Economics May Drive Serverless

We've been following an increasing ephemerality curve to get more and more utilization out of our big brawny boxes. VMs, VMs in the cloud, containers, containers in the cloud, and now serverless, which looks to be our first native cloud infrastructure.

Serverless is said to be about functions, but you really need a zip file of code to do much of anything useful, which is basically a container.

So serverless isn't so much about packaging as it is about not standing up your own chunky persistent services. Those services, like storage, like the database, etc, have moved to the environment.

Your code orchestrates the dance and implements specific behaviours. Serverless is nothing if not a framework writ large.

Serverless also intensifies the developer friendly disintermediation of infrastructure that the cloud started.

Upload your code and charge it on your credit card. All the developer has to worry about their function. Oh, and linking everything together (events, DNS, credentials, backups, etc) through a Byzantine patch panel of a UI; uploading each of your zillions of "functions" on every change; managing versions so you can separate out test, development, and production. But hey, nothing is perfect.

What may drive serverless more than anything else is economics. From markonen

In my book, the innovation in Lambda is, above everything else, about the billing model. My company moved the work of 40 dedicated servers onto Lambda and in doing so decimated our costs. Paying for 1500 cores (our current AWS limit) in 100ms increments has been a game changer.
I'm sure there are upsides to adopting the same programming model with your own hardware or VMs, but the financial benefit of Lambda will not be there.

There are many more quotes likes this, but that's the jist of it. And as pointed out by others, the pay off depends on some utilization threshold. If you can drive the utilization of your instances to some high level then running your own instances makes economic sense.

For the rest of us taking advantage of the aggregation of a big cloud provider is a winner. Setting up a highly available service on the cloud, dealing with instances and all the other overhead is still a huge PITA. Why deal with all that if you don't have to?

Developers pick winners. Developers follow ease of use. Developers follow the money. So serverless is a winner. You'll just have to get over the name.


Stuff The Internet Says On Scalability For July 22nd, 2016

Hey, it's HighScalability time:

It's not too late London. There's still time to make this happen


If you like this sort of Stuff then please support me on Patreon.
  • 40%: energy Google saves in datacenters using machine learning; 2.3: times more energy knights in armor spend than when walking; 1000x: energy efficiency of 3D carbon nanotubes over silicon chips; 176,000: searchable documents from the Founding Fathers of the US; 93 petaflops: China’s Sunway TaihuLight; $800m: Azure's quarterly revenue; 500 Terabits per square inch: density when storing a bit with an atom; 2 billion: Uber rides; 46 months: jail time for accessing a database; 

  • Quotable Quotes:
    • Lenin: There are decades where nothing happens; and there are weeks where decades happen.
    • Nitsan Wakart: I have it from reliable sources that incorrectly measuring latency can lead to losing ones job, loved ones, will to live and control of bowel movements.
    • Margaret Hamilton~ part of the culture on the Apollo program “was to learn from everyone and everything, including from that which one would least expect.”
    • @DShankar: Basically @elonmusk plans to compete with -all vehicle manufacturers (cars/trucks/buses) -all ridesharing companies -all utility companies
    • @robinpokorny: ‘Number one reason for types is to get idea what the hell is going on.’ @swannodette at #curryon
    • Dan Rayburn: Some have also suggested that the wireless carriers are seeing a ton of traffic because of Pokemon Go, but that’s not the case. Last week, Verizon Wireless said that Pokemon Go makes up less than 1% of its overall network data traffic.
    • @timbaldridge: When people say "the JVM is slow" I wonder to what dynamic, GC'd, runtime JIT'd, fully parallel, VM they are comparing it to.
    • @papa_fire: “Burnout is when long term exhaustion meets diminished interest.”  May be the best definition I’ve seen.
    • Sheena Josselyn: Linking two memories was very easy, but trying to separate memories that were normally linked became very difficult
    • @mstine: if your microservices must be deployed as a complete set in a specific order, please put them back in a monolith and save yourself some pain
    • teaearlgraycold: Some people, when confronted with a problem, think “I know, I'll use regular expressions.” Now they have two problems.
    • Erik Duindam:  I bake minimum viable scalability principles into my app.
    • Hassabis: It [DeepMind] controls about 120 variables in the data centers. The fans and the cooling systems and so on, and windows and other things. They were pretty astounded.
    • @WhatTheFFacts: In 1989, a new blockbuster store was opening in America every 17 hours.
    • praptak: It [SRE] changes the mindset from "Failure? Just log an error, restore some 'good'-ish state and move on to the next cool feature." towards "New cool feature? What possible failures will it cause? How about improving logging and monitoring on our existing code instead?"
    • plusepsilon: I transitioned from using Bayesian models in academia to using machine learning models in industry. One of the core differences in the two paradigms is the "feel" when constructing models. For a Bayesian model, you feel like you're constructing the model from first principles. You set your conditional probabilities and priors and see if it fits the data. I'm sure probabilistic programming languages facilitated that feeling. For machine learning models, it feels like you're starting from the loss function and working back to get the best configuration

  • Isn't it time we admit Dark Energy and Dark Matter are simply optimizations in the algorithms running the sim of our universe? Occam's razor. Even the Eldritch engineers of our creation didn't have enough compute power to simulate an entire universe. So they fudged a bit. What's simpler than making 90 percent of matter in our galaxy invisible?

  • Do you have one of these? Google has a Head of Applied AI.

  • Uber with a great two article series on their stack. Part unoPart deux: Our business runs on a hybrid cloud model, using a mix of cloud providers and multiple active data centers...We currently use Schemaless (built in-house on top of MySQL), Riak, and Cassandra...We use Redis for both caching and queuing. Twemproxy provides scalability of the caching layer without sacrificing cache hit rate via its consistent hashing algorithm. Celery workers process async workflow operations using those Redis instances...for logging, we use multiple Kafka clusters...This data is also ingested in real time by various services and indexed into an ELK stack for searching and visualizations...We use Docker containers on Mesos to run our microservices with consistent configurations scalably...Aurora for long-running services and cron jobs...Our service-oriented architecture (SOA) makes service discovery and routing crucial to Uber’s success...we’re moving to a pub-sub pattern (publishing updates to subscribers). HTTP/2 and SPDY more easily enable this push model. Several poll-based features within the Uber app will see a tremendous speedup by moving to push....we’re prioritizing long-term reliability over debuggability...Phabricator powers a lot of internal operations, from code review to documentation to process automation...We search through our code on OpenGrok...We built our own internal deployment system to manage builds. Jenkins does continuous integration. We combined Packer, Vagrant, Boto, and Unison to create tools for building, managing, and developing on virtual machines. We use Clusto for inventory management in development. Puppet manages system configuration...We use an in-house documentation site that autobuilds docs from repositories using Sphinx...Most developers run OSX on their laptops, and most of our production instances run Linux with Debian Jessie...At the lower levels, Uber’s engineers primarily write in Python, Node.js, Go, and Java...We rip out and replace older Python code as we break up the original code base into microservices. An asynchronous programming model gives us better throughput. And lots more.

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Building Highly Scalable V6 Only Cloud Hosting

This is a guest repost by Donatas Abraitis, Lead Systems Engineer at at Hostinger International.

This article is about how we built the new high scalable cloud hosting solution using IPv6-only communication between commodity servers, what problems we faced with IPv6 protocol and how we tackled them for handling more than ten millions active users.

Why did we decide to run IPv6-only network?

At Hostinger we care much about innovation technologies, thus we decided to run a new project named Awex that is based on this protocol. If we can, so why not start since today? Only frontend (user facing) services are running in dual-stack environment, everything else is IPv6-only for west-east traffic.


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Sponsored Post: Cassandra Summit, Scalyr, Gusto, LaunchDarkly, Awake Networks, Aerospike, VividCortex, MemSQL, AiScaler, InMemory.Net

Who's Hiring?

  • IT Security Engineering. At Gusto we are on a mission to create a world where work empowers a better life. As Gusto's IT Security Engineer you'll shape the future of IT security and compliance. We're looking for a strong IT technical lead to manage security audits and write and implement controls. You'll also focus on our employee, network, and endpoint posture. As Gusto's first IT Security Engineer, you will be able to build the security organization with direct impact to protecting PII and ePHI. Read more and apply here.

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Fun and Informative Events

  • Join database experts from companies like Apple, ING, Instagram, Netflix, and many more to hear about how Apache Cassandra changes how they build, deploy, and scale at Cassandra Summit 2016. This September in San Jose, California is your chance to network, get certified, and trained on the leading NoSQL, distributed database with an exclusive 20% off with  promo code - Academy20. Learn more at

  • NoSQL Databases & Docker Containers: From Development to Deployment. What is Docker and why is it important to Developers, Admins and DevOps when they are using a NoSQL database? Find out in this on-demand webinar by Alvin Richards, VP of Product at Aerospike, the enterprise-grade NoSQL database. The video includes a demo showcasing the core Docker components (Machine, Engine, Swarm and Compose) and integration with Aerospike. See how much simpler Docker can make building and deploying multi-node, Aerospike-based applications!  

Cool Products and Services

  • Do you want a simpler public cloud provider but you still want to put real workloads into production? Exoscale gives you VMs with proper firewalling, DNS, S3-compatible storage, plus a simple UI and straightforward API. With datacenters in Switzerland, you also benefit from strict Swiss privacy laws. From just €5/$6 per month, try us free now.

  • High Availability Cloud Servers in Europe: High Availability (HA) is very important on the Cloud. It ensures business continuity and reduces application downtime. High Availability is a standard service on the European Cloud infrastructure of Host Color, active by default for all cloud servers, at no additional cost. It provides uniform, cost-effective failover protection against any outage caused by a hardware or an Operating System (OS) failure. The company uses VMware Cloud computing technology to create Public, Private & Hybrid Cloud servers. See Cloud service at Host Color Europe.

  • Dev teams are using LaunchDarkly’s Feature Flags as a Service to get unprecedented control over feature launches. LaunchDarkly allows you to cleanly separate code deployment from rollout. We make it super easy to enable functionality for whoever you want, whenever you want. See how it works.

  • Scalyr is a lightning-fast log management and operational data platform.  It's a tool (actually, multiple tools) that your entire team will love.  Get visibility into your production issues without juggling multiple tabs and different services -- all of your logs, server metrics and alerts are in your browser and at your fingertips. .  Loved and used by teams at Codecademy, ReturnPath, Grab, and InsideSales. Learn more today or see why Scalyr is a great alternative to Splunk.

  • InMemory.Net provides a Dot Net native in memory database for analysing large amounts of data. It runs natively on .Net, and provides a native .Net, COM & ODBC apis for integration. It also has an easy to use language for importing data, and supports standard SQL for querying data. http://InMemory.Net

  • VividCortex measures your database servers’ work (queries), not just global counters. If you’re not monitoring query performance at a deep level, you’re missing opportunities to boost availability, turbocharge performance, ship better code faster, and ultimately delight more customers. VividCortex is a next-generation SaaS platform that helps you find and eliminate database performance problems at scale.

  • MemSQL provides a distributed in-memory database for high value data. It's designed to handle extreme data ingest and store the data for real-time, streaming and historical analysis using SQL. MemSQL also cost effectively supports both application and ad-hoc queries concurrently across all data. Start a free 30 day trial here:

  • aiScaler, aiProtect, aiMobile Application Delivery Controller with integrated Dynamic Site Acceleration, Denial of Service Protection and Mobile Content Management. Also available on Amazon Web Services. Free instant trial, 2 hours of FREE deployment support, no sign-up required.

  • ManageEngine Applications Manager : Monitor physical, virtual and Cloud Applications.

  • : Monitor End User Experience from a global monitoring network.


If any of these items interest you there's a full description of each sponsor below...

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How Does Google do Planet-Scale Engineering for a Planet-Scale Infrastructure?


How does Google keep all its services up and running? They almost never seem to fail. If you've ever wondered we get a wonderful peek behind the curtain in a talk given at GCP NEXT 2016 by Melissa Binde, Director, Storage SRE at Google: How Google Does Planet-Scale Engineering for Planet-Scale Infrastructure.

Melissa's talk is short, but it's packed with wisdom and delivered in a no nonsense style that makes you think if your service is down Melissa is definitely the kind of person you want on the case. 

Oh, just what is SRE? It stands for Site Reliability Engineering, but a definition is more elusive. It's like the kind of answers you get when you ask for a definition of the Tao. It's more a process than a thing, as is made clear by Ben Sloss 24x7 VP, Google, who defines SRE as:

what happens when a software engineer is tasked with what used to be called operations.

Let that bounce around your head for awhile.

Above and beyond all else one thing is clear: SREs are the custodian of production. SREs are the custodian of customer experience, for both and GCP.

Some of the highlights of the talk for me:

  • The Destructive Incentives of Pitting Uptime vs Features. SRE is an attempt to solve the natural tension between developers who want to push features and sysadmins that want maintain uptime by not pushing features. 
  • The Error Budget. This is the idea that failure is expected. It's not a bad thing. Users can't tell if a service is up 100% of the time or 99.99%, so you can have errors. This reduces the tension between dev and ops. As long as the error budget is maintained you can push out new features and the ops side won't be blamed.
  • Goal is to restore service immediately. Troubleshooting comes later. This means you need a  lot of logging and tooling to debug after a service has been restored. For some reason this made flash on a line from an earlier article, also based on a talk from a Google SRE: Backups are useless. It’s the restore you care about
  • No Boredom Philosophy of Paging. When a page comes in it should be for an interesting and new problem. You don't want SREs being bored handling repetitive problems. That's what bots are for.

Other interesting topics in the talk are: How is SRE structured organizationally? How are devs hired into a role focussed on production and keep them happy? How do we keep the team valued inside of Google? How do we help our teams communicate better and resolve disagreements with data rather than with assertions or power grabs? 

Let's get on with it with it. Here's how Google does Planet-Scale Engineering for a Planet-Scale Infrastructure...

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Stuff The Internet Says On Scalability For July 15th, 2016

Hey, it's HighScalability time:

That little smudge on Jupiter is North America (size comparison). 


If you like this sort of Stuff then please support me on Patreon.
  • <2%: percent of total U.S. electricity consumption used by data centers; $4.99: hourly wage of Amazon Turkers; 8,072: cores in Cassandra cluster; .5: new reward for slaving away in the Bitcoin mines; 11: source code for the original Apollo guidance computer; 10 inverse femtobarns: number of collisions recorded by the Large Hadron Collider; 34 bps: using MEMO to send molecular messages through the air; 200 MB: record for storage in DNA; 10,000+: 3D printed parts are used in a Rolls-Royce Phantom; $43.6bn: IaaS revenue to triple by 2020; 

  • Quotable Quotes:
    • @PokemonGoApp: To ensure all Trainers can experience #PokemonGo, we continue to add new resources to accommodate everyone. Thank you for your patience.
    • @balajis: Pokemon Go is a classic overnight success, 10 years in the making. Ingress database, Google Maps, the Pokemon brand…
    • @avantgame: The math of Pokemon Go is pretty amazing. 21 million players in ONE week, playing 43 minutes on average a day.
    • @icecrime: Does Pokemon Go have generics?
    • @HarvardBiz: When companies start scaling, they often start seeing the future as a threat
    • Jakob Engblom: for the best performance, you want to break the design apart across cut-points with the lowest level of communication across the cut.
    • @peterpur: once again, it becomes obvious that complexity feeds itself, while simplicity needs conscious effort & hard work.
    • @jamesurquhart: Mine is already a microservice because it runs on a microcomputer. Right? Right?
    • Facebook: In our experience, every time we add a new tool, we are surprised that we managed without it.
    • @petecordell: Telling a programmer there's already a library to do X is like telling a songwriter there's already a song about love
    • @linclark: Code that my mom wrote 50 years ago just went up on GitHub
    • @danielbryantuk: "Our monolithic application was so monolithic that we gave it a name - jimmy..." Haha, awesome! @ZalandoTech at #microservices summit
    • Uri Hasson~ even across different languages, our brains show similar activity, or become “aligned,” when we hear the same idea or story.
    • @etherealmind: Bidirectional forwarding detection is most significant advance in Autonomous Routing in the last 20 years.
    • @aphyr: In particular, I'd like to note that @VoltDB has opted to preserve strong serializabilty as the default behavior, despite a latency cost.
    • @swardley: the system is based on a cycle of theft, settlers steal from pioneers forcing them to move on ...
    • Ian Adams: That the actual encoding at the CPU [for erasure coded storage] is generally not the bottleneck, but instead that the network tends to be, especially when you have really “wide” codes, e.g. 17/20 causing tons of traffic across many storage nodes for every request. 
    • Ayende: There is about 10% difference between fsync and fdatasync when using the HDD, but there is barely any difference as far as the SSD is concerned. This is because the SSD can do random updates (such as updating both the data and the metadata) much faster, since it doesn’t need to move the spindle.
    • @cpurdy: As long as flash capacities have an order-of-magnitude advantage over RAM, flash is allowed to be slower ;-)
    • @huntchr: Before you all go nuts re #serverless, #mechanicalsympathy remains important. You still need to understand what is going on under the hood.
    • Gallant: These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.
    • @sheeshee: trying to convince somebody to archive a really old CGI script roughly 1994 for archeological purposes.. old code is important for learning.
    • Kreps & Kleppmann: we advocate a style of application development in which each data storage and processing component focuses on “doing one thing well”. Heterogeneous systems can be built by composing such specialised tools through the simple, general-purpose interface of a log. 

  • This is how you know you are Facebook. Instead of testing your new mobile software on one device you have a datacenter, with a lab, with around 60 custom made rack bristling with 2000 mobile phones, all so you can test all the different combinations and permutations. The mobile device lab at the Prineville data center.

  • The challenge was made. @adrianco: Let me know when you run a 1000 node Cassandra cluster on Kubernetes :-). The challenge was met. Thousand Instances of Cassandra using Kubernetes Pet Set: We deployed 1,009 minion nodes to Google Compute Engine (GCE), spread across 4 zones, running a custom version of the Kubernetes 1.3 beta. We ran this demo on beta code since the demo was being set up before the 1.3 release date. For the minion nodes, GCE virtual machine n1-standard-8 machine size was chosen, which is vm with 8 virtual CPUs and 30GB of memory. It would allow for a single instance of Cassandra to run on one node, which is recommended for disk I/O. 

  • Lures from Pokemon Go have turned out to be amazingly effective. Pokemon Go Is Driving Insane Amounts of Sales at Small Local Businesses. Here's How It Works. Building that kind of native business model driver deep into the game mechanics is the real trick of the game. Also, How the gurus behind Google Earth created 'Pokémon Go'.

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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Why Amazon Retail Went to a Service Oriented Architecture

When Lee Atchison arrived at Amazon, Amazon was in the process of moving from a large monolithic application to a Service Oriented Architecture.

Lee talks about this evolution in an interesting interview on Software Engineering Daily: Scalable Architecture with Lee Atchison, about Lee's new book: Architecting for Scale: High Availability for Your Growing Applications.

This is a topic Adrian Cockcroft has talked a lot about in relation to his work at Netflix, but it's a powerful experience to hear Lee talk about how Amazon made the transition with us having the understanding of what Amazon would later become. 

Amazon was running into the problems of success. Not so much from a scaling to handle the requests perspective, but they were suffering from the problem of scaling the number of engineers working in the same code base.

At the time their philosophy was based on the Two Pizza team. A small group owns a particular piece of functionality. The problem is it doesn’t work to have hundreds of pizza teams working on the same code base. It became very difficult to innovate and add new features. It even became hard to build the application, pass the test suites, and deploy the software.

The solution: move to a Service Oriented Architecture (not microservices).

Organizing around services allowed individual teams to truly own the code base, the support responsibility, and top to bottom responsibility for the functionality.

The result: a dramatic increase in innovation and the ability to grow. After a while the Amazon retail site grew with a constant stream of new capabilities. Maybe too many :-)

The process requires a culture shift, really more of an ownership shift, from being part of a larger group to be an entity of its own that has responsibilities outside of its group as well as responsibilities inside the group.

While the strategy of consciously exploiting team organization as a means of speeding up product development and encouraging innovation is not a new idea now, in the early 2000s it would have been one ballsy move.

Related Articles 


Vertical Scaling Works for Bits and Bites

This is just to delicious a parallel to pass up. 

Here we have Google building a new four story datacenter Scaling Up: Google Building Four-Story Data Centers:


And here we have a new vertical farm from AeroFarms


Both have racks of consumables. One is a rack of bits, the other is a rack of bites. Both used to sprawl horizontally across huge swaths of land and now are building up. Both designs are driven by economic efficiency, extracting the most value per square foot. Both are expanding to meet increased demand. It's a strange sort of convergence.


Stuff The Internet Says On Scalability For July 8th, 2016

Hey, it's HighScalability time:

Juno: 165,000mph, 1.7 billion miles, missed orbit by 10 miles. Dang buggy software. 


If you like this sort of Stuff then please support me on Patreon.
  • $3B: damages awarded to HP from Oracle; 37%: when to stop looking through your search period; 70%: observed Annualized Failure Rate (AFR) in production datacenters for some models of SSDs; 

  • Quotable Quotes:
    • spacerodent: After Christmas there was this huge excess capacity and that is when I first learned of the EC2 project. It was my belief EC2 came out of a need to utilize those extra Gurupa servers during the off season:)
    • bcantrill: That said, I think Sun's problem was pretty simple: we thought we were a hardware company long after it should have been clear that we were a systems company. As a result, we made overpriced, underperforming (and, it kills me to say, unreliable) hardware. And because we were hardware-fixated, we did not understand the economic disruptive force of either Intel or open source until it was too late. 
    • @cmeik: I am not convinced the blockchain and CRDTs *work.*
    • daly: Managers make decisions. Only go to management with your need for a decision and always present the options. They went to management with what was, in essence, a complaint. Worse, it was a complaint that had nothing to do with the business. Clearly they were not keeping the business uppermost in their priority queue. So management made a business decision and fixed the problem.
    • @colettecello: Architect: "we should break this down into 6 microservices" Me: "you have 6 teams who hate each other?" Architect: "how did you know that?"
    • Matt Stats: The differences between BSD and Linux all derive from basic philosophical differences. Once you understand those, everything else falls into place pretty neatly.
    • @wattersjames: "Last year, Johnson & Johnson turned off its last mainframe"
    • Allan Kelly: But in the world of software development this mindset [economies of scale] is a recipe for failure and under performance. The conflict between economies of scale thinking and diseconomies of scale working will create tension and conflict.
    • Jeff G: Today, a large part of my business is migrating companies off the monolithic Java EE containers into lightweight modular containers. Yes, even the tried and true banking and financial industries are moving away from Java EE. 
    • collyw: "Weeks of programming can save hours of planning" is a favorite quote of mine.
    • xiongchiamiov: when I see a team responsible for hundreds of microservices, it's not at all surprising when I find they're completely underwater and struggling to keep up with maintenance, much less new features.
    • Robert Plomin: We're always talking about differences. The only genetics that makes a difference is that 1 percent of the 3 billion base pairs. But that is over 10 million base pairs of DNA. We're looking at these differences and asking to what extent they cause the differences that we observe. 
    • @jmferdegue: Micro services as a cost reduction strategy for project delivery. Marco Cullen from @OpenCredo at #micromanchester
    • J.R.R. Tolkien: I like, and even dare to wear in these dull days, ornamental waistcoats.
    • @johnregehr~ HN commenter has reached enlightenment : In both cases, after about a year, we found ourselves wishing we had not rewritten the network stack.
    • @nigelbabu: OH: 9.9999% uptime is still five 9s.
    • @jessfraz: "We are going to need a floppy and a shaman" @ryanhuber
    • Peter Cohen: So, why the cloud? Because, the developer.
    • @CompSciFact: 'The fastest algorithm can frequently be replaced by one that is almost as fast and much easier to understand.' -- Douglas W. Jones
    • @igrigorik: Improved font loading in WebKit:  - tl;dr: 3s timeout, WOFF2, unicode-range, Font Loading API. hooray!
    • @danielbryantuk: "I've worked on teams with 200+. We had 3 people just to make JPA work" @myfear on scaling issues #micromanchester
    • AWS Origin Story: Jassy tells of an executive retreat at Jeff Bezos’ house in 2003. It was there that the executive team conducted an exercise identifying the company’s core competencies
    • @sheeshee: ".. you are charged for every 100ms your code executes and the number of times your code is triggered." the 1970ies are back. (aws lambda)
    • @KentBeck: accepting mediocrity as the price of scaling misunderstands the power law distribution of payoffs.
    • @cowtowncoder: that is: cost efficiency from AWS et al is for SMALL deployments, and at some point it always, invariably becomes cheaper to DIY
    • Exascale Computing Research priorities: Total power requirements suggest that CPUs will not be suitable commodity processors for supercomputers in the future.

  • Here's how Instagram does it. Instagram + Android: Four Years Later: At the core of this principle is the idea that the Instagram app is simply a renderer of server-provided data, much like a web browser. Almost all complex business logic happens server-side, where it is easier to fix bugs and add new features. We rely on the server to be perfect, enforced through continuous integration testing, and dispense with null-checking or data-consistency checking on the client.

  • Good story on how WePay is moving from a monolith to a services based architecture on top of Kubernetes. Advantages: autoscaling, rolling updates, a pure model independent of software assigned to specific machines.  WePay on Kubernetes: ‘It Changed Our Business’.

  • Julia Ferraioli with a really fun explaination of Kubernetes using legos

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Machine Learning Driven Programming: A New Programming for a New World


If Google were created from scratch today, much of it would be learned, not coded. Around 10% of Google's 25,000 developers are proficient in ML; it should be 100% -- Jeff Dean

Like the weather, everybody complains about programming, but nobody does anything about it. That’s changing and like an unexpected storm the change comes from an unexpected direction: Machine Learning / Deep Learning.

I know, you are tired of hearing about Deep Learning. Who isn’t by now? But programming has been stuck in a rut for a very long time and it's time we do something about it.

Lots of silly little programming wars continue to be fought that decide nothing. Functions vs objects; this language vs that language; this public cloud vs that public cloud vs this private cloud vs that ‘fill in the blank’; REST vs unrest; this byte level encoding vs some different one; this framework vs that framework; this methodology vs that methodology; bare metal vs containers vs VMs vs unikernels; monoliths vs microservices vs nanoservices; eventually consistent vs transactional; mutable vs immutable; DevOps vs NoOps vs SysOps; scale-up vs scale-out; centralized vs decentralized; single threaded vs massively parallel; sync vs async. And so on ad infinitum.

It’s all pretty much the same shite different day. We are just creating different ways of calling functions that we humans still have to write. The real power would be in getting a machine to write the functions. And that’s what Machine Learning can do, write functions for us. Machine Learning might just might be some different kind of shite for a different day.

Machine Learning Driven Programming

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