TL;DR

This is the end of mini series related to Top Domain Model. Let’s quickly go through all the topics we’ve covered.

Top Domain Models

1. In I’m temporal we’ve covered one of the most underused techniques in modelling. Adding an explicit temporal dimension to the model.
2. Reading that I’ve been pivoting all night long brings a lot of different questions about aggregates and modelling them from different perspective, selecting the most useful.
3. In behaviors, processes and reactions we observed that aggregates on their own are meaningless. Why one would capture all the events if none of them caused a reaction?
4. The last chapter reminds to not be so implicit and that capturing the same value twice or more sometimes is required to build a meaningful and useful model.

Summary

I hope you enjoyed these loosely connected articles about modelling. As always, it’s not about the most real models, but the most useful ones.

TL;DR

Make things implicit (more) explicit. This is the mantra we often hear, but when touching the reality of software development, often forget, because of the magic beauty of technology, tooling, etc. Should we repeat this, when modelling domains as well?

Auditing

It doesn’t matter whether you apply DDD or CRUDdy. It’s quite often to hear that a system should provide a some kind of audit. Then we provide a table like History or Audit, where all the “needed” data are stored. What would be the columns of this table? Probably things like:

• user
• operation
• field changed
• value entered

or something similar. Again, depending on the approach these data could flow through the system as some kind of tuple, before hitting the storage.

When dealing with a business process that uses an accountant id, just for registering who’s responsible for tax calculations, would you “reuse” the user from a tuple above, or would you rather create a variable/field that is named accountantId, even if the values in accountantId and user would be the same? What would be your choice?

Event sourcing

ProperyChanged, is one of the worst events ever. Yes, it’s so beautifully generic and so well describing the idea of event sourcing, grasping changes in the model. It can be gathered implicitly, just by comparing an object before and after applying an action. At the same time it’s so useless, as it provides no domain insight and could be blindly used in any case. This approach is often referred to as property sourcing and is simply wrong.

The power of event sourcing, or modelling events at all, is grasping real business deltas of the domain. No playing mambo-jumbo with the tooling and implementing the new minimal change finder. Being explicit is the key to win this, to design a better model.

Summary

There are many more examples of explicit vs implicit and abusing modelling by playing not with the domain but with the tooling. When modelling, analyzing, ask yourself these questions. Should I really reuse it? Should I really make it generic? Quite often the answer will be no, and trying to do this, might hurt your model, and effectively, you.

TL;DR

After pivoting two nights straight (here and here) it’s time to settle down and ask is it possible to stick just to aggregates to model your domain? Is it possible to make it any better with services or maybe another tool is needed?

Actions and state

Aggregates define the boundaries of transactions. That’s their primary target. They narrow down not only our thinking (in a positive sense), but also provide an ability for scaling out our model. If every aggregate sits in its own bucket, there’s nothing easier to save it as a document, or a stream of events within one partition. Their boundaries won’t be ever crossed. The last one is actually a wishful thinking. Who would like to transfer money without ever receiving them on the other side?

Actions and reactions

Think in processes. One account is debited, the other is credited. The second does not have to happen in the same transaction. Moreover, the second, in its nature, is a reaction to the first account being debited. In a natural world reactions often happen at the same time. Modelling them in this way (transactions across all the aggregates) might be more realistic but for sure, is not more useful. The more elements in the transaction, the higher probability of failing… and so on and so forth.

Aggregates + processes

The above is a description of a perfect couple. Aggregates and processes are two tools that you can use to model any domain. Actually, if you dared to say that you don’t need processes, I’d say that you could be wasting your time modelling a really boring and simple domain with a DDD tooling. From the tooling perspective, whether you want to event-source everything all the way or use good old fashioned orm with a message bus, it’s up to you. Just remember, that the true power in your model comes from combining aggregates and processes.

Summary

Modelling aggregates that just work (TM) is impossible. Any, even moderately complex domain contains a lot of reactions and processes that need to be captured in your model to make it usable. Next time, you think about an aggregate reacting to something, ask yourself, what kind of process is this, how should I name it, how to model it?

TL;DR

In the last entry we noticed that we can model out some problems by selecting a different aggregate an event belongs to. In this episode, we will revisit this idea by referring to the good old-fashioned Twitter and its timelines. How timelines are created? Is everyone treated equally? Is it only aggregates’ identities that should be pivoted or maybe our thinking as well?

Fans in, fans out

The model behind Twitter timelines is quite simple. It’s also different for people having 10 followers or 10 millions. The difference is the number of timelines that needs to be updated when Madonna writes a tweet or a @JustAnotherAccountLikingMadonna2019 retweets one of their favorite performer thoughts. Notifying 10 millions people is not that easy and takes a bit longer. Writing to 10 timelines is easy and fast.

Golden customer

A similar reasoning might be added to a golden customer profile or any kind of dimension that distinguishes an aggregate type in your model. Sometimes, a behavior will be slightly altered, sometimes it will be totally rewired. The most important idea is to don’t stick to the initial model. Let yourself ask important questions to distill and clarify it. To discover these golden customers that require different handling, to discover Madonnas that require cargo shipping to deliver tons of their tweets and so on and so forth.

A new type or not

Sometimes, this reasoning will bring you new types of aggregates, sometimes it will be a process based on a dimension distinguishing a Madonna-like aggregate instance. As always, every model will be wrong, but some of them will be more useful than others.

Summary

Don’t stick to the initial aggregate type. Ask more modelling questions, that can bring you new types of aggregates or processes depending on a specific dimension value of already existing aggregate. Pivot not only identities, but also your thinking.

TL;DR

This is the next entry in the Top Domain Model series of loosely coupled posts describing different modelling approaches. In the last one we covered a temporal dimension, it’s time to consider what is an aggregate and where an event belongs to.

Endorsements

We all love receiving endorsements on LinkedIn, don’t we? How would you model such a feature? How would you draw a boundary of the aggregate? Would one aggregate be sufficient enough? Let’s first reason about a single aggregate provided by a user.

Endorsements profile per user

You could easily imagine providing an Endorsements aggregate per user. It could be visualized as a REST subresource of a user like

/user/1231423/endorsements

This could be implemented in a simple way and would handle properly situations where endorsing somebody is not that frequent. In other way, that the probability of having two users endorsing the third at the same time is quite low. What if it was a famous CTO who just created his profile? What if the number of endorsements would be bigger that 100/s? This model would not hold? What would it then?

Pivot to the rescue

When modelling, answering what’s the subject and what’s the action is not that straightforward. You may say that:

An user is endorsed by another

which means that user’s endorsements are modified by another user. But you could also rephrase this to

An user endorses another

Which could be modeled as endorsements storing another users tags in YOUR aggregate. This model, even with a lot of people endorsing is easily scalable: everyone is applying endorsements for other users in their own aggregate. You won’t endorse more than 10 skills a sec, will you?

This approach requires to create a separate, reactive view applying all the events to the read side, so that the famous CTO gets their skills displayed. But this, as it can be eventually consistent, is much easier than overcoming a high throughput scenario for writes.

Summary

When dealing with high volumes of operations ask, if the model can be pivoted? Maybe, selecting another owner of business operation, or even, modelling a new type of an aggregate, can help you deal with requirements you need to satisfy.

TL;DR

This is the first from entries written under wings of Top Domain Model. Nope, it’s not a new TV show for domain driven modellers. It’s a series about domain driven modelling itself. Today, we’ll take a look at models and their temporal dimension.

Reservation

Imagine that you model a system for a major hospital. Unfortunately, the registrations are opened once a year, and multiple people want to register at the same time. It’s even worse, they want to register to get their procedure scheduled in a specific room that has all the needed tools. The first idea could be to model the room as an aggregate. You can imagine hundreds of people trying to update the same aggregate. It won’t work.

Temporal dimension

To address this you could introduce a temporal dimension of the room. Instead of treating it as one room, you could model it as a RoomDay, that represents a schedule for a specific day. This would:

• spread the saturation over multiple aggregates (possibly with a more less natural distribution)
• make the aggregate time-bound (once the day passes, whole aggregate can be archived, deleted)

You could chop even further if needed, nevertheless, adding a temporal dimension to an aggregate can easily help you to simplify challenges related to scale and long lifespan of aggregates.

Summary

When modelling an aggregate, that it’s going to be highly contended, ask yourself is it naturally temporal? Possibly it can be replaced by multiple aggregates, each assigned to one year/month/day.