Getting data out of your system

We have data. It needs sharing.  What are the options?

One must assume that the data is required, so sharing it is necessary.

We could share the database: I’ve written about burning my fingers on doing this as well as the drawbacks of doing it if you’re forced to.  So what are other options?

If we do not provide SQL as our API, then we need to build something to provide that.

We could build an HTTP API to be called.  This API could provide the state of the data now, or we could attempt to provide a solution that provides a flow of events.

We could write data to another database and that is the contract that we share.  This still provides SQL as the API but at least the main application database can change at will as long as the contract into the secondary database is maintained and supported.

We could event the data and another service could listen to the events, reads the data and makes it available in another database and that is the contract with the consumer(s).

It is inevitable that something needs to write the data somewhere for the other application that wants it to gain access to it.  It will depend on what that application is as to what makes the most sense. But something will need to be built to provide the data.

There are many solutions that will hopefully keep your system changeable.  Find one that does not require large amounts of up front work, continuous maintenance, and provides the feedback needed to tells us if we are breaking our consumers.

Advertisements

What are the drawbacks of database integration?

The simplicity of integrating at the database is very appealing.  I’ve been draw into the trap of being caught out and getting my fingers burnt in the past.  So what are the real drawbacks?

TL;DR

  • Why isn’t this the norm for all 3rd party integrations? Those reasons all apply.
  • Avoid writing from multiple applications
  • Experimenting to learn is different to maintaining something long term.
  • Be aware that sharing the entire database is exposing the entire database as the public API.
  • Not knowing what you can change freely introduces friction.Reducing friction is a Good Idea.
  • Provide feedback to know when something has changed that should not have. Contract style tests may help.
  • Take steps to limit the exposed API/contract. Agreeing to share only specific tables may help. Maybe only share tables with a certain prefix.

Why isn’t this the norm for all 3rd party integrations?

Start with thinking about why we generally expose APIs instead of providing SQL database access directly.  All of those reasons will be present whether providing the integration point to a 3rd party company or to any other team – internal or external.

Below are some thoughts on what could be some of those problems.

Avoid writing from multiple applications

There are many patterns that try to help avoid updates from disparate places.  OO discourages direct updates through encapsulation – you cannot touch my data except through my interface.  In DDD you can use the aggregate pattern in order to help reason about changes to data which must all go through the Aggregate Root. The functional solution is to make everything immutable.  When using a pattern like Master Data – we’re trying to solve this problem by making an application / service the single source of truth.  All of these mechanisms are intended to help us reason about software. Software that writes to the database from multiple different codebases can be very hard to reason about.

Writing data from multiple applications violates the Don’t Repeat Yourself principle.  Knowledge about the tables, their defaults and their validations all need to be shared across multiple applications.  It is also hard for a developer to discover that a second application is writing to the table.  Doing a search in the repo they are working in will not find the access.  This can result in applications writing different defaults, validations, data types or not being aware of new columns or the meaning of new data.

A mitigation to this is to build a gem to share.  This is great until we do not keep all the applications on the same gem version and therefore we still have the same problems.  We need to lock step deploy every application using the gem to keep them in sync.  We now are building a distributed monolith.

What about read only – surely that is fine?

Maybe.

If you’re doing it to experiment and learn if something (like a 3rd party tool) is valuable, then definitely learn with direct integration.  After we’ve learnt, consider what the long term implementation is.  The pain that grows with this solution is in the long term maintenance and changeability of the solution, not the short term win.

For a long term solution, if you never change the database schema at all, then this is the quickest, easiest and – if you’re 100% right – most painless mechanism.  If you’re wrong, you’re probably in for pain.  How much pain depends on how rapidly things need to change and potentially how much the consumer is actually interested in.

The biggest problem is that we don’t know what will happen in the future, so assuming that we will never change the database in the future may always be a naïve choice.

What’s the big deal?

When you share your database you share your entire schema to be coupled to.  This is like exposing all of your data via an API where the API is every single SQL query that can be made.  Any change that is made to the schema can impact the consumer. If you give the entire schema that means you may need to ask permission for any change to your database schema as you do not know if the change you are making will impact the consumer.

The database schema is the API contract

Any shared API is a shared contract that the team owning the API guarantees to keep for all consumers.  When changing the API there needs to be negotiation and planning.  When we provide our database to another team to consume, the entire database becomes the contract that we guarantee to maintain. And any changes to that contract must be negotiated.

Why is that bad?

If we need to ask permission to change something it will introduce friction.  We need to remember to talk to another team. We then need to actually talk to the other team.  Other teams have their own priorities.  Given the best will in the world, communication still takes non-zero time.  And it will need to happen for every single change we make.  This slows us down, particularly when we’re making a lot of changes.

Friction slows us down.  Can we solve that?

The friction around needing to ask permission can introduce several responses

  • We could start to work really, really hard to not change data in the database.
  • We could start to think really, really hard up front before we build anything.
  • We could start to think about mechanisms that can reduce the number of things we need to communicate about.

The first two options introduce negative feedback cycles.  We spend more time in order to achieve them instead of less. And, given that things will change and we can’t know everything up front, we may make even more of a mess of the data when optimising for them.

Communication is important, so making sure the things that we’re communicating about are valuable is useful.  It keeps the signal to noise ratio high.  Starting to think of ways to reduce the number of things we are required to communicate about is useful, especially when that does not increase the time spent doing the new thing.  Ideally we should only communicate when the consumer must change.

Can’t we mitigate these problems?

We are smart developers, of course we can introduce mitigations!

We can introduce rules – like adding columns is always allowed.  If we are allowed to add columns that is one change we don’t need to communicate about.  This is a net win as most likely the consumers would generally not notice additive changes. We need to jointly make this rule as it is plausible that consumers could notice and fail.

The consumer could document what they are using.  If we can find out what is being used, then we can self-serve.  The only catch is – what if it isn’t up to date. Humans are fallible so this may happen. This solution moves the work that was done by the publishing team in needing to communicate to the consuming team as they always need to document.  But we’re still doing that work.  A failure may result in a negative feedback cycle – be better! Document more!  Spend more time achieving something that will fail any time a human forgets.

Any mitigation that does not involve feedback from code / tests will potentially fail at some point.  If we can introduce automated mechanisms that tell us that we’re breaking something we could get ahead of that.  Automation does not forget.  If it is setup to run, it will run every time.  If we can get automated feedback, then we know that we’re impacting and can get ahead of the game.

Contract Tests

A good mitigation might be to come up with solutions similar to those for HTTP APIs – perhaps some of the contract testing ideas could help.  For contract tests, the consuming team writes tests that test the API how they use it.  The team exposing the API cannot change these tests.  Their system needs to keep them alive.  This is a great feedback cycle for exposed APIs.  And then the two teams can negotiate about how their usage can change.

The negative part about defining a contract on the base schema of a database is that it represents the base model that we’re trying to build and invest in.  This is where we’re experimenting and learning internally to the team and the system.  Having that coupled to an external consumer makes that experimentation harder

Is there a better way?

Could we define the contract explicitly?  Could we make it work like an HTTP API?  At that point potentially all the rules and expectations that we have around HTTP APIs come into play.

An option could be to populate tables or provide views that the consumer uses. The internal team does not use these tables or views but signs up to them being the API contract for the consumer. This means that we can now apply all the standard mitigations around APIs that we are exposing outside of our system.  This becomes a known space to work in.  This does not make it easy, but it does define the deliberate place where we can support the integration to our database while allowing us to retain control and freely refactor the rest of the database as we wish.

A key idea around refactoring is to build the simplest solution that we can safely refactor out of.  Coupling to a defined, contained interface as a contract feels like a solution that can enable safe refactoring of the underlying code design while keeping the defined contract working.

What does this solution lead to?

Suddenly we have reduced the scope of questions we need to ask when we change the database.  We now know we are fine if the change in the database does not relate to one of the contracted tables / views.  Suddenly a whole category of friction is removed. We can write tests to give us feedback when we are accidentally changing those tables or views.  We do not use them in our systems so we have no reason to change them unless we are changing them at the request of the consumer. Another radical idea would be to use the Dependency Inversion principle and allow the consumer to define what the data should look like.  It is for them after all.

This sounds like a lot of work.  Can’t we just use database refactoring techniques to solve this?

The mechanisms for database refactoring are awesome.  They make your database more fluid just like code refactoring makes your code more fluid.

When you own all of the code, code refactoring flows in small steps that allow you to incrementally deploy software over and over again.  It is an awesome engineering feat.

When you own all of the database, database refactoring allows changes to the database to happen in small steps that allow you to incrementally deploy changes across the software and database over and over again.  It is another awesome engineering feat

Code refactoring slows down and has friction when the edge of the code that you are refactoring is shared by another consumer.  For instance an HTTP API or a class exposed by a Ruby Gem. The steps to quickly change the system run into friction around communication, planning and the potential that you might need to wait a long time before you can finally remove the interim code that is helping make the progression from one form of system to another

Database refactoring slows down and has friction when the database that you are refactoring is shared by another consumer.  The steps to quickly change the system run into friction around communication, planning and the potential that you might need to wait a long time before you can finally remove the interim code that is helping make the progression from one database design to another.

Database refactoring is really useful for helping understand how to change a legacy database shared by another consumer out of your control.  It moves it from static and scary to change, to slow moving and more malleable.

Database refactoring is really useful for helping speed up your changes in data when you own the whole stack.  It allows you to incrementally change the whole system in a far more fluid way.

Database refactoring doesn’t solve the communication and waiting overheads that come with co-ordinating with other consumers.  You can only clean up your refactoring as fast as the consumer follows your changes.  You still need to communicate about every change in case it has impact or introduce mitigations for that as described so far.

Another problem… data is not always meaningful without code

The data in an application’s database might need code to make it mean something to the business domain.  Keeping this in sync across multiple applications accessing the database directly can also lead to pain and violates DRY.  Deliberately providing data that has meaning is far more useful – whether that is directly into a database tables where that interpretation is potentially set or published out in an API that uses the code to do the interpretation.  The temptation here is to introduce a library to share to make sense of the data in the database.  But then we have to ensure every consumer is using the same version of the library…

Change is inevitable, how fast you embrace it is up to you.

I work in fluid environments that accept that code will change.  I accept that we don’t know everything.  We will change the systems we build as new knowledge and designs emerge.  Any team that needs to co-ordinate with another team to ask permission to make a change slows down.  There will be friction in refactoring at the shared edge of our systems.  I prefer to live in hope that we will be able to make more and more changes, to experiment and innovate with new products and ideas simply because we choose not to embrace solutions that will slow us down. Solutions that continue to be reoccurring work aren’t great.  Solutions that stop us from having to do a certain category of work speed us up. Work not done is time available to do other useful work.

Given a new or evolving data schema and database level integration – make sure you keep aware of the drawbacks and protect yourself.

  • Reduce the scope that is consumed.
  • Increase the feedback around what is consumed.
  • Inspect and adapt the pain that you do experience.

Hopefully these thoughts will help some others to avoid getting as many fingers burnt as I have in the past.

 

Building a learning culture

Over the last couple of years, I’ve been focusing on how to push the skill / ability level of the teams that I’ve been working in.  For the last little while that has been focused across multiple teams.

Doing this from inside a team is “easy”.  Time can be spent pairing with anyone willing to pair with me.  To inculcate an attitude of understanding, of questioning, of challenging, and of trying new things to understand what it could look like.  In my experience, keeping an open mind and having respectful conversations enables much learning and growth in any developer – which in turn is more valuable for the company. And I keep on learning new things from developers of every experience level along the way.

Things are different when faced with the challenge of growing a learning culture across multiple teams and not being in any given team.  The following are some of my thoughts and experiments around building a learning culture in an organisation of 4 teams and growing.

Why promote a learning culture?

The software industry has a shortage of skills. It always seems hard to find good software developers to hire.

We have an ever-growing pool of new developers.  A quote going around is “Every 5 years the number of programmers in the world roughly doubles.  So half the programmers in the work have less than 5 years’ experience.” (1)

We are in an industry with a wealth of knowledge that can be consumed – but practical application is needed to truly understand the nuances.

We are in an industry where the learning gained from experience does matter.

Axiom: Experience is valuable.

However

“True learning involves a permanent change in the way you see and act in the world.  The accumulation of information isn’t learning.” – Benjamin Hardy (2)

True experience is more valuable

True experience involves a permanent change in the way you see and act in the world. Experience is not truly valuable unless it is learnt from.  True experience is most valuable when it can be understood in terms of principles and values that were effective (with a good experience) or were broken (for a bad experience).

Experience should be viewed around a common understanding of the values and principles being applied.  The values and principles should be based on the needs of the organisation.  Experience should be respected, discussed and challenged in line with these values and principles.

How can we harness experience to speed up learning?

Given that experience that we learn from is valuable.  How do we better harness the true experience in any given room / team / company?  How can we learn from our experiences and share those learnings with those who have not yet had them most effectively?  How can we extract the years of learning out of the experienced developers’ heads so that we don’t need to have 10+ years to learn it?

What about shared values and principles?

Hypothesis: Teams that value the same things in software will build software more effectively.

Lemma:
If we know vaguely* where you are going
We can all pull vaguely in the same direction

*Vaguely is important.

If we get too precise it limits a team’s ability to innovate and effectively solve the real problems they face.
If it is too ill defined, the teams have no direction and can waste effort duplicating work or going in different or unexpected directions.

Lemma:
Any decision we make should be based on a mental model that can be expressed.  If you can’t express the reasoning, then the reasoning is flawed.

If you don’t like something in a code review – understand why.  If you can’t express the value or principle that is being violated maybe you don’t know why and are just being opinionated.  Understand your opinion first, before expressing it.  It may be that the values and principles are still being met, just in a different way that you aren’t used to.

We need to move from conversations about how to conversations about why.  What are the intentional trade-offs and design decisions that are taking us in this direction or got us here? The how is important – but driven by a deep understanding of why.  This allows us to ensure there is no cargo culting of solutions and no sacred cows being ignored. How do we elevate the conversation from technology details to software truths?

Build a collaborative learning culture.

If we are self-aware and understand the decisions that we are making, then we can discuss these decisions with our teams and build a common understanding of what is a good decision for the team.  The team needs / context outweighs the individual’s needs.

If the team can understand the decisions it is making collectively, then teams can discuss their decisions with other teams and we can build a common understanding of what is a good decision for the organisation.  The organisation’s needs and context outweighs the teams’.

Context

It’s hard to justify why something is good or bad without context.

Without context – if the software does what was asked for, then it is good.  It doesn’t matter if it is spaghetti code.  It doesn’t matter if it is unmaintainable.  It doesn’t matter if it is inefficient.  Being right enough matters for now.  If the software never changes then it is good.  If the software changes, then we might have wanted to optimise for changeability and the current code is no longer good (enough).

Some things that we have tried

My focus has been on building a common understanding of why we do the things we do.  This provides a space for communicating opinions / values / ideas and increases understanding between the things that developers are valuing.

We tried

  • Code appreciation / Code review sessions
  • Code kata and conversation sessions
  • Kata sessions on Friday morning
  • Coaching – though harder to do cross-team
  • Retrospectives – though usually less focused on code

We introduced guilds to encourage cross-team conversations around specific topics.  These included: architecture, continuous delivery, security, databases.

We have an active tech blog, containing knowledge that is useful to remember or to share cross-team – e.g. security fixes made / to be aware of, continuous delivery pipelines of different teams, architectural knowledge base.  This is also a knowledge repository for learning so that new hires can start to get the context of what the rest of the team has already learnt and what their values are.

Code appreciation / code review sessions

This fluctuated through many different forms.  From once a week rotating through a different developer each week across all teams, to sharing just in your team and discussing and then a less frequent cross-team sharing session.

Code kata and conversations

1 hour a week facilitated session on different software topics ranging from TDD to DDD, from SOLID to testing practices and design patterns.  The focus is on practices and discussing and understanding the values and principles that elevate from the exercises.  This is usually in pairs or small groups working on a problem and then we retrospect on learnings as a group.  This started with doing some katas to explore certain ideas but then moved on to many different things and possibly shouldn’t have ‘kata’ in the title any more.

Learnings

We have been successfully changing the conversation from right and wrong and syntax to values and principles and design.  This has been noticeable across the teams.

When we agree on the values and principles, “right” and “wrong” become much easier to articulate.

I have relearnt that not everything is a teachable moment.  Different sessions have different focuses and sometimes in a group environment attempting to ask too many probing questions can be intimidating.

Not everyone will engage.  The key is to ensure that enough do and we focus on what the company needs from building a stronger learning culture and a strong software development team.  Hopefully the rest will pick up from the majority.

Where to from here?  Building a more collaborative learning culture

Everything that we’ve done so far has been to enable a collaborative learning culture.

But it has been focused on bringing the group involved up to a common level and I feel that we are now at a place with enough people engaged and interested.  We need to become truly collaborative in our learning.  This year I hope to see more presenters, more sharing and more growth and understanding across the teams.

The end result will hopefully be a competent, young team that can collaborate effectively around shared values and a common understanding about how to experiment and learn and discuss the right solutions for the organisation.

References:

(1) https://twitter.com/web_goddess/status/804452382536912897) – attributed to Robert Martin. Martin Cronje at Agile NZ 2016 quoted a similar figure.

(2) Via a great presentation by Katlyn Parvin – https://speakerdeck.com/katlyn333/am-i-senior-yet-grow-your-career-by-teaching-your-peers

(3) It looks like Martin is doing similar things to what we’re attempting to do (after moving to NZ) – https://speakerdeck.com/martincronje/agilenz-towards-mastery-establishing-craftsmanship-culture-in-a-team

Know where you’re going

A core idea in agile software development is that we don’t know enough right now so we should only build enough for what we actually do know right now.  This is sensible.  This idea underpins evolutionary / emergent / test driven design – allowing them to thrive.  If we can safely and confidently change the system in any direction from where it is now, then we are in a good place.  We are keeping the XP cost of change curve as flat as possible.

But the cost of change may become high if we need to continuously rework everything. If the knowledge is available to help make more informed design choices earlier, we should use it.

Know where the code is going

Good software developers are intentionally trying to make the code base better.  This is highly desirable.  Good software developers will refactor confidently and allow new designs to emerge.  When you have 6 good developers refactoring the same code base into what in their minds is the best solution it is plausible that you may land up with 6 very different designs going in 6 very different directions.  This might not be ideal.

What would make those 6 good developers into a great team is a shared understanding of how the code is being refactored so that we don’t land up with 6 different designs, but rather one collaborative design that everyone has contributed to – pulling it in the same general direction.

As a team, discussing about where we’re refactoring the code to is important.  A given pair might not reach the best design while working in this sprint and another pair may augment and grow that design in the next sprint.  If we share where we’re going, we can hopefully get somewhere in the region of the shared destination.  And we can more meaningfully converse about changes in design as the requirements change and agree on the next destination.

But don’t inhibit innovation and new ideas

The counter to this is that if a good developer is building something and they choose to build something different to the current design – maybe it is simpler, better, cleaner – this should not be inhibited.  Do not strive for uniformity and accidentally crush the very innovation that you need to channel from a good developer in order to have a great team.

Know where the business is going

A common way of breaking up stories is around the CRUD screens / actions.  It is sometimes easy for a Product Owner to define a series of screens that allow them to administrate some set of information. It is plausible that these screens are built without a conversation of why the screens are being built.  Possibly this is because the PO doesn’t yet know the exact details on how these things are going to interact.  But if this is the case, it is plausible that when the PO finally specifies how these CRUD things are going to interact in a meaningful way that the team could turn around and suggest that, based on how those screens have been built, that isn’t possible – or at least it is very hacky and we’re already building a legacy system with bad design.

Knowing something about where the business is going in the next sprint or 3 is useful.  Use it to track how the design is evolving and hopefully take this at least somewhat into account when designing the code.

But don’t future proof too soon

Don’t worry too much about planning for future needs that are trivial to add in a future sprint.  We don’t need to future proof the design.  We only need to know where it might go so that we can perhaps go there more easily later.  We need to know that the short/medium term goals will be supportable by the design we are creating today.  If it isn’t, it doesn’t matter too much, but the cost of change will probably go up if we need to massively refactor the entire system every sprint – so what can we do to help avoid that?

Know where the architecture is going

Systems are continuously evolving.  As architectural issues arise, an idea of what should be done to solve them should be discussed.  If we can agree on the direction that we need to move the overall architecture and understand what it could look like, then any team may be able to start building that within existing work that they have, in an opportunistic way.  If we have no clue where we would like to go then any team will probably continue to do exactly what they are doing now.

Alternatively, any team may start to innovate in different directions and again we may land up with the architecture of the system being pulling in several different disparate directions which may not be a beneficial in the long run.

If your teams discuss and plan what the architectural changes could look like, then we know where we’re going and we can start working out the baby steps to get there.  Those baby steps may be started to be done inside current work.  But without any clue of where we going we can’t even start to take any baby steps.

But don’t be too constraining

Architecturally knowing where you’re going is important – but the micro details shouldn’t be constrained.  It shouldn’t matter if we use redis or memcache in terms of an architectural solution.  Let the teams converse and decide as a group on the actual solution details when they are needing the solution and allow that to be based on the real experience and constraints they are being experienced in their teams.

Know sort of where you’re going

The precise destination isn’t important – the general one is. The details will vary.  The overview of the destination over the next 2-3 months hopefully will grow and evolve.  Despite that, hopefully it will still be comparable enough to call reasonably the same.

How abstract the destination is depends on the need.  For instance, it might be useful to sketch out the boundaries of several micro-services to have some idea of the destination and to give teams an idea of what they could break out of a larger system.  But don’t be married to those decisions if they turn out to be incorrect.

Use the knowledge just in time

Knowing the destination today should inform the code as a potential destination to refactor towards.  We shouldn’t be focusing solely on the road directly in front of our feet.  We should be looking up into the horizon and gleaning any knowledge we can of what is up ahead.

Knowing where we are going will allow the whole team to pull the code and system in a similar direction.

Knowing sort of where we are going should lead to an informed just in time decision about when to do something with that knowledge.

DDD, Aggregates and designing for change

I have been introducing several domain driven design patterns to the teams that I work with over the last while.  In doing so I have been struck by the repetition of a core principle for many of the DDD patterns.

An example – the aggregate pattern
The aggregate pattern focuses on the business domain, attempting to answer the desire for a software developer to work with fully formed domain objects that interact with each other in a meaningful way.

The aggregate pattern defines the aggregate as a combination of objects that interact as a single unit.  The aggregate is only interacted with via the root of the aggregate.  This ensures that the aggregate root can maintain the integrity of the whole aggregate.

If all interactions with the aggregate are via the root aggregate this means that we can control the implementation of the aggregate behind the interface of the root aggregate.  The aggregate should be designed as a cohesive unit and is decoupled from the rest of the system via the aggregate root’s interface.  The aggregate root’s interface is the API to the aggregate.

Tensions
The complexity is in keeping the aggregate small while useful.  Tension exists when the aggregate gets bigger. The aggregate interface can become complex due to the constraint that all access must occur through the root aggregate.  This tension may push one towards designing smaller interoperable aggregates rather than allowing a large ball of mud to be formed.

Unidirectional?
Just as a design choice could be to have unidirectional models; it may also be an interesting design choice to build an aggregate with unidirectional models – with the root aggregate being the model that knows about (potentially) all the child models.  This may be useful.

My personal preference is to try to keep things unidirectional for as long as it is sensible, but as soon as it isn’t sensible allow connections in both directions.  The aggregate root is controlling the access to the objects in the aggregate.  Allowing objects in the aggregate to know about each other bi-directionally increases the complexity of the aggregate as a whole but, assuming a reasonably small aggregate, the principle of small contained messes is still supported behind the interface exposed by the aggregate root.

The core
Circling back to the core principle for many of the DDD patterns – the factory pattern, the repository pattern and the aggregate pattern all define mechanisms for creating a well-defined interface and hiding the implementation details behind the interface.

The factory pattern provides a creational interface to build an object.  We get well formed objects from it and don’t need to know how they were formed.

The repository pattern provides an interface that abstracts away object storage / query.  We ask it a question and get objects back.  We tell it to persist something, and it happens.  We do not need to worry how.

The root aggregate in the aggregate pattern provides an interface that abstracts away the implementation of the aggregate.

These patterns make code simpler and reduce complexity by clearly defining what should go behind the interface and what should not.  These patterns allow the caller to not worry about the implementation details behind the interface.  All of these patterns support the idea of smaller messes.  If the implementation behind the interface is a little messy, it doesn’t matter, as long as it can be refactored safely later and the caller is not influenced at all.

Decouple the interface that the outside world uses from the implementation underneath.  And know why the code is placed behind the interface.  Design a cohesive unit behind the interface.  This is also how TDD encourages code to be written, assuming you can design the interface well.

In practice
When discussing domain driven design versus other design ideas and using test driven design to build software, there are often questions around which pattern to use or how they should interact.  These questions are often attempting to get black and white answers to a complex contextual problem that probably has many shades of grey in it.

What has struck me most about introducing DDD after introducing TDD and emergent / evolutionary design is that the core principle is to contain the messes behind the interfaces.  Have good interfaces and decouple them.  And ensure the implementation behind the interface is cohesive and can change freely as needed.  That is the core of software design and most patterns.  How do I change later?  How do I keep in control of the code so that when change comes (and it will come! Especially in unexpected ways) it is not accidentally impactful on the rest of the system.

Focus on embracing change
Worry less about the patterns, than what the patterns are trying to teach you.  Use the patterns, understand them, they are a language that can be used effectively among developers.  But the patterns are not the end game, the changeable system that they encourage is.