The microservices approach is made possible in large part by favoring decentralization of software components and data — specifically, by breaking up “monolithic” elements into smaller, easier to change pieces and deploying those pieces on the network.
Their goal, in organizational design terms, is to decentralize decision authority. Instead of having a few people make architectural and software decisions for everyone in the organization, decentralization allows them to distribute decision-making power amongst the people who do the work.
When it comes to data, companies that create individual services for specific business logic frequently feel compelled to consolidate all application data into a single, centralized datastore. Their goal is to make sure that all the data is available for any service that may require it. Managing a single datastore is simple and convenient, and data modeling can be consistent for the entire application to use, regardless of the service that uses it.
However, we would recommend that you avoid doing this. Here are three reasons why centralizing your data is a bad idea and how microservices help in checking the drawbacks.
1. Centralized data is hard to scale
When the data for your entire application is in a single centralized datastore, then as your application grows you must scale the entire datastore to meet the needs of all the services in your application. This is depicted in the diagram below (Figure 1). If you use a separate data store for each service, only the services that have increased demand need to scale, and the database being scaled is smaller. This is shown on the right side of Figure 1.
It’s a lot easier to scale a small database bigger than it is to scale a large database even larger.
How microservices help in scaling?
Scaling is the process of dividing software into smaller units. Scalability refers to the application’s ability to implement more advanced features. It contributes to the application’s security, durability, and maintainability. In the industries, three types of scaling procedures are used. The microservice scaling methodologies include x-axis, y-axis, and z-axis scaling; below is one of the methods along with the corresponding real-world example.
Scaling on the Y-Axis:
Vertical scaling, which includes any resource level scaling, is also referred to as Y-axis scaling. Any DBaaS or Hadoop system can be thought of as Y-axis scaled. The user’s request is redirected and restricted in this type of scaling by implementing some logic.
As an example, consider Facebook. Facebook must handle 1.79 million users every second, so traffic control is a major responsibility for Facebook network engineers. To avoid any danger, they use Y-axis scaling, which entails running multiple servers with the same application at the same time. To control this massive amount of traffic, Facebook redirects all traffic from one region to a specific server, as shown in the image. In architectural terms, this transferring of traffic based on region is known as load balancing.
2. Centralized data is hard to partition later
A common thought that pops up in the mind of every app developer is, “I don’t need to worry about scaling now; I can worry about it later.” As your application grows in popularity, you must consider rethinking architectural decisions to meet increased traffic.
Distributing your datastore into smaller datastores is one of the common architectural changes. It is much more convenient to do at the beginning of the application’s life cycle than it is later. When the application has been around for a few years and all parts of the application have access to all parts of the data, determining which parts of the dataset can be split into a separate datastore without requiring a major rewrite of the code that uses the data becomes extremely difficult. Even simple questions become difficult to answer. What services are making use of the Profiles table? Is there a service that requires both the Systems and Projects tables?
The longer a dataset remains in a single datastore, the more difficult it is to later divide that datastore into smaller segments.
How microservices help in data storing and partition?
A microservice may use one, two, or more databases. Some of the data stored by the microservice may fit well in one type of database while others may fit better in another. There are numerous viable database technologies available today, and I will not compare them here. However, there are some broad database categories to consider when making a decision, such as relational databases, key/value stores, document databases, column stores, and graph databases.
By separating data into separate datastores based on functionality, you avoid issues associated with separating data from joined tables later, and you reduce the possibility of unexpected data correlations in your code.
3. Centralized data makes data ownership impossible
The ability to divide application ownership into distinct and separable pieces is one of the major benefits of dividing data into multiple services. Individual development teams owning applications is a core tenet of modern application development that promotes better organizational scaling and faster response to problems when they arise. The Single Team Oriented Service Architecture (STOSA) development model discusses this ownership model.
This model works well when you have a large number of development teams all contributing to a large application, but it also works well for smaller applications with smaller teams.
The issue is that for a team to own service, they must own both the code and the data for the service. This means that one service (Service A) should not have direct access to the data of another service (Service B). If Service A requires something from Service B, it must call a service entry point for Service B rather than directly accessing the data.
This gives Service B complete control over its data, how it is stored, and how it is maintained.
So, what are your options? Each service in your service-oriented architecture (SOA) should have its data. The data is a component of the service and is incorporated into it.
Designing Microservices: Best Practices
The design of microservices must ensure a weak coupling for services to be modified independently and to operate autonomously. Services that are weakly coupled will benefit fully from the microservice architecture, such as fault tolerance, load adaptation, implementation ease, and so on.
Furthermore, it must be highly cohesive to ensure that exchanges between these services are as coherent as possible via the following rules:
- Design simple microservices that can be composed or linked to others according to the modularity and composability rule.
- Engine interfaces must be isolated according to the separability rule. Internal microservices are not structured, whereas interfaces are.
- Representation rule: design data-controlled microservices that have a simpler operation, are more robust and have better scalability.
- Generation rule: avoid encoding repetitive and trivial things and encode using programs rather than WSDL files to generate code for the interfaces.
- Make the necessary technological and methodological decisions based on the problem to be solved, not on a software catalog or a stack certified by a corporate guru.
Here is how Galaxy can help you
When release cycles take months rather than weeks, your company is frequently unable to deliver modern online experiences. Development bottlenecks impede your ability to update applications, preventing you from innovating and iterating. And an out-of-date or clumsy user experience prevents you from retaining and winning over customers.
Galaxy’s experts will assist you in implementing an end-to-end vision by developing a modern development stack for building enterprise applications with the necessary frontend and microservice technologies for your business. We will help your team quickly build, design, and launch applications based on microservices.
Learn about Galaxy’s Microservices, Frontend, Backend, and DevOps capabilities, which can help your organization build better and faster apps, sites, and portals.
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