Challenges and solutions for distributed data management
Challenge #1: How to define the boundaries of each microservice
Defining microservice boundaries is probably the first challenge anyone encounters. Each microservice has to be a piece of your application and each microservice should be autonomous with all the benefits and challenges that it conveys. But how do you identify those boundaries?
First, you need to focus on the application’s logical domain models and related data. You must try to identify decoupled islands of data and different contexts within the same application. Each context could have a different business language (different business terms). The contexts should be defined and managed independently. The terms and entities used in those different contexts might sound similar, but you might discover that in a particular context, a business concept with one is used for a different purpose in another context, and might even have a different name. For instance, a user can be referred as a user in the identity or membership context, as a customer in a CRM context, as a buyer in an ordering context, and so forth.
The way you identify boundaries between multiple application contexts with a different domain for each context is exactly how you can identify the boundaries for each business microservice and its related domain model and data. You always attempt to minimize the coupling between those microservices. This guide goes into more detail about this identification and domain model design in the section Identifying domain-model boundaries for each microservice later.
Challenge #2: How to create queries that retrieve data from several microservices
A second challenge is how to implement queries that retrieve data from several microservices, while avoiding chatty communication to the microservices from remote client apps. An example could be a single screen from a mobile app that needs to show user information that is owned by the basket, catalog, and user identity microservices. Another example would be a complex report involving many tables located in multiple microservices. The right solution depends on the complexity of the queries. But in any case, you will need a way to aggregate information if you want to improve the efficiency in the communications of your system. The most popular solutions are the following.
API Gateway. For simple data aggregation from multiple microservices that own different databases, the recommended approach is an aggregation microservice referred to as an API Gateway. However, you need to be careful about implementing this pattern, because it can be a choke point in your system, and it can violate the principle of microservice autonomy. To mitigate this possibility, you can have multiple fined-grained API Gateways each one focusing on a vertical “slice” or business area of the system. The API Gateway pattern is explained in more detail in the section in the Using an API Gateway later.
CQRS with query/reads tables. Another solution for aggregating data from multiple microservices is the Materialized View pattern. In this approach, you generate, in advance (prepare denormalized data before the actual queries happen), a read-only table with the data that is owned by multiple microservices. The table has a format suited to the client app’s needs.
Consider something like the screen for a mobile app. If you have a single database, you might pull together the data for that screen using a SQL query that performs a complex join involving multiple tables. However, when you have multiple databases, and each database is owned by a different microservice, you cannot query those databases and create a SQL join. Your complex query becomes a challenge. You can address the requirement using a CQRS approach—you create a denormalized table in a different database that is used just for queries. The table can be designed specifically for the data you need for the complex query, with a one-to-one relationship between fields needed by your application’s screen and the columns in the query table. It could also serve for reporting purposes.
This approach not only solves the original problem (how to query and join across microservices); it also improves performance considerably when compared with a complex join, because you already have the data that the application needs in the query table. Of course, using Command and Query Responsibility Segregation (CQRS) with query/reads tables means additional development work, and you will need to embrace eventual consistency. Nonetheless, requirements on performance and high scalability in collaborative scenarios (or competitive scenarios, depending on the point of view) is where you should apply CQRS with multiple databases.
“Cold data” in central databases. For complex reports and queries that might not require real-time data, a common approach is to export your “hot data” (transactional data from the microservices) as “cold data” into large databases that are used only for reporting. That central database system can be a Big Data-based system, like Hadoop, a data warehouse like one based on Azure SQL Data Warehouse, or even a single SQL database used just for reports (if size will not be an issue).
Keep in mind that this centralized database would be used only for queries and reports that do not need real-time data. The original updates and transactions, as your source of truth, have to be in your microservices data. The way you would synchronize data would be either by using event-driven communication (covered in the next sections) or by using other database infrastructure import/export tools. If you use event-driven communication, that integration process would be similar to the way you propagate data as described earlier for CQRS query tables.
However, if your application design involves constantly aggregating information from multiple microservices for complex queries, it might be a symptom of a bad design—a microservice should be as isolated as possible from other microservices. (This excludes reports/analytics that always should use cold-data central databases.) Having this problem often might be a reason to merge microservices. You need to balance the autonomy of evolution and deployment of each microservice with strong dependencies, cohesion, and data aggregation.
Challenge #3: How to achieve consistency across multiple microservices
As stated previously, the data owned by each microservice is private to that microservice and can only be accessed using its microservice API. Therefore, a challenge presented is how to implement end-to-end business processes while keeping consistency across multiple microservices.
To analyze this problem, let’s look at an example from the eShopOnContainers reference application. The Catalog microservice maintains information about all the products, including their stock level. The Ordering microservice manages orders and must verify that a new order does not exceed the available catalog product stock. (Or the scenario might involve logic that handles backordered products.) In a hypothetical monolithic version of this application, the ordering subsystem could simply use an ACID transaction to check the available stock, create the order in the Orders table, and update the available stock in the Products table.
However, in a microservices-based application, the Order and Product tables are owned by their respective microservices. No microservice should ever include databases owned by another microservice in its own transactions or queries, as shown in Figure 4-9.
Figure 4-9. A microservice cannot directly access a table in another microservice
The Ordering microservice should not update the Products table directly, because the Products table is owned by the Catalog microservice. To make an update to the Catalog microservice, the Ordering microservice should only ever use asynchronous communication such as integration events (message and event-based communication). This is how the eShopOnContainers reference application performs this type of update.
As stated by the CAP theorem, you need to choose between availability and ACID strong consistency. Most microservice-based scenarios demand availability and high scalability as opposed to strong consistency. Mission-critical applications must remain up and running, and developers can work around strong consistency by using techniques for working with weak or eventual consistency. This is the approach taken by most microservice-based architectures.
Moreover, ACID-style or two-phase commit transactions are not just against microservices principles; most NoSQL databases (like Azure Cosmos DB, MongoDB, etc.) do not support two-phase commit transactions. However, maintaining data consistency across services and databases is essential. This challenge is also related to the question of how to propagate changes across multiple microservices when certain data needs to be redundant—for example, when you need to have the product’s name or description in the Catalog microservice and the Basket microservice.
A good solution for this problem is to use eventual consistency between microservices articulated through event-driven communication and a publish-and-subscribe system. These topics are covered in the section Asynchronous event-driven communication later in this guide.
Challenge #4: How to design communication across microservice boundaries
Communicating across microservice boundaries is a real challenge. In this context, communication does not refer to what protocol you should use (HTTP and REST, AMQP, messaging, and so on). Instead, it addresses what communication style you should use, and especially how coupled your microservices should be. Depending on the level of coupling, when failure occurs, the impact of that failure on your system will vary significantly.
In a distributed system like a microservices-based application, with so many artifacts moving around and with distributed services across many servers or hosts, components will eventually fail. Partial failure and even larger outages will occur, so you need to design your microservices and the communication across them taking into account the risks common in this type of distributed system.
A popular approach is to implement HTTP (REST)-based microservices, due to their simplicity. An HTTP-based approach is perfectly acceptable; the issue here is related to how you use it. If you use HTTP requests and responses just to interact with your microservices from client applications or from API Gateways, that is fine. But if you create long chains of synchronous HTTP calls across microservices, communicating across their boundaries as if the microservices were objects in a monolithic application, your application will eventually run into problems.
For instance, imagine that your client application makes an HTTP API call to an individual microservice like the Ordering microservice. If the Ordering microservice in turn calls additional microservices using HTTP within the same request/response cycle, you are creating a chain of HTTP calls. It might sound reasonable initially. However, there are important points to consider when going down this path:
Blocking and low performance. Due to the synchronous nature of HTTP, the original request will not get a response until all the internal HTTP calls are finished. Imagine if the number of these calls increases significantly and at the same time one of the intermediate HTTP calls to a microservice is blocked. The result is that performance is impacted, and the overall scalability will be exponentially affected as additional HTTP requests increase.
Coupling microservices with HTTP. Business microservices should not be coupled with other business microservices. Ideally, they should not “know” about the existence of other microservices. If your application relies on coupling microservices as in the example, achieving autonomy per microservice will be almost impossible.
Failure in any one microservice. If you implemented a chain of microservices linked by HTTP calls, when any of the microservices fails (and eventually they will fail) the whole chain of microservices will fail. A microservice-based system should be designed to continue to work as well as possible during partial failures. Even if you implement client logic that uses retries with exponential backoff or circuit breaker mechanisms, the more complex the HTTP call chains are, the more complex it is implement a failure strategy based on HTTP.
In fact, if your internal microservices are communicating by creating chains of HTTP requests as described, it could be argued that you have a monolithic application, but one based on HTTP between processes instead of intraprocess communication mechanisms.
Therefore, in order to enforce microservice autonomy and have better resiliency, you should minimize the use of chains of request/response communication across microservices. It is recommended that you use only asynchronous interaction for inter-microservice communication, either by using asynchronous message- and event-based communication, or by using HTTP polling independently of the original HTTP request/response cycle.
The use of asynchronous communication is explained with additional details later in this guide in the sections Asynchronous microservice integration enforces microservice’s autonomy and Asynchronous message-based communication.
CAP theorem https://en.wikipedia.org/wiki/CAP_theorem
Eventual consistency https://en.wikipedia.org/wiki/Eventual_consistency
Data Consistency Primer https://msdn.microsoft.com/library/dn589800.aspx
Martin Fowler. CQRS (Command and Query Responsibility Segregation) https://martinfowler.com/bliki/CQRS.html
Materialized View https://docs.microsoft.com/azure/architecture/patterns/materialized-view
Charles Row. ACID vs. BASE: The Shifting pH of Database Transaction Processing http://www.dataversity.net/acid-vs-base-the-shifting-ph-of-database-transaction-processing/
Compensating Transaction https://docs.microsoft.com/azure/architecture/patterns/compensating-transaction
Udi Dahan. Service Oriented Composition http://udidahan.com/2014/07/30/service-oriented-composition-with-video/