I walk on, and get a tap on my shoulder. I turn around to see the cyclist.

“Can you delete that, please?”.

And I do.

Walking back, I think idly “I could probably recover that…”. Legally, I’ve done nothing wrong. And it is a great shot – one of the best I think I’ve taken. But I sigh, and know I won’t. At least this is one shot I can’t claim to have lost due to the Fuji x-pro’s AF.

This talk focuses on the kinds of constraints we should consider when evolving their architecture of our systems in order to enable rapid, frequent release. So much of the conversation about Continuous Delivery focuses on the design of build pipelines, or the nuts and bolts of CI and infrastructure automation. But often the biggest constraint in being able to incrementally roll out new features are the problems in the design of the system itself. I’ll be pulling together a series of patterns that will help you identify what to look for in your own systems when moving towards Continuous Delivery.

For provisioning hosts I am using the Python AWS API Boto. For configuring the hosts once provisioned, I am using Puppet. I remain unconvinced as to the relative merits of PuppetMaster or Chef Server (see my previous post on the subject) and so have decided to stick with using PuppetSolo so I can manage versioning how I would like. This leaves me with a challenge – how do I apply the puppet configuration for the hosts once provisioned with Boto? I also wanted to provide a relatively uniform command-line interface to the development team for other tasks like running builds etc. Some people use cron-based polling for this, but I wanted a more direct form of control. I also wanted to avoid the need to run any additional infrastructure, so mcollective was never something I was particularly interested in.

After a brief review of my “Things I should look at later” list it looked like time to give Fabric a play.

Fabric is a Python-based tool/library which excels at creating command-line tools for machine management. It’s bread and butter is script-based automation of machines via SSH – many people in fact use hand-rolled scripts on top of Fabric as an alternative to systems like Chef and Puppet. The documentation is very good, and I can heartily recommend the Fabric tutorial.

The workflow I wanted was simple. I wanted to be able to checkout a specific version of code locally, run one command to bring up a host and also apply a given configuration set. My potentially naive solution to this problem is to simply tar up my puppet scripts, upload them, and then run puppet. Here is the basic script:

@task
def provision-box():
  public_dns = provision_using_boto()

  local("tar cfz /tmp/end-bundle.tgz path/to/puppet_scripts/*")
  with settings(host_string=public_dns, user="ec2-user", key_filename="path/to/private_key.pem"):
    run("sudo yum install -y puppet")
    put("/tmp/end-bundle.tgz", ".")
    run("tar xf end-bundle.tgz && sudo puppet --modulepath=/home/ec2-user/path/to/puppet_scripts/modules path/to/puppet_scripts/manifests/myscript.pp")

The provision_using_boto() command is an exercise left to the reader, but the documentation should point you in the right direction. If you stuck the above command in your fabfile.py, all you need to do is run fab provision-box to do the work. The first yum install command is there to handle bootstraping of puppet (as it is not on the AMIs we are using) – this will be a noop if the target host already has it installed.

This example is much more simplified than the actual scripts as we have also implemented some logic to re-use ec2 instances to save time & money, and also a simplistic role system to manage different classes of machines. I may write up those ideas in a future post.

As always, you can track the conference (or note that you plan to attend) at Simon & Nat’s startup Lanyrd, which is several shades of awesome.

2011-07-13 00:12:22 www-data 26096 21.4  0.9 160732  5972 ?        D    Jul12   5:30 /usr/sbin/apache2 -k start
2011-07-13 00:12:25 www-data 26096 21.4  0.9 160736  6040 ?        R    Jul12   5:30 /usr/sbin/apache2 -k start
2011-07-13 00:12:22 www-data 26096 21.3  0.9 160732  5972 ?        D    Jul12   5:30 /usr/sbin/apache2 -k start
2011-07-13 00:12:22 root     26179 24.0  0.0   4220   584 ?        S    00:12   0:00 /bin/sh /home/ubuntu/tools/cpu_log

No entries from Postfix – good – but now other processes are having trouble. This was starting to point away from one rogue process gobbling CPU, to high CPU use being a symptom of something else. What can cause very high CPU use? Among other things, swapping memory. A process chewing up all available memory could easily cause these kinds of symptoms. A quick scan through syslog showed me something I should have spotted earlier. If it wasn’t the smoking gun, then at least something pretty close:

Jul 13 00:11:28 domU-12-31-39-01-F0-E5 kernel: [38837.985499] apache2 invoked oom-killer: gfp_mask=0x201da, order=0, oom_adj=0, oom_score_adj=0

This doesn’t mean that Apache is to blame, just that it was a process which oom-killer tried to take out in order to free up memory. And just prior to the outage itself in the apache logs:

[Tue Jul 12 23:48:24 2011] [error] (12)Cannot allocate memory: fork: Unable to fork new process

At this point my mind was already turning to the fact that I hadn’t done *any* tuning of Apache processes or PHP. After googling around for a bit, a few things looked wrong in my config. Here was the untuned default that Ubuntu gave me:

<IfModule mpm_prefork_module>
    StartServers            5
    MinSpareServers         5
    MaxSpareServers        10
    MaxClients            150
    MaxRequestsPerChild     0
</IfModule>

In general MaxClients refers to the maximum number of simultaneous requests that will be served. On prefork Apache, like mine, MaxClients also refers to the max number of child processes that get spawned. A simple ps showed that even after a restart, each apache process was consuming up to 35MB of memory. The host in question has 1GB in RAM – it was clear that even with nothing else running on the box, with that sort of memory footprint I would exhaust memory way before the MaxClients threshold was reached. Even more worrying, the MaxRequestsPerChild was set to zero, meaning that the child processes would never be restarted. If a memory leak was occurring inside the child Apache process, it could carry on eating memory until the box comes crashing to it’s knees. After some quick maths I decided to reduce my MaxClients down to a more manageable 25, but also set MaxRequestsPerChild to 1000. My hope is that this may buy me some more time to try and track down where the memory use is occurring.

This has spurned me on to finally invest some time looking at nginx. This weekend may see me putting in nginx side by side with a view to moving away from Apache – from all reports this may allow me to run my sites with a much lower footprint. But if the last couple of days has taught me anything, it’s that I should be so sure to rush to the conclusion that I’ve finally tracked this problem down. If I’m still having trouble at the weekend, I may well just clone the box and try and reproduce the problem with some performance tests.

What I needed was something that would tell me what was causing the problem. After reaching out to The Hive Mind, Cosmin pointed me in the direction of some awk and ps foo. This little script gets a process listing, and writes out all those rows where the CPU is above 20%, prepended with the current timestamp:

ps aux  | gawk '{ if ( $3 > 20 ) { print strftime("%Y-%m-%d %H:%M:%S")" "$0 } }

My box rarely goes about 5% CPU use, and I was worried about the CPU ramping up so quickly that I didn’t get a sample, so this threshold seemed sensible. The magic is the if ( $3 > 20) – this only emits the line if the third column of input from ps aux (which is the CPU) goes above 20.

I put the one-liner in a script, then stuck the following entry into cron to ensure that every minute, the script gets run. If everything is ok, no output. Otherwise, I’ll get the full process listing. This wouldn’t top the box getting wedged again, but would at least tell me what caused it.

* * * * * root /home/ubuntu/tools/cpu_log >> /var/log/cpu_log

Lo and behold, several hours later and the box got wedged once again. After a restart, the cpu_log showed this:

2011-07-11 17:55:42 postfix   6398 29.6  0.3  39428  2184 ?        S    17:55   0:01 pickup -l -t fifo -u -c
2011-07-11 17:55:42 postfix   6398 29.6  0.3  39428  2180 ?        S    17:55   0:01 pickup -l -t fifo -u -c
2011-07-11 17:55:42 postfix   6398 29.6  0.2  39428  1556 ?        S    17:55   0:01 pickup -l -t fifo -u -c
2011-07-11 17:55:42 postfix   6398 24.6  0.2  39428  1368 ?        S    17:55   0:01 pickup -l -t fifo -u -c
2011-07-11 18:16:43 root      6440 50.0  0.0  30860   344 ?        R    18:16   0:01 pickup -l -t fifo -u -c

Matching what the CloudWatch graphs showed me, the CPU ramped up quote quickly, before I loose all output (the 4th column here is CPU). But this time, we have a culprit – Postfix’s pickup process. I had configured Postfix just a day or two back, so clearly something was amiss. Nonetheless, I can at least now disable Postfix to spend some time diagnosing the problem.

Limiting CPU

Something else that was turned up in my cries for help was cpulimit. This utility would allow me to cap how much CPU a given process used. If and when I re-enable postfix, I’ll almost certainly use this to avoid future outages while I iron out any kinks.