Five-P factors for root cause analysis
One of the problems in doing “root cause analysis” within complex systems is that there’s almost never “one bad thing” that’s truly at the root of the problem, and talking about the incident as if there’s One True Root is probably not productive. It’s important to identify the full range of contributing factors, so that you can do something about those elements individually as well as de-risking the system as a whole.
I recently heard someone talk about struggling to shift the language in their org around root cause, and it occurred to me that adapting Macneil’s Five P factors model from medicine/psychology would be very useful in SRE “blameless postmortems” (or traditional ITIL problem management RCAs). I’ve never seen anything about using this model in IT, and a casual Google search turned up nothing, so I figured I’d write a blog post about it.
The Five Ps (described in IT terms) — well, really six Ps, a problem and five P factors — are as follows:
- The presenting problem is not only the core impact, but also its broader consequences, which all should be examined and addressed. For instance, “The FizzBots service was down” becomes “Our network was unstable, resulting in FizzBots service failure. Our call center was overwhelmed, our customers are mad at us, and we need to pay out on our SLAs.”
- The precipitating factors are the things that triggered the incident. There might not be a single trigger, and the trigger might not be a one-time event (i.e. it could be a rising issue that eventually crossed a threshold, such as exhaustion of a connection pool or running out of server capacity). For example, “A network engineer made a typo in a router configuration.”
- The perpetuating factors are the things that resulted in the incident continuing (or becoming worse), once triggered. For instance, “When the network was down, application components queued requests, ran out of memory, crashed, and had to be manually recovered.”
- The predisposing factors are the long-standing things that made it more likely that a bad situation would result. For instance, “We do not have automation that checks for bad configurations and prevents their propagation.” or “We are running outdated software on our load-balancers that contains a known bug that results in sometimes sending requests to unresponsive backends.”
- The protective factors are things that helped to limit the impact and scope (essentially, your resilience mechanisms). For instance, “We have automation that detected the problem and reverted the configuration change, so the network outage duration was brief.”
- The present factors are other factors that were relevant to the outcome (including “where we got lucky”). For instance, “A new version of an application component had just been pushed shortly before the network outage, complicating problem diagnosis,” or “The incident began at noon, when much of the ops team was out having lunch, delaying response.”
If you think about the October 2021 Facebook outage in these terms, the presenting problem was the outage of multiple major Facebook properties and their attendant consequences. The precipitating factor was the bad network config change, but it’s clearly not truly the “root cause”. (If your conclusion is “they should fire the careless engineer who made a typo”, your thinking is Wrong.) There were tons of contributing factors, all of which should be addressed. “Blame” can’t be laid at the feet of anyone in particular, though some of the predisposing and perpetuating factors clearly had more impact than others (and therefore should be addressed with higher priority).
I like this terminology because it’s a clean classification that encompasses a lot of different sorts of contributing factors, and it’s intended to be used in situations that have a fair amount of uncertainty to them. I think it could be useful to structure incident postmortems, and I’d be keen to know how it works for you, if you try it out.