Stuff The Internet Says On Scalability For July 6th, 2018

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Could RAINB (Redundant Array of Independent Neanderthal ‘minibrains’) replace TPUs as the future AI core? 

 

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.

• $100m: Fortnite iOS revenue in 90 days; $2.5 Billion: SUSE Linux acquisition; 500,000: different orgs on Slack; 6.1%: chance of breaking change in each library; 10: years of the Apple app store; 2021: Japan goes exascale;


• Quoteable Quotes:
  • jedberg: > Yes, that's how Amazon creates lock-in.
    That is the cynical way to look at it. It also creates value because it lets you do more with what you already have.
  • Paul Ingles: We didn’t change our organisation because we wanted to use Kubernetes, we used Kubernetes because we wanted to change our organisation.
  • ThousandEyes: The Internet is made up of thousands of autonomous networks that Continue reading

In Defense of Humanity—How Complex Systems Failed in Westworld **spoilers**

The Westworld season finale made an interesting claim: humans are so simple and predictable they can be encoded by a 10,247-line algorithm. Small enough to fit in the pages of a thin virtual book.

Perhaps my brain was already driven into a meta-fugal state by a torturous, Escher-like, time shifting plot line, but I did observe myself thinking—that could be true. Or is that a thought Ford programmed into my control unit?

To the armies of algorithms perpetually watching over us, the world is a Skinner box. Make the best box, make the most money. And Facebook, Netflix, Amazon, Google, etc. make a lot of money specifically on our predictability.

Even our offline behaviour is predictable. Look at patterns of human mobility. We stay in a groove. We follow regular routines. Our actions are not as spontaneous and unpredictable as we'd surmise.

Predictive policing is a thing. Our self-control is limited. We aren't good with choice. We're predictably irrational. We seldom learn from mistakes. We seldom change.

Not looking good for team human.

It's not hard to see how those annoyingly smug androids—with their perfect bodies and lives lived in a terrarium—could come to Continue reading

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 Continue reading

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

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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 Continue reading

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• For heads of IT/Engineering responsible for building an analytics infrastructure, Etleap is an ETL solution for creating perfect data Continue reading

How Ably Efficiently Implemented Consistent Hashing

This is a guest post by Srushtika Neelakantam, Developer Advovate for Ably Realtime, a realtime data delivery platform.

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 Continue reading

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 Continue reading

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 Continue reading

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. Continue reading

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.

The computer as a creative agent, working in tandem with a human partner, to produce software, in a beautiful act of Continue reading

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.

Causes of Cascading Failures and Designing to Avoid Them

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• For heads of IT/Engineering responsible for building an analytics infrastructure, Etleap is an ETL solution for creating perfect data Continue reading

Strategy: Use TensorFlow.js in the Browser to Reduce Server Costs

One of the strategies Jacob Richter describes (How we built a big data platform on AWS for 100 users for under $2 a month) in his relentless drive to lower AWS costs is moving ML from the server to the client.

Moving work to the client is a time honored way of saving on server side costs. Not long ago it might have seemed like moving ML to the browser would be an insane thing to do. What was crazy yesterday often becomes standard practice today, especially when it works, especially when it saves money:

Our post-processing machine learning models are mostly K-means clustering models to identify outliers. As a further step to reduce our costs, we are now running those models on the client side using TensorFlow.js instead of using an autoscaling EC2 container. While this was only a small change using the same code, it resulted in a proportionally massive cost reduction. 

To learn more Alexis Perrier has a good article on Tensorflow with Javascript Brings Deep Learning to the Browser:

Tensorflow.js has four layers: The WebGL API for GPU-supported numerical operations, the web browser for user interactions, and two APIs: Continue reading

Stuff The Internet Says On Scalability For April 20th, 2018

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Freeman Dyson dissects Geoffrey West's book on universal scaling laws, Scale. (Image: Steve Jurvetson)

If you like this sort of Stuff then please support me on Patreon. And I'd appreciate if you would recommend my new book—Explain the Cloud Like I'm 10—to anyone who needs to understand the cloud (who doesn't?). I think they'll learn a lot, even if they're already familiar with the basics. 

• 5x: BPF over iptables; 51.21%: SSL certificates now issued by Let's Encrypt; 15,000x: acceleration from a genomics co-processor on long read assembly; 100 Million: Amazon Prime members; 20 minutes: time it takes a robot to assemble an Ikea chair; 1.7 Tbps: DDoS Attack; 200 Gb/sec: future network fabric speeds; $7: average YouTube earnings per 1000 views; 800 million: viruses cascading onto every square meter of the planet each day; <10m: error in  Uber's GPS enhancement; $45 million: total value of Bitcoin ransomware extortion; 


• Quotable Quotes:
  • @sachinrekhi: Excited to read the latest Amazon shareholder letter. Amazing the scale they are operating at: 100M prime members, $20B AWS business, >50% of products sold from third-party sellers...Bezos Continue reading

Google: A Collection of Best Practices for Production Services

Fail Sanely

Sanitize and validate configuration inputs, and respond to implausible inputs by both continuing to operate in the previous state and alerting to the receipt of bad input. Bad input often falls into one of these categories:

Incorrect data

Validate both syntax and, if possible, semantics. Watch for empty data and partial or truncated data (e.g., alert if the configuration is N% smaller than the previous version).

Delayed data

This may invalidate current data due to timeouts. Alert well before the data is expected to expire.

Fail in a way that preserves function, possibly at the expense of being overly permissive or overly simplistic. We’ve found that it’s generally safer for systems to continue functioning with their previous configuration and await a human’s approval before using the new, perhaps invalid, data.

Examples

In 2005, Google’s global DNS load- and latency-balancing system received an empty DNS entry file as a result of file permissions. It accepted this empty file and served NXDOMAIN for Continue reading

Stuff The Internet Says On Scalability For April 13th, 2018

Hey, it's HighScalability time:

 

Bathroom tile? Grandma's needlepoint? Nope. It's a diagram of the dark web. Looks surprisingly like a tumor.

If you like this sort of Stuff then please support me on Patreon. And I'd appreciate if you would recommend my new book—Explain the Cloud Like I'm 10—to anyone who needs to understand the cloud (who doesn't?). I think they'll learn a lot, even if they're already familiar with the basics. 

• $23 billion: Amazon spend on R&D in 2017; $0.04: cost to unhash your email address; $35: build your own LIDAR; 66%: links to popular sites on Twitter come from bots; 60.73%: companies report JavaScript as primary language; 11,000+: object dataset provide real objects with associated depth information; 150 years: age of the idea of privacy; 30%~ AV1's better video compression; 100s of years: rare-earth materials found underneath Japanese waters; 67%: better image compression using Generative Adversarial Networks; 1000 bit/sec: data exfiltrated from air-gapped computers through power lines using conducted emissions; 


• Quotable Quotes:
  • @Susan_Hennessey: Less than two months ago, Apple announced its decision to move mainland Chinese iCloud data to state-run servers.
  • @PaulTassi Continue reading

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• 5 Signs You’ve Outgrown DynamoDB. Companies often select a database that seems to be the best choice at first glance, as well as the path of least resistance, and then are subsequently surprised by cost overruns and technology limitations that quickly hinder productivity and put the business at risk. This seems to be the case with many enterprises that chose Amazon Web Service’s (AWS) DynamoDB. In this white paper we’ll cover elements of costing as well as the results of benchmark-based testing. Read 5 Signs You’ve Outgrown DynamoDB to determine if your organization has outgrown this technology.


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Give Meaning to 100 billion Events a Day – The Analytics Pipeline at Teads

This is a guest post by Alban Perillat-Merceroz, Software Engineer at Teads.tv.

In this article, we describe how we orchestrate Kafka, Dataflow and BigQuery together to ingest and transform a large stream of events. When adding scale and latency constraints, reconciling and reordering them becomes a challenge, here is how we tackle it.

Teads for Publisher, one of the webapps powered by Analytics

 

In digital advertising, day-to-day operations generate a lot of events we need to track in order to transparently report campaign’s performances. These events come from:

• Users’ interactions with the ads, sent by the browser. These events are called tracking events and can be standard (start, complete, pause, resume, etc.) or custom events coming from interactive creatives built with Teads Studio. We receive about 10 billion tracking events a day.
• Events coming from our back-ends, regarding ad auctions’ details for the most part (real-time bidding processes). We generate more than 60 billion of these events daily, before sampling, and should double this number in 2018.

In the article we focus on tracking events as they are on the most critical path of our business.

Simplified overview of our technical context with the two main Continue reading

Stuff The Internet Says On Scalability For April 6th, 2018

Hey, it's HighScalability time:

 

Programmable biology - engineered cells execute programmable multicellular full-adder logics. (Programmable full-adder computations)

If you like this sort of Stuff then please support me on Patreon. And I'd appreciate if you would recommend my new book—Explain the Cloud Like I'm 10—to anyone who needs to understand the cloud (who doesn't?). I think they'll learn a lot, even if they're already familiar with the basics. 

• $1: AI turning MacBook into a touchscreen; $2000/month: BMW goes subscription; 20MPH: 15′ Tall, 8000 Pound Mech Suit; 1,511,484 terawatt hours: energy use if bitcoin becomes world currency; $1 billion: Fin7 hacking group; 1.5 million: ethereum TPS, sort of; 235x: AWK faster than Hadoop cluster; 37%: websites use a vulnerable Javascript library; $0.01: S3, 1 Gig, 1 AZ; 


• Quotable Quotes:
  • Huang’s Law~ GPU technology advances 5x per year because the whole stack can be optimized. 
  • caseysoftware: Metcalfe lives here in Austin and is involved in the local startup community in a variety of ways.  One time I asked him how he came up with the law and he said something close Continue reading