What Does It Really Take To Track A Million Cell Phones?


You can find anything and everything on the internet, yet nothing that explains how to track cell phones.

Let us clarify right away, we are not talking about how to track your own cell phone in case it’s lost or stolen. We are talking about tracking everyone that lives, breathes and wears a cell phone.

This is actually incredibly easy and we think that people should be aware of that.

If a representative of a phone service provider with 10 million customers came into my office and asked this question “What would it take to track every move of our 10 million customers?”. My answer would be “An intern and 6 months“. Then we’d insist the intern will need a desk, a computer, basic programming and algebra skills. That’s all it takes.

Imagine for a minute that you are the intern in question. Congratulations and welcome to our company! Your internship begins now, this document will introduce you to everything you need to know.

We’ll go over the basics of cellular networks, geolocation principles, technologies readily available in every cell phone and how to leverage all of that into a truly real-time planet-scale mass surveillance system.

Spoiler Alert: If you are scared of 1984 like scenarios, you may want to stop reading this and bounce to a video with Darth Vader playing the accordion.

A) Foreword

We are in a unique position with cross domain expertise. We combine experience in state-of-the-art tracking systems with past experience in the telecommunication industry.

Whether it’s locating an item in a warehouse, guiding people inside a shopping mall or
following stolen trucks. There are many legitimate use cases for tracking with as many constraints to satisfy: indoors, outdoors, with or without battery, variable precision, etc…

A phone itself comes with numerous technologies built-in: GPS, WiFi, accelerometer, compass, etc…

We’ll focus exclusively on what is needed to achieve easy, effective, reliable, mass-tracking.

B) Requirements

We want to track cell phones. Which one? ALL OF THEM.

Some constraints:

  • Cell phones are out of control
    • No physical access
    • Hardware cannot be modified
    • Software cannot be installed
  • Users are out of control
    • They will not perform any wanted action
    • They will not opt-in to anything
    • They will not consent to anything
  • Must be scalable to millions of cell phones
    • Self-explanatory

Better precision in time and position[1] is better but does not constitute a goal by itself. It has to be balanced against more important parameters like feasibility, scalability, reliability and costs of operation.

For the avoidance of doubt, we’ll call the project an utter success if we find ourselves able to pin point any cell phone being in a specific block inside a specific city, at a specific hour.

[1] A location is always a position AND a time together. It’s important to keep the two dimensions in mind.

C) Multilateration

Most systems work by “triangulation“. It’s possible to triangulate a specific position by comparing some measures to some points of reference. First things first, that’s actually called multilateration.

If you use a service like a GPS, it does all the work and gives out a position with a radius of error.

If you do the hard work yourself, either you are the guy making the GPS or you are trying to mix multiple sensors in a creative way, you need to do the hard work yourself.

Ultimately, it always comes down to 4 methods.

1) Power: Signal power

With information about the transmission power, the reception power and the medium. It’s possible to use physics wave propagation formulas to estimate the distance traveled.

In practice however, this method is extremely unreliable for radio waves, so you NEVER want to use that.

For instance, it’s typical for a long distance radio wave to go up and down 10 fold (+-10 dB) within a single second. It changes all the time and that’s when you are not moving. It gets worse when walls, windows and your head goes in and out of the track.

2) AoA: Angle of Arrival

Note: It’s called triangulation when using angles.

With the angle of a signal, it’s possible to determine that the source is within a line (or a cone). Obviously, it works better with highly directive signals.

You can surely picture a rotating radar like you’ve seen a thousand times in movies.

3) ToA: Time of Arrival

With the time and the speed of a signal, it’s easy to determine the distance. t = d/s.

Challenge: Radio waves travel at the speed of light 299 792 458 m/s.

To measure a distance with 30 cm accuracy requires to measure the time with +- 0.000000001 seconds (1 nanosecond). That is a hard problem.

4) TDoA: Time Difference of Arrival

Also based on time measurement.

It’s possible to use time differences instead of an absolute time.

time difference of arrival principles
The item to be tracked emits a pulse that is received by multiple receivers (Picture Source: Locating Lightning Strikes)

The item to be tracked emits a pulse that is received by multiple receivers. The receivers are at known locations and synchronized in time.

By measuring the time difference between the reception of the signal at the receivers, it’s possible to determine the relative distance of the source to the receivers.

Challenge: It doesn’t only require to measure time with crazy precision but also to synchronize clocks across systems.

D) Cellular Networks Principles

We’ll go through some basics about cellular networks.

1) Base Station (BTS)

A cell phones communicates with a base station.

There are two channels. One for emission (to the BTS), one for reception (from the BTS). They operate at different frequencies.

The emission channel (to the BTS) is shared by all devices. At any time, there can only be one device emitting.

2) Cellular Network

A BTS covers an area around it. Adjacent BTS form a cellular network.

Two adjacent BTS need to have different frequencies to avoid interference.

cellular network
Cellular Network

Each operator runs its own network. It may share or resell network service to other operators.

Some operators are virtual (called MVNO). They have no physical infrastructure, they exist on top of another provider. For example, giffgaff [1] runs on top of O2.

[1] Highly recommended provider in the UK.

3) Cell Density

A base station can only cover a limited amount of users. What happens when there are too many users, like in a city center instead of a village?

dense cellular network
Double the density. Quadruple the capacity.

Trivial, cells can be arranged more densely to increase the capacity.

E) Locating A Cell Phone

We saw the basics of cellular networks and the basics of multilateration.

1) Base Station

Your phone has to be in range of a BTS to work. By the simple virtue of having your phone “online“, the operator knows that you are within the range of his station.

As we said before, the density of towers can be adjusted to accommodate the density of users.

A tower has a theoretical range of up to 35 km radius. In a major city, there could be one every km; in the empty country side, there could be one every 10 km.

That’s enough to locate a phone down to one city.

BTS have to be located carefully to manage their coverage and not jam one another. An operation knows the locations of its BTS. They have to be registered officially to some sort of radio tower registry (the execution varies slightly by country).

P.S. We would like to give some free sites where you can see BTS but they tend to not live long. There is value in providing a good database so it’s never given for free (and if it does, someone will realize their mistake soon).

2) Base Stations x 6


Back to when we were in telecom, a long time ago, we had special test phones provided by the manufacturers.

Think of an old school Nokia phone, except it comes with build-in hardware and software for debugging purpose. One of the build-in tool shows detailed connectivity information, that are otherwise not available to consumers.

With that at hands, we can see that the cell phone, right in ours hands, is able to detect and maintain connectivity with 4 towers simultaneously, at all times.

Why 4? Because there are 4 in our area. The phone could do more!


A $50 cell phone, even one from a decade ago, can be simultaneously “connected” to 6 stations. This may include stations slightly beyond range, having a signal just strong enough to be detected but too weak to be used for actual communications.

As we like to illustrate nowadays in simple terms: Your phone is a wonder of technology, it will go above and beyond to keep the communication going no matter what. When you talk, one word can go to one tower and the next one to another tower, switching as often as necessary.

On a related topic, this is why you cannot find cheap jamming devices against mobiles. Phones are intended to operate in a hostile environment with thousands of phones competing for the air. A jamming device is like a garden hose in a hurricane. It’s physically impossible for any cheap pocket-size device powered by 2 AA batteries to out compete the hurricane.

To conclude this paragraph, your phone is constantly talking to multiple stations, not just one. Instead of being in a disk around a station, you can be located to the intersection of multiple disks. Handsome for tracking, not so much for your privacy.

More importantly, we need multiple points of reference to be able to perform multilateration. Here they are!

3) Angles

We said that a tower covers a radius around it. In practice, this is sub optimal so that’s not how it’s done.

Instead, a station is usually split in 3 independent beams of 120 degrees.

section antenna
A typical base station (Source: Wikipedia)

A typical BTS. Notice the triangle shape, each face covering 120 degrees.

base station setup
The arrangement of Tx and Tx. (Source: Kaithrein)

The technical setup, as recommended by a polish antenna manufacturer.

This allows to limit the positioning to 120 degrees. It’s actually very powerful, it just increased the accuracy a lot and allows for multilateration with only 2 BTS.

Geometry Trivia: The intersection of 2 circles gives 2 points (opposites to each other), it takes a third reference to find which point is the right one. Therefore multilateration always requires 3 references (e.g. the distances from 3 BTS). In practice, an angle is enough to do the distinction most of the time (e.g. angles and distances from 2 BTS).

This method requires information about antennas and directivity. We just checked one BTS database and it’s there so it looks like it’s not a problem to get. The precision will need to be tested in the wild (wave propagation and construction work are not perfect to the degree).

4) RSSI: Received Signal Strength Indicator

A phone emitter has a maximum power of 2 Watts (6 dB). A phone receiver has a typical sensitivity of 0.000000001 Watt (1 nW or -90 dB).

The air can attenuate a signal by a factor of 1 billion and your phone still works. Magic!

In a perfect world of undergraduate physics, the propagation loss in the air can be modeled with that equation.

propagation loss

With L the loss in dB, lambda is the wavelength and d is the distance, lambda and d in the same unit.

In the real world, this doesn’t apply at all. The air is not homogeneous and there are obstacles all over the place. The losses can vary by 2 orders of magnitude at any time (and it does). There is no meaningful value to be measured.

A good usage of Kalman filter may help to filter the samples but that’s both complicated and resource intensive for a mediocre result.

We’ve got much better to do than RSSI so let’s not our waste time discussing that.

5) Timing Advance

A channel is shared between many customers, each one gets very short periods of time allocated. You can read an introduction to GSM frames for details.

The time slot might be unusable in the event of an overlap with the previous or the next slot (dedicated to another phone). One thing that could cause unwanted overlap is the propagation delay from the phone to the station.

timing advance
The signal takes time to travel from a phone to the station. The delay depends how far the phone is.

Each bit is 3.69231 µs long in GSM, a radio wave can travel 1107 meters in that time. That means a phone located multiples of 1107 meters away will be multiple bits late… we don’t want that!

The propagation delay is accounted for and corrected by a mechanism called the timing advance.

The base station measures how late messages arrive and sends a correction parameter, the timing advance, back to the phone.

It’s a number between 0 and 63 indicating how much advance it should take, in multiple of 3.69231 µs.

For the purpose of geolocation, the timing advance allows to locate a cell phone within a 1107 meters annulus around the base station.

For the purpose of being a grammar nazi, the section of a disk inside a concentric disk is called an annulus.

Let’s see what this looks like if we put some circles on top of London.

london trilateration 1 crop
Timing Advance Annuluses

That’s the accuracy a single tower can give with just timing advance (ignoring angles).

 

Let’s see what the intersection of two stations looks like.

london trilateration 2 crop
Timing Advance with two stations.

That gives two possible areas. It takes a third measure to decide for sure (either an angle or a timing advance).

It’s intuitive enough. The more measures, the better.

Remember: Your cell phone is able to talk to 6 towers at all times, that can cooperate in tracking it.

It’s not always accurate but when it is, it can pinpoint you to the block you are walking in.

6) Geometry Quick Thoughts

Two dimensional intersections of disks[1] is high complexity both in terms of computational power and in terms of what a cheap intern might be able to understand.

Intersection of circles is a trivial problem though. There are known formulas that can be computed in constant time.

It can be generalized to N circles by simply applying the formula to each pair of circles. Filter out the points which are not within the intended angle and distance from the station (a basic comparison in constant time[2]).

The resulting points show something that is approximate but quick and easy to compute. Remember that we have millions of people to track in real-time and only an intern for that!

Call for comment: Dear mathematician reader, please comment if you have any advice on how to find the intersection of complex shapes. [3]

[1] Strictly speaking, this should be treated in 3D. The world is a sphere. There are variations in terrains that should be accounted for, especially in mountain regions.

[2] Angles are trivial to play with in polar coordinates (or spherical coordinates).

[3] We checked how design software handle 2D and 3D intersections (SolidWorks, Catia, AutoCad). Sadly, it is advanced mathematics AND it takes a lot of computational power.

7) Summary

Locating a cell phone:

  • A base station locates the phone inside its range (up to 35 km radius)
  • The timing advances locates the phone in a 1107 meter annulus
  • The angle splits locates the phones in a 120 degree section
  • There can be many stations participating in the process
  • They can be interpolated to improve the precision

8) Time

Remember that a position is always implicitly linked to a time. A phone is at a specific place at a specific time.

The phone wants to be connected in permanence. It is adjusting to the environment in real-time all the time. Typically, in a matter of seconds. It is mandatory for the phone to work (calls and messaging).

Being conservative, a phone should be able to be (re)located every minute.


Do the test.

Turn your phone off, send it a message, turn it on, how long to receive the message?

Put your phone in a tin box (to block signal), send it a message, take it out of the box, how long to receive the message?


F) Dependencies

There are some prerequisites to make that tracking system real and deploy it on a large-scale.

1) Base Station Database

The project requires a database of base stations.

Every provider know where they set up their stations, that’s part of the job of being a service provider. It’s a given if making the project as part of an ISP.

It should be easy enough to get a high quality database of base stations for anyone (not to confuse easy with inexpensive).

2) Logging BTS Information

The project requires access to BTS signal information.

First, there is an extensive authentication, roaming and payment system embedded in the network. This is necessary to provide service to the right user at the right time at the right price.

Second, almost every regulation in every country in the world require providers to save some usage information per user, for many years.

There is massive infrastructure already in place to log and audit accesses, down from the station, up to the high level customer subscription.

The values that are needed may or may not be saved already (Cell ID, TA, …), if they are not, they shouldn’t be very hard to add.

3) Matching Identities With Phones

Assuming that we track cell phones. The final step after a phone is located is to match that phone with the identity of a real person.

There is a whole authentication system made built-in the network. There are unique identifiers for customer contracts, sim cards, phones, etc…

Not sure the details of how this works and how this could be abused. Assume that an ISP can match any connected user with the subscriber.

G) The Known Unknown

We saw how to track every cell phone in service, easily done by the ISP of said customers (and by extension easily achieved by the NSA/GCHQ)

There are some unknowns that may affect the scale and the success of the operation. None that can impair it but some that can bring it up to a whole new level!

1) Near Range Tracking

A phone has to discover stations around it. It’s not possible to known which ones are right without trying.

Technically speaking, there is a possibility that the phone might have to broadcast and try to link to all stations in range [1].

If so, any station in an area would be able to follow any phone in proximity. National providers could track everyone everywhere since they are already cover the entire country. Rogue actors could setup dedicated networks for the sole purpose of tracking.

[1] It has to start with timing advance and authentication of the device, thus allowing for multilateration and user identity lookup right away.

2) Cross ISP Traffic

Have you ever been in an area with low reception where the phone displays “emergency services only“.

There is no reception to make regular calls, yet it can make emergency calls, probably by using other networks (read: not the one you subscribe to). This is a legal requirement, cell and service providers have to allow that.

Technically speaking, it means that there is something built-in to allow cell phones to connect to anything through any network and your phone is trying that automatically all the time. (This is similar to the previous point).

If so, it can be abused to track your phone.

3) International Roaming

Ever been to another country? Your phone work just fine, except you’re charged ten times more.

Again, this implies that the phone is connecting to anything. Better though, this implies that other providers are able to reach your current provider somehow, to confirm your access and incur your billing.

Depending on how it’s done in the details, there may or may not be an opportunity to link a cell phone back to its provider and its owner, anywhere in the world.

H) The Known Known

1) Retro and Forward Compatibility

This works on all cell phones and it worked for decades.

The technology has been out and part of every cell phone at least since the first edition of GSM, circa 1991.

There is no change with 3G, 3G+, LTE. Still works like a charm!

2) This Project Can Be Done By An Intern

The technology itself is within reach of a 15 years old. Any student who attends telecom 103 is taught enough to come up with that (if only they listened instead of playing on their phones!).

20 years ago, this might have gone unnoticed or ignored. There were only a few stations and a few users. Limited accuracy, limited user impact. It’s easy to imagine an early proof of concept that found it impossible at the time: “It’s gonna take an entire floppy disk to save the positions of 12000 customers! Oh my gosh. We’ll never have the budget for that.

Nowadays, it’s so trivial it’s frightening. Any cell provider could take an intern and make it happen in 6 months. Gotta save some signal information? It’s already done. Gotta do a bit of algebra? Nothing difficult.

3) Verizon Is Doing That Already

Feel free to read “Verizon” as any major phone provider.

Any service provider automatically gets incredible tracking capabilities and has to keep a history of it. It’s not optional. The first half comes with the phone’s infrastructure, the second half is mandated by regulations.

The core business of a provider is to provide phone service though, not to locate all customers in real-time down to the minute. There is no reason to perfect the techniques written in this document.

4) The NSA Is Doing That Already

Feel free to read the “NSA” as any state sponsored actor.

They want to track every people in the world. That’s one of their main goals. They have lots of resources dedicated to do just that. They have the ability to infiltrate providers and/or to deploy their own rogue infrastructure.

Ironically, the most awesome mass surveillance system ever invented is out there already and quite easy to use.

What are the odds that they figured it out? I’d say pretty high.

Conclusion



What’s the difference between a Nokia 3310 and an iPhone 7?

There isn’t any! As long as they are turned on, they can both locate you in real-time, 24/7, with a precision better than 1 square kilometer

 

 

 

mobile cellular subscriptions (per 100 people)
Mobile Cellular Subscriptions per 100 people (Source: The World Bank)

 

what if i told you it took 25 years to equip every human being with a personal tracking device
…and we made them pay for it!

 

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The 187 Million Dollars Gmail Bug


Discovery

The scene takes place at a multi-billion dollars company, running some of the most well known e-commerce sites in the world.

It’s a cold day of winter, as every last Tuesday of the month, it’s the on-boarding for new employees.

Teams and leaders are giving various presentations about the business, the teams, their products, the partners, the competitions and many more topics. Some talks are actually quite interesting.

One of the last presenter requests participants to use the service, find something we want and go through the purchase as if we were intending to buy it.

The point of this exercise is obviously to get some opinions and feedbacks…

I’ll spare you the full narrative of the session. Except, one insignificant details toward the end, one person in the back of the room saying she cannot login to the site.

If a decade of experience has taught me anything, it’s that for every user who complains, there can be another million who experience the same issue, making your product plain unusable for all of them. Always follow on feedback, no matter how stupid or irrelevant they seem, they can be the tree that hide the forest.

What can be so special that it doesn’t work with her? Let’s find out.

Debugging

She cannot login, because she forgot her password. She did the “reset password” many times and it does NOT work.

Why would this not work? Let’s have her try and reset it one more time.

Forgot Password => Reset => “Instructions have been sent to …”

The email is allegedly sent… and it is received by her gmail.

screenshot gmail subject
New message in the inbox!

She opens it and click the reset link and it doesn’t work.

Why does this not work? The page says “invalid or expired link, try again to be sent a new email“.

So, let’s click again. Except this time, it’s my turn.

We open the gmail, it shows that there is a new unread message again. Great!

Looking carefully, there is something unusual on the bottom of the email conversation…

screenshot gmail hidden
New messages are not shown. They need to be manually expanded. (Desktop Client View)

The email is here. It’s just hidden!

It turns out that gmail is hiding new messages when they look too similar to the previous one.

All the password reset messages are there, hidden on the bottom, each with it’s own unique link. We try the latest one and it does work! Password resetted.

it's not a bug, it's a feature
Debugging complete

She’s been reading the same old email every single time. She never noticed the hidden messages on the bottom (note that they are quite difficult to spot on mobile).

Reset links are unique and invalidated when a new one is made, hence the errors about invalid links.

Let’s remember of that as a lesson in user accessibility. What may be noticed by one user may be missed by another.

Impact

This password reset procedure is for a billion dollar e-commerce site, used by millions of customers, in most of the countries in the world.

I should say that users buy things sparingly. They buy something once, go on with their lives, maybe come back one day far away. It’s reasonable to expect a sizeable user base to forget their password as often as “every single time“.

The issue is impacting all users who forgot their password (read: already registered on the site), use gmail (not sure about other clients) and don’t notice the hidden messages on the bottom.

 

Let’s see with the data team what’s the impact of this. Incidentally, they just introduced themselves during one of the presentations.

Assuming some percentages of some percentages of some statistics of our sales. (Sorry, private numbers ^^).

The direct impact of this bug is a direct loss of revenues of $187M dollars per year, simply accounting for people who are unable to login and place any order.

 

I should add that, as shown time and again during demonstrations, participants will switch to competitors within a few minutes of frustration, especially the ones who are already familiar with them. I don’t know if they are giving up faster or slower than regular users, either way that’s food for thought.

The impact accounting for direct losses, plus indirect losses, plus recurring losses, plus reputation loss, plus word of mouth loss, plus competitors stealing our business, well, recurrently stealing our business, etc, is hard to put an estimate on. It should be within some multipliers.

 

Last but not least. This is on a single business and we’ve got more than just one.

What else is impacted has yet to be determined. That will vary by how the password resets are done.

Fix

We need to force gmail to NOT collapse emails.

Having different subjects should do the trick.

Let’s append the current time to the subject. That is minor change in the line of code that generates the subject.

i dont often write code but when i do its 3m dollars per chara
What to say next time you’re asked to code in an interview

Conclusion

All the internet is potentially affected. You should check whether your business is.

pic - if google fixes google the internet could go up by 1percent of 1percent
There is a percentage of truth behind every meme.

 

What’s The Best Data Warehouse Solution? RedShift vs BigQuery vs Hadoop


That can be explained with a simple flowchart:

datawarehouse flowchart
Full Size ImagePDF Version

 

See also:

 

What’s The Best NoSQL Database? Cassandra vs MongoDB vs Redis vs ElasticSearch


That can be explained with a simple flowchart:

nosql flowchart
Full Size ImagePDF Version

 

See also:

 

Moby/Docker in Production: An Update


The previous article Docker in Production: A History of Failure was quite a hit.

After long discussions, hundreds of feedbacks, thousands of comments, meetings with various individuals and major players, more experimentation and more failures, it’s time for an update on the situation.

We’ll go over the lessons learned from all the recent interactions and articles, but first, a reminder and a bit of context.

Disclaimer: Intended Audience

The large amount of comments made it clear that the world is divided in 10 kind of people:

1) The Amateur

Running mostly test and side projects with no real users. May think that using Ubuntu beta is the norm and call anything “stable” obsolete.

I dont always make workin code but when I do it works on my machine
Can’t blame him. It worked on his machine.

2) The Professional

Running critical systems for a real business with real users, definitely accountable, probably get a phone call when shit hits the fan.

one-does-not-simply-say-well-it-worked-on-my-machine.jpg
Didn’t work on the machine that served his 586 million customers.

What Audience Are You?

There is a fine line between these worlds and they clash pretty hard when they ever meet. Obviously, they have very different standards and expectations.

One of the reason I love finance is because that it has a great culture of risk. It doesn’t mean to be risk-averse contrary to a popular belief. It means to evaluate potential risks and potential gains and weight them against each other.

You should take a minute to think about your standards. What do you expect to achieve with Docker? What do you have to lose if it crashes all systems it’s running on and corrupt the mounted volumes? These are important factor to drive your decisions.

What pushed me to publish the last article was a conversation with a guy from a random finance company, just asking my thoughts about Docker, because he was considering to consider it. Among other things, this company -and this guy in particular- manages systems that handle trillions of dollars, including the pensions of millions of Americans.

Docker is nowhere ready to handle my mother’s pension, how could anyone ever think that??? Well, it seemed the Docker experience wasn’t documented enough.

What Do You Need to Run Docker?

As you should be aware by know, Docker is highly sensitive to the kernel, the host and the filesystem it’s using. Pick the wrong combination and you’re talking kernel panic, filesystem corruption, Docker daemon lock down, etc…

I had time to collect feedback on various operating conditions and test a couple more myself.

We’ll go over the results of the research, what has been registered to work, not work, experience intermittent failures, or blow up entirely in epic proportions.

Spoiler Alert: There is nothing with or around Docker that’s guaranteed to work.

Disclaimer: Understand the Risks and the Consequences

I am biased toward my own standards (as a professional who has to handle real money) and following the feedback I got (with a bias toward reliable sources known for operating real world systems).

For instance, if a combination of operating system and filesystem is marked as “no-go: registered catastrophic filesystem failure with full volume data loss“. It is not production ready (for me) but it is good enough for a student who has to do a one-off exercise in a vagrant virtual machine.

You may or may not experience the issues mentioned. Either way, they are mentioned because they are certified to be present in the wild as confirmed by the people who hit them. If you try an environment that is similar enough, you are on the right path to become the next witness.

The worst that can -and usually- happen with Docker is that it seems okay during the proof of concepts and you’ll only begin to notice and understand issues far down the line, when you cannot easily move away from it.

CoreOS

CoreOS is an operating that can only run containers and is exclusively intended to run containers.

Last article, the conclusion was that it might be the only operating system that may be able to run Docker. This may or may not be accurate.

We abandoned the idea of running CoreOS.

First, the main benefit of Docker is to unify dev and production. Having a separate OS in production only for containers totally ruins this point.

Second, Debian (we were on Debian) announced the next major release for Q1 2017. It takes a lot of effort to understand and migrate everything to CoreOS, with no guarantee of success. It’s wiser to just wait for the next Debian.

CentOS/RHEL

CentOS/RHEL 6

Docker on CentOS/RHEL 6 is no-go: known filesystem failures, full volume data loss

  1. Various known issues with the devicemapper driver.
  2. Critical issues with LVM volumes in combination with devicemapper causing data corruption, container crash, and docker daemon freeze requiring hard reboot to fix.
  3. The Docker packages are not maintained on this distribution. There are numerous critical bug fixes that were released in the CentOS/RHEL 7 packages but were not back ported to the CentOS/RHEL 6 packages.
ship crash shipt it revert
The only sane way to migrate to Docker in a big company still running on RHEL 6 => Don’t do it!

CentOS/RHEL 7

Originally running the kernel 3, RedHat has been back porting the kernel 4 features into it, which is mandatory for running Docker.

It caused problems at time because Docker failed to detect the custom kernel version and the available features on it, thus it cannot set proper system settings and fails in various mysterious ways. Every time this happens, this can only be resolved by Docker publishing a fix on feature detection for specific kernels, which is neither a timely nor systematic process..

There are various issues with the usage of LVM volumes, depends on the version.

Otherwise, it’s a mixed bag. Your mileage may vary.

As of CentOS 7.0, RedHat recommended some settings but I can’t find the page on their website anymore. Anyway, there are a tons of critical bugfixes in later version so you MUST update to the latest version.

As of CentOS 7.2, RedHat recommends and supports exclusively XFS and they give special flags for the configuration. AUFS doesn’t exist, OverlayFS is officially considered unstable, BTRFS is beta (technology preview).

The RedHat employees are admitting themselves that they struggle pretty hard to get docker working in proper conditions, which is a major problem because they gotta resell it as part of their OpenShift offering. Try making a product on an unstable core.

If you like playing with fire, it looks like that’s the OS of choice.

Note that for once, it is a case where you surely wants to have RHEL and not CentOS, meaning timely updates and helpful support at your disposal.

Debian

Debian 8 jessie (stable)

A major cause of the issues we experienced was because our production OS was Debian stable, as explained in the previous article.

Basically, Debian froze the kernel to a version that doesn’t support anything Docker needs and the few components that are present are rigged with bugs.

Docker on Debian is major no-go: There is a wide range of bugs in the AUFS driver (but not only), usually crashing the host, potentially corrupting the data, and that’s just the tip of the iceberg.

Docker is 100% guaranteed suicide on Debian 8 and it’s been since the inception of Docker a few years ago. It’s killing me no one ever documented this earlier.

I wanted to show you a graph of AWS instances going down like dominoes but I didn’t have a good monitoring and drawing tool to do that, so instead I’ll illustrate with a piano chart that looks the same.

docker-crash-illustrated
Typical docker cascade failure in our test systems.

Typical Docker cascading failure on our test systems. A test slave crashes… the next one retries two minutes later… and dies too. This specific cascade took 6 tries to go past the bug, slightly more than usual, but nothing fancy.

You should have CloudWatch alarms to restart dead hosts automatically and send a crash notifications.

Fancy: You can also have a CloudWatch alarm to automatically send a customized issue report to your regulator whenever there is an issue persisting more than 5 minutes.

Not to brag but we got quite good at containing Docker. Forget about Chaos Monkey, that’s child play, try running trading systems handling billions of dollars on Docker [1].

[1] Please don’t do that. That’s a terrible idea.

Debian 9 stretch

Debian stretch is planned to become the stable edition in 2017. (Note: might be released as I write and edit this article).

It will feature the kernel 4.10 which is the latest LTS, published simultaneously.

At the time of release, Debian Stretch will be the most up to date stable operating system and it will allegedly have all the shiny things necessary to run Docker (until the Docker requirements change again).

It may resolve a lot of the issues and it may make a tons of new ones. We’ll see how it goes.

Ubuntu

Ubuntu has always been more up to date than the regular server distributions.

Sadly, I am not aware of any serious companies than run on Ubuntu. This has been a source of much misunderstanding in the docker community because dev and amateur bloggers try things on the latest Ubuntu (not even the LTS [1]) yet it’s utterly non representative of production systems in the real world (RHEL, CentOS, Debian or one of the exotic Unix/BSD/Solaris).

I cannot comment on the LTS 16 as I do not use it. It’s the only distribution to have Overlay2 and ZFS available, that gives some more options to be tried and maybe find something working?

The LTS 14 is a definitive no-go: Too old, don’t have the required components.

[1] I received quite a few comments and unfriendly emails of people saying to “just” use the latest Ubuntu beta. As if migrating all live systems, changing distribution and running on a beta platform that didn’t even exist at the time was an actual solution.


Update: I said I’m never coming back to Docker and certainly not to spend an hour on digging up references but I guess I have to now that they are handed to me in spectacular ways.

I received a quite insulting email from a guy who is clearly in the amateur league to say that “any idiot can run Docker on Ubuntu” then proceed to give a list of software packages and advanced system tweaks that are mandatory to run Docker on Ubuntu, that allegedly “anyone could have found in 5 seconds with Google“.

At the heart of his mail is this bug report, which is indeed the first Google result for “Ubuntu docker not working” and “Ubuntu docker crash: Ubuntu 16.04 install for 1.11.2 hangs.

This bug report, published on June 2016 highlights that the Ubuntu installer simply doesn’t work at all because it doesn’t install some dependencies which are required by Docker to run, then it’s a see of comments, user workarounds and not-giving-a-fuck #WONTFIX by Docker developers.

The last answer is given by an employee 5 months later to say that the Ubuntu installer will never be fixed, however the next major version of Docker may use something completely different that won’t be affected by this issue.

A new major version (v1.13) just got released (8 months since the report), it is not confirmed whether it is affected by the bug or not (but it is confirmed to come with breaking changes).

It’s fairly typical of what to expect from Docker. Checklist:

  • Is everything broken to the point Docker can’t run at all? YES.
  • Is it broken for all users, of say a major distribution? YES.
  • Is there a timely reply to acknowledge the issue? NO.
  • Is it confirmed that the issue is present and how severe it is? NO.
  • Is there any fix planned? NO.
  • Is there a ton of workarounds of various danger and complexity? YES.
  • Will it ever be fixed? Who knows.
  • Will the fix, if it ever comes, be backported? NEVER.
  • Is the ultimate answer to everything to just update to latest? Of course.

AWS Container Service

AWS has an AMI dedicated to running Docker. It is based on an Ubuntu.

As confirmed by internal sources, they experienced massive troubles to get Docker working in any decent condition

Ultimately, they released am AMI for it, running a custom OS with a custom docker package with custom bug fixes and custom backports. They went and are still going through extensive efforts and testing to keep things together.

If you are locked-in on Docker and running on AWS, your only salvation might be to let AWS handles it for you.

Google Container Service

Google offers containers as a service. Google merely exposes a Docker interface, the containers are run on internal google containerization technologies, that cannot possibly suffer from all the Docker implementation flaws.

Don’t get me wrong. Containers are great as a concept, the problem is not the theoretical aspect, it’s the practical implementation and tooling we have (i.e. Docker) which are experimental at best.

If you really want to play with Docker (or containers) and you are not operating on AWS, that leaves Google as the single strongest choice, better yet, it comes with Kubernetes for orchestration, making it a league of its own.

That should still be considered experimental and playing with fire. It just happens that it’s the only thing that may deliver the promises and also the only thing that comes with containers AND orchestration.

OpenShift

It’s not possible to build a stable product on a broken core, yet RedHat is trying.

From the feedback I had, they are both struggling pretty hard to mitigate the Docker issues, with variable success. Your mileage may vary.

Considering that they both appeal to large companies, who have quite a lot to lose, I’d really question the choice of going for that route (i.e. anything build on top of Docker).

You should try the regular clouds instead: AWS or Google or Azure. Using virtual machines and some of the hosted services will achieve 90% of what Docker does, 90% of what Docker doesn’t do, and it’s dependable. It’s also a better long-term strategy.

Chances are that you want to do OpenShift because you can’t do public cloud. Well, that’s a tough spot to be in. (Good luck with that. Please write a blog in reply to talk about your experience).

Summary

  • CentOS/RHEL: Russian roulette
  • Debian: Jumping off a plane naked
  • Ubuntu: Not sure Update: LOL.
  • CoreOS: Not worth the effort
  • AWS Containers: Your only salvation if you are locked-in with Docker and on AWS
  • Google Containers: The only practical way to run Docker that is not entirely insane.
  • OpenShift: Not sure. Depends how good the support and engineers can manage?

A Business Perspective

Docker has no business model and no way to monetize. It’s fair to say that they are releasing to all platforms (Mac/Windows) and integrating all kind of features (Swarm) as a desperate move to 1) not let any competitor have any distinctive feature 2) get everyone to use docker and docker tools 3) lock customers completely in their ecosystem 4) publish a ton of news, articles and releases in the process, increasing hype 5) justify their valuation.

It is extremely tough to execute an expansion both horizontally and vertically to multiple products and markets. (Ignoring whether that is an appropriate or sustainable business decision, which is a different aspect).

In the meantime, the competitors, namely Amazon, Microsoft, Google, Pivotal and RedHat all compete in various ways and make more money on containers than Docker does, while CoreOS is working an OS (CoreOS) and competing containerization technology (Rocket).

That’s a lot of big names with a lot of firepower directed to compete intensively and decisively against Docker. They have zero interest whatsoever to let Docker locks anyone. If anything, they individually and collectively have an interest in killing Docker and replacing it with something else.

Let’s call that the war of containers. We’ll see how it plays out.

Currently, Google is leading the way, they are replacing Docker and they are the only one to provide out of the box orchestration (Kubernetes).

Conclusion

Did I say that Docker is an unstable toy project?

Invariably some people will say that the issues are not real or in the past. They are not in the past, the challenges and the issues are very current and very real. There is definite proof and documentation that Docker has suffered from critical bugs making it plain unusable on ALL major distributions, bugs that ran rampant for years, some still present as of today.

If you look for any combination of “docker + version + filesystem + OS” on Google, you’ll find a trail of issues with various impact going back all the way to docker birth. It’s a mystery how something could fail that bad for that long and no one writes about it. (Actually, there are a few articles, they were just lost under the mass of advertisement and quick evaluations). The last software to achieve that level of expectation with that level of failure was MongoDB.

I didn’t manage to find anyone on the planet using Docker seriously AND successfully AND without major hassle. The experiences mentioned in this article were acquired by blood, the blood of employees and companies who learned Docker the hard way while every second of downtime was a $1000 loss.

Hopefully, you can learn from our past, as to not repeat it.

mistake - it could be that the purpose of your life is only to serve as a warning to others

If you were wondering whether you should have adopted docker years ago => The answer is hell no, you dodged a bullet. You can tell that to your boss. (It’s still not that much useful today if you don’t proper have orchestration around it, which is itself an experimental subject).

If you are wondering whether you should adopt it now… while what you run is satisfactory and you have any considerations for quality => The reasonable answer is to wait until RHEL 8 and Debian 10. No rush. Things need to mature and the packages ain’t gonna move faster than the distributions you’ll run them on.

If you like to play with fire => Full-on Google Container Engine on Google Cloud. Definitive high risk, probable high reward.

Would this article have more credibility if I linked numerous bug reports, screenshots of kernel panics, personal charts of system failures over the day, relevant forum posts and disclosed private conversations? Probably.

Do I want to spend yet-another hundred hours to dig that off, once again? Nope. I’d rather spend my evening on Tinder than Docker. Bye bye Docker.

Moving On

Back to me. My action plan to lead the way on Containers and Clouds had a major flaw I missed out, the average tenure in tech companies is still not counted in yearS, thus the year 2017 began by being poached.

Bad news: No more cloud and no more Docker where I am going. Meaning no more groundbreaking news. you are on your own to figure it out.

Good news: No more toying around with billions dollars of other people’s money… since I am moving up by at least 3 orders of magnitude! I am moderately confident that my new immediate playground may include the pensions of a few millions of Americans, including a lot of people who read this blog.

docker your pension fund 100% certified not dockeri
Rest assured: Your pension is in good hands! =D

Google Cloud is 50% cheaper than AWS


Let’s revisit Google and Amazon pricing since the AWS November 2016 Price Reduction.

We’ll analyse instance costs, for various workloads and usages. All prices are given in dollars per month (720 hours) for servers located in Europe (eu-west-1).

Shared CPU Instances

Shared CPU instances give only a bit of CPU. The physical processor is over allocated and shared with many other instances running on the same host. A shared CPU instance may burst to 100% CPU usage for short periods but it may also be starved of CPU and paused. Note that these instances are cheap but they are not reliable for non-negligible continuous workloads.

google cloud vs aws pricing shared CPU instances

The smallest instances on both cloud is 500MB and a few percent of CPU. That’s the cheapest instance. It’s usable for testing and minimal needs (can’t do much with only 5% of CPU and 500MB).

The infamous t2.small and it’s rival the g1-small are usually the most common instance types in use. They come with 2GB of memory and a bit of CPU. It’s cheap and good enough for many use cases (excluding production and time critical processing, which need dedicated CPU time).

The Cheapest Production Instances

Production instances are all the instances with dedicated CPU time (i.e. everything but the shared CPU instances).

Most services will just run on the cheapest production instance available. That instance is very important because it determines the entry price and the specifications for everything.

google cloud vs aws pricing cheapest production instances

The cheapest production instance on Google Cloud is the n1-standard-1 which gives 1 CPU and 4 GB of memory.

AWS is more complex. The m3.medium is 1 CPU and 4 GB of memory. The c4.large is 2 CPU and 4 GB of memory.

m3/c3 are the previous family generation (pre-2015), using older hardware and an ancient virtualisation technology. c4/m4 are the current generation, it has enhanced networking and reserved bandwidth for EBS, among other system improvements.

Either way, the Google entry-level instance is significantly cheaper than both AWS entry-level instances. There will be a lot of these running, expect massive costs savings by using Google cloud.


I’m a believer that one should optimize for manageability and not raw costs. That means adopting c4/m4 as the standard for deployments (instead of c3/m3).

Given this decision, the smallest production instance on AWS is the c4.large (2 CPU, 4GB memory), a rather big instance when compared to the n1-standard-1 (1 CPU, 4GB memory). Why are we forced to pay for two CPUs as the minimal choice on AWS? That does set a high base price.

Not only Google is cheaper because it’s more competitive but it also offers more tailored options. The result is a massive 68% discount on the most commonly used production instance.

Personal Note: I would criticize the choice of AWS to discontinue the line of m4.medium instance type (1 CPU).


Instances by usage

A server has 3 dimensions of specifications: CPU performances, memory size and network speed.

Most applications only have a hard requirement in a single dimension. We’ll analyse the pricing separately for each usage pattern.

google cloud vs aws pricing instances by usage

Network Heavy

Typical Consumers: load balancers, file transfers, uploads/downloads, backups and generally speaking everything that uses the network.

What should we order to have  1Gbps and how much will it be?

  • The minimum on Google Cloud is the n1-highcpu-4 instance (4 CPU, 4 GB memory).
  • The minimum on AWS is the c4.4xlarge instance (16 CPU, 30 GB memory).

AWS bandwidth allowance is limited and correlated to the instance size. The big instances -with decent bandwidth- are incredibly expensive.

To give a point of comparison, the c4|m4|r3.large instances have a hard cap at 220 Mbits/s of network traffic (Note: It also applies internally within a VPC).

figure2_7001
Source: Network and cloud storage benchmark in 2015

All Google instances have significantly faster network than the equivalent [and even bigger] AWS instances, to the point where they’re not even playing in the same league.

Google has been designing networks and manufacturing their own equipment for decades. It’s fair to assume than AWS doesn’t have the technology to compete.

CPU

Typical Consumers: web servers, data analysis, simulations, processing and computations.

Google is cheaper per CPU.

Google CPU instances have half the memory of AWS CPU instances[1]. While that could have justified a 10% difference, it doesn’t justify double[2].

Note: The performances per CPU are equivalent on both cloud (though the CPU models and serial numbers may vary).

[1] A sane design decision. Most CPU bound workloads don’t need much memory. (Note: if they do, they can be run on “standard” instances).

[2] Pricing is mostly linked to CPU count. Additional memory is cheap.

Memory

Typical Consumers: database, caches and in-memory workloads.

Google is cheaper per GB of memory.

Google memory instances have 15% less memory than AWS CPU instances. While that could have justified a few percent difference, it sure as hell doesn’t justify double[2].

[2] Pricing is mostly linked to CPU count. Additional memory is cheap.

Local SSD and Scaling Up

There are software that can only scale up, typically SQL databases. A database holding tons of data will require fast local disks and truckloads of memory to operate non-sluggishly.

Scaling up is the most typical use case for beefy dedicated servers, but we’re not gonna rent a single server in another place just for one application. The cloud provider will have to accommodate that need.

Google allows to attach local 400GB SSDs to any instance type ($85 a month per disk).

Some AWS instances comes with small local SSD (16-160GB), you’re out of luck if you need more space than that. The only option to get big local SSD is the special i2 instances family, they have specifications in powers of 800GB local SSD + 4 CPU + 15 GB RAM (for $655 a month).

The Google SSD model is superior. It’s significantly more modular and cheaper (and more performant but that’s a different topic).

aws-vs-gce-pricing-instances-with-local-ssd
The requirements to fulfil are between parenthesis.

Disk Intensive Load: A job that requires high volume fast disks (i.e. local SSD) but not much memory.

AWS forces you to buy a big instance (i2.xlarge) to get enough SSD space whereas Google allows you to attach a SSD to a small instance (n1-highcpu-4). The lack of flexibility from AWS has a measurable impact, the AWS setup is 406% the costs of the Google setup to achieve the same need.

Database: A typical database. Fast storage and sizeable memory.

Bigger Database: Sometimes there is no choice but to scale up, to whatever resources are commanded by the application.

On AWS (i2.8xlarge) 32 cores, 244GB memory, 2 x 800 GB local SSD in RAID1 (+ 6 SSD unused yet gotta pay for it).

On Google Cloud (n1-highmem-32): 32 cores, 208 GB memory, 4 x 375 GB local SSD in RAID10.

This last number is meant to show that the lack of flexibility of AWS can (and will) snowball quickly. Only a very particular instance can fulfil the requirements, it comes with many cores and 4800 GB of unnecessary local SSD. The AWS bill is $4k (273%) higher than the equivalent setup on Google Cloud.

Custom Instances

Google offers custom machine types. You can pick how much CPU and memory you want, you’ll get that exact instance with a tailored pricing.

It is quite flexible. For instance, we could recreate any instance from AWS on Google Cloud.

Of course, there are physical bounds inherent to hardware (e.g. you can’t have a single core with 100 GB of memory).

Reserved Instances

Reserved Instances are bullshit!

Reserving capacity is a dangerous and antiquated pricing model that belongs to the era of the datacenter.

The numbers given in this article do not account for any AWS reservation. However, they all account for Google sustained use discount (30% automatic discount on instances that ran for the entire month).

If your infrastructure is so small that you can reserve all your 4 instances upfront, you should reconsider why you use AWS in the first place. There are more appropriate and cheaper options available.

If your infrastructure is big enough that you have dozens of servers (or thousands), you should already be aware that:

  1. Long term commitment is a huge risk. Most people underestimate it.
  2. Predictions are always off. Most people are overconfident in their predictions.
  3. You are no exception to most people.
  4. Reservation is a mess when having many AWS accounts (dev, staging, prod).
  5. Anything that is testing/transient is too short-lived to be reserved.
  6. Less than 50% of reservable stuff can actually be reserved (margin for change/error).

Most people managers are stubborn. If you your manager is stubborn and really insists on reserving instances, you should bet exclusively on “1 year full upfront“.

fishing with gr
Safety Warning: There is no confirmation button when you purchase reserved instances. You can absolutely spend $73185 without seeing nor confirming an invoice.

Conclusion

google cloud vs aws pricing summary relative costs

AWS was the first generation of cloud, Google is the second. The second generation is always better because it can learn from the mistakes of the first and it doesn’t have the old legacy to support.

2016 should be remembered as the year Google became a better choice than AWS. If 50% cheaper is not a solid argument, I don’t know what is.


References:

Cloud Storage Performance, a benchmark with graphs on network performance.

Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network, A Google Research Paper, the story on what powers their internal network.

Amazon does everything wrong, and Google does everything right, A message by an employee from Amazon than Google, not directly relevant but still a good read.

Before And After Docker: How To Deploy An Application


Docker is a packaging and deployment system. It allows you to package an application as a “docker image“, then deploy it easily on some servers with a single “docker start <image>” command.

Packaging an application

Packaging an application without Docker

built pipeline without docker
The Standard Build Pipeline
  1. A developer pushes a change
  2. The CI sees that new code is available. It rebuilds the project, runs the tests and generates a package
  3. The CI saves all files in”dist/*” as build artifacts

The application is available for download from “ci.internal.mycompany.com/<project>/<build-id>/dist/installer.zip

Packaging an application with Docker

build pipeline with docker
The Build Pipeline with Docker
  1. A developer pushes a change
  2. The CI sees that new code is available it. It rebuilds the project, runs the tests and generates a docker image
  3. The docker image is saved to the docker registry

The application is available for download as a docker image named “auth:latest” from the registry “docker-registry.internal.mycompany.com”.

You need a CI pipeline

A CI pipeline requires a source code repository (GitLab, GitHub, VisualSVN Server) and a continuous integration system (Jenkins, GitLab CI, TeamCity). Docker also needs a docker registry.

A functional CI pipeline is a must-have for any software development project. It will ensure that your application(s) are automatically re-run, re-tested and re-packaged on every change.

The developers gotta write scripts to build their application, to run tests and to generate packages. Only the developers of an application can do that because they are the only ones to have the knowledge about how things are supposed/expected to work.

Generally speaking, the CI jobs should mostly consist into calling external scripts, like “./build.sh && ./tests.sh”. The scripts themselves must be part of the source code, they’ll evolve with the application.

You need to know your applications

Please answer the following questions:

  • What does the application need to be built?
  • What’s the command/script to build it?
  • What does the application need to run?
  • What configuration file is needed and where to put it?
  • What’s the command to start/stop the application?

You need to be able to answer all these questions, for all the applications you’re writing and managing.

If you don’t know the answers, you have a problem and Docker is NOT the solution. You gotta figure out how things works and write documentation! (Better hope the guys who were in charge are still working here and gave a thought about all that).

If you know the answers, then you’re good. You know what has to be done. Whether it will be executed by bash, ansible, DockerFile, spec or zip is just an implementation detail.

Deploying an application

Deploying an application without Docker

  1. Download the application
  2. Setup dependencies, services and configuration files
  3. Start the application
# ansible pseudo code 
hosts: hosts_auth
serial: 1 #rolling deploy, one server at a time
become: yes
 
tasks:
  name: instance is removed from the load balancer
  elb_instance:
    elb_name: auth
    instance_id: "{{ ansible_ansible_id }}"
    state: absent
 
  name: service is stopped
  service:
    name: auth
    state: stopped
 
  name: existing application is deleted
  file:
    path: /var/lib/auth/
    match: "*"
    recursive: yes
    state: absent
 
  name: application is deployed
  unarchive: 
    url: https://ci.internal.mycompany.com/auth/last/artifacts/installer.zip
    destination:: /var/lib/auth
 
  name: virtualenv is setup
  pip:
    requirements: /var/lib/auth/requirements.txt
    virtualenv: /var/lib/auth/.venv
 
  name: application configuration is updated
  template:
    src: auth.conf
    dst: /etc/mycompany/auth/auth.conf

  name: service configuration is updated
  template: 
    src: auth.service
    dst: /etc/init.d/mycompany-auth
 
  name: service is started
  service:
    name: auth
    state: running
 
  name: instance is added to the load balancer
  elb_instance:
    elb_name: auth
    instance_id: "{{ ansible_ansible_id }}"
    state: present

Deploying an application with Docker

  1. Create a configuration file
  2. Start the docker image with the configuration file
# ansible pseudo code
hosts: hosts_auth
serial: 1 #rolling deploy, one server at a time
become: yes
 
tasks:
  name: instance is removed from the load balancer
  elb_instance:
    elb_name: auth
    instance_id: "{{ ansible_ansible_id }}"
    state: absent
 
  name: container is stopped
  docker:
    name: auth
    state: stopped
 
  name: configuration is updated
  template: 
    src: auth.conf
    dst: /etc/mycompany/auth/auth.conf

  name: container is started
  docker:
    name: auth
    image: docker-registry.internal.mycompany.com/auth:latest
    state: started
    mount:
      /etc/mycompany/auth/auth.conf:/etc/mycompany/auth/auth.conf
    port:
      8101:8101
 
  name: instance is added to the load balancer
  elb_instance:
    elb_name: auth
    instance_id: "{{ ansible_ansible_id }}"
    state: present

Notable differences

With docker, the python setup/virtualenv and the service configuration is done during the image creation rather than during the deployment. (The commands are the same, they’re just done in an earlier build stage).

The configuration files are deployed on the host and mounted inside Docker. It would be possible to bake the configuration file into the image but some configurations might only be determined at deployment time and we’d rather not store secrets in the image.

Infrastructure

Docker is only a packaging and deployment tool.

Docker doesn’t handle auto scaling, it doesn’t have service discovery, it doesn’t reconfigure load balancers, it doesn’t move containers when servers fail.

Orchestration systems (notably Kubernetes) are supposed to help with that. Currently, they are quite experimental and very difficult to setup [beyond a proof of concept]. The lack of proper orchestration will limit Docker to only be a hype packaging & deployment tool for the foreseeable future.

Docker [even with Kubernetes] needs an existing environment to run, including servers and networks. It ain’t gonna install and configure itself either.

All of that has to be done manually. Order servers in the cloud. Create OS images with Packer. Configure VPC and networking with Terraform. Setup the servers and systems with Ansible. Install and deploy the applications (including docker images) with Ansible.

Cheat Sheet

  1. Figure out what is required and how to build the applications
  2. Write build, test and packaging scripts
  3. Document that in the README
  4. Setup a CI system
  5. Configure automatic builds after every change
  6. Figure out the application dependencies and how to run it
  7. Add that to the README
  8. Write deploy and setup scripts (with Ansible or Salt)

Conclusion

Packaging and deploying applications is a real and challenging job. A Debian package has some good practices and standards to follow whereas Docker comes with no good practices and no rules whatsoever. Docker is a [marketing] success in part because it gives the illusion that the task is easy, with a sense of coolness.

In practise though, it is hard and there is no way around it. You’ll have to figure out your needs and decide on a practical way to deploy and package your applications that will be tailored just for you. Docker is not the solution to the problem, it’s just a random tool among many others, that may or may not help you.

It’s fair to say that the docker ecosystem is infinitely complex and has a long learning curve. If you have neat applications with clear and limited dependencies, they should be relatively manageable and docker can’t make it any easier. On the contrary, it has the potential to make it harder.

Docker shines to package applications with complex messy dependencies (typical NodeJS and Ruby environments). The dependency hell is taken away from the host and moved into the image and the image creation scripts.

Docker is handsome for dev and test environments. It allows to run multiple applications easily on the same host, isolated from each other. Better yet, some applications have conflicting dependencies and would be impossible to run on a single host otherwise.

You should investigate a configuration management system (Ansible) if you don’t already have one. It will help you to manage, configure and setup [numerous] remote servers, à la SSH on steroid. It’s way more general and practical than Docker (and you’re gonna need it to install docker and deploy images anyway).

Reminder: In spite of the practical use cases, docker should be considered as a beta tool not quite ready for serious production.

Moby/Docker in Production: A History of Failure


April 2017: Updated the title. The Docker CEO decided to rename Docker to Moby overnight, without notice. I’ve tried to warn the world about unexpected Docker changes and breakages. That’s the order of magnitude you have to be prepared for.

Introduction

My first encounter with docker goes back to early 2015. Docker was experimented with to find out whether it could benefit us. At the time it wasn’t possible to run a container [in the background] and there wasn’t any command to see what was running, debug or ssh into the container. The experiment was quick, Docker was useless and closer to an alpha prototype than a release.

Fast forward to 2016. New job, new company and docker hype is growing like mad. Developers here have pushed docker into production projects, we’re stuck with it. On the bright side, the run command finally works, we can start, stop and see containers. It is functional.

We have 12 dockerized applications running in production as we write this article, spread over 31 hosts on AWS (1 docker app per host [note: keep reading to know why]).

The following article narrates our journey with Docker, an adventure full of dangers and unexpected turns.

so it begins, the greatest fuck up of our time

Production Issues with Docker

Docker Issue: Breaking changes and regressions

We ran all these versions (or tried to):

1.6 => 1.7 => 1.8 => 1.9 => 1.10 => 1.11 => 1.12

Each new version came with breaking changes. We started on docker 1.6 early this year to run a single application.

We updated 3 months later because we needed a fix only available in later versions. The 1.6 branch was already abandoned.

The versions 1.7 and 1.8 couldn’t run. We moved to the 1.9 only to find a critical bug on it two weeks later, so we upgraded (again!) to the 1.10.

There are all kind of subtle regressions between Docker versions. It’s constantly breaking unpredictable stuff in unexpected ways.

The most tricky regressions we had to debug were network related. Docker is entirely abstracting the host networking. It’s a big mess of port redirection, DNS tricks and virtual networks.

Bonus: Docker was removed from the official Debian repository last year, then the package got renamed from docker.io to docker-engine. Documentation and resources predating this change are obsolete.

Docker Issue: Can’t clean old images

The most requested and most lacking feature in Docker is a command to clean older images (older than X days or not used for X days, whatever). Space is a critical issue given that images are renewed frequently and they may take more than 1GB each.

The only way to clean space is to run this hack, preferably in cron every day:

docker images -q -a | xargs --no-run-if-empty docker rmi

It enumerates all images and remove them. The ones currently in use by running containers cannot be removed (it gives an error). It is dirty but it gets the job done.

The docker journey begins with a clean up script. It is an initiation rite every organization has to go through.

Many attempts can be found on the internet, none of which works well. There is no API to list images with dates, sometimes there are but they are deprecated within 6 months. One common strategy is to read date attribute from image files and call ‘docker rmi‘ but it fails when the naming changes. Another strategy is to read date attributes and delete files directly but it causes corruption if not done perfectly, and it cannot be done perfectly except by Docker itself.

Docker Issue: Kernel support (or lack thereof)

There are endless issues related to the interactions between the kernel, the distribution, docker and the filesystem

We are using Debian stable with backports, in production. We started running on Debian Jessie 3.16.7-ckt20-1 (released November 2015). This one suffers from a major critical bug that crashes hosts erratically (every few hours in average).

Linux 3.x: Unstable storage drivers

Docker has various storage drivers. The only one (allegedly) wildly supported is AUFS.

The AUFS driver is unstable. It suffers from critical bugs provoking kernel panics and corrupting data.

It’s broken on [at least] all “linux-3.16.x” kernel. There is no cure.

We follow Debian and kernel updates very closely. Debian published special patches outside the regular cycle. There was one major bugfix to AUFS around March 2016. We thought it was THE TRUE ONE FIX but it turned out that it wasn’t. The kernel panics happened less frequently afterwards (every week, instead of every day) but they were still loud and present.

Once during this summer there was a regression among a major update, that brought back a previous critical issue. It started killing CI servers one by one, with 2 hours in average between murders. An emergency patch was quickly released to fix the regression.

There were multiple fixes to AUFS published along the year 2016. Some critical issues were fixed but there are many more still left. AUFS is unstable on [at least] all “linux-3.16.x” kernels.

  • Debian stable is stuck on kernel 3.16. It’s unstable. There is nothing to do about it except switching to Debian testing (which can use the kernel 4).
  • Ubuntu LTS is running kernel 3.19. There is no guarantee that this latest update fixes the issue. Changing our main OS would be a major disruption but we were so desperate that we considered it for a while.
  • RHEL/CentOS-6 is on kernel 2.x and RHEL/CentoS-7 is on kernel 3.10 (with many later backports done by RedHat).

Linux 4.x: The kernel officially dropped docker support

It is well-known that AUFS has endless issues and it’s regarded as dead weight by the developers. As a long-standing goal, the AUFS filesystem was finally dropped in kernel version 4.

There is no unofficial patch to support it, there is no optional module, there is no backport whatsoever, nothing. AUFS is entirely gone.

[dramatic pause]

.

.

.

How does docker work without AUFS then? Well, it doesn’t.

[dramatic pause]

.

.

.

So, the docker guys wrote a new filesystem, called overlay.

OverlayFS is a modern union filesystem that is similar to AUFS. In comparison to AUFS, OverlayFS has a simpler design, has been in the mainline Linux kernel since version 3.18 and is potentially faster.” — Docker OverlayFS driver

Note that it’s not backported to existing distributions. Docker never cared about [backward] compatibility.

Update after comments: Overlay is the name of both the kernel module to support it (developed by linux maintainers) and the docker storage driver to use it (part of docker, developed by docker). They are two different components [with a possible overlap of history and developers]. The issues seem mostly related to the docker storage driver, not the filesystem itself.

The debacle of Overlay

A filesystem driver is a complex piece of software and it requires a very high level of reliability. The long time readers will remember the Linux migration from ext3 to ext4. It took time to write, more time to debug and an eternity to be shipped as the default filesystem in popular distributions.

Making a new filesystem in 1 year is an impossible mission. It’s actually laughable when considering that the task is assigned to Docker, they have a track record of unstability and disastrous breaking changes, exactly what we don’t want in a filesystem.

Long story short. That did not go well. You can still find horror stories with Google.

Overlay development was abandoned within 1 year of its initial release.

[dramatic pause]

.

.

.

Then comes Overlay2.

The overlay2 driver addresses overlay limitations, but is only compatible with Linux kernel 4.0 [or later] and docker 1.12” — Overlay vs Overlay2 storage drivers

Making a new filesystem in 1 year is still an impossible mission. Docker just tried and failed. Yet they’re trying again! We’ll see how it turns out in a few years.

Right now it’s not supported on any systems we run. We can’t use it, we can’t even test it.

Lesson learnt: As you can see with Overlay then Overlay2. No backport. No patch. No retro compatibility. Docker only moves forward and breaks things. If you want to adopt Docker, you’ll have to move forward as well, following the releases from docker, the kernel, the distribution, the filesystems and some dependencies.

Bonus: The worldwide docker outage

On 02 June 2016, at approximately 9am (London Time). New repository keys are pushed to the docker public repository.

As a direct consequence, any run of “apt-get update” (or equivalent) on a system configured with the broken repo will fail with an error “Error https://apt.dockerproject.org/ Hash Sum mismatch

This issue is worldwide. It affects ALL systems on the planet configured with the docker repository. It is confirmed on all Debian and ubuntu versions, independent of OS and docker versions.

All CI pipelines in the world which rely on docker setup/update or a system setup/update are broken. It is impossible to run a system update or upgrade on an existing system. It’s impossible to create a new system and install docker on it.

After a while. We get an update from a docker employee: “To give an update; I raised this issue internally, but the people needed to fix this are in the San Francisco timezone [8 hours difference with London], so they’re not present yet.

I personally announce that internally to our developers. Today, there is no Docker CI and we can’t create new systems nor update existing systems which have a dependency on docker. All our hope lies on a dude in San Francisco, currently sleeping.

[pause waiting for the fix, that’s when free food and drinks come in handy]

An update is posted from a Docker guy in Florida at around 3pm (London Time). He’s awake, he’s found out the issue and he’s working on the fix.

Keys and packages are republished later.

We try and confirm the fix at around 5pm (London Time).

That was a 7 hours interplanetary outage because of Docker. All that’s left from the outage is a few messages on a GitHub issue. There was no postmortem. It had little (none?) tech news or press coverage, in spite of the catastrophic failure.

Docker Registry

The docker registry is storing and serving docker images.

Automatic CI build  ===> (on success) push the image to ===> docker registry
Deploy command <=== pull the image from <=== docker registry

There is a public registry operated by docker. As an organization, we also run our own internal docker registry. It’s a docker image running inside docker on a docker host (that’s quite meta). The docker registry is the most used docker image.

There are 3 versions of the docker registry. The client can pull indifferently from any:

Docker Registry Issue: Abandon and Extinguish

The docker registry v2 is as a full rewrite. The registry v1 was retired soon after the v2 release.

We had to install a new thing (again!) just to keep docker working. They changed the configuration, the URLs, the paths, the endpoints.

The transition to the registry v2 was not seamless. We had to fix our setup, our builds and our deploy scripts.

Lesson learnt: Do not trust on any docker tool or API. They are constantly abandoned  and extinguished.

One of the goal of the registry v2 is to bring a better API. It’s documented here, a documentation that we don’t remember existed 9 months ago.

Docker Registry Issue: Can’t clean images

It’s impossible to remove images from the docker registry. There is no garbage collection either, the doc mentions one but it’s not real. (The images do have compression and de-duplication but that’s a different matter).

The registry just grows forever. Our registry can grow by 50 GB per week.

We can’t have a server with an unlimited amount of storage. Our registry ran out of space a few times, unleashing hell in our build pipeline, then we moved the image storage to S3.

Lesson learnt: Use S3 to store images (it’s supported out-of-the-box).

We performed a manual clean-up 3 times in total. In all cases we had to stop the registry, erase all the storage and start a new registry container. (Luckily, we can re-build the latest docker images with our CI).

Lesson learnt: Deleting any file or folder manually from the docker registry storage WILL corrupt it.

To this day, it’s not possible to remove an image from the docker registry. There is no API either. (One of the point of the v2 was to have a better API. Mission failed).

Docker Issue: The release cycle

The docker release cycle is the only constant in the Docker ecosystem:

  1. Abandon whatever exists
  2. Make new stuff and release
  3. Ignore existing users and retro compatibility

The release cycle applies but is not limited to: docker versions, features, filesystems, the docker registry, all API…

Judging by the past history of Docker, we can approximate that anything made by Docker has a half-life of about 1 year, meaning that half of what exist now will be abandoned [and extinguished] in 1 year. There will usually be a replacement available, that is not fully compatible with what it’s supposed to replace, and may or may not run on the same ecosystem (if at all).

We make software not for people to use but because we like to make new stuff.” — Future Docker Epitaph

The current status-quo on Docker in our organization

Growing in web and micro services

Docker first came in through a web application. At the time, it was an easy way for the developers to package and deploy it. They tried it and adopted it quickly. Then it spread to some micro services, as we started to adopt a micro services architecture.

Web applications and micro services are similar. They are stateless applications, they can be started, stopped, killed, restarted without thinking. All the hard stuff is delegated to external systems (databases and backend systems).

The docker adoption started with minor new services. At first, everything worked fine in dev, in testing and in production. The kernel panics slowly began to happen as more web services and web applications were dockerized. The stability issues became more prominent and impactful as we grew.

A few patches and regressions were published over the year. We’ve been playing catchup & workaround with Docker for a while now. It is a pain but it doesn’t seem to discourage people from adopting Docker. Support and demand is still growing inside the organisation.

Note: None of the failures ever affected any customer or funds. We are quite successful at containing Docker.

Banned from the core

We have some critical applications running in Erlang, managed by a few guys in the ‘core’ team.

They tried to run some of their applications in Docker. It didn’t work. For some reasons, Erlang applications and docker didn’t go along.

It was done a long time ago and we don’t remember all the details. Erlang has particular ideas about how the system/networking should behave and the expected load was in thousands of requests per second. Any unstability or incompatibility could justify an outstanding failure. (We know for sure now that the versions used during the trial suffered from multiple major unstability issues).

The trial raised a red flag. Docker is not ready for anything critical. It was the right call. The later crashes and issues managed to confirm it.

We only use Erlang for critical applications. For example, the core guys are responsible for a payment system that handled $96,544,800 in transaction this month. It includes a couple of applications and databases, all of which are under their responsibilities.

Docker is a dangerous liability that could put millions at risk. It is banned from all core systems.

Banned from the DBA

Docker is meant to be stateless. Containers have no permanent disk storage, whatever happens is ephemeral and is gone when the container stops. Containers are not meant to store data. Actually, they are meant by design to NOT store data. Any attempt to go against this philosophy is bound to disaster.

Moreover. Docker is locking away processes and files through its abstraction, they are unreachable as if they didn’t exist. It prevents from doing any sort of recovery if something goes wrong.

Long story short. Docker SHALL NOT run databases in production, by design.

It gets worse than that. Remember the ongoing kernel panics with docker?

A crash would destroy the database and affect all systems connecting to it. It is an erratic bug, triggered more frequently under intensive usage. A database is the ultimate IO intensive load, that’s a guaranteed kernel panic. Plus, there is another bug that can corrupt the docker mount (destroying all data) and possibly the system filesystem as well (if they’re on the same disk).

Nightmare scenario: The host is crashed and the disk gets corrupted, destroying the host system and all data in the process.

Conclusion: Docker MUST NOT run any databases in production, EVER.

Every once in a while, someone will come and ask “why don’t we put these databases into docker?” and we’ll tell some of our numerous war stories, so far, no-one asked twice.

Note: We started going over our Docker history as an integral part of our on boarding process. That’s the new damage control philosophy, kill the very idea of docker before it gets any chance to grow and kill us.

A Personal Opinion

Docker is gaining momentum, there is some crazy fanatic support out there. The docker hype is not only a technological liability any more, it has evolved into a sociological problem as well.

The perimeter is controlled at the moment, limited to some stateless web applications and micro services. It’s unimportant stuff, they can be dockerized and crash once a day, I do not care.

So far, all people who wanted to use docker for important stuff have stopped after a quick discussion. My biggest fear is that one day, a docker fanatic will not listen to reason and keep pushing. I’ll be forced to barrage him and it might not be pretty.

Nightmare scenario: The future accounting cluster revamp, currently holding $23M in customer funds (the M is for million dollars). There is already one guy who genuinely asked the architect “why don’t you put these databases into docker?“, there is no word to describe the face of the architect.

My duty is to customers. Protecting them and their money.

Surviving Docker in Production

gif-what-docker-pretends-to-be
What docker pretends to be.
gif-what-docker-really-is
What docker really is.

Follow releases and change logs

Track versions and change logs closely for kernel, OS, distributions, docker and everything in between. Look for bugs, hope for patches, read everything with attention.

ansible '*' -m shell -a "uname -a"

Let docker crash

Let docker crash. self-explanatory.

Once in a while, we look at which servers are dead and we force reboot them.

Have 3 instances of everything

High availability require to have at least 2 instances per service, to survive one instance failure.

When using docker for anything remotely important, we should have 3 instances of it. Docker die all the time, we need a margin of error to support 2 crashes in a raw to the same service.

Most of the time, it’s CI or test instances that crash. (They run lots of intensive tests, the issues are particularly outstanding). We’ve got a lot of these. Sometimes there are 3 of them crashing in a row in an afternoon.

Don’t put data in Docker

Services which store data cannot be dockerized.

Docker is designed to NOT store data. Don’t go against it, it’s a recipe for disaster.

On top, there are current issues killing the server and potentially destroying the data so that’s really a big no-go.

Don’t run anything important in Docker

Docker WILL crash. Docker WILL destroy everything it touches.

It must be limited to applications which can crash without causing downtime. That means mostly stateless applications, that can just be restarted somewhere else.

Put docker in auto scaling groups

Docker applications should be run in auto-scaling groups. (Note: We’re not fully there yet).

Whenever an instance is crashed, it’s automatically replaced within 5 minutes. No manual action required. Self healing.

Future roadmap

Docker

The impossible challenge with Docker is to come with a working combination of kernel + distribution + docker version + filesystem.

Right now. We don’t know of ANY combination that is stable (Maybe there isn’t any?). We actively look for one, constantly testing new systems and patches.

Goal: Find a stable ecosystem to run docker.

It takes 5 years to make a good and stable software, Docker v1.0 is only 28 months old, it didn’t have time to mature.

The hardware renewal cycle is 3 years, the distribution release cycle is 18-36 months. Docker didn’t exist in the previous cycle so systems couldn’t consider compatibility with it. To make matters worse, it depends on many advanced system internals that are relatively new and didn’t have time to mature either, nor reach the distributions.

That could be a decent software in 5 years. Wait and see.

Goal: Wait for things to get better. Try to not go bankrupt in the meantime.

Use auto scaling groups

Docker is limited to stateless applications. If an application can be packaged as a Docker Image, it can be packaged as an AMI. If an application can run in Docker, it can run in an auto scaling group.

Most people ignore it but Docker is useless on AWS and it is actually a step back.

First, the point of containers is to save resources by running many containers on the same [big] host. (Let’s ignore for a minute the current docker bug that is crashing the host [and all running containers on it], forcing us to run only 1 container per host for reliability).

Thus containers are useless on cloud providers. There is always an instance of the right size. Just create one with appropriate memory/CPU for the application. (The minimum on AWS is t2.nano which is $5 per month for 512MB and 5% of a CPU).

Second, the biggest gain of containers is when there is a complete orchestration system around them to automatically manage creation/stop/start/rolling-update/canary-release/blue-green-deployment. The orchestration systems to achieve that currently do not exist. (That’s where Nomad/Mesos/Kubernetes will eventually come in, there are not good enough in their present state).

AWS has auto scaling groups to manage the orchestration and life cycle of instances. It’s a tool completely unrelated to the Docker ecosystem yet it can achieve a better result with none of the drawbacks and fuck-ups.

Create an auto-scaling group per service and build an AMI per version (tip: use Packer to build AMI). People are already familiar with managing AMI and instances if operations are on AWS, there isn’t much more to learn and there is no trap. The resulting deployment is golden and fully automated. A setup with auto scaling groups is 3 years ahead of the Docker ecosystem.

Goal: Put docker services in auto scaling groups to have failures automatically handled.

CoreOS

Update after comments: Docker and CoreOS are made by separate companies.

To give some slack to Docker for once, it requires and depends on a lot of new advanced system internals. A classic distribution cannot upgrade system internals outside of major releases, even if it wanted to.

It makes sense for docker to have (or be?) a special purpose OS with an appropriate update cycle. It may be the only way to have a working bundle of kernel and operating system able to run Docker.

Goal: Trial the CoreOS ecosystem and assess stability.

In the grand scheme of operations, it’s doable to separate servers for running containers (on CoreOS) from normal servers (on Debian). Containers are not supposed to know (or care) about what operating systems they are running.

The hassle will be to manage the new OS family (setup, provisioning, upgrade, user accounts, logging, monitoring). No clue how we’ll do that or how much work it might be.

Goal: Deploy CoreOS at large.

Kubernetes

One of the [future] major breakthrough is the ability to manage fleets of containers abstracted away from the machines they end up running on, with automatic start/stop/rolling-update and capacity adjustment,

The issue with Docker is that it doesn’t do any of that. It’s just a dumb container system. It has the drawbacks of containers without the benefits.

There are currently no good, battle tested, production ready orchestration system in existence.

  • Mesos is not meant for Docker
  • Docker Swarm is not trustworthy
  • Nomad has only the most basic features
  • Kubernetes is new and experimental

Kubernetes is the only project that intends to solve the hard problems [around containers]. It is backed by resources that none of the other projects have (i.e. Google have a long experience of running containers at scale, they have Googley amount of resources at their disposal and they know how to write working software).

Right now, Kubernetes is young & experimental and it’s lacking documentation. The barrier to entry is painful and it’s far from perfection. Nonetheless, it is [somewhat] working and already benefiting a handful of people.

In the long-term, Kubernetes is the future. It’s a major breakthrough (or to be accurate, it’s the final brick that is missing for containers to be a major [r]evolution in infrastructure management).

The question is not whether to adopt Kubernetes, the question is when to adopt it?

Goal: Keep an eye on Kubernetes.

Note: Kubernetes needs docker to run. It’s gonna be affected by all docker issues. (For example, do not try Kubernetes on anything else than CoreOS).

Google Cloud: Google Container Engine

As we said before, there is no known stable combination of OS + kernel + distribution + docker version, thus there is no stable ecosystem to run Kubernetes on. That’s a problem.

There is a potential workaround: Google Container Engine. It is a hosted Kubernetes (and Docker) as a service, part of Google Cloud.

Google gotta solve the Docker issues to offer what they are offering, there is no alternative. Incidentally, they might be the only guys who can find a stable ecosystem around Docker, fix the bugs, and sell that ready-to-use as a cloud managed service. We might have a shared goal for once.

They already offer the service so that should mean that they already worked around the Docker issues. Thus the simplest way to have containers working in production (or at-all) may be to use Google Container Engine.

Goal: Move to Google Cloud, starting with our subsidiaries not locked in on AWS. Ignore the rest of the roadmap as it’s made irrelevant.

Google Container Engine: One more reason why Google Cloud is the future and AWS is the past (on top of 33% cheaper instances with 3 times the network speed and IOPS, in average).


Why docker is not yet succeeding in production, July 2015, from the Lead Production Engineer at Shopify.

Docker is not ready for primetime, August 2016.

Docker in Production: A retort, November 2016, a response to this article.

How to deploy an application with Docker… and without Docker, An introduction to application deployment, The HFT Guy.


Disclaimer (please read before you comment)

A bit of context missing from the article. We are a small shop with a few hundreds servers. At core, we’re running a financial system moving around multi-million dollars per day (or billions per year).

It’s fair to say that we have higher expectations than average and we take production issues rather (too?) seriously.

Overall, it’s “normal” that you didn’t experience all of these issues if you’re not using docker at scale in production and/or if you didn’t use it for long.

I’d like to point out that these are issues and workarounds happening over a period of [more than] a year, summarized all together in a 10 minutes read. It does amplify the dramatic and painful aspect.

Anyway, whatever happened in the past is already in the past. The most important section is the Roadmap. That’s what you need to know to run Docker (or use auto scaling groups instead).

How to present a GitHub project for your resume


Introduction

Companies ask for a GitHub profile. Recruiters ask for a GitHub profile. The question “Do you contribute to open-source?” is now one of the most common questions asked in phone screens.

If people want a GitHub, we shall give them a GitHub. This article will explain how to present a GitHub project for use in a resume.

The given advice can be read from two point of views. As a candidate, it is what to write to introduce and present a software (not necessary on GitHub). As an interviewer (or a fellow developer), it is what to look for to judge the experience of the developer(s) and the quality of a software.

submit application form with a github link
When having a GitHub is mandatory, just like having a name.

Link to a specific project

Put a link to your GitHub in your resume and every application forms you have to fill.

That link must send directly to a project. Never link to the root of your GitHub profile, it doesn’t show anything useful and it’s hard to navigate from there.

It means that you must have ONE project to show. A single demonstration project is enough, don’t need more.

This project will be the “landing page” in web buzzword. This is the first page the employer will see. They will rarely go past it (and they shouldn’t have to) so the page should be a good enough by itself. If they go past it, it’s only because the page grabbed their interests and they wanted to see more.

We’ll write the project page to give a good first impression and show off skills as a software engineer.

Project Structure

A software project can be judged in 5 seconds by looking at the directory structure.

An inexperienced developer is easy to spot. His project doesn’t have any structure. Files are either in unpredictable places or all in the top directory.

There is one project structure to rule them all. There MUST be separate directories for source, test, libraries, compiled binaries, etc…

Whether the naming convention will be “doc” or “docs” is an unimportant detail. For example, here are Simple Folder Structure Conventions for GitHub projects:

.
├── build                   # Compiled files (alternatively `dist`)
├── docs                    # Documentation files (alternatively `doc`)
├── src                     # Source files (alternatively `lib` or `app`)
├── test                    # Automated tests (alternatively `spec` or `tests`)
├── tools                   # Tools and utilities
├── LICENSE
└── README.md
software project structure
A well-organized project

Have a README

Have a README to:

  • Describe the purpose of the project
  • Screenshots/videos
  • Usage
  • Link to the installer/webpage

Have screenshots in the readme

A picture is worth a thousand words.

People are not going to install the application just to see it. Give them screenshots.

Have videos in the readme

A picture is worth a thousand words. A video is worth a thousand pictures.

There is nothing better than a video when it comes to giving a demonstration or showing off an application.

snake game preview animated gif
Great demo from a random snake project on GitHub

Note: GitHub does not allow to embed video files in the readme, use animated gif instead.

Link to a website or an installer

Link to the site if it’s a web application project. Of course, a web application should be running somewhere and publicly accessible, that’s the point of web applications.

Link to the installer if it’s a desktop application project. It’s unlikely that the user will install it but that looks professional, that’s how desktop applications are distributed after all.

Integrate GitHub tools

GitHub has a rich ecosystem of free tools for building, packaging, testing and much more. All these tools are mandatory for professional software development.

It used to be hell to setup the tooling but now everything is readily available for free through GitHub and the setup is dead simple. There is no excuse to not use the tooling.

github-integration-icons

This one is a sample C++ project for a Connect Four. From left to right:

  1. Build on Linux (Travis CI)
  2. Build on Windows (AppVeyor)
  3. Unit tests and coverage analysis (Coveralls)

What about the source code?

Nobody cares about your code. It was quite a shocking moment when I learned this in my programming career. I would take great care in polishing my code only to find out nobody actually cares. It’s not the code that counts, it’s the product. ” — Source

A paragraph to explain the purpose of the application is 10 times faster than guessing it. A quick start video of an [non-trivial] application is 100 times faster than figuring it out. A design diagram is 1000 times faster than reverse engineering the application. All of these could be achieved by reading the source code, at the cost of orders of magnitude more time and headache. It’s extremely slow and difficult to read code (or should we say to decode code). It should only ever be a last resort.

Lesson #1: Noone cares about your source code. Noone is gonna read it.

Lesson #2: Don’t expect people to read it. Don’t force them to.

What if I don’t have big projects to show?

Good. Smaller projects are easier to show, easier to explain and easier to understand for the interviewer. For instance, everyone can grasp a good old Connect Four.

It is not a trivial project despite what it looks like at first. Write a decent UI, put some colors, allow a two players option, add a “hint” to show the best next move, add an AI to play against.

While the game is conceptually simple there is a lot of work to turn it into a good and polished software. That leaves plenty of depth to talk about in a face-to-face interview.

Did you know that the first player in a connect four game always wins? [if playing perfectly] Did you know that the second player can always draw the game, if the first player doesn’t take the middle position as his first move?

Source: A Knowledge-based Approach of Connect-Four, The Game is Solved: White Wins, Victor Allis

Do interviewers really look at GitHub?

As a matter of fact: No, they don’t.

github traffic statistics
GitHub Traffic Statistics

We’ve done the tests. Here are the statistics after sending a bunch of resumes. The 3 views are from myself, I accessed the project while writing this article, without being authenticated to GitHub. Oops, my bad.

From personal experience from the last time I looked for a job. After a dozen of phone interviews (1 dev per call) and a couple of on-sites (4 to 7 devs per on-site), there was only 1 visit to my profile.

Conclusion: Noone cares about GitHub. Noone is gonna read it. Everyone is gonna ask for it nonetheless, cause it’s hype.

Bonus: Since noone will check the link they’re given, you too can refuse to participate in the GitHub masquerade by answer all GitHub request with the ultimate hello world repository. This is a repository of hello world in hundreds of languages.

Cheat Sheet

  1. Structure the project
  2. Have a README
  3. Write a paragraph to explain the purpose of the project
  4. Put screenshots and videos
  5. Distribute an installer (desktop app) or give the website (web app)
  6. Integrate development tools (CI, unit test, packager, etc…)

That’s good practices for software projects. It’s not limited to GitHub.