The shared kernel relationship is a bounded context relationship that involves two or more bounded contexts sharing a common codebase, such as libraries or databases. The shared kernel relationship implies that the shared components are an essential part of the core domain and therefore must be consistent across all the contexts that use them.

In other words, the shared kernel relationship is a way to collaborate between multiple teams or contexts while ensuring that the core domain components are always in sync. It is typically used in situations where there is a high degree of interdependence between multiple contexts or when developing a new system from scratch.

The shared kernel relationship has both advantages and disadvantages, which should be carefully considered before adopting it in a project. Some of the benefits of a shared kernel include reduced development time, improved consistency, and a higher degree of collaboration. However, it can also lead to tight coupling between different parts of the system, which can make it difficult to maintain and evolve over time. It may also require significant coordination between teams to ensure that changes to the shared kernel are properly managed and communicated.

Implications

The shared kernel relationship in DDD refers to the situation where two or more bounded contexts share a common subset of the domain model, and they agree to collaborate on the maintenance and evolution of that shared part.

Pros

• Avoiding duplication of effort and reducing complexity by reusing a common model across multiple contexts.
• Promoting consistency and a common language across different teams and systems, reducing the risk of misunderstanding and misalignment.
• Facilitating integration and interoperability between systems, since they can share the same model and data structures.

Cons

• Tight coupling between contexts, since changes in the shared model can have ripple effects across different systems, making it harder to evolve them independently.
• Potential conflicts between teams, since changes to the shared model may require coordination and consensus, which can slow down development and reduce autonomy.
• Complexity and maintenance costs, since the shared model may need to be more generic and flexible to accommodate multiple contexts, which can make it harder to understand and modify.

Therefore, a shared kernel relationship should be used carefully and only when the benefits outweigh the drawbacks, and when the teams involved have a high level of trust and collaboration.

Example

Let’s consider a hypothetical example of an e-commerce platform that has two bounded contexts: “Ordering” and “Inventory Management”.

In the “Ordering” context, the system handles the creation and management of orders, and in the “Inventory Management” context, the system manages the available inventory for products. Since both contexts deal with product information, it might make sense to establish a shared kernel relationship between them.

With a shared kernel relationship, the two teams responsible for each context can agree on a common set of data models and business rules related to products, such as the product catalog, pricing, and stock levels. Both teams would have access to this shared kernel and would be responsible for maintaining it.

This approach can be beneficial because it allows the teams to work independently on their own bounded context while ensuring that they are using the same definitions and rules for product-related information. However, this relationship can also create a strong coupling between the two contexts, which can make it difficult to make changes without affecting both contexts.

For example, if the product catalog needs to be updated, both teams would need to be involved in the change. Additionally, if one team makes a change to the shared kernel that affects the other team, coordination and communication would be necessary to ensure that the change is properly integrated and tested.

Therefore, a shared kernel relationship can work well when the two contexts have a high degree of interdependence and there is a need for shared data models and business rules. However, it should be used with caution and only when necessary to avoid creating unnecessary coupling between contexts.

When does it work?

For a shared kernel relationship to work effectively, the following conditions need to be met:

1. The shared code should be small and focused: The shared code should only contain domain elements that are genuinely shared between the bounded contexts. If the shared code becomes too large or complex, it can cause issues for both bounded contexts.
2. The shared code should be stable: Any changes made to the shared code should be done in a way that doesn’t affect the other bounded contexts. This means that the shared code should be well-tested and stable, and changes should be made carefully.
3. There should be a high level of trust between the teams: Since the shared code is used by multiple teams, there needs to be a high level of trust and collaboration between them. Any changes made to the shared code should be communicated clearly to all the teams involved.
4. The teams should have a good understanding of each other’s domains: The teams involved in the shared kernel relationship should have a good understanding of each other’s domains. This helps to ensure that the shared code is used appropriately and that changes to the shared code are made in a way that is consistent with the overall architecture.

When does it not work?

The shared kernel relationship can be risky as it tightly couples the two bounded contexts together. Therefore, if one bounded context changes the shared kernel, it can have a significant impact on the other bounded context. This makes it challenging for the two teams to work independently, which may cause friction and slow down the development process. Therefore, the shared kernel relationship should only be used when the two bounded contexts have a strong affinity, and both teams are working closely together. In addition, it is critical to have clear agreements and processes in place to manage changes to the shared kernel.