Reliability Engineering Quotes

If it hurts, do it more frequently, and bring the pain forward.

I'm not supposed to be the reliable one here! I'm supposed to be the one who stabs people for money, and this lovesick bullshit of yours is seriously cramping my style!

...every new genetically engineered plant is a unique event in nature, bringing its own set of genetic contingencies. This means that the reliability or safety of one genetically modified plant doesn't necessarily guarantee the reliability or safety of the next.

Never forget that the word that best describes reliable science is not consensus, but engineering.

Without the low operating costs, high efficiency, high reliability, and great durability of diesel engines, it would have been impossible to reach the extent of globalization that now defines the modern economy.

generally speaking, no one should be directly logging in to a database.

team size should not scale directly with service growth.

When a team must allocate a disproportionate amount of time to resolving tickets at the cost of spending time improving the service, scalability and reliability suffer.

Making all the components highly reliable will not necessarily make the system safe.

Releasing software is too often an art; it should be an engineering discipline.

Hope is not a strategy.

Leibniz had little engineering skill and did not surround himself with those who did. So, like many great theorists who lacked practical collaborators, he was unable to produce reliably working versions of his device.

At this risk of being redundant, everything must be checked in to code. This includes the following: DB object migrations Triggers Procedures and functions Views Configurations Sample datasets for functionality Data cleanup scripts

The train swung around the curve, the engine puffing with short, heavy blasts, and they passed smoothly from sight that way, with that quality about them of shabby and timeless patience, of static serenity: that blending of childlike and ready incompetence and paradoxical reliability that tends and protects them it loves out of all reason and robs them steadily and evades responsibility and obligations by means too barefaced to be called subterfuge even and is taken in theft or evasion with only that frank and spontaneous admiration for the victor which a gentleman feels for anyone who beats him in a fair contest, and withal a fond and unflagging tolerance for whitefolk's vagaries like that of a grandparent for unpredictable and troublesome children, which I had forgotten.

Bill Baker, a distinguished engineer at Microsoft, quipped that we used to treat servers like pets: “You name them and when they get sick, you nurse them back to health. [Now] servers are [treated] like cattle. You number them and when they get sick, you shoot them.

As electrical energy can create mechanical vibrations (perceived as sound by the human ear), so in turn can mechanical vibrations create electrical energy, such as the previously mentioned ball lightning. It could be theorized, therefore, that with the Earth being a source for mechanical vibration, or sound, and the vibrations being of a usable amplitude and frequency, then the Earth's vibrations could be a source of energy that we could tap into. Moreover, if we were to discover that a structure with a certain shape, such as a pyramid, was able to effectively act as a resonator for the vibrations coming from within the Earth, then we would have a reliable and inexpensive source of energy.

However, interrupting technology workers is easy, because the consequences are invisible to almost everyone, even though the negative impact to productivity may be far greater than in manufacturing. For instance, an engineer assigned to multiple projects must switch between tasks, incurring all the costs of having to re-establish context, as well as cognitive rules and goals.

Things are getting out of hand: Tony finds herself channeling this opinion at least once a day. The crazed weather. The vicious, hate-filled politics. The myriad glass high-rises going up like 3-D mirrors, or siege engines. The municipal garbage collection: Who can keep all those different-coloured bins straight? Where to put the clear plastic food containers, and why isn't the little number on the bottom a reliable guide? And the vampires. You used to know where you stood with them--smelly, evil,undead--but now there are virtuous vampires and disreputable vampires, and sexy vampires and glittery vampires, and none of the old rules about them are true any more. Once you could depend on garlic, and on the rising sun, and on crucifixes. You could get rid of the vampires once and for all. But not any more.

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In the early 1980s, managers at the National Aeronautics and Space Administration (NASA) estimated that the flights would be 99.999 percent reliable, which represents a failure rate of only 1 in 100,000. According to the physicist Richard Feynman, who was a member of the commission that investigated the January 1986 Challenger accident, in which the shuttle broke apart shortly into its flight, killing all seven astronauts on board, this “would imply that one could put a Shuttle up each day for 300 years expecting to lose only one.” He wondered, “What is the cause of management’s fantastic faith in the machinery?” Engineers, who were more familiar with the shuttle itself and with machines in general, predicted only a 99 percent success rate, or a failure every 100 launches. A range safety officer, who personally observed test firings during the developmental phase of the rocket motors, expected a failure rate of 1 in 25. The Challenger accident proved that estimate to be the actual failure rate, giving a success rate of 96 percent after exactly 25 launchings.

Once again, infrastructure had set a limit on the pace of development of a new technology. But by the second decade of the nineteenth century, malleable-iron rails were beginning to replace brittle cast iron on the wagonways of England, and stone or wooden sleepers strengthened the roadbeds for heavy locomotives. With such improvements, Trevithick’s circular track on vacant land in London would open out into a network of fast, reliable transportation—but not before its inventors and engineers endured a final long haul of challenges and trials.

When his troops reached the rocket camp, they found almost everything of value gone and Stalin was reportedly furious. “This is absolutely intolerable,” he said, according to reliable defectors. “We defeated Nazi armies; we occupied Berlin and Peenemünde, but the Americans got the rocket engineers. What could be more revolting and more inexcusable? How and why was this allowed to happen?” In a way, the Red Army’s race toward Peenemünde was symbolic: It was, without anyone knowing it, the beginning of the race for outer space, or what Winston Churchill once called “the wizard war.

It’s hard to blame Representative Petri for missing the point. The value of studying fireflies is endlessly surprising. For example, before 1994, Internet engineers were vexed by spontaneous pulsations in the traffic between computers called routers, until they realized that the machines were behaving like fireflies, exchanging periodic messages that were inadvertently synchronizing them. Once the cause was identified, it became clear how to relieve the congestion. Electrical engineers devised a decentralized architecture for clocking computer circuits more efficiently, by mimicking the fireflies’ strategy for achieving synchrony at low cost and high reliability.

What was happily proved by this early revolution is something that we perhaps need to be reminded of again today: that neither exact science nor engineering is proof against the irrationality of those that operate the system. Above all, that the strongest and most efficient of megamachines can be overthrown, that human errors are not immortal. The collapse of the Pyramid Age proved that the megamachine exists on a basis of human beliefs, which may crumble, of human decisions, which may prove fallible, and human consent, which, when the magic becomes discredited, may be withheld. The human parts that composed the megamachine were by nature mechanically imperfect: never wholly reliable. Until real machines of wood and metal could be manufactured in sufficient quantity to take the place of most of the human components, the megamachine would remain vulnerable.

The foundation of our thinking is the Theory of Jobs to Be Done, which focuses on deeply understanding your customers’ struggle for progress and then creating the right solution and attendant set of experiences to ensure you solve your customers’ jobs well, every time. “Theory” may conjure up images of ivory tower musings, but I assure you that it is the most practical and useful business tool we can offer you. Good theory helps us understand “how” and “why.” It helps us make sense of how the world works and predict the consequences of our decisions and our actions. Jobs Theory5, we believe, can move companies beyond hoping that correlation is enough to the causal mechanism of successful innovation. Innovation may never be a perfect science, but that’s not the point. We have the ability to make innovation a reliable engine for growth, an engine based on a clear understanding of causality, rather than simply casting seeds in the hopes of one day harvesting some fruit.

Leonid Gavrilov, a researcher at the University of Chicago, argues that human beings fail the way all complex systems fail: randomly and gradually. As engineers have long recognized, simple devices typically do not age. They function reliably until a critical component fails, and the whole thing dies in an instant. A windup toy, for example, works smoothly until a gear rusts or a spring breaks, and then it doesn’t work at all. But complex systems—power plants, say—have to survive and function despite having thousands of critical, potentially fragile components. Engineers therefore design these machines with multiple layers of redundancy: with backup systems, and backup systems for the backup systems. The backups may not be as efficient as the first-line components, but they allow the machine to keep going even as damage accumulates. Gavrilov argues that, within the parameters established by our genes, that’s exactly how human beings appear to work. We have an extra kidney, an extra lung, an extra gonad, extra teeth.

Let the center be your home: To be centered is considered desirable; when they feel distracted or scattered, people often say, “I lost my center.” But if there is no person inside your head, if the ego’s sense of I, me, mine is illusory, where’s the center? Paradoxically, the center is everywhere. It is the open space that has no boundaries. Instead of thinking of your center as a defined spot—the way people point to their hearts as the seat of the soul—be at the center of experience. Experience isn’t a place; it’s a focus of attention. You can live there, at the still point around which everything revolves. To be off center is to lose focus, to look away from experience or block it out. To be centered is like saying “I want to find my home in creation.” You relax into the rhythm of your own life, which sets the stage for meeting yourself at a deeper level. You can’t summon the silent witness, but you can place yourself close to it by refusing to get lost in your own creation. When I find myself being overshadowed by anything, I can fall back on a few simple steps: • I say to myself, “This situation may be shaking me, but I am more than any situation.” • I take a deep breath and focus my attention on whatever my body is feeling. • I step back and see myself as another person would see me (preferably the person whom I am resisting or reacting to). • I realize that my emotions are not reliable guides to what is permanent and real. They are momentary reactions, and most likely they are born of habit. • If I am about to burst out with uncontrollable reactions, I walk away. As you can see, I don’t try to feel better, to be more positive, to come from love, or to change the state I’m in. We are all framed by personalities and driven by egos. Ego personalities are trained by habit and by the past; they run along like self-propelled engines. If you can observe the mechanism at work without getting wrapped up in it, you will find that you possess a second perspective, one that is always calm, alert, detached, tuned in but not overshadowed. That second place is your center. It isn’t a place at all but a close encounter with the silent witness.

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In typical DevOps transformations, as we progress from deployment lead times measured in months or quarters to lead times measured in minutes, the constraint usually follows this progression: Environment creation: We cannot achieve deployments on-demand if we always have to wait weeks or months for production or test environments. The countermeasure is to create environments that are on demand and completely self-serviced, so that they are always available when we need them. Code deployment: We cannot achieve deployments on demand if each of our production code deployments take weeks or months to perform (i.e., each deployment requires 1,300 manual, error-prone steps, involving up to three hundred engineers). The countermeasure is to automate our deployments as much as possible, with the goal of being completely automated so they can be done self-service by any developer. Test setup and run: We cannot achieve deployments on demand if every code deployment requires two weeks to set up our test environments and data sets, and another four weeks to manually execute all our regression tests. The countermeasure is to automate our tests so we can execute deployments safely and to parallelize them so the test rate can keep up with our code development rate. Overly tight architecture: We cannot achieve deployments on demand if overly tight architecture means that every time we want to make a code change we have to send our engineers to scores of committee meetings in order to get permission to make our changes. Our countermeasure is to create more loosely-coupled architecture so that changes can be made safely and with more autonomy, increasing developer productivity.

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I am assured that this is a true story. A man calls up his computer helpline complaining that the cupholder on his personal computer has snapped off, and he wants to know how to get it fixed. “Cupholder?” says the computer helpline person, puzzled. “I’m sorry, sir, but I’m confused. Did you buy this cupholder at a computer show or receive it as a special promotion?” “No, it came as part of the standard equipment on my computer.” “But our computers don’t come with cupholders.” “Well, pardon me, friend, but they do,” says the man a little hotly. “I’m looking at mine right now. You push a button on the base of the unit and it slides right out.” The man, it transpired, had been using the CD drawer on his computer to hold his coffee cup. I bring this up here by way of introducing our topic this week: cupholders. Cupholders are taking over the world. It would be almost impossible to exaggerate the importance of cupholders in automotive circles these days. The New York Times recently ran a long article in which it tested a dozen family cars. It rated each of them for ten important features, among them engine size, trunk space, handling, quality of suspension, and, yes, number of cupholders. A car dealer acquaintance of ours tells us that they are one of the first things people remark on, ask about, or play with when they come to look at a car. People buy cars on the basis of cupholders. Nearly all car advertisements note the number of cupholders prominently in the text. Some cars, like the newest model of the Dodge Caravan, come with as many as seventeen cupholders. The largest Caravan holds seven passengers. Now you don’t have to be a nuclear physicist, or even wide awake, to work out that that is 2.43 cupholders per passenger. Why, you may reasonably wonder, would each passenger in a vehicle need 2.43 cupholders? Good question. Americans, it is true, consume positively staggering volumes of fluids. One of our local gas stations, I am reliably informed, sells a flavored confection called a Slurpee in containers up to 60 ounces in size. But even if every member of the family had a Slurpee and a personal bottle of

As strangeness becomes the new normal, your past experiences, as well as the past experiences of the whole of humanity, will become less reliable guides. Humans as individuals and humankind as a whole will increasingly have to deal with things nobody ever encountered before, such as super-intelligent machines, engineered bodies, algorithms that can manipulate your emotions with uncanny precision, rapid man-made climate cataclysms and the need to change your profession every decade. What is the right thing to do when confronting a completely unprecedented situation? How should you act when you are flooded by enormous amounts of information and there is absolutely no way you can absorb and analyse it all? How to live in a world where profound uncertainty is not a bug, but a feature? To survive and flourish in such a world, you will need a lot of mental flexibility and great reserves of emotional balance. You will have to repeatedly let go of some of what you know best, and feel at home with the unknown. Unfortunately, teaching kids to embrace the unknown and to keep their mental balance is far more difficult than teaching them an equation in physics or the causes of the First World War. You cannot learn resilience by reading a book or listening to a lecture. The teachers themselves usually lack the mental flexibility that the twenty-first century demands, for they themselves are the product of the old educational system. The Industrial Revolution has bequeathed us the production-line theory of education. In the middle of town there is a large concrete building divided into many identical rooms, each room equipped with rows of desks and chairs. At the sound of a bell, you go to one of these rooms together with thirty other kids who were all born the same year as you. Every hour some grown-up walks in, and starts talking. They are all paid to do so by the government. One of them tells you about the shape of the earth, another tells you about the human past, and a third tells you about the human body. It is easy to laugh at this model, and almost everybody agrees that no matter its past achievements, it is now bankrupt. But so far we haven’t created a viable alternative. Certainly not a scaleable alternative that can be implemented in rural Mexico rather than just in upmarket California suburbs.

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engage in development tasks, because the service basically runs and repairs itself: we want systems that are automatic, not just automated. In practice, scale and new features keep SREs on their toes.

In general, an SRE team is responsible for the availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning of their service(s). We

And taking the historical view, who, then, looking back, might be the first SRE? We like to think that Margaret Hamilton, working on the Apollo program on loan from MIT, had all of the significant traits of the first SRE.5 In her own words, “part of the culture was to learn from everyone and everything, including from that which one would least expect.

1.  What kind of information are you thinking of measuring? The more the object to be measured resembles inanimate matter, the more likely it is to be measureable: that is why measurement is indispensable in the natural sciences and in engineering. When the objects to be measured are influenced by the process of measurement, measurement becomes less reliable. Measurement becomes much less reliable the more its object is human activity, since the objects—people—are self-conscious, and are capable of reacting to the process of being measured. And if rewards and punishments are involved, they are more likely to react in a way that skews the measurement’s validity. By contrast, the more they agree with the goals of those rewards, the more likely they are to react in a way that enhances the measurement’s validity.

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We, the engineers building these systems, have a responsibility to carefully consider those consequences and to consciously decide what kind of world we want to live in.

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If a human operator needs to touch your system during normal operations, you have a bug. The definition of normal changes as your systems grow. Carla Geisser, Google SRE

Urmson and his colleagues learned that as their technology improved, the humans using it would become less reliable. “We realized that this was going to be much much harder to do than we had thought,” said engineer Dave Ferguson. “It just became clear that, look, if we can’t rely on the driver to pay attention, or to hand over control to, then basically we just have to solve the whole problem. We need to have this vehicle not need the driver.

Prefer experimentation over analysis. It’s far more reliable to get good at cheap validation than it is to get great at consistently picking the right solution. Even if you’re brilliant, you are almost always missing essential information when you begin designing. Analysis can often uncover missing information, but it depends on knowing where to look, whereas experimentation allows you to find problems that you didn’t anticipate.

Which models are the most reliable? Well, obviously, the models that come from hard science and engineering are the most reliable models on this earth. And engineering quality control—at least the guts of it that matters to you and me and people who are not professional engineers—is very much based on the elementary mathematics of Fermat and Pascal: It costs so much, and you get so much less likelihood of it breaking if you spend this much. It’s all elementary high school mathematics.

Every major change in the technologies underlying our lifestyles, from gunpowder to steel to the internal combustion engine to the rise of electricity, has required a leap of faith and a major break from the past. Imagine the fear, traveling long distances aboard a rickety vehicle that burned an explosively flammable liquid and rode upon black rubber tubes filled with air. What could possibly go wrong?! Yet people quickly overcame their fear of cars and focused instead on how to improve safety and reliability.

Quantum computing is not only faster than conventional computing, but its workload obeys a different scaling law—rendering Moore’s Law little more than a quaint memory. Formulated by Intel founder Gordon Moore, Moore’s Law observes that the number of transistors in a device’s integrated circuit doubles approximately every two years. Some early supercomputers ran on around 13,000 transistors; the Xbox One in your living room contains 5 billion. But Intel in recent years has reported that the pace of advancement has slowed, creating tremendous demand for alternative ways to provide faster and faster processing to fuel the growth of AI. The short-term results are innovative accelerators like graphics-processing unit (GPU) farms, tensor-processing unit (TPU) chips, and field-programmable gate arrays (FPGAs) in the cloud. But the dream is a quantum computer. Today we have an urgent need to solve problems that would tie up classical computers for centuries, but that could be solved by a quantum computer in a few minutes or hours. For example, the speed and accuracy with which quantum computing could break today’s highest levels of encryption is mind-boggling. It would take a classical computer 1 billion years to break today’s RSA-2048 encryption, but a quantum computer could crack it in about a hundred seconds, or less than two minutes. Fortunately, quantum computing will also revolutionize classical computing encryption, leading to ever more secure computing. To get there we need three scientific and engineering breakthroughs. The math breakthrough we’re working on is a topological qubit. The superconducting breakthrough we need is a fabrication process to yield thousands of topological qubits that are both highly reliable and stable. The computer science breakthrough we need is new computational methods for programming the quantum computer.

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Empowering Connections: Unveiling the Essence of Pioneer Powers' Copper Cable Lugs In the dynamic world of electrical engineering, where precision and reliability reign supreme, one name stands out as a beacon of excellence – Pioneer Powers. Renowned for its innovative solutions and unwavering commitment to quality, Pioneer Powers has carved a niche for itself as a leading provider of power solutions, with its copper cable lugs leading the charge.

As Gordon’s book The Rise and Fall of American Growth went to press in early 2016 (its publication facilitated by digital technologies), the internet continued to disrupt countless industries while the media fanned fears of an impending ‘second machine age’, in which robots replace human workers. Gordon’s Northwestern colleague Joel Mokyr suggested that a ‘shortfall of imagination [is] largely responsible for much of today’s pessimism’. Mokyr listed a number of revolutionary new technologies then under development, including 3D printing, graphene and genetic engineering, to which might be added autonomous cars and clean energy.19 Finance writer William Bernstein accused secular stagnationists of conflating what they couldn’t conceive with that which was not possible.20 Hansen made the same mistake. The most reliable prediction, Bernstein concluded, is to assume that past economic trends continue.

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Hope is not a strategy. Traditional SRE saying

By design, it is crucial that SRE teams are focused on engineering. Without constant engineering, operations load increases and teams will need more people just to keep pace with the workload.

Christy Lodwick, the Vice President of Tyde Systems, brings reliable skills and knowledge to the organization. Having served in the Health Care industry as an Administrator and Consultant, Christy Lodwick has the experience required to manage high-end projects at the company. Many clients benefit from her project managerial and network engineering skills.

In software development, engineers are often pressured to deliver features instead of fixing defects, addressing reliability issues, or working on internal improvement projects. As a result, so called “technical debt”§§ adds up, leading to more complex problems that are increasingly difficult to solve. Consequently, Marty Cagan, a product expert who has trained generations of product leaders on building software products that customers love, stresses the importance for product and engineering leaders to allocate at least 20% of engineering’s time to proactively fix issues before they snowball into catastrophic ones, such as having to “rewrite the entire codebase from scratch.

These and other obscure insights from my early career as Buckminster Fuller’s chief engineer left me with a deep appreciation for systems. Fuller was quick to point out that most people’s education hadn’t prepared them to see systems. He believed that ever-increasing specialization threatened our ability to appreciate how systems work. Moreover, in school we learn to break problems down into parts, which enables focus and progress in many fields of knowledge but blinds us to larger patterns and relationships. Traditional management systems similarly break work down into parts, inhibiting collaboration and innovation in favor of reliability and efficiency.

Control system overlays must be faster and more reliable than the underlying systems being controlled. The Nyquist-Shannon Sampling Theorem, first introduced in 1928, explains why. A receiver (sensor) must sample at least twice the rate of the sender (the thing being monitored and controlled) to accurately measure and control a system. This theorem forms the basis of all things digital, including telecommunications, medical imaging systems, astronomy, and more. In reality, to control a complex engineered or biological system, the receiver and controller must be much faster to maintain resilience and agility. This has stark implications for top-down management. For instance, if reports are generated and reviewed once a week, they can be used to control (manage) only situations that change no faster or more frequently than every two weeks. Anything faster moving may not be detected or is not controllable. This explains why exemplary organizations are typically characterized by overlays of people in supportive roles that are uncharacteristic of their lower-performing peers. That is not “overhead” but absolutely necessary bandwidth for sustaining high performance of fast-moving, complex, dynamic systems.

Blameless culture originated in the healthcare and avionics industries where mistakes can be fatal. These industries nurture an environment where every “mistake” is seen as an opportunity to strengthen the system. When postmortems shift from allocating blame to investigating the systematic reasons why an individual or team had incomplete or incorrect information, effective prevention plans can be put in place. You can’t “fix” people, but you can fix systems and processes to better support people making the right choices when designing and maintaining complex systems.

A key principle of any effective software engineering, not only reliability-oriented engineering, simplicity is a quality that, once lost, can be extraordinarily difficult to recapture.

So it’s that kind of engineering, quality engineering, reliability engineering, that has to go into every one of those single point failure modes.

By analogy Marshall’s Wiley C. Bunn, Director of Reliability and Quality Assurance, describes the engineering work and data that constitute the Rationale for Retention, necessary for all C 1 items:

My late mother knew absolutely nothing about cars, but had an eagle eye for people.* It would have been interesting to set her the task of buying ten cars based on her instincts about the people selling them, while at the same time tasking ten automotive engineers with acquiring ten cars at auction. I’m confident the cars my mother bought would have been every bit as reliable as the cars chosen by the engineers, perhaps more so.

    System.out.println("Invalid Card");     break;

Throughout the heart of the Rockies, tourism and recreation have either replaced resource extraction industries as the major sources of revenue and employment or are on the verge of doing so. In particular, national parks and other reserves that dot the region are proving to be among its most reliable economic engines both for local communities and for the states and provinces that host the protected areas. The aim of connecting those dots for ecological reasons goes hand in hand with ensuring long-term financial stability and prosperity.

There is no doubt that 'force multipliers' - squad automatic weapons - have changed the character of warfare once again, just as their predecessors did during the First World War, if perhaps not to quite the same degree. In the immediate future it seems that most armies will be using some form of 5.56mm machine-gun at squad level, be it a box-fed LSW or belt-fed SAW. If there is a cloud on the horizon where modern light machine-guns are concerned it is that they are not powerful enough for long-range work, or for penetrating cover and light armour. Nevertheless, the new generation of light machine-guns will remain in use well into the next century, not least because they are popular with the soldiers who operate them, the machine-gunners. Likewise, there will still be a place for the heavier GPMG, which does have the 'punch' that the LSW lacks. Machine-guns themselves have become lighter, and their operating principles both more secure and more efficient; the ammunition they use has shrunk to a quarter of its original size and become almost 100 percent reliable. The one important thing which has not changed dramatically is the human component; the attitude with which man faces the prospect of death in battle, and how he prepares himself to face that possibility quite deliberately, for it was the original invention of the machine-gun which reformed that. More than any other single 'advance' in weapons technology, the machine-gun allowed an individual (or actually, a small team of men) to dominate a sector of the battlefield. They had an inhuman advantage which simply had to be exploited if they were to be on the winning side, whether their opponents were Zulus, Sioux, or Dervishes, or other industrialized nations to be beaten into last place in the race toward economic supremacy. Whether the machine-gun has been as important, in any sense at all of the word, as it near-contemporary, the internal combustion engine - or even, date one say it, the bicycle or sewing machine - is still to be decided, but there is one clear, irrefutable fact connected with its short history: it has killed tens of millions of men, women and children and blighted the lives of tens of millions more.

The companies that didn’t suffer, including Netflix, knew how to design for reliability; they understood resilience, spreading data across zones, and a whole lot of reliability engineering.

DevOps and its resulting technical, architectural, and cultural practices represent a convergence of many philosophical and management movements (including): Lean, Theory of Constraints, Toyota production system, resilience engineering, learning organizations, safety culture, Human factors, high-trust management cultures, servant leadership, organizational change management, and Agile methods.

The deal [between product owners and] engineering goes like this: Product management takes 20% of the team’s capacity right off the top and gives this to engineering to spend as they see fit. They might use it to rewrite, re-architect, or re-factor problematic parts of the code base...whatever they believe is necessary to avoid ever having to come to the team and say, ‘we need to stop and rewrite [all our code].’ If you’re in really bad shape today, you might need to make this 30% or even more of the resources. However, I get nervous when I find teams that think they can get away with much less than 20%.

While by definition, starship engine rooms should have been predictable, uneventful places that operated according to the reliable mathematics of warp physics, she’d come to believe that, more often than not, they were in fact the nexi of entropy. Order battled chaos in these places with an almost dependable regularity. And engineers, she secretly suspected, functioned as avatars of both these forces, keeping them carefully balanced so that neither overwhelmed the other. Thus, warp drive worked, but the best engineers could still find a new wrinkle in the laws of physics when circumstances required it. Bhatnagar

Over-alerting causes Operations engineers to be woken up in the middle of the night for protracted periods of time, even when there are few actions that they can appropriately take. The

Chuck Rossi, Director of Release Engineering at Facebook, described, “All the code supporting every feature we’re planning to launch over the next six months has already been deployed onto our production servers. All we need to do is turn it on.

Software engineering has this in common with having children: the labor before the birth is painful and difficult, but the labor after the birth is where you actually spend most of your effort. Yet

Electric light, the first reliable internal combustion engine, and wireless transmission were all invented within the same three-month period at the end of 1879. Within

When an engineer with years of familiarity in a problem space begins designing a product, it’s easy to imagine a utopian end-state for the work. However, it’s important to differentiate aspirational goals of the product from minimum success criteria (or Minimum Viable Product). Projects can lose credibility and fail by promising too much,

We will actively manage this technical debt by ensuring that we invest at least 20% of all Development and Operations cycles on refactoring, investing in automation work and architecture and non-functional requirements (NFRs, sometimes referred to as the “ilities”), such as maintainability, manageability, scalability, reliability, testability, deployability, and security. Figure 11: Invest 20% of cycles on those that create positive, user-invisible value (Source: “Machine Learning and Technical Debt with D. Sculley,” Software Engineering Daily podcast, November 17, 2015,

Between 1931 and 1946, Pan American Airways had 28 flying boats known as “Clippers,” These four radial engine aircraft were S-40’s and 42’s built in 1934, later replaced by Boeing 314 Clippers, that became the familiar symbol of the company. Following the war, Pan American Airways flew land based airliners such as the Boeing 377 Stratocruiser, developed from the C-97, Stratofreighter, and a military derivative of the B-29 Superfortress, used as a troop transport, and the DC-4 series, converted from the blueprints of the C-54 Skymaster. Both of these airliners were originally developed for the United States Army Air Corps, during World War II. On January 1950 Pan American Airways Corporation adopted the name it had been unofficially called since 1943, and formally became “Pan American World Airways, Inc.” That September Pan American bought out American Airlines’ overseas division and simultaneously placed an order for 45 DC-6Bs, replacing their DC-4’s. Throughout Pan-American was known simply as Pan-Am. The Douglas DC-6 is a four engine “Double Wasp” radial piston-powered airliner manufactured for long flights. It was built by the Douglas Aircraft Company from 1946 until 1958. More than 700 were built between those years and some are still flying today. The rugged, reliable DC-6B, was regarded as the ultimate piston-engine airliner, from the perspective of having excellent handling qualities and relatively economical operations.

To achieve market orientation, we won’t do a large, top-down reorganization, which often creates large amounts of disruption, fear, and paralysis. Instead, we will embed the functional engineers and skills (e.g., Ops, QA, Infosec) into each service team, or provide their capabilities to teams through automated self-service platforms that provide production-like environments, initiate automated tests, or perform deployments.

The deal [between product owners and] engineering goes like this: Product management takes 20% of the team’s capacity right off the top and gives this to engineering to spend as they see fit. They might use it to rewrite, re-architect, or re-factor problematic parts of the code base...whatever they believe is necessary to avoid ever having to come to the team and say, ‘we need to stop and rewrite [all our code].’ If you’re in really bad shape today, you might need to make this 30% or even more of the resources. However, I get nervous when I find teams

Traditional managers will often object to hiring engineers with generalist skill sets, arguing that they are more expensive and that ‘I can hire two server administrators for every multi-skilled operations engineer.’” However, the business benefits of enabling faster flow are overwhelming. Furthermore, as Prugh notes, “[ I] nvesting in cross training is the right thing for [employees’] career growth, and makes everyone’s work more fun.

Putting alerts into email and hoping that someone will read all of them and notice the important ones is the moral equivalent of piping them to /dev/null: they will eventually be ignored.

Dan Milstein, one of the principal engineers at Hubspot, writes that he begins all blameless post-mortem meetings by saying, “We’re trying to prepare for a future where we’re as stupid as we are today.” In other words, it is not acceptable to have a countermeasure to merely “be more careful” or “be less stupid”— instead, we must design real countermeasures to prevent these errors from happening again.

Automation is a force multiplier, not a panacea.

100% is the wrong reliability target for basically everything (pacemakers and anti-lock brakes being notable exceptions).

We cannot achieve deployments on demand if each of our production code deployments take weeks or months to perform (i.e., each deployment requires 1,300 manual, error-prone steps involving up to three hundred engineers). The countermeasure is to automate our deployments as much as possible, with the goal of being completely automated so they can be done self-service by any developer.

One of the most significant things they did to help change the outcomes of deployments was to have all Facebook engineers, engineering managers, and architects rotate through on-call duty for the services they built. By

However, a real implementation may still have to include code to handle the case where something happens that was assumed to be impossible, even if that handling boils down to printf("Sucks to be you") and exit(666) — i.e., letting a human operator clean up the mess [93]. (This is arguably the difference between computer science and software engineering.)

the SRE Way in mind: thoroughness and dedication, belief in the value of preparation and documentation, and an awareness of what could go wrong, coupled with a strong desire to prevent it.

To this end, Google always strives to staff its SRE teams with a mix of engineers with traditional software development experience and engineers with systems engineering experience.

Don’t be afraid to provide white glove customer support for early adopters to help them through the onboarding process. Sometimes automation also entails a host of emotional concerns, such as fear that someone’s job will be replaced by a shell script. By working one-on-one with early users, you can address those fears personally, and demonstrate that rather than owning the toil of performing a tedious task manually, the team instead owns the configurations, processes, and ultimate results of their technical work. Later adopters are convinced by the happy examples of early adopters.

The launch cycle at Internet companies is markedly different. Launches and rapid iterations are far easier because new features can be rolled out on the server side, rather than requiring software rollout on individual customer workstations.

The Role of the Launch Coordination Engineer Our Launch Coordination Engineering team is composed of Launch Coordination Engineers (LCEs), who are either hired directly into this role, or are SREs with hands-on experience running Google services. LCEs are held to the same technical requirements as any other SRE, and are also expected to have strong communication and leadership skills

Any organization that aspires to be serious about running an effective SRE arm needs to consider training. Teaching SREs how to think in a complicated and fast-changing environment with a well-thought-out and well-executed training program has the promise of instilling best practices within a new hire’s first few weeks or months that otherwise would take months or years to accumulate.

Hiring SREs well is critical to having a high-functioning reliability organization, as explored in “Hiring Site Reliability Engineers” [Jon15]. Google’s hiring practices have been detailed in texts like Work Rules! [Boc15],1 but hiring SREs has its own set of particularities. Even by Google’s overall standards, SRE candidates are difficult to find and even harder to interview effectively.

Making the jump from a previous company or university, while changing job roles (from traditional software engineer or traditional systems administrator) to this nebulous Site Reliability Engineer role is often enough to knock students’ confidence down several times. For more introspective personalities (especially regarding questions #2 and #3), the uncertainties incurred by nebulous or less-than-clear answers can lead to slower development or retention problems.

Which backends of this server are considered “in the critical path,” and why? What aspects of this server could be simplified or automated? Where do you think the first bottleneck is in this architecture? If that bottleneck were to be saturated, what steps could you take to alleviate it?

In the course of their jobs, they will come across systems they’ve never seen before, so they need to have strong reverse engineering skills.

At scale, there will be anomalies that are hard to detect, so they’ll need the ability to think statistically, rather than procedurally, to uncloak problems.

It’s important to establish credibility by delivering some product of value in a reasonable amount of time. Your first round of products should aim for relatively straightforward and achievable targets — ones without controversy or existing solutions. We