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· 3 min read
Alvaro Jose

Why would I do this?

Docker-desktop is a paid product, their licensing mode is by user, and it provides value not for the software side but for their cloud offering (registry, etc). For this, if the intent of you're a company is to use containers locally to facilitate software development, the cost tends to be high.

What is podman?

Podman (short for Pod Manager) is an open-source, Linux-native tool designed to develop, manage, and run containers and container images. It offers a Docker-compatible command-line interface (CLI) that does not rely on a daemon, but directly interacts with the Image registry, container, and image storage, and container process operations.

Migration Steps

1. Clean-up Docker Desktop (Optional)

you will need to run the next bash script


# Uninstall Script

if [ "${USER}" != "root" ]; then
echo "$0 must be run as root!"
exit 2

while true; do
read -p "Remove all Docker Machine VMs? (Y/N): " yn
case $yn in
[Yy]* ) docker-machine rm -f $(docker-machine ls -q); break;;
[Nn]* ) break;;
* ) echo "Please answer yes or no."; exit 1;;

echo "Removing Applications..."
rm -rf /Applications/

echo "Removing docker binaries..."
rm -f /usr/local/bin/docker
rm -f /usr/local/bin/docker-machine
rm -r /usr/local/bin/docker-machine-driver*
rm -f /usr/local/bin/docker-compose

echo "Removing boot2docker.iso"
rm -rf /usr/local/share/boot2docker

echo "Forget packages"
pkgutil --forget io.docker.pkg.docker
pkgutil --forget io.docker.pkg.dockercompose
pkgutil --forget io.docker.pkg.dockermachine
pkgutil --forget io.boot2dockeriso.pkg.boot2dockeriso

echo "All Done!"

2. Install Homebrew

Homebrew is the defacto command line package manager for OSX, if you don't have it is very recommendable to have it.

/bin/bash -c "$(curl -fsSL"

3. Install Podman

On Mac, each Podman machine is backed by a QEMU based virtual machine. Once installed, the podman command can be run directly from the Unix shell in Terminal, where it remotely communicates with the podman service running in the Machine VM.

For Mac, Podman is provided through Homebrew. Once you have set up brew, you can use the brew install command to install Podman:

brew install podman

Next, create and start your first Podman machine:

podman machine init
podman machine start

You can then verify the installation information using:

podman info

At this point, podman should have created a proxy file in /usr/local/bin/docker, if it does not exist you will have to create it with:

sudo vim /usr/local/bin/docker

add in that file the content:

[ -e /etc/containers/nodocker ] || \
echo "Emulate Docker CLI using podman. Create /etc/containers/nodocker to quiet msg." >&2
exec podman "$@"

the script needs to be made executable by:

chmod +x /usr/local/bin/docker

you should now be able to run a docker as normal

docker run -it

4. Use podman-mac-help

You should consider using podman-mac-help to migrate transparently to Podman on macOS.

  • Continue using familiar Docker commands.
  • Take advantage of the benefits of Podman on macOS.
  • Your tools, such as Maven or Testcontainers, communicate with Podman without reconfiguration.

The podman-mac-helper tool provides a compatibility layer that allows you to use most Docker commands with Podman on macOS. The service redirects /var/run/docker to the fixed user-assigned UNIX socket location.

To enable this, you just need to run:

sudo podman-mac-helper install

5. Install Podman Desktop (Optional)

Finally, to have a better compatibility and a UI to work with as with docker desktop, you can install Podman desktopby running:

brew install podman-desktop

· 2 min read
Alvaro Jose

As we develop a product over time, changes need to be made as we need to accommodate new functionality. As most of our systems don't run isolated, and we have clients that used them (ex. public API), We have to keep compatibility at least on a temporary basis. How do we achieve this?


A common practice is to have different versions for the multiple clients. While simple, it also requires significant effort to maintain as whenever an issue or bug is spotted, multiple places are affected, meaning there are more possibility of side effects.
It also makes it more difficult to make a case for clients to migrate from one to the other due to the contract changes.

This affect mostly negatively the next DORA 4 metrics:

  • Lead Time for change

Versionless: Expand & Contract

As the name says, this strategy intents to have only one state of truth and not a multitude of them. Versionless has been heavily adopted as a principle by GraphQL, for example.
We can achieve this in any code base by implementing a strategy for parallel changes called Expand & Contract, it's call this way due to the phases code goes through. Let's see for example we want to migrate from using one field value to a similar field with a more complex representation.

  • Expand: We add the new 'field' to the existing contract, and add the code to support this strategy on the existing code.
  • Contract: We monitor the usage of the old 'field' to understand when it is possible to deprecate, at that point we remove the old code.

With this, we have a clean source code that we can evolve indefinitely as required by the business.

This affect the next DORA 4 metrics:

  • ✔️ Lead Time for change

· 3 min read
Alvaro Jose

I have already written some other post on this topic. I will go straight to the point on comparing Git Flow (a legacy strategy that most companies use) and Trunk-Based Development.

Gitflow: The Bad & The Ugly

Why do I call it the bad and the ugly? Because it does not allow you to achieve Continuous Deployment.
The idea is that every developer works isolated on their branch, validate on their branch and ask through a merge request to add their code to the X stage branch.

There are multiple issues with this:

  • Code does not exist isolated, we don't deploy isolated code, so the isolated test is not valid as it will require retesting.
  • The peer review process happens at the end, causing a very slow feedback loop. Having to rewrite code that could be avoided.
  • The more time the branch lives, the more it diverges from the original behavior and the more complex it is to merge.
  • Merging can cause complex conflicts that require revalidation, and it might have side effect in other features.
  • As there needs to be validations of the merges, it's normal to have multiple environments that give a false sense of security, increases the $ cost and increases the lead time.
  • Egos and preferences become part of the review process, as it has become an 'accepted' practice that the 'experts' or 'leads' do the reviews.

All of this is red tape to go through is a problem that makes delivery slower, and create a lack of ownership mentality farther away from what happen to the individual branch.

This affects mostly negatively, most of DORA 4 metrics:

  • Deployment frequency
  • Lead Time for change
  • Mean Time To Recovery

Is there a simpler and better way to collaborate on code way?

Trunk-Based Development: The Good

What happens if we all commit to the same branch.

Most of the expressed issues are solved, in this scenario by:

  • Code is never isolated, as we all push code to the same place.
  • Teams that do this practices also practice pair programming, making the peer review process is continuous and synchronous.
  • As individuals push multiple times a day, merge conflicts are non-existent or small.
  • Does not require revalidation, as validation is a continuous stream in the single environment.
  • No ego environment tent to appear as there is no centralize approver of code, so it's not a matter of preference but a team effort and ownership.

As we have seen before, having unfinished code does not need to affect users, as it is common practice to use feature flags and/or branching by abstraction.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery


Simplicity is king. Having a simpler structure enables speed and quality of delivery, as it allow teams to work closely, take shared ownership and act faster related to a smaller change.

· 2 min read
Alvaro Jose

Before we enable code for our clients, we need to test and validate it does what is expected. This could be an entire series of its own (please let me know if you want one), so I will keep it on a high level.


I could probably spend hours sharing different types of testing strategies and where and why to use them.
In reality, the most important thing, is to make sure we use the correct ratio of the different types of tests, as it will highly affect the time and location of your testing.

This ratio has always been shown as a pyramid with:

  • Unit test: validate individual pieces of logic that are isolated.
  • Integration test: validates interactions with multiple parts of your system or other systems.
  • Integrated test: They test the system as a whole.

Tests are divided in these layers because there is a cost in time and complexity.

This affect the next DORA 4 metrics:

  • ✔️ Change Failure Rate


Validation differs from testing as it's the confirmation that the behavior is what the user expected, for now, humans are the only ones that can discern this.
As we have seen in the previous chapter, the recommendation is to do this in production, so you get:

  • Get real behaviors of interactions with other systems
  • Get real performance

This affect the next DORA 4 metrics:

  • ✔️ Change Failure Rate

· 3 min read
Alvaro Jose

Now that we know where our code lives, we need to make sure our users get access to the features. For this, we need to get our code to the environment we want to deploy to, and control the rollout (if you are not a big bang release fan).

Blue/Green Deployment: Getting to prod with 0 downtime

What is this?, The concept is simple, we have a set of machines (ex. blue) where we currently have our app running, and we want to deploy. The intent is to create a new set of machines (ex. green) where our new version of the code will run. We would like to validate as much as possible (ex. automated e2e tests) that this new version is up to par with the previous one before moving the traffic and destroy the previous version.

You can see the process in the next graph:

With this, we are trying to achieve a 0 downtime while deploying a new version of our code. This is critical for teams that practice continuous deployment, as you want to avoid having systems down as you deploy multiple times a day.

Enabling feature access to users

there are multiple ways to enable access to users, in between them:

Big Bang Releases

This is the plug and pray solution. Pushing the code and expecting it to work as it's enabled for all users. This is a very dangerous strategy as your blast radius is all your users.

Canary Releases

This is a practice that comes from the mining industry, The idea was the next one:

If a canary is in the same place where humans are inside the mine, when there is a problem with the breathable air it will be the first one to perish.

If we translate this to software, the idea is to have deployed the changes only to one or a few servers. With this, we can monitor this canary instances and act if any issue happens, we reduce the blast radius of issues to only the users who go through that server.

This affect the next DORA 4 metrics:

  • ✔️ Change Failure Rate

This approach provides us a way to reduce the blast radius from a big bang release. Nevertheless, it does not help us to prevent or act faster upon a bug in our code.

Feature Flag Releases

To improve upon the canary release strategy, we can move towards feature flags.

Feature Flags are hiding our code behind a 'flag' this can help decide if the code is enabled or disabled, as in the next image.

There are a multitude of services, libraries & SDKs that allow you to create flags in your code. They help by:

  • Decouple activation of features from the release pipeline.
  • Solving incidents in a matter of seconds.
  • Do a controlled rollout. For example:
    • Enable only for team.
    • Enable for X% of the traffic.
    • Enable for users in a specific country.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Mean Time To Recovery
  • ✔️ Change Failure Rate

· 3 min read
Alvaro Jose

Our services need to run somewhere, so our users can access it. It's a very common practices to have multiple environments like dev, staging, and prod. Is this actually a good practices?

CI vs. CD vs. CD

when people talk about continuous integration, delivery and deployment, they normally talk about it as a whole.

Nevertheless, let's reflect why these are 3 different practices. As they are steps in a journey, you can do one and not the next one.

  • Continuous integration: allows making reproducible states of the code in multiple places.
  • Continuous Delivery: Now that it's reproducible, it needs to be marked as potentially deployable and provide the ability to deploy it.
  • Continuous Deployment: Delivers the code to your clients and not only to your team as you commit.

The trap of Multiple Environments

As you can imagine, with the previous definition of CI/CD, having multiple environments will never allow you to achieve Continuous Deployment.

The intent of having multiple environments is to reduce change failure rate, are we actually achieving this with the practices? The answer is normally not due to:

  • A non-production environment will never be the same as a production.
    • Different data
    • Different performance
    • Different security practices
    • Etc…
  • Stress and ownership of moving things to production
  • Accumulation of code in lower environments (meaning more bugs).
  • Longer feedback loop.
  • Continuous misalignment due to development cycles in between different teams.

As you can see, this makes a fake sense of safety, but it does not affect positively the change failure rate.

This affects mostly negatively, most of DORA 4 metrics:

  • Deployment frequency
  • Lead Time for change
  • Mean Time To Recovery
  • 〰️ Change Failure Rate

Achieving Continuous Deployment, Only prod, is it so crazy?

How can a team Continuous deployment? The answer tends to be simple, making every commit go to production and testing in it.
Be aware this does not mean to have our users experience possible bugs or see test data, as we can hide functionalities behind toggles, headers, or parameters that allow access to only the development team. As we will see in future installments of this series.

An example strategy is the one in the next diagram.

This allows us to keep only one environment that discriminates in between test and non-test data that can be clean periodically, while it provides the real environment with the real behavior. With this, we solved:

  • Real performance & behavior.
  • Continuous alignment with other teams.
  • Smaller feedback cycles.
  • Control of rollout.
  • Smaller $ cost.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery
  • 〰️ Change Failure Rate


There is no one size fit all, but modern practices tend to go towards simplicity and fast feedback loops. There are many practices involved on this simplicity that enables us to feel comfortable with only production environments. We will talk about them on this series.

· 3 min read
Alvaro Jose

When we talk about observability, we talk about:

Capability of developers to understand the health and status of their application.

We don't want users or clients to be the ones noticing something is wrong. For this, there are multiple tools that fall under the observability category.



This is the first line of defense against issues, the intent is to get notified if any potential issue arises.
The intent of this is to provide a notification if any parameter of our application is out of range (ex. to many 5xx).

This allows us to use our mental bandwidth to focus in creating value and not continuously check if the parameters are in range.

This affect the next DORA 4 metrics:

  • ✔️ Mean Time To Recovery


As the name says, this is a set of measurements we track from our code, it allows us to understand the health of individual parts of our system.

This metrics are shown in dashboards that allow us to visually understand what is happening. We can divide metrics dashboards in 2 types:

  • Status: It will give us a really fast overview of the health of the system.
  • Details: It will not tell us what is wrong, but will provide more detailed information to dig deeper into a specific area.

It's important to not mix this 2 together, as they have different purposes. Like with alarms, it helps focus our mental bandwidth in the correct place.

As you see in the previous image, the left represents a detail dashboard that makes it difficult to know on a single view if there is an issue. For this, as in the image on the right, we have a status dashboard that in a single glance we can spot where to look next.

This affect the next DORA 4 metrics:

  • ✔️ Mean Time To Recovery


This is the lower level you want to go. It should tell you where in the code is your issue, so you can go and fix it.

When thinking about logging, it is significant not log everything. Due to the added noise that this can bring.

This affect the next DORA 4 metrics:

  • ✔️ Mean Time To Recovery


let's get practical on how would this work.

  • Implement your service
  • Create metrics and send them to your metrics system (ex. Datadog, Grafana)
  • Create logs and send them to your logging system (ex. Datadog, Kibana, CloudWatch).
  • Create dashboards:
    • Single Status dashboard. Use only simple boxes with green and red backgrounds that represent in one view the health of your system & subsystems.
    • Multiple Detail dashboards. Create a dashboard for each subsystem with as much data as necessary to understand where the issue is, so you can later pinpoint the root cause in your logs.
  • Create alarms based on the status dashboard boxes.
  • Connect your notification system (ex. Opsgenie, PagerDuty, Slack channel) to the created alarms, so you get push notifications as something goes wrong.

· 4 min read
Alvaro Jose

When we start our journey towards continuous integration & delivery, the first thing to take in count is the mentality. There are a few of them that will make or break our intent. Let's see the most important and also some practices.


You build it, you run it

create a DevOps culture, not a Devs vs Ops

This mentality is the idea that the same people who develop the software re in charge to maintain it in good health by observing it.

For many years, this was not the case. Operations & development were handled by different teams. This caused a dystopian situation where each group had a different goal:

  • Devs: deliver as fast as possible. By pushing code to production without observing the side effects of it.
  • Ops: keep system stability.

With the 'you build it, you run it' mentality, devs focus on their service or work, while Ops becomes a product team that focus on providing the correct tooling for Developers.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery
  • ✔️ Change Failure Rate

Embrace Ownership in Failure Culture

the problem is not breaking things, is the inability to recover from it

Normally, developers feel they need a safety net to feel comfortable to introduce changes to production, this tends to translate in delegating the ownership to others trough peer review or other validation step.
This lack of ownership have massive effects on the capacity to recover and the gates that code needs to go through, affecting the feedback cycle.

To improve this failure culture is necessary to promote this behavior, having no blame reduces the amount of stress people go through.

If something fails is not an issue of the individual but of the process itself.

Imagine that every commit goes to production, changes will be so small that fixing or rolling back can be done in minutes or seconds. At the same time, developers will be able to create the correct tooling to feel more comfortable with this practice.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery
  • ✔️ Change Failure Rate

Be a Boy Scout

Don’t continue the same path if you think something can be done better

As individuals, need to bring change to our products. If we see any new practice, tool, services… that can support the work of the team, bring it forward. Don't shy away because the team is currently doing it.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery
  • ✔️ Change Failure Rate

Learn & Adapt

Not everything is solved in the same way, don't follow:

If your only tool is a hammer then every problem looks like a nail

For this, learn and take your time for it. When you have a new problem, as it's possible, you don't have the correct tool in your toolbox.

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery
  • ✔️ Change Failure Rate


Firefighter Role

The firefighter role is a rotating role inside the team. They are responsible for being the first responder to incidents and helping solve them.
At the same time, to make sure this person does not suffer from cognitive load due to context switching, this person is not involved on the normal pair rotation and development tasks.
In exchange, they focus during the week in improving the specific tooling of the project (ex. DB migration tooling).

This affect the next DORA 4 metrics:

  • ✔️ Deployment frequency
  • ✔️ Lead Time for change
  • ✔️ Mean Time To Recovery
  • ✔️ Change Failure Rate

On Call Rotation

As the development team is also in charge of running the service, some of them will require after working hour support. On call is just this, the disposition of team members to take care of their services around the clock.
This tends to sound bad, but there are ways to not make this suck. I can't express it better than Chris Ford has already done in this page.

This affect the next DORA 4 metric:

  • ✔️ Mean Time To Recovery


These are the starting point to feel comfortable running things in production without the concern that any issue is a catastrophic thing. Failing is not an issue, the important part is to be able to recover as soon as possible from any problem that arises.

· 3 min read
Alvaro Jose

This is a series I am really looking forward to writing. I have been doing this presentation for the last 3 years in multiple places.

Am I Crazy?

The answer is no, most of the thing you will see on this series comes from practices derived from Extreme Programming, that show how to build quality and value into products. So bear with me for some time.


A few years ago, I read the book Accelerate that is derived of the analysis of the state of DevOps report that happens in a regular basis.

The book does not speak only about technology but also speaks about communication, organization, etc. And how this affects effectiveness in teams & companies. I recommend reading the entire book.

4 key metrics

Nevertheless, most of the people resume this book (erroneously) in the next table.

It does a comparison on a what are called the 4 key metrics, and provide a classification of performance (teams & companies, since 2017 this classification has evolved).

What does these 4 key metrics mean:

  • Deployment frequency: is how often does the team deploy to production.
  • Lead Time for change: is how much time does a story take to get to production.
  • Mean Time To Recovery: is how fast can we solve a production issues.
  • Change Failure Rate: is how frequently do we break things in production.

All this metrics is helping teams understand their feedback cycle and stability. In the case of the team, I currently work with:

  • Deployment Frequency: once per commit to trunk (while doing trunk-based development) what ends up translating to a few times per day.
  • Lead Time for change: below 1h. We can activate a feature as soon as the code is deployed by the CI/CD using feature flags.
  • Mean Time To Recovery: In minutes. We can activate and deactivate feature flags on the fly if any of the code breaks, and we have a good observability and alarming, so we are the first one to notice.
  • Change Failure Rate: We don't optimize for this, as MTTR is more important for us (I will explain why later). Nevertheless, we currently only had 2 minor production issues in the last year, so we are way below 1%. Our CI/CD validations help a lot on this.

The intent of this series is to share the Extreme programming practices that we use to achieve being on the elite classification of DORA 4.

Note of Caution

As this twitter thread shows, this is not one size fits all, the challenges of a team are not the challenges of another one. There is no silver bullet or common root cause to the issue, and each team should use this metrics to track improvements in an unbiased way. For this, the 4 key metrics do not mean anything at company level and should not be used to compare teams.


In the following installments, I will walk backwards from having something in production and how to keep it running in a healthy manner stress-free up to coding techniques that enable Trunk-based development.

· 4 min read
Alvaro Jose

In software development, over the last years we always talk about on cross-functional teams, as a good split of responsibilities to provide autonomy in teams. What does that mean? Why is this so? And what does it look like?

History & types of teams

It's probably easier to see the evolution of team culture as a chronology, as it has been an evolving thing.

Specialization-Based Teams

Traditionally, when we had only big monolithic applications, teams have been split by their expertise. This meaning all the quality assurance, Frontend, Backend roles will be in a team with their expertise-based peers. This might look like the next image:

What are the pros and cons of this model:

  • ✔️ Improve depth of knowledge from peers.
  • ✔️ No dependency on individuals, the Bus factor tends to be bigger than 1.
  • ❌ Bottlenecks in between teams, due to different priorities and timelines.
  • ❌ Lack of breath of knowledge.
  • ❌ Low domain expertise due to coverage of all domains.
  • ❌ Continuous context switch due to support of multiple domains.
  • ❌ Design issues due Conway's Law relation in between communication patterns and architecture.
  • ❌ Eventually, teams grow too big and have management issues due to Dunbar's Number on human relationships.

Specialized Cross-functional Teams

Due to the shortcomings of the previous model and the raise of microservices and some concepts from DDD, the intention of splitting teams was to make sure a specific domain and their solutions were cover by the same people.
This allows more independence and control over what is required to fulfill the needs of that domain.

This might look like the next image:

What are the pros and cons of this model:

  • ✔️ Common domain expertise, allowing faster and informed decisions.
  • ✔️ Single domain will not require a lot of context switch.
  • ✔️ Helps design on microservices environments due to Conway's Law.
  • ✔️ Teams tend to stay small and follow Dunbar's Number on human relationships (ex. Amazon 2 large pizza team size).
  • ❌ Bottlenecks in between team members, due to process dependency.
  • ❌ Lack of depth of knowledge from peers.
  • ❌ Lack of breath of knowledge being shared.
  • ❌ Bus factor tends to be small.

T-shaped Cross-Functional Teams

The previous organization helped many teams to be able to focus and do the right thing in the right moment.

Nevertheless, it lacked the focus on collaboration and support inside the team, as each person has their small set of responsibilities can easily cause bottlenecks inside a single team.

T-shaped development tries to solve this by making sure all team members can work in every part of the solution (represented by the horizontal part of the 'T'). Nevertheless, each member can have his own preferred field of expertise (represented by the vertical part of the 'T').
This has been enabled by the lower complexity on the tooling and entry-level learning curve to most of the expertises.

What are the pros and cons of this model:

  • ✔️ No bottlenecks as all team members can chip in to the different needs.
  • ✔️ Common domain expertise, allowing faster and informed decisions.
  • ✔️ Single domain will not require a lot of context switch.
  • ✔️ Helps design on microservices environments due to Conway's Law.
  • ✔️ Teams tend to stay small and follow Dunbar's Number on human relationships (ex. Amazon 2 large pizza team size).
  • ✔️ Shared tasks improve a team member depth of knowledge.
  • ✔️ Shared tasks improve a team member breath of knowledge.
  • ✔️ As knowledge is spread inside the team, the Bus Factor is not an issue.


Time has improved things for all teams, and we are probably not at the end of the transformation of teams. Nevertheless, it is good for companies and individuals to adapt to changes in the environment.