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Docker Desktop for Mac networking stops working after upgrade to MacOS 11 Big Sur

If you see strange connection problems when building docker images on MacOS after updating to Big Sur the reason might be a change in some implementations in the network layer. And the Docker Desktop update doesn’t take these into account because fixing the issue means to delete everything previously built on your machine.

Spcifically, open the Docker Desktop app and klick on the bug icon in the upper right corner:

When the dialog opens, click on “Clean / Porge data”

This will take some time since docker will restart after cleaning. But then your network problems should be gone.

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Import .env settings in shell script

Sometimes you have a set of environment variables in a .env file. And sometimes you would like to consume them in a shell script. You can do that like this:

export $(cat .env | xargs)

In some special cases you might have the .env file somewhere on the net and would like to include it. Here curl comes in handy:

export $(curl -SsL | xargs)

This URL also can be a link to some raw file on GitHub or GitLab.

But be careful about what you include from somewhere on the net!


Running Dash in Docker

This is part one of a short series of posts about Dash.
The repository for this blog posts is here.

Dash is an application framework to build dashboards (hence the name) or in general data visualization heavy largely customized web apps in Python. It’s written on top of the Python (web) micro-framework Flask and uses plotly.js and react.js.

The Dash manual neatly describes how to setup your first application showing a simple bar chart using static data, you could call this the ‘Hello World’ of data visualization. I always like to do my development work (and that of my teams) in a dockerized environment, so it OS agnostic (mostly, haha) and the everyone uses the same reproducible environment. It also facilitates deployment.

So let’s gets started with a docker-compose setup that will probably be all you need to get started. I use here a setup derived from my Django docker environment. If you’re interested in that one too, I can publish a post on that one as well. The environment I show here uses (well, not for the Hello World example, but at some point you might need a database, so I provide one, too) PostgreSQL, but setups with other databases (MySQL/MariaDB for relational data, Neo4J for graph data, InfluxDB for time series data … you get it) are also possible.

We’ll start with a requirements.txt file that will hold all the pyckages that need to be installed to run the app:


Psycopg is the Python database adapter for PostgreSQL and Dash is … well, Dash. Here you will add additional database adaptors or other dependencies your app might use.

Next is the Dockerfile (and call it Dockerfile.dash) to create the Python container:

FROM python:3

RUN mkdir /code

COPY requirements.txt /code/
RUN pip install -r requirements.txt
COPY . /code/

We derive our image from the current latest Python 3.x image, the ENV line sets the environment variable PYTHONUNBUFFERED for Python to one. This means, that stdin, stdout and stderr are completely unbuffered, going directly to the container log (we’ll talk about that one later).
Then we create a directory named code in the root directory of the image and go there (making it the current work directory) with WORKDIR.
Now we COPY the requirements.txt file into the image, and RUN pip to install whatever is in there.
Finally we COPY all the code (and everything else) from the current directory into the container.

Now we create a docker-compose.yml file to tie all this stuff together and run the command that starts the web server:

version: '3'


    image: postgres
    container_name: dash_pgsql
      - POSTGRES_DB=postgres
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - "5432:5432"
      - ./pgdata:/var/lib/postgresql/data

      context: .
      dockerfile: Dockerfile.dash
    container_name: dash_dash
    command: python
      - .:/code
      - "80:8080"
      - pgsql

We create two so called services: a database container running PostgreSQL and a Python container running out app. The PostgreSQL container uses the latest prebuilt image, we call it dash_pgsql and we set some variables to initiate the first database and the standard database user. You can later on certainly add additional users and databases from the psql command line. To do this we export the database port 5432 to the host system so you can use any database you already have tool to manage what’s inside that database. Finally we persist the data using a shared volume in the subdirectory pgdata. This makes sure we see all the data again when we restart the container.
Then we set up a dash container using our previously created Dockerfile.dash to build the image and we call it dash_dash. This sounds a bit superfluous but this way all containers in this project will be prefixed with “dash_“. If you leave that out docker-compose will use the projects directory name as a prefix and append a “_1” to the end. If you later use Docker swarm you will possibly have multiple containers for the same service running and then they will be numbered.
The command that will be run when we start the container is python We export port 8080 (which we set in the, bear with me) to port 80 on our host. You might have some other process using that port. In this case change the 80 to whatever you like (8080 for example). Finally we declare that this container needs the PostgreSQL service to run before starting. This currently is not needed but will come handy later, since the PostgreSQL containers might be a bit slow in startup. And then your app might start without a valid database resource.

The last building block is our app script itself:

import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['']

app = dash.Dash(name, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
  html.H1(children='Hello Dash'),

    Dash: A web application framework for Python.

      'data': [
        {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
        {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
      'layout': {
        'title': 'Dash Data Visualization'

if __name__ == '__main__':
  app.run_server(host='', port=8080, debug=True)

I won’t explain too much of that code here, because this just is the first code example from the Dash manual. But note that I changed the parameters of app.run_server(). You can use any parameter here that the Flask server accepts.

To fire this all up, use first docker-compose build to build the Python image. Then use docker-compose up -d to start both services in the background. To see if they run as ppalnned use docker-compose ps. You should see two services:

Name         Command                         State  Ports
dash_dash    python                   Up>8080/tcp
dash_pgsql postgres   Up>5432/tcp

Now point your browser to http://localhost (or appending whatever port you have used in the docker-compose file) and you should see:

You now can use any editor on your machine to modify the sourcecode in the project directory. Changes will be loaded automatically. If you want to look at the log output of the dash container, use docker-compose logs -f dash and you should see the typical stdout of a Flask application, including the debugger pin, something like:

dash_1 | Running on
dash_1 | Debugger PIN: 561-251-916
dash_1 | * Serving Flask app "app" (lazy loading)
dash_1 | * Environment: production
dash_1 | WARNING: This is a development server. Do not use it in a production deployment.
dash_1 | Use a production WSGI server instead.
dash_1 | * Debug mode: on
dash_1 | Running on
dash_1 | Debugger PIN: 231-410-660

Here you will also see when you save a new version of and the web server reloads the app. To stop the environment first use CTRL-c to exit the log tailing and issue a docker-compose down. In an upcoming episode I might show you some more things you cound do with Dash and a database.

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Excel is Maslow’s hammer of our time

The law of the instrument states that if your only tool is a hammer, you are tempted to treat everything as a nail. In modern business this hammer often is Excel. Every time someone needs a little (or larger) tool to do some work, in many companies the result is an Excel spreadsheet with lots of logic, programming and formatting in it. It’s tempting since in a corporate environment nearly everyone has an office suite on their computer. An obvious solution is hacked together pretty fast and often never again understood, but more on that later.
There are however some fundamental problems with applications becoming spreadsheets:

  • They are inherently not multi-user capable.
  • They are not versioned. Everyone has some version or the other of the file. Nobody knows which is the newest.
  • They are not maintainable or understandable for someone else as the author or later on even by the author.
  • Data, code and visualization literally live within the same space.

Why are those things generally and inherently bad? Aren’t there legitimate use cases for „Excel apps“? Let’s give all the shortcomings a closer look.

Not multi-user capable

Without using special server components Office documents are not multi-user capable. The start of all misery often is a mail, spreading a document among several people. Then some or all of them start to make changes. If they don’t synchronize who works in which order on the document (and sending around updated versions of the corpus delicti!) the proliferation of conflicting versions will drive you mad some time later.

No versioning

Different people will have different versions of the document. Even you yourself might have different versions, if you chose not to overwrite the existing file: My_Important_Calculation_2019-01-05.xlsx, My_Important_Calculation_2019-01-07.xlsx, My_Important_Calculation_2019-01-07_better_version.xlsx … sounds familiar, eh? I hope I don’t need to explain why this is bad.

Lack of maintainability

Ever tried do decipher a formula as long as the Panamericana crammed in a cell the size of a fruit fly? And now imagine trying to understand what the whole worksheet does by clicking into each and every cell and try to figure out what happens therein. I know there are people who are able to grasp an overall view of a worksheet they never saw before. But I prefer to use my brains computational power to solve the underlying business case or problem instead of an encrypted version of it in a worksheet.

One-pot solution

Your data (the numbers and texts in cells), your code (formulas) and visualization (some inline created graphics) literally live in the same place, your worksheet. You could try to separate the code by using Visual Basic for applications or some more modern replacement but this solves the problem only partially. And you lose some of the just in time synchronous tooling which is one of the few advantages of the Office solution.

So what?

Writing good readable and maintainable code is empathy with your team and your future self. The hardship you go through when you rely on office “applications” for your business mostly was created by yourself and / or your team. At least for the cases you and your team is responsible, you can do better.
Set up an application dedicated to your business problem. There are technical solutions that allow you to do so in a fast and agile way. You can put that solution on a central server so everyone has access. You can back up that server or service to prevent data loss. And your solution will have a much better user experience.
And don’t tell me your company is not a software company because you produce goods or sell services. Thinking this way is sort of a déformation professionnelle (french term for “looking at things from the point of view of one’s profession”). If you deploy some code (in whatever environment), at least part of your company indeed is a software company. Don’t take me for that, maybe you put more confidence in General Electric’s CEO Jeff Immelt who said in an interview with Charlie Rose “Every company has to be a software company”.

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The good, the bad and the ugly – about career ladders in engineering


Nearly everyone working in software development has seen or was confronted with career ladders. These nifty listings of levels for employees. Many people think about the relevance of those ladders for their own career or their teams and companies. Career ladders can be a good way to get some orientation about the inherent questions of our job:

  • Where am I now in the sense of career or growth path?
  • Where am I going? What are my opportunities?

However since career ladders is a very central point and also an often tacitly used one, let me point out some things I learned from my own experience as a developer and engineering manager and from other people. I will list some resources at the bottom of this post.

Lessons learned

  1. Career ladders should not be superimposed to teams from the outside. The creation and usage can and should be triggered and respected by the organization. But let it be some sort of team work. Let people contribute on a voluntary basis. Discuss the content and the usage. When there is some agreement about that, communicate back to the rest of the team making sure everyone understands that this was a team effort. And once again collect and discuss feedback.
  2. Be aware that teams maybe have a heterogeneous background. So on very tightly connected teams with a shared background only some information around the career ladder and the levels therein maybe needed but on other teams you need to be more explicit and detailed in the level definition and things needed to advance. So some extra details and information might be superfluous for some team members but very well used by others.
  3. Think about career paths. There might be more than one linear path and more than one end point if any at all. Some people like to work in tech intensive roles like developers for a certain period of time and then like to advance into more people centric roles. Some tech people don’t like the idea at all to let go of at least some of their time developing and coding and instead deal with those awkward carbon based species. Be sure to offer a way to grow also for those people. Team lead is not the natural next step after being senior engineer for some time. Maybe it will be some sort of architect role, sometimes also called principal engineer.
  4. It’s hard if not impossible to come up with a engineering career ladder from scratch. Have a look around what other organozations do. An increasing number of companies make their career paths transparent and public. See the resources section at the bottom.
  5. With a small team you normally don’t start with a career ladder right away. When your organization grows and your team size reaches the generally accepted management span (the number of people you can manage or care for on a direct report basis) of around 8 to 12 people this is a good point to start to think about that.
  6. A career ladder not only consists of the levels and paths and their definition but also of the way you use and apply it, in performance reviews and promotions for example. Don’t define level transitions by the number of years of experience someone has but by their abilities. People develop with different speed and emphasis. Take that into account. Promote people to levels they already have shown to be able to do.
  7. It’s your job as a manager to create an environment where it is OK to fail. This not only creates an emotionally save culture but also lets people try out new steps on their path to grow.Be sure to create opportunities where people can hop in and learn to grow into that new role or ability.

What about contractors?

I often see discussions about the role of contractors or freelancers in organizations. This certainly also applies to a career ladder. How you see freelancing coworkers and how you manage them depends on the role you assign to them. Traditionally contractors have one of two applications:

  1. Firefighters: You’re project is late and you need more people working on these tasks but can not hire them permanently for any reason.
  2. Knowledge transfer: You need some special knowledge that no one on your team has. Maybe you need it only for some time or maybe it is so rare or obscure (think of COBOL developers) that you were not able to find someone permanently.

These two are the classical or old fashioned “mercenary scenarios” of contractors. But there is another one. Some people just like to be a bit more independent or come from another background. But they will stay with your team pretty much as long as any permanent team member. The modus of payment just is different. In this case I would take those people into account with your career ladder the same way you do with permanently hired coworkers. I for example once was promoted from developer to project manager when I worked as a contractor on a team. This was the start of my “manager’s path”.


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Agility in different domains


Working with agile processes is a best practice approach by now for the software development industry. It allows for small but fast steps towards a greater goal of a project or product vision. You could also apply agile thinking to research projects. Classical research is not that different from an agile process, nay? You take some small step, test a hypothesis, evaluate the result and continue with a next step.

Agility in science

But there is a fundamental difference in the concept of these processes: research allows to dismiss one or even several of the previous steps. In software development you normally don’t throw away the artifacts you built to move where you are now. For research to be agile it needs to be allowed to discard previous steps, or as Alfred North Whitehead said in “Adventures of ideas”:

A science that hesitates to forget its founders is lost.

It’s part of changing paradigms in the classical sense of Thomas S. Kuhn’s ideas in “The structure of scientific revolutions”.

There and back again

Having broadened the view on agility for research, let’s look back at software development. In agile sprints your teams makes decisions that will influence the path of the whole product. So you eventually make fast paced decisions whose effects will stay for a long time. Most software development teams are prepared to make quick technological decisions based on the problem at hand. They are normally not prepared to make estimates on the long term feasability of said decisions.

Take away

So you may accumulate technological debt even if you try to avoid. Take some pressure out of the technological decision making process by allowing for revision of architectural decisions. And last but not least establish a culture allowing to make errors. Because being under pressure to avoid errors and increase velocity makes developers susceptible for making errors. But this is a topic for another post.

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Management problems and how to avoid them

The last days I listened to some episodes of a german management podcast. They discussed some common management problems or mistakes and how to avoid them. This got me thinking about my own experiences and this post is a combination of ideas from the podcast and my own thoughts. And it certainly is by no means complete or authoritative.


One of the most demotivating habits is not only to set a goal but also explicitly to define how to reach it. If you are at every stage of a project informed about every detail and progress of every task, if you always work on the important tasks yourself, if you think you know more than your coworkers and can do better, if you are able to jump in with any task at hand you probably are micromanaging your team. This is demotivating since you express a lack of trust. Try to delegate tasks. Have confidence in your coworkers because without they don’t have either. Sometimes the line between giving enough information to work on a task and giving too much information so the task can be done without thinking about is a fine one. Especially founders often never learned to delegate by specifying a task at hand as detailed as needed but not more. When the results don’t match their expectations they feel confirmed in their distrust.
Additionally don’t request status information each and every day. This holds true for management, but does certainly not apply to agile processes. So what is the difference between a manager requesting to be updated every day and a daily standup, you may ask. Well the standup is the spreading of information you as a reporting team member think is noteworthy for all (!) other team members. Reporting to your manager socio-dynamically is a totally different process. There is an imbalance in power right from the setting. The (im)balance of power in conversations in general is a topic worth its own post.

The undercover expert

This is a special type of micromanager. All by themselves they think that they can accomplish the technical problems better than anybody else because there is nothing they love as much as fiddling with details and technology. The result is that they work in their company not on their company. And since they are the A-Team, made of Chuck Norris and MacGyver in one person, their idea of management is to control everything. But they will think of this as support for their coworkers. Such a manager also will never give up control, even if they try. This is because stepping out of the way because they trusts someone else to do the job is not part of their worldview.


To pay too much is bad, but to pay poor is even worse. So don’t pay less than average. In fact if you like to get excellent work pay more than average. Money is not everything but money can be a form of appreciation. Or rather paying poor wages is a sign of lack of appreciation. If you now say that you cannot afford to pay competitive wages then you perhaps should not hire additional employees. This sounds sharp but if your revenue is so bad that you need to cut on the wages maybe your business model or way to manage is not optimal. The laws of economy say that you can not expect to buy an excess of any good by paying poor. If you pay poor for any deliverable you have to add for the risk to do so. If you take that into account you have a bit more margin to pay.
And while we’re at it: don’t work with a bonus system. Bonuses have shown to be counterproductive to create engagement with a company or job position. Most people will do anything to comply to the results defined in their agreement on objectives. This includes taking shortcuts that in the short term work to reach a goal but might harm your company if it is not the intended way to reach the goal.


Be authentic. Don’t try. Be it. If you commit to deliver something do so. If you give deadlines or goals, be specific. „Somewhere around March“ is not a date. “Monday, the 25 March at 10am“ is a date. If you define dates or deadlines, take a note and write a mail so everyone knows and can keep track. If you give positive feedback, do so in public, if you have to express criticism, do it in a one-on-one. Meetings with two or more managers and one employee are not feedback but a jury. Avoid this setting in any case. You remember my remark on imbalance of power from above?


While we are at one-on-ones: it’s typical german management style to have exactly one annual personnel talk. This meeting is thought to contain feedback, objectives, personal growth (if this is a topic at all) and salary. That’s a lot of stuff that should not be combined and once a year is too infrequent. There are two main reasons why managers avoid one-on-ones.
First they might think they have not enough time for every coworker to meet once a week or biweekly. This signals „you are not important to me“. And let’s just do some math. The typical management span in a tech company is somewhere up to 10 team members. 10 conversations of half an hour make 5 hours. Every two weeks which has 80 hours if your full time week has 40 hours. That’s not very much for one of your core tasks.
Or they are afraid of the talk. Being afraid to talk to colleagues may have one of two reasons. One is you might be afraid of what you will hear. The other is that you just don’t like to communicate so much at all. If one of those applies to you you might want to think about your job choice. There is a swabian proverb: „Not beaten is praised enough“. Don’t live by that. It never has been true.

Photo by Freepik

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Why I don’t like hackathons

Disclaimer: I never took part in a hackathon for the reasons I explain here. So all views are deduced from observing those events and their outcome.


Under which conditions would you say a software project goes bad? Let me gather some I really dislike:

  • We’re in a hurry i.e. we are short in time
  • We are actually so short in time that we can’t complete each and every task
  • We don’t have a good set of requirements
  • Outputting something counts more than doing it right (“quick and dirty is better than nothing”)
  • We have so much work to do that we ignore our health condition (at least for some “sprint time”)

Nearly every condition I mentioned above can be found in a hackathon. Because it is a challenge. Work fast, achieve most. And who attends a hackathon? Young people. Developers starting their career in tech.

What do they learn in a hackathon? Work fast, complete as many tasks as possible. Doing it quick and dirty is perfectly OK. If you don’t have a specification for a task, guess whatever you think will work and implement that. It’s OK to sit 3 days (and I mean DAYS including the nights) in a room and hack right away, ignoring how your body feels.

NO. Just fucking no.

I don’t want people to learn that quick and dirty is the standard way to do things! I don’t want them to learn that scarcity in manpower, time and quality is perfectly acceptable!

To be clear: in every project I worked there are phases when time is short and we needed to do something quick and dirty. But I always try to implement things the best way I can.

“We need people who can fix things quickly!”

Training people to quick and dirty as a standard might be exactly what the organizers aim at. I prefer my coworkers to learn to do things first right and then fast. And to find shortcuts where needed.

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WoC: Detect non-iterable objects in foreach time to time we use iterable objects, arrays, objects implementing the \Traversable interface (which iterators also do).

In the old days, real programmers didn’t check for data types, they set sort of canary flags like this:

$obj = SOME DATA;
$isarray = false;
foreach ($obj as $item) {
    $isarray = true;
    // do some stuff like output of $item
if ($isarray === false) {
    // didn't get a data array, so output some default message

This implements some sort of foreach {…} else {…} construct that PHP doesn’t have. Sure, it works. If you don’t turn on warnings, because if you do, you will be overwhelmed by warnings about foreach trying to act on a non-array/iterable object.

There is a solution: test your data type before running into the foreach! Yes, this means if you can not be 100% sure what sort of object you get, you have to enclose each and every foreach with an if construct. Since PHP 7.1 this block can make use of the is_iterable() function, which returns true, if the given parameter is any kind of iterable object:

$obj = SOME DATA;
if (is_iterable($obj)) {
    foreach ($obj as $item) {
        // do some stuff like output of $item
} else {
    // didn't get a data array, so output some default message

For me this looks much better. The result is the same, but without warnings and the purpose is directly intelligible. The former example needs some thinking about the wtf factor of the code.

For PHP versions below 7.1 you can use some sort of polyfill function:

if (!function_exists('is_iterable')) {
    function is_iterable($obj) {
        return is_array($obj) || (is_object($obj) && ($obj instanceof \Traversable));

Thanks to the commentators of the PHP manual for this hint.

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Same procedure as last year: switch dates for daylight savings time in PHP

The same procedure every year: part of the world switches to daylight savings time (DST). Don’t get me started on the topic if there is any sense in doing so. We have to deal with it.

There certainly are functions that deliver the correct time for the current timezone. But what if you would like to know the switch dates in advance? The rule for DST switch dates in Germany is quite simple:we switch to summertime at 2:00 in the morning on the last sunday in march and back to wintertime at 2:00 in the morning of the last sunday in october. So these dates are variable.

Here the PHP DateTimeZone comes to the rescue. The steps are simple enough:

  1. Get a DateTimeZone object for the timezone you’re interested in
  2. Get the transition dates for the year you are interested in by specifying the start and end dates to search for
  3. Clean up the returned array, since it always contains the start date itself

And here is a short piece of code to use:



// Get start and end date to search for

$timezone = new DateTimeZone("Europe/Berlin");
$transitions = $timezone->getTransitions($t1, $t2);

// Delete first element since getTransitions() always returns 3 array elements
// and the first is always the start day itself


The result is an array with two elements. For 2018 in Germany the results are:

    [0] => Array
            [ts] => 1521939600
            [time] => 2018-03-25T01:00:00+0000
            [offset] => 7200
            [isdst] => 1
            [abbr] => CEST

    [1] => Array
            [ts] => 1540688400
            [time] => 2018-10-28T01:00:00+0000
            [offset] => 3600
            [isdst] =>
            [abbr] => CET