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This chapter is from the book

This chapter is from the book

Rule: Design Small Aggregates

We can now thoroughly address this question: What additional cost would there be for keeping the large-cluster Aggregate? Even if we guarantee that every transaction would succeed, a large cluster still limits performance and scalability. As SaaSOvation develops its market, it’s going to bring in lots of tenants. As each tenant makes a deep commitment to ProjectOvation, SaaSOvation will host more and more projects and the management artifacts to go along with them. That will result in vast numbers of products, backlog items, releases, sprints, and others. Performance and scalability are nonfunctional requirements that cannot be ignored.

Keeping performance and scalability in mind, what happens when one user of one tenant wants to add a single backlog item to a product, one that is years old and already has thousands of backlog items? Assume a persistence mechanism capable of lazy loading (Hibernate). We almost never load all backlog items, releases, and sprints at once. Still, thousands of backlog items would be loaded into memory just to add one new element to the already large collection. It’s worse if a persistence mechanism does not support lazy loading. Even being memory conscious, sometimes we would have to load multiple collections, such as when scheduling a backlog item for release or committing one to a sprint; all backlog items, and either all releases or all sprints, would be loaded.

To see this clearly, look at the diagram in Figure 10.3 containing the zoomed composition. Don’t let the 0..* fool you; the number of associations will almost never be zero and will keep growing over time. We would likely need to load thousands and thousands of objects into memory all at once, just to carry out what should be a relatively basic operation. That’s just for a single team member of a single tenant on a single product. We have to keep in mind that this could happen all at once with hundreds or thousands of tenants, each with multiple teams and many products. And over time the situation will only become worse.

Figure 10.3.

Figure 10.3. With this Product model, multiple large collections load during many basic operations.

This large-cluster Aggregate will never perform or scale well. It is more likely to become a nightmare leading only to failure. It was deficient from the start because the false invariants and a desire for compositional convenience drove the design, to the detriment of transactional success, performance, and scalability.

If we are going to design small Aggregates, what does “small” mean? The extreme would be an Aggregate with only its globally unique identity and one additional attribute, which is not what’s being recommended (unless that is truly what one specific Aggregate requires). Rather, limit the Aggregate to just the Root Entity and a minimal number of attributes and/or Value-typed properties.3 The correct minimum is however many are necessary, and no more.

Which ones are necessary? The simple answer is: those that must be consistent with others, even if domain experts don’t specify them as rules. For example, Product has name and description attributes. We can’t imagine name and description being inconsistent, modeled in separate Aggregates. When you change the name, you probably also change the description. If you change one and not the other, it’s probably because you are fixing a spelling error or making the description more fitting to the name. Even though domain experts will probably not think of this as an explicit business rule, it is an implicit one.

What if you think you should model a contained part as an Entity? First ask whether that part must itself change over time, or whether it can be completely replaced when change is necessary. Cases where instances can be completely replaced point to the use of a Value Object rather than an Entity. At times Entity parts are necessary. Yet, if we run through this design exercise on a case-by-case basis, many concepts modeled as Entities can be refactored to Value Objects. Favoring Value types as Aggregate parts doesn’t mean the Aggregate is immutable since the Root Entity itself mutates when one of its Value-typed properties is replaced.

There are important advantages to limiting internal parts to Values. Depending on your persistence mechanism, Values can be serialized with the Root Entity, whereas Entities can require separately tracked storage. Overhead is higher with Entity parts, as, for example, when SQL joins are necessary to read them using Hibernate. Reading a single database table row is much faster. Value objects are smaller and safer to use (fewer bugs). Due to immutability it is easier for unit tests to prove their correctness. These advantages are discussed in Value Objects (6).

On one project for the financial derivatives sector using Qi4j [Öberg], Niclas Hedhman4 reported that his team was able to design approximately 70 percent of all Aggregates with just a Root Entity containing some Value-typed properties. The remaining 30 percent had just two to three total Entities. This doesn’t indicate that all domain models will have a 70/30 split. It does indicate that a high percentage of Aggregates can be limited to a single Entity, the Root.

The [Evans] discussion of Aggregates gives an example where having multiple Entities makes sense. A purchase order is assigned a maximum allowable total, and the sum of all line items must not surpass the total. The rule becomes tricky to enforce when multiple users simultaneously add line items. Any one addition is not permitted to exceed the limit, but concurrent additions by multiple users could collectively do so. I won’t repeat the solution here, but I want to emphasize that most of the time the invariants of business models are simpler to manage than that example. Recognizing this helps us to model Aggregates with as few properties as possible.

Smaller Aggregates not only perform and scale better, they are also biased toward transactional success, meaning that conflicts preventing a commit are rare. This makes a system more usable. Your domain will not often have true invariant constraints that force you into large-composition design situations. Therefore, it is just plain smart to limit Aggregate size. When you occasionally encounter a true consistency rule, add another few Entities, or possibly a collection, as necessary, but continue to push yourself to keep the overall size as small as possible.

Don’t Trust Every Use Case

Business analysts play an important role in delivering use case specifications. Much work goes into a large and detailed specification, and it will affect many of our design decisions. Yet, we mustn’t forget that use cases derived in this way don’t carry the perspective of the domain experts and developers of our close-knit modeling team. We still must reconcile each use case with our current model and design, including our decisions about Aggregates. A common issue that arises is a particular use case that calls for the modification of multiple Aggregate instances. In such a case we must determine whether the specified large user goal is spread across multiple persistence transactions, or if it occurs within just one. If it is the latter, it pays to be skeptical. No matter how well it is written, such a use case may not accurately reflect the true Aggregates of our model.

Assuming your Aggregate boundaries are aligned with real business constraints, it’s going to cause problems if business analysts specify what you see in Figure 10.4. Thinking through the various commit order permutations, you’ll see that there are cases where two of the three requests will fail.5 What does attempting this indicate about your design? The answer to that question may lead to a deeper understanding of the domain. Trying to keep multiple Aggregate instances consistent may be telling you that your team has missed an invariant. You may end up folding the multiple Aggregates into one new concept with a new name in order to address the newly recognized business rule. (And, of course, it might be only parts of the old Aggregates that get rolled into the new one.)

Figure 10.4

Figure 10.4. Concurrency contention exists among three users who are all trying to access the same two Aggregate instances, leading to a high number of transactional failures.

So a new use case may lead to insights that push us to remodel the Aggregate, but be skeptical here, too. Forming one Aggregate from multiple ones may drive out a completely new concept with a new name, yet if modeling this new concept leads you toward designing a large-cluster Aggregate, that can end up with all the problems common to that approach. What different approach may help?

Just because you are given a use case that calls for maintaining consistency in a single transaction doesn’t mean you should do that. Often, in such cases, the business goal can be achieved with eventual consistency between Aggregates. The team should critically examine the use cases and challenge their assumptions, especially when following them as written would lead to unwieldy designs. The team may have to rewrite the use case (or at least re-imagine it if they face an uncooperative business analyst). The new use case would specify eventual consistency and the acceptable update delay. This is one of the issues taken up later in this chapter.

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