Stuff The Internet Says On Scalability For June 29th, 2018

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Rockets. They're big. You won't believe how really really big they are. (Corridor Crew)

Do you like this sort of Stuff? Please lend me your support on Patreon. It would mean a great deal to me. And if you know anyone looking for a simple book that uses lots of pictures and lots of examples to explain the cloud, then please recommend my new book: Explain the Cloud Like I'm 10. They'll love you even more.

• 200TB: GitLab Git data; $100 Billion: Instagram; ~250k: Walmart peak events per second; 10x: data from upgraded Large Hadron Collider; .3mm: smallest computer; 9.9 million: spam or automated accounts identified by Twitter per week; 1 million: facial image training set; 1/3: industrial robots installed in China; 24%: never backup; 7 billion: BuzzFeed monthly page views; 


• Quotable Quotes:
  • @jason: would love to do a https://www.founder.university/  for Immigrants -- but we might need to do it in Canada or Mexico, so that, umm.... potential immigrants can actually attend! #america 
  • @kellabyte: LOL at racking up an AWS bill of $140,000 in 4 hours of compute time.
  • @kellabyte: Recently I got to work on a project that really stressed Amazon AWS scalability. You want to talk scale? We spun up a cluster of 100,000 AWS instances multiple times. 2+ million CPU cores. I got to work on something so big a cloud provider learned about their scale bottlenecks
  • jedberg: Despite being a strong advocate for AWS, this is where I will say Google completely outshines Amazon. Google's approach to pricing is, "do it as efficiently and quickly as possible, and we'll make sure that's the cheapest option". AWS's approach is more, "help us do capacity planning and we'll let you get a price break for it.". Google applies bulk discounts after the fact, AWS makes you ask for them ahead of time.
  • Mark Lapedus: Costs of developing a complex chip could run as high as $1.5B, while power/performance benefits are likely to decrease.
  • Quirky: Thus there is a tradeoff: separateness enables inventors to create heterodox ideas, but strong cohesive networks are likely to be better for getting them implemented.
  • karmakaze: The key difference is that NoSQL was being developed and used to solve specific issues with using traditional databases. They were implementations of solutions to problems. Blockchain is a solution in search of problems.
  • slivym: FPGAs in industry are used for a very small number of specific applications: Smart NICs, Early stages of wireless networks (5G whilst the standards are being hammered out), military (where you need high performance with no consideration of cost), and embedded, Prof Video (where the custom I/O is essential). Generally, unless you're doing something that fits those applications well, the FPGA will not look good, and there are the same mistakes made in research time after time. For data centre these are twice as bad.
  • @troyhunt: We [Cloudflare] peaked at 44M requests to @reporturi in an hour yesterday. The busiest single minute I saw was 949k requests or an *average* of 16k requests per second for that minute. 
  • NSA: Right now, almost all NSA’s mission is being done in [IC GovCloud], and the productivity gains and the speed at which our analysts are able to put together insights and work higher-level problems has been really amazing.
  • Robert Graham: This is the most pressing lesson organizations need to learn, the one they are ignoring. They need to do more to prevent desktops from infecting each other, such as through port-isolation/microsegmentation. They need to control the spread of administrative credentials within the organization. A lot of organizations put the same local admin account on every workstation which makes the spread of NotPetya style worms trivial. They need to reevaluate trust relationships between domains, so that the admin of one can't infect the others.
  • smidgie82: This article totally ignores the devops side of Docker. Sure, you can cobble together Docker-like process isolation using namespaces and cgroups, and you can run the process using a custom set of libraries using chroot -- though I definitely don't agree that for the average developer that approach is anywhere near as easy as Docker.
  • Valeriy Kravchuk: Partitioning bugs do not get proper attention from Oracle engineers. We see bugs with wrong status and even a bug with a clear test case and a duplicate that is "Open" for 4 years. Some typical use cases are affected badly, and still no fixes (even though since 5.7 we have native partitioning in InnoDB and changing implementation gave good chance to review and either fix or re-check these bugs).
  • Don't stop here. Follow the clues and you shall...

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Stuff The Internet Says On Scalability For June 22nd, 2018

Hey, it's HighScalability time:

4th of July may never be the same. China creates stunning non-polluting drone swarm firework displays. Each drone is rated with a game mechanic and gets special privileges based on performance (just kidding). (TicToc)

Do you like this sort of Stuff? Please lend me your support on Patreon. It would mean a great deal to me. And if you know anyone looking for a simple book that uses lots of pictures and lots of examples to explain the cloud, then please recommend my new book: Explain the Cloud Like I'm 10. They'll love you even more.

• $40 million: Netflix monthly spend on cloud services; 5%: retention increase can increase profits 25%; 50+%: Facebook's IPv6 traffic from the U.S, for mobile it’s over 75 percent; 1 billion: monthly Facebook, err, Instagram users; 409 million: websites use NGINX; 847 Tbps: global average IP traffic in 2021; 200 million: Netflix subscribers by 2020; $30bn: market for artificial-intelligence chips by 2022;

• Quotable Quotes:

  • @evacide: Just yelled “Encryption of data in transit is not the same as encryption of data at rest!” at a journalist on the car radio before slamming it off. I am a hit at parties.

  • Drako: I deal with the security industry, where more than 90% of the security cameras are manufactured in China. The chips in those cameras used to be made in a lots of different places. They’ve since migrated to China, and a lot of the government customers I engage with are unwilling in any way, shape, or form to deploy those cameras. They have a huge problem sourcing cameras that are not based on those chips. There is a lot of concern about the Trojans in chips and Trojans near the chips. It’s the first situation I’ve encountered where the customer is honestly concerned about this.

  • Memory Guy: But there are even more compelling reasons for certain applications to convert from today’s leading technologies (like NAND flash, DRAM, NOR flash, SRAM, and EEPROM) to one of these new technologies, and that is the fact that the newer technologies all provide considerable energy savings in computing environments...Something consistent about all of them is that they are nonvolatile, so they don’t need to be refreshed like DRAM, they use faster and lower-energy write mechanisms than either NAND or NOR flash, and their memory cells can be shrunk smaller than current memory technologies’ scaling limits, which means that they should eventually be priced lower than today’s memory chips.

  • Charlie Demerjian: what does Intel have planned for their server roadmap? Why is it causing such consternation among OEMs, ODMs, and major customers? For the same reason the 14/10nm messaging is causing consternation among investors, but the server side is in much worse shape. How bad is it? Three major roadmap updates in 29 days with serious spec changes, and it got worse from there.

  • Xtracerx: for me the biggest value to serverless functions is how nicely they tie in to the ecosystem of a cloud provider. using them to respond to storage events on s3 or database events or auth events is super easy and powerful.

  • Daniel Lemire: So Roaring bitmaps can be thousands of times faster than a native JavaScript Set. they can can be two orders of magnitude faster than FastBitSet.js. That Roaring bitmaps could beat FastBitSet.js is impressive: I wrote FastBitSet.js and it is fast!

  • Bryan William Jones: Cool. "But it turns out that the Orbiters' photos were actually super-high-rez, shot on 70mm film and robotically developed inside the orbiters, with the negs raster-scanned at 200 lines/mm and transmitted to ground stations using... lossless analog image-compression technology."

  • Bcrescimanno: I worked at Netflix when OpenConnect was introduced and I don't remember anyone internally thinking it was unnecessary (though, even at the time, the company was large enough that you didn't know everyone). Quite contrary, this was the era of the 250GB / month cap from Comcast and we could observe clearly that they were throttling Netflix traffic. OpenConnect, the ability to deploy the CDN directly into the internal network of these ISPs served multiple purposes--not the least of which was to expose the fact that they were holding Netflix for ransom. So, to say that was the executive foreseeing things is a bit of revisionist history. It doesn't lessen the impact or importance of OpenConnect; but, it grew out of a very real impasse with a very large ISP. Ultimately, Netflix did end up paying Comcast in 2014 and, surprise surprise, the throttling stopped.

  • Eitally: there are a few critical differences between GCP and AWS or Azure. Setting aside the network quality & performance, which is objectively superior with Google, outside of GCE almost every other GCP product is offered as a managed service. Beyond that, there are several -- like Spanner -- that don't exist anywhere else. I fully appreciate a desire to avoid vendor lock-in, but there are plenty of situations where allying with a vendor that offers a superior product/service for your specific business need is absolutely the correct decision.

  • There are more quotes, more everything. Get them while they're hot.

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How Ably Efficiently Implemented Consistent Hashing

This is a guest post by Srushtika Neelakantam, Developer Advovate for Ably Realtime, a realtime data delivery platform. You can view the original article—How to implement consistent hashing efficiently—on Ably's blog.

Ably’s realtime platform is distributed across more than 14 physical data centres and 100s of nodes. In order for us to ensure both load and data are distributed evenly and consistently across all our nodes, we use consistent hashing algorithms.

In this article, we’ll understand what consistent hashing is all about and why it is an essential tool in scalable distributed system architectures. Further, we’ll look at data structures that can be used to implement this algorithm efficiently at scale. At the end, we’ll also have a look at a working example for the same.

Hashing revisited

Remember the good old naïve Hashing approach that you learnt in college? Using a hash function, we ensured that resources required by computer programs could be stored in memory in an efficient manner, ensuring that in-memory data structures are loaded evenly. We also ensured that this resource storing strategy also made information retrieval more efficient and thus made programs run faster.

The classic hashing approach used a hash function to generate a pseudo-random number, which is then divided by the size of the memory space to transform the random identifier into a position within the available space. Something that looked like the following:

location = hash(key) mod size

So, why can’t we use the same method for handling requests over the network?

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Stuff The Internet Says On Scalability For June 15th, 2018

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Scaling fake ratings. A 5 star 10,000 phone Chinese click farm. (English Russia)

Do you like this sort of Stuff? Please lend me your support on Patreon. It would mean a great deal to me. And if you know anyone looking for a simple book that uses lots of pictures and lots of examples to explain the cloud, then please recommend my new book: Explain the Cloud Like I'm 10. They'll love you even more.

• 1.6x: better deep learning cluster scheduling on k8s; 100,000: Large-scale Diverse Driving Video Database; 3rd: reddit popularity in the US; 50%: increase in Neural Information Processing System papers, AI bubble? 420 tons: leafy greens from robot farms; 75%: average unused storage on EBS volumes; 12TB: RAM on new Azure M-series VM; 10%: premium on Google's single-tenant nodes; $7.5B: Microsoft's cost of courting developers; 100th: flip-flop invention anniversary; 1 million: playlist dataset from Spotify; 38GB torrent: Stackoverflow public database; 85%: teens use YouTube; 20%-25%: costs savings using Aurora; 80%: machine learning Ph.D.s work at Google or Facebook; 18: years of NASA satellite data; >1TB: Ethereum blockchain; 200,000 trillion: IBM's super computer calculations per second; 


• Quotable Quotes:
  • Michael Pollan: “I have no doubt that all that Hubbard LSD all of us had taken had a big effect on the birth of Silicon Valley.”
  • @hisham_hm: Strongly disagree. Most of use who started coding in the 80s started with BASIC. We turned out just fine. The first thing that your first language should teach you is the _joy of coding_.
  • @bryanmikaelian: OH: GraphQL is SOAP for millennials
  • @JoeEmison: Another thing that serverless architectures change: how do you software development. I find myself using a local dev environment to do infra config mgmt, but then often use the web consoles for writing functions and testing; so much faster that way.
  • Jürgen Schmidhuber: And now we can see that all of civilisation is just a flash in world history. In just a flash, the guy who had the first agriculture was almost the same guy who had the first spacecraft in 1957. And soon we are going to have the first AIs that really deserve the name, the first true AIs.
  • Dave Snowden~ A key principle of complex design is shift a system to an adjacent possible. Once there's enough stability more conventional approaches can be used. Architect for discovery before architecting for delivery. Starting with delivery misses the discovery phases which misses opportunities as well as threats. Fractal engagement is they way to achieve change. People don't make decisions about what other people do. People make decision about what they can do tomorrow within their own sphere of influence. The system as a whole orientates through multiple actions. You scale be decomposition and recombination, not through aggregation, and not by imitation.
  • Alex Lindsay: When possibility is greater than circumstance you get action.
  • Andrew Barron: These are all this family of traits that at one time were considered to be the thing that separated humans from all other animals, and then was slowly recognized to appear in primates and then large-brained mammals. And then suddenly we’re recognizing that something like a honey bee, with less than a million neurons, is able to do all of these things.
  • John Hennessy & David Patterson~ We're entering a new golden age [in processors]. The end of Dennard Scaling and Moore's Law means architecture is where we have to innovate to improve performance, cost, and energy. Rasing the level of abstraction using Domain Specific Languages makes it easier for programmers and architects to innovate. Domain Specific Architectures are getting 20x and 40x improvements, not just 5-10%.
  • Hungry for more? Get a plate, click through, and pile it high.

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Stuff The Internet Says On Scalability For June 8th, 2018

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Slovenia. A gorgeous place to break your leg. Highly recommended.

Do you like this sort of Stuff? Please lend me your support on Patreon. It would mean a great deal to me. And if you know anyone looking for a simple book that uses lots of pictures and lots of examples to explain the cloud, then please recommend my new book: Explain the Cloud Like I'm 10. They'll love you even more.

• 294: slides in Internet trends 2018 deck; 110 TB: Hubble Space Telescope data; $124 million: daily App store purchases; 10 billion: monthly Siri requests; 1000 billion: yearly photos taken on iOS; one exabyte: Backblaze storage by year end; 837 million: spam taken down by Facebook in Q1; 86%: of passwords are terrible; 10 Million: US patents; 72: Transceiver Radar Chip; C: most energy efficient language; $138 Billion: global games market; $50 billion: 2017 Angry Birds revenue; 50 million: cesium atomic clock time source; 1.3 Million: vCPU grid on AWS at $30,000 per hour; $296 million: Fortnite April revenue; 4000: Siri requests per second; 


• Quotable Quotes:
  • @adriancolyer: Microsoft's ServiceFabric runs across over 160K machines and 2.5M cores, powering core Azure services as well as end-user applications. 
  • @StabbyCutyou: To summarize: Tech is not easy, but people are always harder. Until the robots come to replace us all, you won't be able to avoid dealing with people.
    Focus on the above skills, and "technology harder" as a way to increase the scope of where you can be useful to other engineers.
  • Mani Doraisamy: I would rather optimize my code than fundraise.
  • Backblaze: The failure rates of all of the larger drives (8-, 10- and 12 TB) are very good, 1.2% AFR (Annualized Failure Rate) or less. The overall failure rate of 1.84% is the lowest we have ever achieved, besting the previous low of 2.00% from the end of 2017.
  • @rbranson: Things I once held as sincere beliefs: * Databases are bad queue backends * Static typing is a waste of time * Monoliths are always worse * Strong consistency isn't worth fighting for * Using the "right" tool always trumps using the one you know * The JVM sucks
  • @aleksbu: Since I switched back to SSR for my product, productivity went through the roof. SPAs has been a fun journey for me but all this added complexity makes it super expensive. My product doesn’t need any of the benefits that SPAs bring at the moment so switching to SSR was natural.
  • @ajaynairthinks: Real car convo with my 5YO: "V:Papa What's a Lambda" [after hearing my call] Me: Well its a way to run code without servers V: What's code Me: Its like algorithms [from a game he plays] V: Ohh so Lambda is cool because I know what code is and because I dunno what a server is Me: [Yes!]
  • @OpenAI: AI and Compute: Our analysis showing that the amount of compute used in the largest AI training runs has had a doubling period of 3.5 months since 2012 (net increase of 300,000x)
  • @JeffDean: TPUv3!  So hot it needs help with cooling: first liquid cooled devices in our data centers. A TPUv3 pod is 8X as powerful as the TPUv2 pod announced at IO last year, offering more than 100 petaflops of ML compute, allowing us to tackle bigger problems & build better products.
  • Platformonomics: As I keep repeating, CAPEX is both a prerequisite to play in the big boy cloud and confirmation of customer success. Both IBM and Oracle are tens of billions of dollars in cloud infrastructure CAPEX behind Amazon, Google, and Microsoft.
  • Eugene Wei: I believe the core experience of Twitter has reached most everyone in the world who likes it. Let's examine the core attributes of Twitter the product 
  • Harrison Jacobs: The joke used to be that Chinese people like to live near good public schools, Liyan Chen, the manager of international corporate affairs at Alibaba, told Business Insider. “The joke now in China is that they want to live where the Hemas are, because then they can get everything delivered to them really easily.”...The technological advancements Alibaba has brought to Hema — easy in-app ordering, ultrafast delivery, price matching, facial-recognition payment, tailored stocking based on spending habits, etc. — Amazon could easily bring to Whole Foods. And in my opinion, given Amazon's obsession with efficiency, it's a matter of not if, but when.
  • @lizthegrey: Some uses of ML today in Google production: predicting user clicks on ads, prefetching next memory/file accesses, scheduling jobs and capacity planning, speech recognition, fraud detections, smart responses, and machine vision. #SREcon
  • @lizthegrey: What's ML look like in prod? "Don't worry, it's just another data pipeline" 10% of the effort is offline training -- transforming/training prod data using TPUs, validating, and producing a trained model. We then push the trained model to prod. #SREcon
  • Joab Jackson: [Facebook] developed a tool, called Packager, which uses machine learning to automate the process of deciding which files to bundle into a package for a specific end user. It relies heavily on statistical analysis: Which files will the users need right away? Which will they need eventually? Which files have been updated? Some files get updated constantly; others not so much so.
  • It has been a while so you know there's a lot more...

  Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

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Gone Fishin'

Well, not exactly Fishin', but I'll be on a month long vacation starting today. I won't be posting new content, so we'll all have a break. Disappointing, I know. Please use this time for quiet contemplation and other inappropriate activities. Se vidiva kmalu!

If you really need a quick fix there's always all the back catalog of Stuff the Internet Says. Odds are there's a lot you didn't read yet.

Stuff The Internet Says On Scalability For April 27th, 2018

Hey, it's HighScalability time:

Did ancient Egyptians invent Wi-Fi? @sherifhanna

Do you like this sort of Stuff? Please lend me your support on Patreon. It would mean a great deal to me. And if you know anyone looking for a simple book that uses lots of pictures and lots of examples to explain the cloud, then please recommend my new book: Explain the Cloud Like I'm 10. They'll love you even more.

• $17,500: price to give up Google search; 51.8,31.2,18.79: % using AWS, Azure, Google for IoT; 400: items per second shipped by peak Amazon; 43%: music revenues came from streaming; 800%: boost in downloads from apps featured by the Apple App Store; 45: average age of startup founder; 


• Quotable Quotes:
  • Broad Band: By the mid-twentieth century, computing was so much considered a woman’s job that when computing machines came along, evolving alongside and largely independently from their human counterparts, mathematicians would guesstimate their horsepower by invoking “girl-years,” and describe units of machine labor as equivalent to one “kilogirl.”
  • thegayngler: Most engineers would not hire themselves. That has been apparent to me for awhile now. I’m not sure why they expect people to be to be better than they were when they were hired. I don’t expect engineers to be better than me. I have but one qualification. Can they do the job? Are they strong enough that I can guide them into the position I need them at if it is required.
  • Where Wizards Stay Up Late: Heart liked working with small, tightly knit groups composed of very bright people. He believed that individual productivity and talent varied not by factors of two or three, but by factors of ten or a hundred. Because Heart had a knack for spotting engineers who could make things happen, the groups he had supervised at Lincoln tended to be unusually productive.
  • @AllenDowney: Can someone explain why, if you write an idea in math notation, that's "theory", which provides deep understanding of the math "behind" it, but if you write the same idea in a programming language, it's just hacking? This bizarre prejudice is the bane of my professional life.
  • @kwchang: 'The internet went from a democratizing free space to having power very centralized; crypto decentralization is a reaction to that' - @starkness #Angels #cryptointro
  • Lynn Langit: To me, containers are the new VMs. All this frenzy about containers, and more specifically container management systems — look, somebody has to manage the things. I want to pay the cloud providers to do it so I don’t have to.
  • @kellabyte: Many-core servers are a huge problem. We have no idea how to write software to actually use the hardware properly. For example, in Go, there’s no IO library that can go faster than 10GbE.
  • Markus Winand: Don’t say relational database when referring to SQL databases. SQL is really more than just relational.
  • Catalin Cimpanu: A loud sound emitted by a fire suppression system has destroyed the hard drives of a Swedish data center, downing Nasdaq operations across Northern Europe.
  • dmoy: My mother in law is already paranoid, warning us not to talk about certain topics with her. Scary stuff. I should point out that my MIL is a completely apolitical person who worked in the Chinese government for her entire career, spending most of that time just helping poor people, without a shred of corruption. She has nothing to worry about, doesn't care about politics, and even she's paranoid about this.
  • vkjv: +1 for Neon! The best part about Rust is that it changes this question to "what language AND Rust?" Node + Rust is a great experience.
  • There are lots more quotes. Click through for complete enlightenment.

Update: What will programming look like in the future?

Update: Ólafur Arnalds built a robotic music system to accompany him on the piano. He calls his system of two semi generative, self playing pianos—STRATUS. You can hear his most recent song re:member. There's more explanation in The Player Pianos pt. II and a short Facebook live session. His software reacts to his playing in real-time. Then he has to react to the robots because he's not sure what they're going to do. He's improvising like a jazz band would do, but it's with robots. It's his own little orchestra. The result is beautiful. The result is also unexpected. Ólafur makes the fascinating point that usually your own improvisation is limited by your own muscle memory. But with the randomness of the robots you are forced to respond in different ways. He says you get a "pure unrestricted creativity." And it's fun he says with a big smile on his face.

Maybe programming will look something like the above video. Humans and AIs working together to produce software better than either can separately.

Maybe AIs won't destroy the world as we all fear, but help us by working together to build a better world?

The computer as a creative agent, working in tandem with a human partner, to produce software, in a beautiful act of co-creation.

The alternative vision—The Coming Software Apocalypse—is a dead end.

Better requirements and better tools have already been tried and found wanting. Requirements are a trap. They don't work. Requirements are no less complex and undiscoverable than code.

Tools are another trap. Tools are just code that encode an inflexible solution to a problem that's already been solved. Why?

I remember a lot of tools over the years. Lisp machines would change everything. Software through pictures would change everything. Then there's every kind of smart compiler, new language, functional this, OOP that, high level specification language, IDL, graphical program generators, program verifiers, bug trackers, source code control systems, build systems, test systems and so on.

I've written a lot of tools. For example, for a lot of real-time embedded systems I worked on I wrote a state machine generator (https://github.com/ToddHoff/fgen), that could generate entire sophisticated agents with timers, events, complicated message protocols, timeouts, retries, etc. Very powerful. I've written dozens more like that.

So I like tools. I build tools. I use tools. Tools aren't bad. Tools aren't enough either.

All those tools did nothing to change the face of software. Improvements were linear and incremental.

That's because tools are static by their nature. A hammer is a tool for pounding nails. Tools encode an approach to solving a particular problem. Need a variation and you're out of luck. Even hackable tools only allow evolution in one or two dimensions before you are simply rewriting a new and different tool.

So I guess I'm cheating by saying something that goes beyond the bounds of toolness is no longer a tool, it's more of a co-creator. You can't as far as I know say that about any programming tools today.

What we need in software is something that puts us on an exponential growth curve. All we've had so far are tools that are more like cruise control than fully autonomous self-driving cars.

To put it another way, tools have been sustaining innovations rather than disruptive innovations. Even the cloud has largely served as a medium for implementing software as we have always implemented it, just with a twist. A powerful twist to be sure, but the cloud has not been disruptive from the perspective of writing code. It merely continues the process of enfolding of abstractions upstack, or up the value chain, which is inherently linear and sustaining.

And I realize this is all just bloviation in that I have no idea how it would work, even though I've given it a lot of thought.

When I see how the pianist and the AI controlled pianos work together making beautiful music together, there's a core their that resonates deeply with building software.

There are the seeds of how it has already started:

• Jeff Dean On Large-Scale Deep Learning At Google 
• Machine Learning Driven Programming: A New Programming For A New World

Here's what we do know: neither tools or requirements are a silver bullet, they are a method of incrementally improving software quality. Software production quantity is not increased at all.

What we need is a manufacturing process that puts software production on an exponential curve. The only conceivable tool we have at the moment to put software on an exponential production curve is AI. That's the only way software can truly eat the world.

Right now, limited as we are by human programmers using methods that haven't changed much in 30 years, software is just nibbling at the world. And that won't scale. We need more software. A lot more software. And humans are the bottleneck.

Are humans and AIs working together to co-create software the solution? I don't know, but what else is there?

Related Articles

• On HackerNews
• More disussion on the general topic: Is Uncle Bob serious? (on HackerNews), Tools are not the Answer

Google: Addressing Cascading Failures

Like the Spanish Inquisition, nobody expects cascading failures. Here's how Google handles them.

This excerpt is a particularly interesting and comprehensive chapter—Chapter 22 - Addressing Cascading Failures—from Google's awesome book on Site Reliability Engineering. Worth reading if it hasn't been on your radar. And it's free!

Written by Mike Ulrich

If at first you don't succeed, back off exponentially."

Dan Sandler, Google Software Engineer

Why do people always forget that you need to add a little jitter?"

Ade Oshineye, Google Developer Advocate

A cascading failure is a failure that grows over time as a result of positive feedback.¹⁰⁷ It can occur when a portion of an overall system fails, increasing the probability that other portions of the system fail. For example, a single replica for a service can fail due to overload, increasing load on remaining replicas and increasing their probability of failing, causing a domino effect that takes down all the replicas for a service.

We’ll use the Shakespeare search service discussed in Shakespeare: A Sample Service as an example throughout this chapter. Its production configuration might look something like Figure 22-1.

Figure 22-1. Example production configuration for the Shakespeare search service

Causes of Cascading Failures and Designing to Avoid Them

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