complicated (MMM doesn’t deal with that setup). Other “new” components we use are lighttpd, haproxy, and Zimbra (new in the sense that our old external infra used different tech). Most systems (not all, yet) are redundant/expendable and run on a mix of Linode instances and our own machines. Doing these things for your own infra is particularly educational, it provides extra perspective. The result is, I believe, pretty decent. Failures generally won’t cause major disruption any more, if at all. Of course, it’s still work in progress.

Running costs of this “farm”? I’ll tell later, as I think it’s a good topic for a poll and I’m curious: how much do you spend on server infrastructure per month?

Background for non-Anglophones: “eating your own dogfood” refers to a company doing themselves what they’re recommending to their clients and in general. Also known as “leading by example”, but I think it’s also about trust and credibility. On the other hand, there’s the “dentist’s tooth-ache” which refers to the fact that doctors are their own worst patients ;-)


PlanetMySQL Voting: Vote UP / Vote DOWN

Advanced Squid Caching in Scribd: Hardware + Software Used

Август 4th, 2009

After the previous post in this caching related series I’ve received many questions on hardware and software configuration of our servers so in this post I’ll describe our server’s configs and the motivation behind those configs.

Hardware Configuration

Since in our setup Squid server uses one-process model (with an asynchronous requests processing) there was no point in ordering multi-core CPUs for our boxes and since we have a lots of pages on the site and the cache is pretty huge all the servers ended up being highly I/O bound. Considering these facts we’ve decided to use the following hardware specs for the servers:

CPU: One pretty cheap dual-core Intel Xeon 5148 (no need in multiple cores or really high frequencies – even these CPUs have ~1% avg load)
RAM: 8Gb (basically to reduce I/O pressure by caching hot content in RAM)
Disks: 4 x small SAS 15k drives in JBOD mode (no RAIDS – we’ve tried all kinds of RAID configs and it did not help with the I/O performance)

So, once again: nothing is as important in a squid box as I/O throughput.

Here is a sample CPU load graph from one of the boxes:

squid-cpu-graph

Software Configuration

This could be a long story, but in a few words our experience with different squid versions was the following.

First, when I’ve started working on this caching project I’ve just installed squid using Debian’s apt-get install squid command. As the result we’ve got some ancient squid 2.6 release that for some reason (still unclear to me) was painfully slow in I/O operations and it had some leaking file descriptors problem so after a few hours under production load the box would simply stop processing requests.

When the first approach failed, I’ve decided to go to the squid web site, download the latest production release and install it from sources (yes, we do it all the time when OS vendor ships too old or buggy releases). Result – freaking fast and stable squid 3.0 which worked flawlessly for about 5 months.

Few months ago we’ve found out about the stale-* extensions available in squid 2.7 and I’ve started wondering if we should change our perfectly stable 3.0 setup to 2.7. And some time later I’ve decided to use Vary HTTP header in our caching architecture and then I found out that vary-caching correctly implemented only in 2.7 and since 3.0 is a complete rewrite of the 2.X branch, vary-caching is not yet implemented there (or not in a way we’d want it to be implemented).

So, the final result: at this moment in time we’re using custom-built Squid 2.7STABLE6 and really happy with it, it is stable, fast and feature-rich caching proxy server.

Caching Cluster Configuration

Apparently we have more than one squid server in scribd and this makes it a bit harder to use those servers (comparing to one box when you’d send all requests to one IP:port pair). We’ve tried to use round-robin balancing for the squid boxes + ICP-based neighbor checks but it was adding more latency to our responses and we’ve decided to put haproxy load balancer between nginx and squid farm and set up URL hash based balancing to distribute requests evenly amongst squid backends.

This scheme worked pretty nice, but we had one serious problem with this setup: if one squid box would go down, haproxy would quickly detect the problem and would remove it from the pool… And here comes the problem – removing a server from the pool completely changes hashing keys space and all cached requests become invalid. To solve this problem we’ve developed a nginx balancer module that performs consistent hashing of URLs and we’re testing this module now in production. What is really good about this module is that it removes one hop from the chain if http proxies between the site and a user.

So, this was a short description of what hardware we use for our caching cluster and why do we use it. In the next posts of this series we’ll talk about cache control and objects invalidation.