DDD EU 2022

Here are my notes from DDD EU 2022. It was a great conference this year with a focus on models, growth, managing complexity and dealing with legacy.

Will definitely review these notes in the near future and see how I can apply them to my work day to day.

Opening Keynote


  • Andrew Harmel-Law
  • Diana Montalion
  • Mike Rozinsky
  • Gayathri Thiyagarajan

The advice process:

  1. When working on something, get advice from two groups:
    1. Those affected by the decision
    2. Experts in the area
  2. Then it's up to you to decide

The goal is to free up decision bottlenecks from making decisions and to democratise architecture.

No checks, no barriers, whatever you decide goes.

"Lead by example" vs "exert control".

Discussed amongst ourselves: Would this work or fail? Why?

We realised that most of us already followed this process to some degree. The more senior someone is, the more they would seek advice but ultimately the decision would be theirs.

A great mindset for growing self reliance and independence, a bad mindset for those used to a standard tayloristist model, i.e. "I decide, you do".

Would involce a junior being able to release whatever they want without review, however, they would have the choice to wait for an approval before release.

Kind of breaks the PR approval process, must have a review beforehand, but given the nature of PRs, if the PR is the first time someone is seeing the concept, then they're not likely to spot bugs anyway.

Requires trust and psychological safety.

Residulaity theory

Speaker: Barry O'Reilly

Software is Ordered (predicable, static) Business processes and people are disordered (unpredicable, adaptive)

The focus os this theory is on the software side,

Where did this talk come from?

  1. Some architects consistently design systems that can handle unknown stressors
    • I.e. they build resiliant systems
  2. Overall industry success rate of building resiliant systems is 17%

What are these architects doing that others aren't?

We can handle some of the known unknowns, but those aren't what breaks your system, it's the unknown unknowns that getcha.

Figure out the knowns and known unknowns:

Requirements gathering is like throwing darts at a wall with a circle somewhere in it.

  • Sometimes we hit the circle and that's requirement for change
  • Other times we hit outside and that's something that should stay the same

I.e. a random simulation of the environment => requirement gathering

Every system has attractors, be these for success paths or failure paths

  • See chaos theory

We design systems areound happy path attractors and ignore failure path attractors

No wonder our systems are brittle

A connected network has square root of N boolean states

Kauffman networks: NKp N = Nodes K = Connection count p = ... I'll get back to you on that one

We need to make the simulation more random

Residue: What's left over when a stressor breaks a system

Building a resiliant system steps:

  1. Map out what parts of your system work and what breaks under a stressor
  2. Build up a map of residues for different stressors and attractors
  3. Try to merge and build a system that can handle all

Handling one stressor tends to handle many due to nature of attractors

There is coupling we cannot see until a stressor affects a system This coupling is called "Hyperliminal coupling"

Testing your resiliancy:

  1. Take a naive system (N)
  2. Create a stressor list
  3. Use half to create residues and design a robust system (R)
    • Dont need to build it just yet
  4. Use other half to verify if system R can handle stressors better than N


R = Residue count X = New stressors handled by N Y = New stressors handled by R

      Y - X
Ri = -------

Ri > 0 = System can handle the unknown

This is called residual analysis

Important caveat: Only works for software, not suitable for organisations and people.

Speaker has a PHD based on this very topic

DDD at Scale

Speaker: Xin Yao https://speakerdeck.com/xinyaodk/domain-driven-architecture-at-scale-ddd-eu-2022

Chief Architect at Deutsch Bank (I think)

They spotified and inverse conwayed

Issues withg compounding complexity:

  • Reshuffled teams
  • Complex domains
  • Legacy systemns

Noticed a recurring problem due to self organised teams They'd optimised the parts (teams) not the connectons (relationships)

    (t)   <--- Good :)
   /   \
  /     \ <--- Bad :(
(t)     (t)

Alignment does not necessarily bring coherence

Common purpose can be an invisible leader

Value Bets:

  • North Star
  • Inputs

Works Bets:

  • Opportunities
  • Implementations

Value bets are strategic and high level

(This is their own language for describing this process)

Basically a hierarchy: North Star => Inputs => Opportunities => Implementations

Involves implementors in North Start and Inputs collection

Opportunities are solution agnostic

Software at Scale suffers from temporal anomalies:

  • Language drifts
  • Models fragement

When strategising think of your vision of the future through a future cone.

We can see the proable, plausible and the maybe

Strategic goal: Can we improve our odds of deciding and acting with better correspondance to an emergent reality?

Revenge of Conways law:

  • Shipping org charts
  • Empowerment not helping

Problem: Teams have different initiatives affecting the same solution space Result: No collaboration

Goal: How do we add our change with reducing optionality for other teams operating in the solution space?

Being a maker is a multplier

  • We can improve things and increase momentum

Functional Aggregate Design

Speaker: Thomas Ploch https://slides.com/tploch/what-is-a-ddd-aggregate

Process theorises why something happens Variant theorises how something happens

Process is temporal Variant is state based

Four types of process theory:

  1. Evolution: Change through time
  2. Dialectic: How power shifts and causes change (e.g. politics)
  3. Telelogical: Plan of action
  4. Lifecycle: End state and how to get there

Aggregates are experiments into implementing a process theory

State machines have limitiations:

  • No parallel processing per aggregate instance (sync)
  • Does not support random ordering of messages

Transducer (new term) take state and output events

Types of objects:

  • Commands
  • Behaviour
  • Invariants
  • Events
  • Policies

Distributed Pain


  • Sara Pellegrini
  • Milan Savić

Mostly a talk on async messaging problems and strategies.

Avoid building a distributed system for as long as you can, they open up many can of worms.

Discussed various techniques:

  • Retry
  • Circuit Breaker
  • Idempotence
    • Command ID when theres no aggregates

Most of this is already written in patterns around distributed systems, talk is more of primer in the concepts.

Two types of solution:

  1. Technical
  2. Design

Design is the best place to fix, if you can design your system so the problem doesn't exist then you're in a better position.

Be evolution ready:

  • Stay responsive
  • Accept responsibility
  • Embrace failures

Critically Interacting with Models


  • Mathias Verraes
  • Rebecca Wirfs-Brock

Take three business models:

  1. Hierarchical

    _|_   _|_
    | | | | | |
  2. Social Network

    /       \ /
    (5)       (6)---(7)
  3. Value creation

    [Work] => €€€

All three have different belief systems and are potentially imcompatible.

  1. Hierarchical believes that top down is the ideal structure for how decisions are made
  2. Social networks believe that talking fluidly and empowering individuals leads to better systems
  3. Value creation believes that value creation is king and everthing else is in service

All models have belief systems. When incorporating different models watch for inconsistencies and address them.

The status quo is that adopted models are assimilated without much thought Examples: Agile, Scrum, Safe, Lean, etc...

Models mess with you, we start thinking of the models as reality and not, well, a model that's missing details

There's no such thing as a pure model, no such thing as a pure abstraction.


  • Compare models to figure out what each adds or omits, emphasises or downplays
  • Does it address or solve problems you are interested in

Switching between org models comes with risks Beware of reorganisation fatigue!

Avoid Model Fitting:

  • Trying to generalise two models is often a bad idea
  • It always feels possible, but you often lose key details that makes the models unique
    • I.e. you strip the models of the very things that make them useful

If you care about something have a building block for it

Steps for working critically with models:

  1. Scrutinize multiple models
  2. Adapt the model to your context
    • Requires a redesign, not just a nudge
  3. Gather feedback and reshape

Fractal Geometry of Software Design

Speaker: Vladik Khononov https://speakerdeck.com/vladikk/fractal-geometry-of-software-design

Energy supply systems are the foundation for all growing systems, be they organic or artificial

These are systems sustained through energy supplying a hierarchical network E.g.

  • Bodies
  • Cities
  • Companies
  • Trees

There is a direct correlation between size and energy consistent across systems

Energy is abstract and depends on the context.

Knowledge is the energy supply of software design.

Four types of knowledge coupling:

  1. Implementation: Not intended for inter component intergration
  2. Functional: Business logic, use-cases
  3. Model: Data structures, core components of functional
  4. Contract: APIs

In energy systems growth is not linear, systems usually get more efficient as they grow

This is called sub-linear growth E.g.

  • Metabolic rate by mass
    • Energy requirement grows slower than size

Systems also have super linear grwoth, were growth takes more energy each time

Very few systems have linear growth between size and energy, most are sub linear or super linear

Growth increases the efficiency of all systems, whether they're beneficial or not

In software this manifests as:

  1. Components (modules) grow sub linearly
  2. Connections between components grow super linearly (complexity)

NB: Cognitive load limit is static, no growth

Once knowledge complexity surpases cognitive load we can no longer understnad the system and change risk increases super linearly

Most energy systems follow fractcal topolology networks I.e. there is self similarity between the different levels of the system

Also called the "Self similarity principle"

Key examples:

  • City roads
  • Tree
  • Vascular system

Strenth (S) is the amount of shared knowledge Cost of change is proportional to the Distance (D) between components

Low Distance x Low Stength = Low complexity High Distance x Low Stength = Loose coupling Low Distance x High Stength = High cohesion High Distance x High Stength = Global complexity

The last one is obviously bad and massively increases cognitive load, that's why we avoid it

Expressed as an equations: M = S^D

High strength should have low distance

  • aggregate or module
  • High cohesion

Low strength should have high distance

  • System to system
    • Bounded content to bounded context
  • Low coupling

How Complex Software Impacts Cognitive Load

Speaker: Corstian Boerman

Cognitive ability:

  • Senses
  • Thoughts
  • Experiences

Mental wellbeing is the no.1 priority ✙

For a system to function it must accomodate the weakess link in the chain O-O-O-O-O-o-O

Rights cannot exist without responsibility

Surplus of cognitive capacity improves resiliance

A system running at maximum efficiancy cannot handle change, it will implode

Expections > Abilities = Burnout

  • Overtime this will decrease cognitive ability
    • Positive feedback loop can occur further destroying it

Cognitive depletion is an occupational hazard

How to handle it:

  1. Recude code complexity
  2. Recude cognitive load

Closing keynote


  • Indu Alagarsamy
  • Olivia Cheng

When system complexity bleeds over we lose site of the user, get stuck focussing in the details

We need to center the uner in everything we build, it exists to serve their needs, no matter how far away

If it's not solving a problem for the user then it's not worth it.

We don't build resiliant system for fun, we do it to enable customer success.

I also spoke at DDD EU this year on Using Test Pain as a Design Guide. I don't have notes for that talk, since that would just be the script I wrote for it. :D

Here are the slides though incase you're interested, it got some very positive feedback: https://slides.com/barryosull/using-test-pain-as-a-design-guide-c930e9


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