Cloud Native Database – This content is an excerpt from the eBook, Architecting Cloud Native .NET Applications, available in .NET Docs or can be read offline as a free downloadable PDF.
As we’ve seen throughout this book, a cloud-native approach changes the way you design, deploy, and manage applications. It also changes the way you manage and store data
Cloud Native Database
, business service components are integrated into a shared service layer and share data from a relational database
Distributed Principles In A Cloud Native Database
In many ways, a database simplifies data management Querying data across multiple tables is simple Update data together or rollback them all ACID transactions guarantee strong and immediate stability
Designed to be cloud-native, we take a different approach On the right side of Figure 5-1, notice how business functionality is broken down into smaller, independent microservices. Each microservice encapsulates a specific business capability and its own data Monolithic databases decompose into a distributed data model with many smaller databases, each parallel to a microservice. When the smoke clears, we come out with a design that makes an exposure
This database offers many advantages to microservices, especially for systems that need to evolve rapidly and support large scale. With this model…
Data segregation also enables each microservice to implement the type of data store that is best suited to its workload, storage needs, and read/write patterns. Choices include relational, document, key-value, and graph-based data stores
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Reliance databases remain relevant for microservices with complex data, while NoSQL databases have gained considerable popularity. They offer wide scale and high availability Their schemaless nature allows developers to move away from the typed data classes and architectures of ORMs that make changes expensive and time-consuming. We cover NoSQL databases later in this chapter
While encapsulating data into discrete microservices can increase agility, performance, and scalability, it also presents several challenges. In the next section, we discuss these challenges in discussing patterns and practices
While microservices are independent and focus on specific activities, such as inventory, shipping or ordering, they often require integration with other microservices. Often the integration includes a microservice
In the previous figure, we see a shopping basket microservice that adds an item to a user’s shopping basket. The data store for this microservice contains basket and line item data, it does not manage product or pricing data. Instead, those data items are owned by the catalog and pricing microservices This aspect presents a problem How can the Shopping Basket Microservice add a product to a user’s shopping basket when it does not have product or pricing data in its database?
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One option discussed in Chapter 4 is a direct HTTP call from the shopping basket to the catalog and pricing microservices. However, in Chapter 4, we discussed synchronous HTTP calls
We can also implement a request-response pattern with separate inbound and outbound queues for each service. However, this pattern is complex and requires plumbing to correlate request and response messages When this backend microservice dequeues the call, the calling service still has to wait for the call to complete synchronously. Network problems, transient errors, or an overloaded microservice and this can lead to prolonged and even failed operations.
Instead, a widely accepted pattern for eliminating cross-service dependencies is the materialized view pattern shown in Figure 5-4.
) in the shopping basket service This table contains a general copy of the data required from product and pricing microservices Copying data directly into the shopping basket microservice eliminates the need for expensive cross-service calls. By adding local data to the service, you improve the response time and reliability of the service Additionally, having your own copy of the data makes the shopping basket service more stable Should the Catalog Service become unavailable, this will not directly affect the Shopping Basket Service The shopping basket can continue to work with data from its own store
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Data replication is an established practice in cloud-native systems and is not considered an anti-pattern, or bad practice. Remember this
A data set can be owned and have rights over it You need to synchronize the read models when the system of record is updated. Synchronization is typically implemented through asynchronous messaging with a publish/subscribe pattern, as shown in Figure 5.4.
Querying data across microservices is difficult, executing a transaction across multiple microservices is even more complex. The inherent challenge of maintaining data consistency across independent data sources in different microservices cannot be underestimated. The lack of distributed transactions in cloud-native applications means you must manage distributed transactions programmatically. You leave a world
In the previous diagram, five independent microservices participate in a distributed transaction that creates a queue. Each microservice manages its own data store and implements a local transaction for its store To create order, local transaction for
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The operation must be aborted and rolled back While there is built-in transaction support available within each of the microservices, there is no support for distributed transactions that would span all five services to keep data consistent.
A popular pattern for adding distributed transaction support is the Saga pattern This is done by grouping local transactions together programmatically and sequentially If any local transaction fails, Saga aborts the operation and invokes a set of compensating transactions. Compensating transactions restore data consistency by undoing changes made by previous local transactions Figure 5-6 shows a failed transaction with the Saga pattern
The operation failed on the inventory microservice Saga calls a set of compensation transactions (in red) to adjust inventory counts, cancel payments and orders, and return data to a consistent state for each microservice.
Saga patterns are usually choreographed as a sequence of related events, or orchestrated as a set of related instructions. In Chapter 4, we discussed the Service Aggregator pattern that will be the foundation for an orchestrated Saga implementation. We also discussed the Azure Service Bus and Azure Event Grid topics in the evening which will be the foundation for a Choreograph Saga implementation.
Cloud Native Databases
Large cloud-native applications often support high-volume data requirements In these situations, traditional data storage techniques can be disruptive For complex systems that are deployed at a large scale, both command and query responsibility segregation (CQRS) and event sourcing can improve application performance.
CQRS, an architectural pattern that helps maximize performance, scalability, and security. The pattern separates operations that read data from operations that write data
However, a high volume data view may benefit from separate model and data tables for reading and writing. To improve performance, read operations can query against a highly unstructured representation of data to avoid expensive iterative table joins and table locks. The
, will update against a fully normalized representation of the data to ensure consistency Then you need to implement a mechanism to keep both representations in sync Normally, when the write table is modified, it emits an event that replicates the change to the read table.
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In the previous diagram, separate command and query models are implemented Each data write operation is saved in the write store and then propagated to the read store Note how the data persistence process ultimately works into the persistence policy The read model eventually synchronizes with the write model, but there may be some delay in this process We will discuss final stability in the next section
This separation enables reads and writes to be scaled independently Read operations use a schema optimized for queries, while writes use a schema optimized for updates. Reading queries go against redundant data, while complex business logic can be applied to writing models. Similarly, you can impose stricter protection on write operations than on read exposures
Implementing CQRS can improve application performance for cloud-native services However, this results in a complex design Apply this policy carefully and strategically to those parts of your cloud-native application that will benefit from it. For more on CQRS, see the Microsoft book .NET Microservices: Architecture for Containerized .NET Applications.
A system typically stores the state of a data entity If a user changes their phone number, for example, the customer record is updated with the new number We always know the state of a data entity, but each update overwrites the previous state
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In most cases, this model works well In high-volume systems, however, transaction locking and frequent update operations can limit database performance, responsiveness, and scalability.
Event sourcing takes a different approach to data capture Each operation that affects data resides in an event store Instead of updating the state of a data record, we add each change to a serialized list of the past—like an accountant’s ledger. The event store becomes the system of record for the data It is used to propagate various object views within the bounded context of a microservice Figure 5.8 shows the sample
In the previous figure, notice how each entry (in blue) for a user’s shopping cart is added to an underlying event store. In the adjacent materialized view, the system projects the current state by replaying all the events associated with each shopping cart. View, or read
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