Pragmatic Dictator

My current company has for obvious business reasons got a serious interest in delivering a quality website experience during the World Cup and thus I’ve been spending a lot of time focused on our own performance and capacity management of late.

P&C is one of those 80/20 tradeoffs. There’s always more one can do or measure or test, equally getting the basics in place will deliver substantial benefit. I’d go further and argue that without a solid grasp of the basics, one cannot easily determine what else beyond that might be required. Here then are the basics that I’ve found myself repeating over and over:

• Have an enquiring mind – anomalies are not to be ignored or dismissed on the basis of pure speculation. Determining root cause is essential to prevent surprises in production. Some recent examples:
  1. In one test we noticed that every so often we’d get a substantial blip in disk I/O on servers that should be processing entirely out of memory. Along with that blip there’d be a corresponding reduction in throughput, we could have ignored it, after all things sorted themselves out relatively quickly but we chose to investigate. All these servers were periodically running a cleanup job the developers were unaware of and had not factored into their capacity calculations. The implications for production would have been a regularly overloaded, badly performing website. We’ve since tuned the jobs, adjusted their schedules and increased our capacity to ensure we can always spread the load around enough to accommodate them.
  2. An examination of the distribution of load on the boxes behind our load-balancers revealed a higher than expected amount of variance in CPU and connections. A review of the application revealed that any particular user’s traffic is sticky to one box, unfortunate as it’s stateless, time for a code change. We also spent time looking at the monitoring infrastructure and discovered that in certain cases we’d get false reports of 100% CPU utilisation, that one will be fixed with an OS patch.
• Gather the right data – there’s no value in allowing oneself to be limited by what is easily available via some set of tools people are comfortable with. One tool we were using had an unreasonably low ceiling on the number and rate of samples it could handle such that any graphs it produced showed hardly anything of the true profile of e.g. CPU utilisation, memory consumption or I/O. Forming any opinion about system behaviour in respect of load was going to be an exercise in speculation. We junked the tool and are looking for a replacement, in the meantime we’ve fallen back to making use of low level performance counters which we can sample local to the machine and whack onto disk for later analysis via scripts, opensource tools etc.
• Design tests that support reasoning – One should indeed try and replicate production load behaviours to judge overall system behaviour. The challenge of such testing is that it can be difficult to relate performance data back to exactly what was going on during some period of a test and make a diagnosis or be confident of an improvement. There are a number of things we can do to improve the situation:
  1. Ensure tests are deterministic such that any given run can be compared against other runs. This isn’t as simple as it looks when e.g. you wish to gradually increase load at a fixed rate that is being produced by more than one box.
  2. Have tests produce sufficient logging that one can easily identify what was going on at particular points in the sampled data. Logging of course can actually affect test behaviour and that isn’t always desirable.
  3. Build additional tests that target particular user journey’s through the system. Doing this for all possible journey’s can be costly so it makes sense to focus on testing those which are most popular with users. These kinds of tests restrict the reasoning tree making analysis, diagnosis and solution identification much easier.
• Measure what customers care about – they don’t care about CPUs, I/O or memory, they worry about things like response times. It is important to focus on maintaining a quality user experience not endlessly improving system efficiency. Considering user factors such as response times stops us expending huge effort on CPU utilisation when we should be focusing on say, network I/O, browser performance or reducing the amount of data we push to the browser before a page can render.
• Beware of averages – it is very tempting to combine datasets via the use of averaging unfortunately such a practice can easily hide spikes that might be indicative of a problem. On more than one occasion an engineer has presented a graph that tracks the average CPU and a table that summarises min, avg and max. After which they’ve pronounced load testing was a success and yet they have no explanation for why the average is never more than 50% but the max is 100% and whether or not this is good or bad.




• More than load – excessive focus on measuring the effect of a particular load can make us blind to another important metric, resource cost per unit of work – these are the collection of tests and analysis that help us understand what to tune and how much to keep our appetite for boxes and bandwidth reasonable. One simple thing teams can do per sprint (assuming you’re agile, why wouldn’t you be?) is point a profiler at each component and look for the low hanging fruit that is poor algorithm selection or inefficient code (e.g. repeated scanning of lists where a hashmap would be better or repeatedly computing something that could be cached).