Systems Engineering: A great definition.

by allspaw on July 18, 2011

Ben Rockwood said something last December about the re-emergence of the Systems Engineer and I agree with him, 100%.

NASA Systems Engineering Handbook, 2007

To add to that, I’d like to quote the excellent NASA Systems Engineering handbook’s introduction. The emphasis is mine:

Systems engineering is a methodical, disciplined approach for the design, realization, technical management, operations, and retirement of a system. A “system” is a construct or collection of different elements that together produce results not obtainable by the elements alone. The elements, or parts, can include people, hardware, software, facilities, policies, and documents; that is, all things required to produce system-level results. The results include system-level qualities, properties, characteristics, functions, behavior, and performance. The value added by the system as a whole, beyond that contributed independently by the parts, is primarily created by the relationship among the parts; that is, how they are interconnected. It is a way of looking at the “big picture” when making technical decisions. It is a way of achieving stakeholder functional, physical, and operational performance requirements in the intended use environment over the planned life of the systems. In other words, systems engineering is a logical way of thinking.

Systems engineering is the art and science of developing an operable system capable of meeting requirements within often opposed constraints. Systems engineering is a holistic, integrative discipline, wherein the contributions of structural engineers, electrical engineers, mechanism designers, power engineers, human factors engineers, and many more disciplines are evaluated and balanced, one against another, to produce a coherent whole that is not dominated by the perspective of a single discipline.

Systems engineering seeks a safe and balanced design in the face of opposing interests and multiple, sometimes conflicting constraints. The systems engineer must develop the skill and instinct for identifying and focusing efforts on assessments to optimize the overall design and not favor one system/subsystem at the expense of another. The art is in knowing when and where to probe. Personnel with these skills are usually tagged as “systems engineers.” They may have other titles—lead systems engineer, technical manager, chief engineer— but for this document, we will use the term systems engineer.

The exact role and responsibility of the systems engineer may change from project to project depending on the size and complexity of the project and from phase to phase of the life cycle. For large projects, there may be one or more systems engineers. For small projects, sometimes the project manager may perform these practices. But, whoever assumes those responsibilities, the systems engineering functions must be performed. The actual assignment of the roles and responsibilities of the named systems engineer may also therefore vary. The lead systems engineer ensures that the system technically fulfills the defined needs and requirements and that a proper systems engineering approach is being followed. The systems engineer oversees the project’s systems engineering activities as performed by the technical team and directs, communicates, monitors, and coordinates tasks. The systems engineer reviews and evaluates the technical aspects of the project to ensure that the systems/subsystems engineering processes are functioning properly and evolves the system from concept to product. The entire technical team is involved in the systems engineering process.

I would imagine that successful organization understands this concept of systems engineering, but I don’t think I’ve ever seen it put so well.

NASA’s engineers have both common and conflicting goals, just like we do in web operations. They weigh trade-offs in efficiency and thoroughness, and wade into the constraints of better, cheaper, faster, and hopefully: more resilient.

This re-emergence of the systems engineering (or “full-stack” engineering) notion is excellent and exciting to me, and I’m hoping that everyone in our field, when they hear “DevOps” (and/or how Theo says *Ops) what they mean is taking a systems engineering view.

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Training Organizational Resilience in Escalating Situations

by allspaw on May 10, 2011

This little ramble of thoughts are related to my talk at Velocity coming up, but I know I’ll never get to this part at the conference, so I figured I’d post about it here.

Building resilience from a systems point of view means (amongst other things) understanding how your organization deals with failure and unexpected situations. Generally this means having a development and operations teams that can work well together under pressure, with fluctuating amounts of uncertainty, bringing their own domain expertise to the table when it matters.

This is what drives some of my favorite Ops candidate interview questions. Knowing Unix commands, network architectures, database behaviors, and scripting languages are obviously required, but comprise only one facet of the gig.  The real mettle comes from being able easily zoom in and out of the whole system under scrutiny, splitting up troubleshooting responsibilities amongst your team (and trusting their results) and differentiating red herring symptoms from truly related ones. It also comes from things like:

• Staying away from distracting conversation during the outage response. Nothing kills a TTR like unrelated talk in IRC or a conf call.
• Trusting your information. This is where the UI challenges of dashboard design can make or break an outage response. “Are those units milli, or mega?”
• Balancing too much communication and too little amongst team members. Troubleshooting outage verbosity is a fickle mistress.
• Stomping actions. OneThingAtATime™ methods aren’t easy to stick to, especially when things escalate.
• Keeping outage fatigue at bay, and recognizing when brains are melting and need to take a break.

To make matters worse, determining causality can be tenuous at best when you’re working with complex systems, so being able to recognize when a failure has a single root cause (hint: with the big outages – almost never) and when it has multiple contributing causes is a skill that isn’t easily gained without seeing a lot of action in the past.

So it’s not a surprise that working well within a team under stressful scenarios is something other fields try to train people for.  Trauma surgeons, FBI agents, military teams, air traffic control, etc. all have drills, exercises, and simulations for teaching these skills, but they are all done within the context of what those escalating situations look like in their specific fields.

So this brings a question that has come up before in my circles:

Can this sort of organizational resilience be taught, within the context of web operations?

GameDay exercises could certainly be one avenue for testing and training team-based outage response, but most of the focus there (at least those discussed publicly by companies who hold GameDay exercises) is testing the infrastructure and application-level components, and even then under controlled conditions and relatively narrow failure modes.

So the confidence-building value of GameDay drills lie elsewhere, and don’t really exercise the cognitive load that real-world failures can produce on the humans (i.e. the troubleshooting dev and ops teams) like the spectacular Amazon AWS outage recently.

But! Some smart folks have been thinking about this question, at a higher-level:

Is it possible to construct non-contextual and generic drills that can train competencies for this sort of on-the-fly, making-sense-of-unfamiliar-failure-modes, and sometimes disorienting troubleshooting?

At the Lund University in Sweden, there’s an excellent article on building organizational resilience in escalating situations, which I believe resulted in a chapter in the Resilience Engineering in Practice book, and also references another excellent article by David Woods and Emily Patterson called How Unexpected Events Produce An Escalation Of Cognitive And Coordinative Demands.

The parts I want to highlight here are best practices for designing scenarios meant to train these skills. If you’re looking to design a good drill meant to educate and/or train Ops and Devs on what cognitive muscles to develop for handling large-scale outages, this is a pretty damn good list (quoted from both of those sources above):

• Try to force people beyond their learned roles and routines. The scenario can contain problems that are not solvable within those roles or routines, and forces people to step out of those roles and routines.
• Contain a number of hidden goals, at various times during the scenario, that people could pursue (e.g. different ways of escaping the situation or de-escalating it), but that they have to vocalize and articulate in order to begin to achieve them (as they cannot do so by themselves).
• Include potential actions of which the consequences are both important and difficult to foresee (and that might significantly influence people’s ability to control the problem in the near future). This can force people into pro-active thinking and articulation of their expectations of what might happen.
• Be able to trap people in locking onto one solution that everybody is fixedly working towards. This can be done by garden-pathing; making the escalating problem look initially (with strong cues) like something the crew could already familiar with, but then letting it depart (with much weaker cues) to see whether the crew is caught on the garden path and lets the situation escalate.
• Or the scenario, by creating so much cognitive noise in terms of new warnings and events, should be able to trip people into thematic vagabonding—the tendency to redirect attention and change diagnosis with each incoming data piece, which results in a fragmentation of problem-solving.

Think that such a scenario could be constructed?

I want to think so, but of course nothing teaches like the hindsight of a real production outage, eh?

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Resilience Engineering: Part I

by allspaw on April 7, 2011

I’ve been drafting this post for a really long time. Like most posts, it’s largely for me to get some thoughts down. It’s also very related to the topic I’ll be talking about at Velocity later this year.

When I gave a keynote talk at the Surge Conference last year, I talked about how our field of web engineering is still young, and would do very well to pay attention to other fields of engineering, since I suspect that we have a lot to learn from them. Contrary to popular belief, concepts such as fault tolerance, redundancy of components, sacrificial parts, automatic safety mechanisms, and capacity planning weren’t invented with the web. As it turns out, some of those ideas have been studied and put into practice in other fields for decades, if not centuries.

Systems engineering, control theory, reliability engineering…the list goes on for where we should be looking for influences, and other folks have noticed this as well. As our field recognizes the value of taking a “systems” (the C. West Churchman definition, not the computer software definition) view on building and managing infrastructures with a “Full Stack Programmer” perspective, we should pull our heads out of our echo chamber every now and again, because we can gain so much from lessons learned elsewhere.

Last year, I was lucky to convince Dr. Richard Cook to let us include his article “How Complex Systems Fail” in Web Operations. Some months before, I had seen the article and began to poke around Dr. Cook’s research areas: human error, cognitive systems engineering, safety, and a relatively new multi-discipline area known as Resilience Engineering.

What I found was nothing less than exhilarating and inspirational, and it’s hard for me to not consider this research mandatory reading for anyone involved with building or designing socio-technical systems. (Hint: we all do, in web operations) Frankly, I haven’t been this excited since I saw Jimmy Page in a restaurant once in the mid-90s. Even though Dr. Cook (and others in his field, like Erik Hollnagel, David Woods, and Sidney Dekker) historically have written and researched resilience in the context of aviation, space transportation, healthcare and manufacturing, their findings strike me as incredibly appropriate to web operations and development.

Except, of course, accidents in our field don’t actually harm or kill people. But they almost always involve humans, machines, high stress, and high expectations.

Some of the concepts in resilience engineering run contrary to the typical (or stereotypical) perspectives that I’ve found in operations management, and that’s what I find so fascinating. I’m especially interested in organizational resilience, and the realization that safety in systems develops not in spite of us messy humans, but because of it.

For example:

Historical approaches taken towards improving “safety” in production might not be best

Conventional wisdom might have you believe that the systems we build are basically safe, and that all they need is protection from unreliable humans. This logically stems from the myth that all outages/degradations occur as the result of a change gone wrong, and I suspect this idea also comes from Root Cause Analysis write-ups ending with “human error” at the bottom of the page. But Dekker, Woods, and others in Behind Human Error suggest that listing human error as a root cause isn’t where you should end, it’s where you should start your investigation. Getting behind what led to a ‘human error’ is where the good stuff happens, but unless you’ve got a safe political climate (i.e., no one is going to get punished or fired for making mistakes) you’ll never get at how and why the error was made. Which means that you will ignore one of the largest opportunities to make your system (and organization) more efficient and resilient in the face of incidents. Mismatches, slips, lapses, and violations…each one of those types of error can lead to different ways of improving. And of course, working out the motivations and intentions of people who have made errors isn’t straightforward, especially engineers who might not have enough humility to admit to making an error in the first place.

Root Cause Analysis can be easily misinterpreted and abused

The idea that failures in complex systems can literally have a singular ‘root’ cause, as if failures are the result of linear steps in time, is just incorrect. Not only is it almost always incorrect, but in practice that perspective can be harmful to an organization because it allows management and others to feel better about improving safety, when they’re not, because the solution(s) can be viewed as simple and singular fixes (in reality, they’re not). James Reason’s pioneering book Human Error is enlightening on these points, to say the least. In reality (and I am guilty of this as anyone) there are motivations to reduce complex failures to singular/linear models, tipping the scales on what Hollnagel refers to as an ETTO, or Efficiency-Thoroughness Trade-Off, which I think will sound familiar to anyone working in a web startup. Because why spend extra time digging to find details of that human error-causing outage, when you have work to do? Plus, if you linger too long in that postmortem meeting, people are going to feel even worse about making a mistake, and that’s just cruel, right?

PostMortems or accident investigations is not the only way an organization can improve “safety”

Only looking at failures to guide your designs, tools, and processes drastically minimizes your ability to improve, Hollnagel says. Instead of looking at the things that go wrong, looking at the things that go right is a better strategy to improve resiliency. Personally, I think that engineering teams who practice continuous deployment intuitively understand this. Small and frequent changes made to production by a growing number of developers ascribe to a particular culture of safety, whether they know it or not. It requires what Hollnagel refers to as a “constant sense of unease”, and awareness of failure is what helps bridge that stereotypical development and operations divide.

Resilience should be a 4th management objective, alongside Better/Faster/Cheaper

The definition goes like this:

Resilience is the intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions. Since resilience is about being able to function, rather than being impervious to failure, there is no conflict between productivity and safety.

This sounds like one of those commonsense ideas, right? In an extremely self-serving way, I find some validation in that definition that optimizing for MTTR is better than optimizing for MTBF. My gut says that this shouldn’t be shocking or a revelation; it’s what mature engineering is all about.

Safety might not come from the sources you think it comes from

“…so safety isn’t about the absence of something…that you need to count errors or monitor violations, and tabulate incidents and try to make those things go away…..it’s about the presence of something. But the presence of what? When we find that things go right under difficult circumstances, it’s mostly because of people’s adaptive capacity; their ability to recognize, adapt to, and absorb changes and disruptions, some of which might fall outside of what the system is designed or trained to handle.”

- Sidney Dekker

My plan is to post more about these topics, because there are just too many ideas to explain in a single go. Apparently, Ashgate Publishing has owned this space, with a whole series of books. The newest one, Resilience Engineering in Practice, is in my bag, and I can’t put it down. Examples of these ideas in real-world scenarios (hospital and medical ops, power plants, air traffic control, financial services) are juicy with details, and the chapter “Lessons from the Hudson” goes into excellent detail about the trade-offs that go on in the mind of someone in high-stress failure scenarios, like Chesley Sullenberger.

I’ll end on this decent introduction to some of the ideas that includes the above quote, from Sidney Dekker. There’s some distracting camera work, but the ideas get across:

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Etsy’s Chef Repo, 2010

by allspaw on December 31, 2010

Etsy’s Chef Repo, 2010 from jspaw on Vimeo.

Delicious InfoViz courtesy of Gource.

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MTTR is more important than MTBF (for most types of F)

by allspaw on November 7, 2010

This week I gave a talk at QCon SF about development and operations cooperation at Etsy and Flickr.  It’s a refresh of talks I’ve given in the past, with more detail about how it’s going at Etsy. (It’s going excellently )

There’s a bunch of topics in the presentation slides, all centered around roles, responsibilities, and intersection points of domain expertise commonly found in development and operations teams. One of the not-groundbreaking ideas that I’m finally getting down is something that should be evident for anyone practicing or interested in ‘continuous deployment’:

Being able to recover quickly from failure is more important than having failures less often.

This has what should be an obvious caveat: some types of failures shouldn’t ever happen, and not all failures/degradations/outages are the same. (like failures resulting in accidental data loss, for example)

Put another way:

MTTR is more important than MTBF

(for most types of F)

(Edited: I did say originally “MTTR > MTBF”)

What I’m definitely not saying is that failure should be an acceptable condition. I’m positing that since failure will happen, it’s just as important (or in some cases more important) to spend time and energy on your response to failure than trying to prevent it. I agree with Hammond, when he said:

If you think you can prevent failure, then you aren’t developing your ability to respond.

In a complete steal of Artur Bergman‘s material, an example in the slides of the talk is of the Jeep versus Rolls Royce:

Artur has a Jeep, and he’s right when he says that for the most part, Jeeps are built with optimizing Mean-Time-To-Repair, not the classical approach to automotive engineering, which is to optimize Mean-Time-Between-Failures. This is likely because Jeep owners have been beating the shit out of their vehicles for decades, and every now and again, they expect that abuse to break something. Jeep designers know this, which is why it’s so damn easy to repair. Nuts and bolts are easy to reach, tools are included when you buy the thing, and if you haven’t seen the video of Army personnel disassembling and reassembling a Jeep in under 4 minutes, you’re missing out.

The Rolls Royce, on the other hand, likely don’t have such adventurous owners, and when it does break down, it’s a fine and acceptable thing for the car to be out of service for a long and expensive fixing by the manufacturer.

We as web operations folks want our architectures to be built optimized for MTTR, not for MTBF. I think that the reasons should be obvious, and the fact that practices like:

• Dark launching
• Percentage-based production A/B rollouts
• Feature flags

are becoming commonplace should verify this approach as having legs.

The slides from QConSF are here:

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Go or No-Go: Operability and Contingency Planning (Surge)

by allspaw on November 3, 2010

Last month I had the honor of speaking at the Surge Conference in Baltimore, put together by OmniTI.

It was a most excellent conference, and the expertise levels were ridiculously high. I count myself lucky to be considered the same league as the rest of the presenters. I did give a Keynote talk, and I haven’t uploaded those slides yet. The talk I gave on the second day of the conference was about how we plan for feature launches at Etsy, which follows a similar pattern we had at Flickr.

So, here are the slides for that talk:

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Nagios alerts on the iPhone – deleting boatloads

by allspaw on October 27, 2010

Protip: if you’re getting Nagios alerts on an iPhone, and you have your contact set as:  xxx-xxx-xxxx@txt.att.net, you’ll get messages from a ‘sender’ that looks like: “1 (410) 000-173″. This is not someone in Maryland, it’s a special address so that AT&T can route a reply back to the sender if need be.

The side affect of this is when/if you get a boatload of alerts (which can happen in cascading failure scenarios where you don’t have any Nagios dependencies or event handlers set up) you’re gonna have to spend a proportional boatload of time swiping and deleting those alerts one by one.

This, of course, is a major bummer.

A solution is to set your contact info in nagios instead to xxx-xxx-xxxx@mms.att.net, which will properly set a “from” address on your iPhone, so when it comes time to delete the boatload of messages, you can do it in a single ‘delete conversation’ swipe.

Caveat: If you do this (set to mms.att.net, instead of txt.att.net) you’ll lose the ability to reply to a Nagios alert. This presumably will affect those smart folks who have set up the ability to acknowledge an alert from their phone via a reply/procmail mechanism.

Bonus protip: make it so that you don’t ever get boatloads of Nagios alerts at once.  That will help, too.

Implied bonus protip: event handlers and dependencies are the sign of an evolved ops organization. It’s not too difficult to set up, and you’ll feel joy after you do!

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Ops Meta-Metrics: Velocity 2010 Slides

by allspaw on June 24, 2010

As expected, Velocity was excellent this year. What an awesome time to be in this field.

Caveat for those who didn’t see/hear my talk: the graphs and numbers in the slides are, for the most part, made up. But they’re also in line with what I’ve seen at Flickr and Etsy.

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