Keeping track of 300 servers

Since broke 10 million pageviews today, I thought it would be a good time to talk a little bit about keeping track of all the servers that run, Akismet,, Ping-o-matic, etc. Currently we have over 300 servers online in 5 different data centers across the country. Some of these are collocated, and others are with dedicated hosting providers, but the bottom line is that we need to keep track of them all as if they were our children! Currently we use Nagios for server health monitoring, Munin for graphing various server metrics, and a wiki to keep track of all the server hardware specs, IPs, vendor IDs, etc. All of these tools have suited us well up until now, but there have been some scaling issues.

  • MediaWiki — Like Wikipedia, we have a MediaWiki page with a table that contains all of our server information, from hardware configuration to physical location, price, and IP information. Unfortunately, MediaWiki tables don’t seem to be very flexible and you cannot perform row or column-based operations. This makes simple things such as counting how many servers we have become somewhat time consuming. Also, when you get to 300 rows, editing the table becomes a very tedious task. It is very easy to make a mistake throwing the entire table out of whack. Even dividing the data into a few tables doesn’t make it much easier. In addition, there is no concept of unique records (nor do I really think there should be) so it is very easy to end up with 2 servers that have the same IP listed or the same hostname.
  • Munin — Munin has become an invaluable tool for us when troubleshooting issues and planning future server expansion. Unfortunately, scaling munin hasn’t been the best experience. At about 100 hosts, we started running into disk IO problems caused by the various data collection, graphing and HTML output jobs munin runs. It seemed the solution was to switch to the JIT graphing model which only drew the graphs when you viewed them. Unfortunately, this only seemed to make the interface excruciatingly slow and didn’t help the IO problems we were having. At about 150 hosts we moved munin to a dedicated server with 15k RPM SCSI drives in a RAID 0 array in an attempt to give it some more breathing room. That worked for a while, but we then started running into problems where the process of polling all the hosts actually took longer than the monitoring interval. The result was that we were missing some data. Since then, we have resorted to removing some of the things we graph on each server in order to lighten the load. Every once in a while, we still run into problems where a server is a little slow to respond and it causes the polling to take longer than 5 minutes. Obviously, better hardware and reducing graphed items isn’t a scalable solution so something is going to have to change. We could put a munin monitoring server in each datacenter, but we currently sum and stack graphs across datacenters. I am not sure if/how that works when the data is on different servers. The other big problem I see with munin is that if one host’s graphs stop updating and that host was part of a totals graph, the totals graph will just stop working. This happened today — very frustrating.
  • Nagios — I feel this has scaled the best of the 3. We have this running on a relatively light server and have no load or scheduling issues. I think it is time, however, to look at moving to Nagios’ distributed monitoring model. The main reason for this is that since we have multiple datacenters, each of which have their own private network, it is important for us to monitor each of these networks independently in addition to the public internet connectivity to each datacenter. The simplest way to do this is to put a nagios monitoring node in each data center which can then monitor all the servers in that facility and report the results back to the central monitoring server. Splitting up the workload should also allow us to scale to thousands of hosts without any problems.

Anyone have recommendations on how to better deal with these basic server monitoring needs? I have looked at Zabbix, Cacti, Ganglia, and some others in the past, but have never been super-impressed. Barring any major revelations in the next couple weeks, I think we are going to continue to scale out Nagios and Munin and replace the wiki page with a simple PHP/MySQL application that is flexible enough to integrate into our configuration management and deploy tools.

Additional Capacity

So, I haven’t blogged much lately but there is a reason. Over the past month we have been hard at work expanding the infrastructure behind and Akismet. Here are some of the things that we have done over the past month or so:

  • Migrated out of San Diego
  • Brought online almost 100 new servers in 3 new datacenters — Dallas, TX, San Antonio, TX, and San Francisco, CA
  • Tripled the database hardware behind
  • Now serving blogs out of 3 datacenters in real-time
  • Akismet is now served from 2 datacenters

Here are a couple pictures of some new hardware racked and powered on just before we put it into production last week.

From top to bottom (left):

  • 21 x HP DL145
  • 4 x HP DL365

From top to bottom (right):

  • 18 x HP DL145
  • 4 x HP DL365
  • 1 x 3U HP Storage Array
  • 1 x HP DL385


And the back….


Thanks to Evan League and Brian Maples of Layered Tech for doing the build-out pictured above and sending the photos over.

New servers for

We are getting ready to place an order for an additional 37 servers in a new datacenter. This new point of presence will serve as the 3rd active node for Over the past few weeks, I have been doing lots of testing and seemingly endless negotiation with various hosting companies.


The model we have adopted is to use commodity hardware to serve all the functions of the site. We do not rely on SANs or super-expensive multi-processor systems. Our web servers are usually either single or dual processor machines with 1-2GB of RAM and a small, inexpensive hard drive. Our database servers are single or dual processor machines with 4-8GB of RAM and 2-4 fast SCSI drives in a RAID array using a hardware RAID controller. Because there is redundancy built into the architecture that several of these machines can fail at once at the site is unaffected, the individual machines do not need to be extremely robust. Historically, CPU time has been the most precious resource on the web servers and disk I/O on the DBs.


  • Provide the hardware outlined above
  • Support Debian AMD64 or similar
  • Provide a Gigabit private backend network for inter-server communication
  • Ability to deploy additional servers quickly and painlessly
  • Sales and support team that is competent and easy to work with
  • US Datacenter and not Dallas or San Diego

Initial Impressions

Dedicated server providers seem to fall into 2 classes:

1) No-frills provider that offers server between $79 – $149/month. No phone support or advanced services. This would be fine, but they usually cannot provide the DB-class machines we need and do not provide Gigabit backend networks.

2) Full-service provider that offers servers beginning at $250/month and up. These providers are usually not the best fit for us because they justify their higher prices by saying they have superior support. We don’t really need “superior” support, just someone that can take care of hardware issues when needed. Seems like a waste to pay for something you are never going to use and don’t really need. Also, in my experience, the more expensive the hosting company, the more painful the sales process is. Sometimes I feel like I am buying a car….

The Finalists

Over the past month, we have narrowed the field from about 10 different possible providers down to the following 3. Each of these fit somewhere between no-frills and full-service as mentioned above, but I definitely get the feeling that they lean one way or the other.

  • ServePath
    Based in San Francisco, California (where Automattic is also based) they seem to lean towards the full-service side, offering 24×7 phone support, higher-end servers, load balancers, and firewalls. Their sales process has been pretty agonizing. It is now going on 45 days of back and forth, price changes, configuration changes, conference calls, and about 50 one-on-one calls with our sales guy. I had chance to visit their offices and tour their datacenter earlier this week so I got a feel of how things work there — it appears to be a well-run organization. One thing that struck me as a bit odd was that they do not deploy any rack-mounted servers in their datacenter. All of their servers, which are built in house, are in tower (white-box) chassis like you would see under an office desk. This is something I would expect in the lower-end market, but at $500+ per month and rather large RAID arrays (300GB SCSI x 6) I would expect to see more rack-mounted chassis to take advantage of the superior cooling.
  • Server Beach
    Based in San Antonio, Texas and now owned by Peer1, Server Beach is definitely more on the no-frills side. When we were looking at new datacenters about 6 months ago, Server Beach could not provide what we wanted — they did not offer SCSI RAID or Gigabit privatenet, but said it was coming. Well, those things are now available. Kudos to Server Beach on a super-simple and painless sales process. Once I contacted them with the configuration we wanted, they scheduled a conference call to discuss the details. They had members of their support, operations, and sales teams on the line. About 30 minutes later we were all done and a day later they had sent over a proposal. It was right the first time – had everything we asked for and at a very fair price.
  • Joyent/TextDrive
    Honestly, we probably will not go with TextDrive for this deployment, as it is a pretty radical departure from our current configuration, and they don’t technically meet the requirements laid out above. They are worth mentioning, however, because they are doing some pretty cool stuff with OpenSolaris, zones, and ZFS, and it sounds like it could be a good fit for our architecture model. Their storage is super-fast, the container model allows you to replicate existing containers with a single command — there is no need to provision and setup a new physical server. ZFS offers all sorts of cool stuff like snapshots, compression, and built-in data consistency checks. Utilizing this architecture, would require that we maintain 2 completely separate environments — Linux and Solaris — and there is a definite time investment in doing so. I think that we will probably look at moving some of our services to Solaris containers in the near future, but I am not sure it will be

The Verdict

First, let me say that we still have yet to make the final decision, but hope to do so by the end of the week. Both ServePath and Server Beach seem like they will be great companies to work with. ServePath is local, so we can walk over to their offices and meet with them if needed – there is something to be said for working with local vendors. Server Beach has been a pleasure to work with thus far and I have some experience working with them in the past. Peer1’s VP of Marketing also blogs on Pricing and server configurations are almost identical, so that really isn’t as much of a factor as one would think.

Anyone have experience working with either of these companies? Suggestions? Feedback?

Interesting stats

As a follow up to Matt’s September wrap up post, I thought it would be fun to post some more technical stats about the current infrastructure:

  • 80 physical processors
  • 139GB of RAM
  • 91 hard drives with a combined total of over 15 terabytes of storage space
  • 2000 database queries per second spread over 40 MySQL instances
  • Over 8 million objects stored in memcached serving over 8000 requests per second

The best part is that this is just the beginning! There are many more exciting things to come. Next up — expansion to 2 additional datacenters in the US.

Load balancer testing

Over the past week here at, we have been doing some experimentation with various software load balancers. In the latest test, we are using Pound with a weighted round-robin algorithm between all of the web servers in a given datacenter. The individual weight takes into account hardware differences and any other tasks that server may be doing. So, a dual processor server would receive more traffic than a single processor machine. The tests have been going really well so far.  Once the testing is complete and we have decided on a solution, I will post about the various things we tried, pros and cons of each, etc.  The graph below shows the load averages of the various web servers over a one week period. I’ll let you guess when the test began 🙂 Pretty dramatic difference, eh?


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