Incidents as keys to a lot of value

One of the workhorses of the modern software world is the key-value store. there are key-value services such as Redis or Dynamo, and some languages build key-value data structures right in to the language (examples include Go, Python and Clojure). Even relational databases, which are not themselves key-value stores, are frequently built on top of a data structure that exposes a key-value interface.

Key-value data structures are often referred to as maps or dictionaries, but here I want to call attention to the less-frequently-used term associative array. This term evokes the associative nature of the store: the value that we store is associated with the key.

When I work on an incident writeup, I try to capture details and links to the various artifacts that the incident responders used in order to diagnose and mitigate the incident. Examples of such details include:

dashboards (with screenshots that illustrate the problem and links to the dashboard)
names of feature flags that were used or considered for remediation
relevant Slack channels where there was coordination (other than the incident channel)

Why bother with these details? I see it as a strategy to tackle the problem that Woods and Cook refer to in Perspectives on Human Error: Hindsight Biases and Local Rationality as the problem of inert knowledge. There may be information about that we learned at some point, but we can’t bring it to bear when an incident is happening. However, we humans are good at remembering other incidents! And so, my hope is that when an operational surprise happens, someone will remember “Oh yeah, I remember reading about something like this when incident XYZ happened”, and then they can go look up the incident writeup to incident XYZ and see the details that they need to help them respond.

In other words, the previous incidents act as keys, and the content of the incident write-ups act as the value. If you make the incident write-ups memorable, then people may just remember enough about them to look up the write-ups and page in details about the relevant tools right when they need them.

Second-class interactions are a first-class risk

Below is a screenshot of Vizceral, a tool that was built by a former teammate of mine at Netflix. It provides a visualization of the interactions between the various microservices.

Vizceral uses moving dots to depict how requests are currently flowing through the Netflix microservice architecture. Vizceral is able to do its thing because of the platform tooling, which provides support for generating a visualization like this by exporting a standard set of inter-process communication (IPC) metrics.

What you don’t see depicted here are the interactions between those microservices and the telemetry platform that ingest these metrics. There’s also logging and tracing data, and those get shipped off-box via different channels, but none of those channels show up in this diagram.

In fact, this visualization doesn’t represent interactions with any of the platform services. You won’t see bubbles that represent the compute platform or the CI/CD platform represented in a diagram like this, even though those platform services all interact with these application services in important ways.

I call the first category of interactions, the ones between the application services, as first-class, and the second category, the ones where the interactions involve platform services, as second-class. It’s those second-class interactions that I want to say more about.

These second-class interactions tend to have a large blast radius, because successful platforms by their nature have a large blast radius. There’s a reason why there’s so much havoc out in the world when AWS’s us-east-1 region has a problem: because so many services out there are using us-east-1 as a platform. Similarly, if you have a successful platform within your organization, then by definition it’s going to see a lot of use, which means that if it experiences a problem, it can do a lot of damage.

These platforms are generally more reliable than the applications that run atop them, because they have to be: platforms naturally have higher reliability requirements than the applications that run atop them. They have these requirements because they have a large blast radius. A flaky platform is a platform that contributes to multiple high-severity outages, and systems that contribute to multiple high-severity outages are the systems were reliability work gets prioritized.

And a reliable system is a system whose details you aren’t aware of, because you don’t need to be. If my car is very reliable, then I’m not going to build an accurate mental model of how my car works, because I don’t need to: it just works. In her book Human-Machine Reconfigurations: Plans and Situated Actions, the anthropologist Lucy Suchman used the term representation to describe the activity of explicitly constructing a mental model of how a piece of technology works, and she noted that this type of cognitive work only happens when we run into trouble. As Suchman puts it:

[R]epresentation occurs when otherwise transparent activity becomes in some way problematic

Hence the irony: these second-class interactions tend not to be represented in our system models when we talk about reliability, because they are generally not problematic.

And so we are lulled into a false sense of security. We don’t think about how the plumbing works, because the plumbing just works. Until the plumbing breaks. And then we’re in big trouble.

Expect it most when you expect it least

Homer Simpson saying "It's probably the person you least suspect." — Homer Simpson: philosopher

Yesterday, CrowdStrike released a Preliminary Post-Incident Review of the major outage that happened last week. I’m going to wait until the full post-incident review is released before I do any significant commentary, and I expect we’ll have to wait at least a month for that. But I did want to highlight one section of the doc from the section titled What Happened on July 19, 2024, emphasis mine

On July 19, 2024, two additional IPC Template Instances were deployed. Due to a bug in the Content Validator, one of the two Template Instances passed validation despite containing problematic content data.

Based on the testing performed before the initial deployment of the Template Type (on March 05, 2024), trust in the checks performed in the Content Validator, and previous successful IPC Template Instance deployments, these instances were deployed into production.

And now, let’s reach way back into the distant past of three weeks ago, when the The Canadian Radio-television and Telecommunications Commission (CRTC) posted an executive summary of a major outage, which I blogged about at the time. Here’s the part I want to call attention to, once again, emphasis mine.

Rogers had initially assessed the risk of this seven-phased process as “High.” However, as changes in prior phases were completed successfully, the risk assessment algorithm downgraded the risk level for the sixth phase of the configuration change to “Low” risk, including the change that caused the July 2022 outage.

In both cases, the engineers had built up confidence over time that the types of production changes they were making were low risk.

When we’re doing something new with a technology, we tend to be much more careful with it, it’s riskier, we’re shaking things out. But, over time, after there haven’t been any issues, we start to gain more trust in the tech, confident that it’s a reliable technology. Our internal perception of the risk adjusts based on our experience, and we come to believe that the risks of these sorts of changes are low. Any organization who concentrates their reliability efforts on action items in the wake of an incident, rather than focusing on the normal work that doesn’t result in incidents, is implicit making this type of claim. The squeaky incident gets the reliability grease. And, indeed, it’s rational to allocate your reliability effort based on your perception of risk. Any change can break us, but we can’t treat every change the same. How could it be otherwise?

The challenge for us is that large incidents are not always preceded by smaller ones, which means that there may be risks in your system that haven’t manifested as minor outages. I think these types of risks are the most dangerous ones of all, precisely because they’re much harder for the organization to see. How are you going to prioritize doing the availability work for a problem that hasn’t bitten you yet, when your smaller incidents demonstrate that you have been bitten by so many other problems?

This means that someone has to hunt for weak signals of risk and advocate for doing the kind of reliability work where there isn’t a pattern of incidents you can point to as justification. The big ones often don’t look like the small ones, and sometimes the only signal you get in advance is a still, small sound.

Book review: How Life Works

In the 1980s, the anthropologist Lucy Suchman studied how office workers interacted with sophisticated photocopiers. What she found was that people’s actions were not determined by predefined plans. Instead, people decided what act to take based on the details of the particular situation they found themselves in. They used predefined plans as resources for helping them choose which action to take, rather than as a set of instructions to follow.

I couldn’t help thinking of Suchman when reading How Life Works. In it, the British science writer Philip Ball presents a new view of the role of DNA, genes, and the cell in the field of biology. Just as Suchman argued that people use plans as resources rather than explicit instructions, Ball discusses how the cell uses DNA as resources. Our genetic code is a toolbox, not a blueprint.

Imagine you’re on a software team that owns a service, and an academic researcher who is interested in software but doesn’t really know anything about it comes to and asks, “What function does redis play in your service? What would happen if it got knocked out?”. This is a reasonable question, and you explain the role that redis plays in improving performance through caching. And then he asks another question: “What function does your IDE’s debugger play in your service?” He notices the confused look on your face and tries to clarify the question by asking, “Imagine another team had to build the same service, but they didn’t have the IDE debugger? How would the behavior of the service be different? Which functions would be impaired” And you try to explain that you don’t actually know how it would be different. That the debugger, unlike redis, is a tool, which is sometimes used to help diagnose problems. But there are multiple ways to debug (for example, using log statements). It might not make any difference at all if that new team doesn’t happen to use the debugger. There’s no direct mapping of the debugger’s presence to the service’s functionality: the debugger doesn’t play a functional role in the service the way that redis does. In fact, the next team that builds a service might not end up needing to use the debugger at all, so removing it might have no observable effect on the next service.

The old view sees these DNA segments like redis, having an explicit functional role, and the new view sees them more like a debugger, as tools to support the cell in performing functions. As Ball puts it, “The old view of genes as distinct segments of DNA strung along the chromosomes like beads, interspersed with junk, and each controlling some aspect of phenotype, was basically a kind of genetic phrenology.” The research has shown that the story is more complex than that, and that there is no simple mapping between DNA segments in our chromosomes and observed traits, or phenotypes. Instead, these DNA segments are yet another input in a complex web of dynamic, interacting components. Instead of focusing on these DNA strands of our genome, Ball directs our attention on the cell as a more useful unit of analysis. A genome, he points out, is not capable of constructing a cell. Rather, a cell is always the context that must exist for the genome to be able to do anything.

The problem space that evolution works in is very different from the one that human engineers deal with, and, consequently, the solution space can appear quite alien to us. The watch has long been a metaphor for biological organisms (for example, Dawkins’s book “The Blind Watchmaker”), but biological systems are not like watches with their well-machined gears. The micro-world of the cell contains machinery at the scale of molecules, which is a very noisy place. Because biological systems must be energy efficient, they function close to the limits of thermal noise. That requires a very different types of machines than the ones we interact with at our human scales. Biology can’t use specialized parts with high tolerances, but must instead make do with more generic parts that can be used to solve many different kinds of problems. And because the cells can use the same parts of the genome to solve different problems, asking questions like “what does that protein do” becomes much harder to answer: the function of a protein depends on the context in which the cell uses it, and the cell can use it in multiple contexts. Proteins are not like keys designed to fit specifically into unique locks, but bind promiscuously to different sites.

This book takes a very systems-thinking approach, as opposed to a mechanistic one, and consequently I find it very appealing. This is a complex, messy world of signaling networks, where behavior emerges from the interaction of genome and environment. There are many connections here to the field of resilience engineering, which has long viewed biology as a model (for example, see Richard Cook’s talk on the resilience of bone). In this model, the genome acts as a set of resources which the cell can leverage to adapt to different challenges. The genome is an example, possible the paradigmatic example, of adaptive capacity. Or, as the biologists Michael Levin and Rafael Yuste put it, whom Ball quotes: “Evolution, it seems, doesn’t come up with answers so much as generate flexible problem-solving agents that can rise to new challenges and figure things out on their own.”

Quick takes on Rogers Network outage executive summary

The Canadian Radio-television and Telecommunications Commission (CRTC) has posted an executive summary of a report on a major telecom outage that happened in 2022 to Rogers Communications, which is one of the major Canadian telecom companies.

The full report doesn’t seem to be available yet, and I’m not sure if it ever will be publicly released. I recommend you read the executive summary, but here are some quick impressions of mine.

Note that I’m not a network engineer (I’ve only managed a single rack of servers in my time), so I don’t have any domain expertise here.

Migration!

When you hear “large-scale outage”, a good bet is that involved a migration. The language of the report describes it as an upgrade, but I suspect this qualifies as a migration.

In the weeks leading to the day of the outage on 8 July 2022, Rogers was executing on a seven-phase process to upgrade its IP core network. The outage occurred during the sixth phase of this upgrade process.

I don’t know anything about what’s involved a telecom upgrading its IP core network, but I do have a lot of general opinions about migrations, and I’m willing to bet they apply here as well.

I think of migrations as high-impact, bespoke changes that the system was not originally designed to accommodate.

They’re high-impact because things can go quite badly if something goes wrong. If you’ve worked at a larger company, you’ve probably experienced migrations that seem to take forever, and this is one of the reasons why: there’s a lot of downside risk in doing migration work (and often not much immediate upside benefit for the people who have to do the work, but that’s a story for another day).

Migrations are bespoke in the sense that each migration is a one-off. This makes migrations even more dangerous because:

The organization doesn’t have any operational muscles around doing any particular migration, because each one is new.
Because each migration is unique, it’s not worth the effort to build tooling to support doing the migration. And even if you build tools, those tools will always be new, which means they haven’t been hardened through production use.

There’s a reason why you hear about continuous integration and continuous delivery but not continuous migration, even though every org past a certain age will have multiple migrations in flight.

Finally, migrations are changes that the system was not originally designed to accommodate. In my entire career, during the design of a new system, I have never heard anyone ask, “How are we going to migrate off of this new system at the end of its life?” We just don’t design for migrating off of things. I don’t even know if it’s possible to do so.

Saturation!

Rogers staff removed the Access Control List policy filter from the configuration of the distribution routers. This consequently resulted in a flood of IP routing information into the core network routers, which triggered the outage. The flood of IP routing data from the distribution routers into the core routers exceeded their capacity to process the information. The core routers crashed within minutes from the time the policy filter was removed from the distribution routers configuration. When the core network routers crashed, user traffic could no longer be routed to the appropriate destination. Consequently, services such as mobile, home phone, Internet, business wireline connectivity, and 9-1-1 calling ceased functioning.

Saturation is a term from resilience engineering which refers to a system receiving being pushed to the limit of the amount of load that it can handle. It’s remarkable how many outages in distributed systems are related to some part of the system being overloaded, or hitting a rate limit, or exceeding some other limit. (For example, see Slack’s Jan 2021 outage). This incident is another textbook example of a brittle system, which falls over when it becomes saturated.

Perception of risk

I mentioned earlier that migrations are risky, and everyone knows migrations are risky. Roger engineers knew that as well:

Rogers had initially assessed the risk of this seven-phased process as “High.”

Ironically, the fact that the migration had gone smoothly up until that point led them to revise their risk assessment downwards.

However, as changes in prior phases were completed successfully, the risk assessment algorithm downgraded the risk level for the sixth phase of the configuration change to “Low” risk, including the change that caused the July 2022 outage.

I wrote about this phenomenon in a previous post, Any change can break us, but we can’t treat every change the same. The engineers gained confidence as they progressed through the migration, and things went well. Which is perfectly natural. In fact, this is one of the strengths of the continuous delivery approach: you build enough confidence that you don’t have to babysit every single deploy anymore.

But the problem is that we can never perfectly assess the risk in the system. And no matter how much confidence we build up, that one change that we believe is safe can end up taking down the whole system.

I should note that the report is pretty blame-y when it comes to this part:

Downgrading the risk assessment to “Low” for changing the Access Control List filter in a routing policy contravenes industry norms, which require high scrutiny for such configuration changes, including laboratory testing before deploying in the production network.

I wish I had more context here. How did it make sense to them at the time? What sorts of constraints or pressures were they under? Hopefully the full report reveals more details.

Cleanup

Rogers staff deleted the policy filter that prevented IP route flooding in an effort to clean up the configuration files of the distribution routers.

Cleanup work has many of the same risks as migration work: it’s high-impact and bespoke. Say “cleanup script” to an SRE and watch the reaction on their face.

But not cleaning up is also a risk! The solution can’t be “never do cleanup” in the same way it can’t be “never do migrations”. Rather, we need to recognize that this work always involve risk trade-offs. There’s no safe path here.

Failure mode makes incident response harder

At the time of the July 2022 outage, Rogers had a management network that relied on the Rogers IP core network. When the IP core network failed during the outage, remote Rogers employees were unable to access the management network. …

Rogers staff relied on the company’s own mobile and Internet services for connectivity to communicate among themselves. When both the wireless and wireline networks failed, Rogers staff, especially critical incident management staff, were not able to communicate effectively during the early hours of the outage.

When an outage affects not just your customers but also your engineers doing incident response, life gets a whole lot harder.

This brings to mind the Facebook outage from 2021:

[A]s our engineers worked to figure out what was happening and why, they faced two large obstacles: first, it was not possible to access our data centers through our normal means because their networks were down, and second, the total loss of DNS broke many of the internal tools we’d normally use to investigate and resolve outages like this.

Component substitution fallacy

The authors point out that the system was not designed to handle this sort of overload:

Absence of router overload protection. The July 2022 outage exposed the absence of overload protection on the core network routers. The network failure could have been prevented had the core network routers been configured with an overload limit that specifies the maximum acceptable number of IP routing data the router can support. However, the Rogers core network routers were not configured with such overload protection mechanisms. Hence, when the policy filter was removed from the distribution router, an excessive amount of routing data flooded the core routers, which led them to crash.

This is a great example of the component substitution fallacy, which fails to acknowledge explicit trade-offs that are made within organizations about which parts of the system to work on. Note that the Rogers engineers will certainly build in router overload protection now, but it means that’s engineering effort that won’t be spent building protections against other failure modes that haven’t happened yet.

Acknowledging trade-offs

To the authors’ credit, they explicitly acknowledge the tradeoffs involved in the overall design of the system.

The Rogers network is a national Tier 1 network and is architecturally designed for reliability; it is typical of what would be expected of such a Tier 1 service provider network. The July 2022 outage was not the result of a design flaw in the Rogers core network architecture. However, with both the wireless and wireline networks sharing a common IP core network, the scope of the outage was extreme in that it resulted in a catastrophic loss of all services. Such a network architecture is common to many service providers and is an example of the trend of converged wireline and wireless telecom networks. It is a design choice by service providers, including Rogers, that seeks to balance cost with performance.

I really hope the CRTC eventually releases the full report, I’m looking forward to reading it.

Efficiency and bad outcomes

Yossi Kreinin has a new blog post out about how the push for greater efficiency in organizations leads to negative outcomes, because it leads to teams pursuing local efficiency goals rather than doing what’s genuinely best for the business.

A concrete example of this phenomenon in action is Mihail Eric’s first-hand account on how Amazon fell behind in the AI race, despite having a significant head start. There’s a ton of great detail there, I’ll excerpt just one example from the piece:

I remember on one occasion our team did an analysis demonstrating that the annotation scheme for some subset of utterance data was completely wrong, leading to incorrect data labels.

That meant for months our internal annotation team had been mislabeling thousands of data points every single day. When we attempted to get the team to change their annotation taxonomy, we discovered it would require a herculean effort to get even the smallest thing modified.

We had to get the team’s PM onboard, then their manager’s buy-in, then submit a preliminary change request, then get that approved (a multi-month-long process end-to-end).

And most importantly, there was no immediate story for the team’s PM to make a promotion case through fixing this issue other than “it’s scientifically the right thing to do and could lead to better models for some other team.” No incentive meant no action taken.

Since that wasn’t our responsibility and the lift from our side wasn’t worth the effort, we closed that chapter and moved on.

For all I know, they could still be mislabeling those utterances to this day.

And now, back to Kreinin’s original post. This bit jumped out at me, where he contrasts the pathologies of human organizational systems with the non-pathologies of biological ones:

Your legs don’t fight your heart, brain and each other for the oxygen budget; every organ only uses what it needs, and is optimized for efficiency.

It’s actually more interesting than that. Our organs don’t use only what they need: they have some surplus capacity, which they are able to lend to other organs when your body anticipates that those other organs will need the additional resources. This phenomenon is called allostasis. I find myself turning back to Peter Sterling’s paper Allostasis: A Model of Predictive Regulation:

Efficiency requires organs to trade-off resources, that is, to grant each other short-term loans. For example, resting skeletal muscle uses ~1.2 liters of oxygenated blood per minute, but peak effort requires ~22 l/min, nearly 20-fold more. Cardiac output increases, but that is insufficient, and although muscle can store fuel (glycogen and fatty acids), it cannot store much oxygen. Nor would it help to maintain a reservoir of de-oxygenated blood because at peak demand the lungs operate at full capacity. So a reservoir of de-oxygenated blood would require a reservoir of lung and heart. In turn, these would require increased capacities for digestion, absorption, excretion, and cooling. Consequently, for a non-storable resource subject to variable demand, it is most efficient to borrow.

What Kreinin points out is a real risk of what David Woods refers to as brittleness in organizations. As he points out in his theory of graceful extensibility, resilient systems require that agents within a system lend out resources to each other in times of need. But the natural inclination of management to incentivize efficiency pushes the system in the exact opposite direction. This leads to behaviors that Woods and Branlat refer to as locally adaptive but globally maladaptive.

Kreinin believes that this type of behavior is inevitable in competently managed organizations, where goals set out by management are clear, and so he argues for the potential virtues of an incompetently managed organization, where individuals are left to come up with their own goals.

If someone invites you to work for a company that’s run very badly, there might well be a good story there – this is far from guaranteed, but you might want to hear the details. And by “a good story”, I don’t mean “yay, here’s a place to slack off at,” but “maybe I can finally get some work done that I hardly ever get the chance to do.”

Ouch.

You can’t judge risk in hindsight

A while back, the good folks at Google SRE posted an article titled Lessons Learned from Twenty Years of Site Reliability Engineering. There’s some great stuff in here, but I wanted to pick on the first lesson: The riskiness of a mitigation should scale with the severity of the outage. Here are some excerpts from the article (emphasis mine)

Let’s start back in 2016, when YouTube was offering your favorite videos such as “Carpool Karaoke with Adele” and the ever-catchy “Pen-Pineapple-Apple-Pen.” YouTube experienced a fifteen-minute global outage, due to a bug in YouTube’s distributed memory caching system, disrupting YouTube’s ability to serve videos.
…
We, here in SRE, have had some interesting experiences in choosing a mitigation with more risks than the outage it’s meant to resolve. During the aforementioned YouTube outage, a risky load-shedding process didn’t fix the outage… it instead created a cascading failure.

We learned the hard way that during an incident, we should monitor and evaluate the severity of the situation and choose a mitigation path whose riskiness is appropriate for that severity.

The question I had reading this was: how did the authors make the judgment that the load-shedding mitigation was risky? In particular, how was the risk of the mitigation perceived in the moment? Note: this question is still relevant, even if the authors/contributors were the actual responders!

When a bad outcome happens, it’s easy to say with hindsight that the action was risky. But we can really only judge the riskiness based on what was understood by the operators at the time they had to make the call. As the good Dr. Cook noted in the endlessly quotable How Complex Systems Fail, all practitioner actions are gambles:

After accidents, the overt failure often appears to have been inevitable and the
practitioner’s actions as blunders or deliberate willful disregard of certain impending failure. But all practitioner actions are actually gambles, that is, acts that take place in the face of uncertain outcomes. The degree of uncertainty may change from moment to moment. That practitioner actions are gambles appears clear after accidents; in general, post hoc analysis regards these gambles as poor ones. But the converse: that successful outcomes are also the result of gambles; is not widely appreciated.

I have no firsthand knowledge of this particular incident. But, just as nobody ever wakes up and says “I’m going to do a bad job today”, nobody wakes up and says “I’m going to take unnecessary risks today.” Doing operations work means making risk trade-offs under uncertainty. We generally don’t know in advance how risky a particular mitigation will be. I think the real lessons is to recognize the inherent challenge that operators face in these scenarios.

The problem with a root cause is that it explains too much

The recent performance of the stock market brings to mind the comment of a noted economist who was once asked whether the market is a good leading indicator of general economic activity. Wonderful, he replied sarcastically, it has predicted nine of the last four recessions. – Alfred L. Malabre Jr., 1968 March 4, The Wall Street Journal

In response to my previous post, Peter Ludemann made the following observation on Mastodon:

This post makes the case for why I would still call these contributors rather than root causes, even though they certainly sound root-cause-y. (They’re also fantastic examples of risks that are very common in the types of systems we work in, but that’s not the topic of this particular post).

Let’s take the first one, “a configuration system that makes mistakes easy.” I’d ask the question, “does an incident occur every single time somebody uses the configuration system?” I don’t know the details of the particular incident(s) that Peter is alluding to, but I’m willing to bet that this isn’t true. Rather, I assume what he is saying is that the configuration system is fundamentally unsafe in some way (e.g., it’s too easy to unintentionally take a dangerous action), and every once in a while a dangerous mistake would happen and an incident would occur.

What this means is that the unsafe configuration system by itself isn’t sufficient for the incident to occur! The config system enables incidents to occur, but it doesn’t, by itself, create the incident. Rather, it’s a combination of the configuration system, and some other factors, that trigger incidents. Maybe incidents only manifests when there is a particular action a user is trying to take, or maybe some people know how to work around the sharp edges and others don’t, or other things.

This may sound like sophistry. After all, the configuration system is an unsafe operator interface. The lesson from an incident is that we should fix it! However, here’s the problem with that line of thinking. The truth is that there are many types of these sorts of problems in a system. I like to call these problems vulnerabilities, even though people usually reserve that term in a security context. Peter gives three examples, but our systems are really shot through with these sorts of vulnerabilities. There are all sorts of unsafe operator interfaces, assumptions that have become invalidated with change, dangerous potential interactions between components, and so on. These vulnerabilities are the sorts of issues that the safety researcher James Reason referred to as latent pathogens. Reason is the one who proposed the Swiss cheese model, with the latent pathogens being the holes in the cheese.

My problem with labeling these vulnerabilities as root causes is that this obscures how our systems actually spend most of their time up, even though these vulnerabilities are always present. Let’s say you were able to identify every vulnerability you had in a system. If you label each one as a root cause of an outage, then your system should be down all of the time, because these vulnerabilities are all present in your system!

But your system isn’t down all of the time: in fact, it’s up more often than it’s down, even though these vulnerabilities are omnipresent. And the reason your system is up more than it’s down is that these vulnerabilities are not, by themselves, sufficient to take down a system. If you label these vulnerabilities as root causes, you make it impossible to understand to how your system actually succeeds. And if you don’t know how it succeeds, you can’t understand how it fails. You’re like the economist predicting recessions that don’t happen.

Now, whether we label these vulnerabilities as root causes or not, they clearly represent a risk to your system. But we have an additional problem: we live in the adaptive universe. That means we don’t actually have the resources (in particular, the time) to identify and patch all of these vulnerabilities. And, even if we could stop the world, find them all, and fix them all, and start the world again, our system keeps changing over time, and new vulnerabilities would set in. And that doesn’t even take into account how patching these vulnerabilities can create new ones. The adaptive universe also teaches us that our work will inevitably introduce new vulnerabilities because we only have a finite amount of time to actually do that work. Mistaking problems with individual components with the general problem of finite resources is the component substitution fallacy.

In short, labeling vulnerabilities as root causes is dangerous because it blinds us to the nature of how complex systems manage to stay up and running most of the time, even though vulnerabilities within the system are always with us. Now, these vulnerabilities are still risks! However, they may or may not manifest as incidents. In addition, we can’t predict which ones will bite us, and we don’t have the resources to root all of them out. We use “this just bit us so we should address it because otherwise it will bite us again” a heuristic, but it’s an implicit one. What we should be asking is “given that we have limited resources, is spending the time addressing this particular vulnerability worth the opportunity cost of delaying other work?”

The error term isn’t Pareto distributed

You’re probably familiar with the 80-20 rule: when 80% of the X stems from only 20% of the Y. For example, 80% of your revenue comes from only 20% of your customer, or 80% of the logs that you’re storing are generated from only 20% of the services. Talk to anybody who is looking to reduce cloud costs in their organization, and chances are they’re attacking the problem by looking for the 20% of services that are generating 80% of the costs, rather than trying to reduce cloud usage uniformly across all services.

Not all phenomena follow the 80-20 rule, but it’s common enough in the systems we encounter that it’s a good rule of thumb. The technical term for it is the Pareto principle, and distributions that exhibit this 80-20 phenomena are an example of Pareto distributions, also known as power law distributions.

A common implicit assumption is that availability problems are Pareto-distributed. If you look at incidents and keep track of their causes, you should be able to identify a small number of causes that lead to the majority of the incidents. Because of this, we should attribute a cause to an incident, and then look to see which causes most often contribute to incident in order to identify interventions that will have the largest impact: those 20% of improvements that should yield 80% improvements. If you believe in the RCA (root cause analysis) model of incidents, that’s a reasonable assumption to make: identify the root cause of each incident, track these across incidents, and then invest in projects that attack the most expensive root causes.

If we can identify the problematic red dots, we can achieve significant improvements

But if you’re a frequent reader of this blog, you know there’s an alternative model of how incidents come to be. I’m fond of referring to the alternative as the LFI (learning from incidents) model. However, in the safety science research community this alternative model is more commonly associated with terms such as the New View, the New Look, or Safety-II.

The contrast of the LFI model with the RCA model is captured well in Richard Cook’s famous monograph, How Complex Systems Fail:

Post-accident attribution accident to a ‘root cause’ is fundamentally wrong.

Because overt failure requires multiple faults, there is no isolated ‘cause’ of an accident. There are multiple contributors to accidents. Each of these is necessary insufficient in itself to create an accident. Only jointly are these causes sufficient to create an accident. Indeed, it is the linking of these causes together that creates the circumstances required for the accident.

In this model, incidents don’t happen because of a single cause: rather, it’s through the interaction of multiple contributors.

If incidents stem from problematic interactions rather than problematic components, then focusing on components will lead us astray

Under an incident model where incidents are a result of interactions, we wouldn’t expect there to be a Pareto distribution of causes. This means that if we look at a distribution of incidents by contributor (or cause, or component), we’re unlikely to see any one of these stand out as being the source of a large number of incidents. Instead, by looking at the components instead of the interactions, we’re unlikely to see much of any pattern at all.

Turning back to How Complex Systems Fail again:

Complex systems are heavily and successfully defended against failure.

The high consequences of failure lead over time to the construction of multiple layers of defense against failure. These defenses include obvious technical components (e.g.
backup systems, ‘safety’ features of equipment) and human components (e.g. training, knowledge) but also a variety of organizational, institutional, and regulatory defenses (e.g. policies and procedures, certification, work rules, team training). The effect of these measures is to provide a series of shields that normally divert operations away from accidents.

All of these explicit and implicit components of the system work together to keep things up and running. You can think of this as a process that most of the time generates safety (or availability), but sometimes doesn’t. You can think of these failure cases as a sort of error or residual term, they’re the leftover, the weird cases at the edges of our system. I think treating incidents as an error term is a useful metaphor because we don’t fall into the trap of thinking about error terms as looking like Pareto distributions.

This doesn’t mean that there aren’t patterns of failure in our incidents: there absolutely are. But it means that the patterns we need to look for aren’t going to visible if we don’t ask the right questions. It’s the difference between asking “which services were involved?” and “what were the goal conflicts that the engineers were facing?“

Green is the color of complacency

Here are a few anecdotes about safety from the past few years.

In 2020, the world was struck by the COVID-19 pandemic. The U.S. response was… not great. Earlier in 2019, before the pandemic struck, the Johns Hopkins Center for Health Security released a pandemic preparedness assessment that ranked 195 countries on how well prepared they were to deal with a pandemic. The U.S. was ranked number one: it was identified as the most well-prepared country on earth.

With its pandemic playbook, “The U.S. was very well prepared,” said Eric Toner, senior scholar at the Johns Hopkins Center for Health Security. “What happened is that we didn’t do what we said we’d do. That’s where everything fell apart. We ended up being the best prepared and having one of the worst outcomes.”

On October 29, 2018, Lion Air Flight 610 crashed 13 minutes after takeoff, killing everyone on board. This plane was a Boeing 737 MAX, and a second 737 MAX had a fatal crash a few months later. Seven days prior to the Lion Air crash, the National Safety Council presented the Boeing Company with the Robert W. Campbell Award for leadership in safety:

“The Boeing Company is a leader in one of those most safety-centric industries in the world,” said Deborah A.P. Hersman, president and CEO of the National Safety Council. “Its innovative approaches to EHS excellence make it an ideal recipient of our most prestigious safety award. We are proud to honor them, and we appreciate their commitment to making our world safer.”

On April 20th, 2010, an explosion on the Deepwater Horizon offshore drilling rig killed eleven workers and led to the largest marine oil spill in the history of the industry. The year before, the U.S. Minerals Management Service issued its SAFE award to Deepwater Horizon:

MMS issued its SAFE award to Transocean for its performance in 2008, crediting the company’s “outstanding drilling operations” and a “perfect performance period.” Transocean spokesman Guy Cantwell told ABC News the awards recognized a spotless record during repeated MMS inspections, and should be taken as evidence of the company’s longstanding commitment to safety.

When things are going badly, everybody in the org knows it. If you go into an organization where high-severity incidents are happening on a regular basis, where everyone is constantly in firefighting mode, then you don’t need metrics to tell you how bad things are: it’s obvious to everyone, up and down the chain. The problems are all-too-visible. Everybody can feel them viscerally.

It’s when things aren’t always on fire that it can be very difficult to assess whether we need to allocate additional resources to reduce risk. As the examples above show, absence of incidents do not indicate an absence of risk. In fact, these quiet times can lull is into a sense of complacency, leading us to think that we’re in a good spot, when the truth is that there’s a significant risk that’s hidden beneath the surface.

Personally, I don’t believe it’s even possible to say with confidence that “everything is ok with right now”. As the cases above demonstrate, when things are quiet, there’s a limit to how well we can actually assess the risk based on the kinds of data we traditionally collect.

So, should you be worried about your system? If you find yourself constantly in firefighting mode, then, yes, you should be worried. And if things are running smoothly, and the availability metrics are all green? Then, also yes, you should be worried. You should always be worried. The next major incident is always just around the corner, no matter how high your ranking is, or how many awards you get.