Sql Database Design Pattern Framework
Sibyl Piccuillo
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As already mentioned in a comment, the GoF patterns are likely all in use in the .NET framework. Where is not exactly the easiest to answer as the framework is massive and unless MS publishes as such listed in some of the examples given it is not always obvious. The more familiar one is with a pattern the more likely you would notice a framework class that was employing it.
Additionally, dofactory has a for sale kit ($79-99) that is about teaching how to use/implement GoF patterns in .NET BUT they do list on the reading they will also explain where MS uses them in the Framework.
CQRS stands for Command and Query Responsibility Segregation, a pattern that separates read and update operations for a data store. Implementing CQRS in your application can maximize its performance, scalability, and security. The flexibility created by migrating to CQRS allows a system to better evolve over time and prevents update commands from causing merge conflicts at the domain level.
In traditional architectures, the same data model is used to query and update a database. That's simple and works well for basic CRUD operations. In more complex applications, however, this approach can become unwieldy. For example, on the read side, the application may perform many different queries, returning data transfer objects (DTOs) with different shapes. Object mapping can become complicated. On the write side, the model may implement complex validation and business logic. As a result, you can end up with an overly complex model that does too much.
There is often a mismatch between the read and write representations of the data, such as additional columns or properties that must be updated correctly even though they aren't required as part of an operation.
Having separate query and update models simplifies the design and implementation. However, one disadvantage is that CQRS code can't automatically be generated from a database schema using scaffolding mechanisms such as O/RM tools (However, you will be able to build your customization on top of the generated code).
For greater isolation, you can physically separate the read data from the write data. In that case, the read database can use its own data schema that is optimized for queries. For example, it can store a materialized view of the data, in order to avoid complex joins or complex O/RM mappings. It might even use a different type of data store. For example, the write database might be relational, while the read database is a document database.
If separate read and write databases are used, they must be kept in sync. Typically this is accomplished by having the write model publish an event whenever it updates the database. For more information about using events, see Event-driven architecture style. Since message brokers and databases usually cannot be enlisted into a single distributed transaction, there can be challenges in guaranteeing consistency when updating the database and publishing events. For more information, see the guidance on idempotent message processing.
The read store can be a read-only replica of the write store, or the read and write stores can have a different structure altogether. Using multiple read-only replicas can increase query performance, especially in distributed scenarios where read-only replicas are located close to the application instances.
Messaging. Although CQRS does not require messaging, it's common to use messaging to process commands and publish update events. In that case, the application must handle message failures or duplicate messages. See the guidance on Priority Queues for dealing with commands having different priorities.
Eventual consistency. If you separate the read and write databases, the read data may be stale. The read model store must be updated to reflect changes to the write model store, and it can be difficult to detect when a user has issued a request based on stale read data.
Collaborative domains where many users access the same data in parallel. CQRS allows you to define commands with enough granularity to minimize merge conflicts at the domain level, and conflicts that do arise can be merged by the command.
Task-based user interfaces where users are guided through a complex process as a series of steps or with complex domain models. The write model has a full command-processing stack with business logic, input validation, and business validation. The write model may treat a set of associated objects as a single unit for data changes (an aggregate, in DDD terminology) and ensure that these objects are always in a consistent state. The read model has no business logic or validation stack, and just returns a DTO for use in a view model. The read model is eventually consistent with the write model.
Scenarios where performance of data reads must be fine-tuned separately from performance of data writes, especially when the number of reads is much greater than the number of writes. In this scenario, you can scale out the read model, but run the write model on just a few instances. A small number of write model instances also helps to minimize the occurrence of merge conflicts.
The CQRS pattern is often used along with the Event Sourcing pattern. CQRS-based systems use separate read and write data models, each tailored to relevant tasks and often located in physically separate stores. When used with the Event Sourcing pattern, the store of events is the write model, and is the official source of information. The read model of a CQRS-based system provides materialized views of the data, typically as highly denormalized views. These views are tailored to the interfaces and display requirements of the application, which helps to maximize both display and query performance.
Using the stream of events as the write store, rather than the actual data at a point in time, avoids update conflicts on a single aggregate and maximizes performance and scalability. The events can be used to asynchronously generate materialized views of the data that are used to populate the read store.
Because the event store is the official source of information, it is possible to delete the materialized views and replay all past events to create a new representation of the current state when the system evolves, or when the read model must change. The materialized views are in effect a durable read-only cache of the data.
As with any system where the write and read stores are separate, systems based on this pattern are only eventually consistent. There will be some delay between the event being generated and the data store being updated.
The pattern adds complexity because code must be created to initiate and handle events, and assemble or update the appropriate views or objects required by queries or a read model. The complexity of the CQRS pattern when used with the Event Sourcing pattern can make a successful implementation more difficult, and requires a different approach to designing systems. However, event sourcing can make it easier to model the domain, and makes it easier to rebuild views or create new ones because the intent of the changes in the data is preserved.
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