Surely, you’ve heard the fairytales from microservices and monoliths. Or on a similar note, the tales about distributed (big) balls of mud from people like Simon Brown.

Usually these posts point out what goes wrong and how unexperienced teams go for a "hype-driven/tunnel-vision architecture". But how do you actually cut microservices? How do you design interfaces? What are techniques to find weak points in your application or system architecture?
In this post I digest the intends and views of a talk on the software architecture summit in Munich.


Herbert Dowalil @hdowalil on Twitter
Stefan Zörner @STefanZoerner on Twitter

Microservices vs. Monolith

The spectrum of architecture definitely isn’t as binary as most blog posts suggest it is. There are way more types, here is a short recap of some of the most famous ones.


There are different definitions out there, but most of them share key points like a domain-driven modules cut, outstanding flexibility and network distribution. One (generally accepted) definition in a FAQ-style by Jimmy Bogard.

Self-contained Systems (SCS)

Similar to microservices, but usually bigger services and fewer in a system.

Deployment Monolith(aka. Modulith)

You have a valuable product, but it does not suite a SaaS business model? Most of your customers won’t offer a Kubernetes clusters or you have a hard time building a secure deployment pipeline? This does not stop good architecture! You can still cut your software in great modules and deploy it as one package.

Architecture Monolith

Such a monolith has no defined structure. It consists of modules, but they are referenced across the whole system. Looks like this:


A blueprint of doing application architecture to generate standard services showing actual business processes steps. The main benefit should’ve been the orchestration of services to full processes and composition of new processes.
While SOA might have failed for most, it is "survived by its offsprings" (Anne Thomas Manes: SOA is Dead; Long Live Services). For a comparison between SOA and microservices see this O’REILLY report.

All these types focus on cutting a big software block into modules. Microservices for example focus on the smallest autonomous boundaries, SOA on reusability and composition. Because of this, the following ideas ‘how to define modules’ can be used for microservers, but don’t stop there. You can use them to define Java packages, C++ namespaces or create C# assemblies.


Often we start with question groups like "How many parts do I need?" and "Where do I cut?"

Sometimes we don’t even know if we should initially start with a monolith (Martin Fowler: MonolithFirst) or with microservices (Stefan Tilkov: Don’t start with a monolith). We can answer this particular question now: it doesn’t really matter when designing modules and interfaces. When you need a push for microservices think about upcoming complexity. The speakers phrased it nicely:

Modularization makes complexity manageable. Investment at beginning makes complexity manageable at the end.

For the first questions, we follow an enhanced version of the SOLID criterias by Robert C. Martin. Herbert Dowalil calls them "5C". Such principles sound nice, but what does strong cohesion or lose coupling mean?

"If you can’t measure it – you can’t manage it"

The key here is using "old school" metrics like cyclomatic complexity or average relative visibility. That’s it; here are the 5C:


Cut your modules in a way where they only have one concern/a single responsibility. Try to maximize cohesion inside a module.
Metrics: e.g. LCOM4, Relational-Cohersion, cyclomatic complexity


Hide the internals of a module inside. Nothing outside the module needs to know about the technologies used or any work (changes) done inside.
Metrics: e.g. low relative visibility, low average relative visibility and low global relative visibility


Design small, specific and easy to understand interfaces between modules.
Metrics: e.g. Depth-of-Inherence


Explicitly declare every connections between modules.
Metrics: e.g. RACD and NCCD from John Lakos. Stability from the software package metrics.


Build new modules by connecting modules together. Move from a lower level of modules to a bigger view on the system. For higher level, modules follow the same patterns as for lower level ones.
Metics: e.g. automated tools like ArchUnit, Sonorgraph or Teamscale

For a deeper dive, see the "Architektur Spicker" (GER only) and follow @Herbert Dowalil on Twitter for updates on his new book.

Once you’ve cut your modules you can decide if you want to distribute them over the network. Pros can be:

  • Independent technological decisions:
    This reduces macro architecture from your software and lets you use specialized tools and languages for each module (like .NET for a web API and python for the underlying data science or spring boot for your user handling and c++ for a calculation core).
  • Technology roll-over:
    You can upgrade your technology components step by step. An upgrade from the Java or Node.js runtime might improve performance and eliminate security threads or you can change a module from an outdated version to the newest one (Angular.js to Angular). This can be useful when a system runs longer than anticipated.
  • Developer autonomy:
    Every part of your software can be developed and deployed independently. This shortens feedback cycles and enables developers to evaluate ideas faster.

While this sounds awesome (and it is!), there are also downsides:

  • Troubleshooting and debugging:
    Following calls and processes through your distributed software is harder than debugging a single component. Tracing makes troubleshooting easier, but replicating an error state across your whole application can be hard.
  • Complicated Ops:
    Operating your software can be way harder. For example, there might be issues with creating secure pipelines and authorization concepts through company boarders.
  • Consistency:
    There are ways to build around eventual consistency and distribution and sacrifice strong consistency. Examples would be the 2 phase commit (but you shouldn’t) or the saga pattern. While these are working and are proven successful, the initial set up is harder and they certainly don’t simplify troubleshooting.

As usual, this trade-off needs to be evaluated individually.


Modularization is hard. If you decide to go with microservices or any other way, doesn’t really matter in this context. You can distribute your modules to enforce principles, but if you cut your modules wrong, things will only get harder.
Don’t be afraid of "old school" or "university-style" metrics. Identify metrics with your team. Collaboratively search for weak points in your software (architecture). Enforce selected metrics, but never let a metric break the build and never dictate metrics top down!


Das Microservice-Praxisbuch by Eberhard Wolff


Software systems architecture by Nick Rozanski and Eoin Woods

Microservice Patterns by Chirs Richards

[Modulith First Der angemessene weg zu microservices]() by Herbert Dowalil

Architektur Spicker #8

Microservice FAQ by Jimmy Bogard

SOA is dead; long live services by Anne Thomas Manes

What distinguishes a junior developer from a senior and the senior from a software architect? This is a commonly asked question and there are plenty of very good sources out there. One argument can be found on every blog post about this topic: a junior mostly makes small decisions and consumes knowledge. With the level of seniority, the level of decision making and knowledge sharing increases. This is why we often find people like "advocates", "fellows" or "heroes" on conferences and summits.
I went to one of these summits (Munich Software Architecture Summit) and want to share my experiences about the sessions and talks here. Let’s start with the key note of the first day. Weiterlesen

Die Auswertung vom Windows Event Log auf Fehler und Muster darin, ist mit dem Event Viewer nur begrenzt möglich und sehr mühsam. Eine einfache Alternative ist der Export der Events mit der Powershell zu CSV und der Import mit Analyse in Power BI. Weiterlesen

Worum gehts?

Konferenzen und E-Books sind voll davon: Container. Wenn über Container in der Software-Entwicklung gesprochen wird, fällt es meist zusammen mit dem Wort Docker. Dieser Blog-Post beschäftigt sich mit Docker und den Containern und soll einen kurzen Überblick über die damit im Zusammenhang stehenden Begriffe geben. Weiterlesen

Powershell Skripte können je nach Anwendungsfall komplex werden. Daher sind auch für Robustheit und Qualität automatisierte Tests wichtig. Das Powershell Module Pester hat sich hierfür bewährt und lässt wenig Wünsche offen.

Powershell kommt aber auch häufig zum Einsatz, wenn es darum geht verschiedene Systeme oder Dienste zu integrieren, also für Dateiaustausch oder Deployment. Das Mocken von Funktionen kommt dabei schnell an Grenzen. Tests gegen existirerende Systeme sind nicht immer möglich oder schwer zu isolieren.

Eine mögliche Lösung ist hier Docker Container zu verwenden. Im Test-Setup können die benötigten Dienste und Server als Container instanziiert werden und im Tear-Down einfach wieder gelöscht werden. Zur besseren Integration von Powershell und Docker habe ich dazu PSDocker entwickelt. Weiterlesen

Azure Cosmos DB

Working with Azure Cosmos DB usually is a pleasure. The initial setup process is fast and easy, working with documents very intuitive and once you go to production you don’t encounter any scaling barriers. It might not be the cheapest database out there, but you get what you pay for. Weiterlesen

Since Powershell Core supports Linux, you might want to test your Powershell Module if it works on Linux as it works on Windows. You could setup a virtual machine, which is a litte time consuming and cumbersome. You could use a continous integration service like AppVeyor which does not allow to debug if the test fails.
Another option would be Docker. But anyway i tested the Windows Linux Subsystem (WLS) and it’s pretty good! Weiterlesen

DevOps findet immer größere Verbreitung bei IT-Projekten. Ob im StartUp, beim Mittelständler oder im Konzern-Umfeld, kann der DevOps-Modus für Projekte handfeste Vorteile bringen, die sich von Fall zu Fall leicht unterscheiden können. Sei es Strukturierung der Prozesse für kleine Projekte, wo Trennung von Entwicklung, Betrieb und Qualitätssicherung nicht wirtschaftlich waren. Oder sei es für große Projekte, in denen Planung, Kommunikation und Abnahmen zu größeren Zeitspannen zwischen Anforderung und GoLive führen und damit zum Projektrisiko wurden. Allgemein sollen sich Qualität, Geschwindigkeit und Soziales Klima verbessern. BI-Projekte nehmen hier häufig eine Sonderrolle ein. Das ist aber nicht notwendig.

Steffen Kampmann von der Tekaris GmbH hat dazu einen Vortrag bei der PASS Bayern gehalten. Der Vortag erklärt die zentralen Konzepte und Methoden von DevOps, die Besonderheiten in BI-Projekten, die technischen Lösungsansätze von Continuous Deployment in Microsoft SQL Server mit ihren Alltagsproblemen und einen Erfahrungsbericht.

Dazu sind auch Folien Github Repository der PASS Bayern veröffentlicht worden.