This article will discuss the approach of decentralized data management. It will explore some advanced data management architectural patterns that facilitate decentralized applications. By default, the decentralised database management philosophy favors microservices in different aspects of software design. This focuses primarily on decentralization. It does not guide the organization’s business logic. But it guides how data persists.
In the traditional database management approach, software designs are usually monolithic. They typically have monolithic data stores, such as SQL server. These data stores contain a single database with many tables. In this architecture, a centralized DB acts as an engine for data persistence. The SQL server may offload different portions of the application. These portions include complex joins, queries, or stored procedures.
Microservice Architecture Mostly Favors Decentralized Data Management.
About REST (Representational State Transfer)
To organize enterprise data in a decentralized structure, you must understand how to model data using REST. REST stands for Representational State Transfer. REST guides the development of different stateless database systems now. The fundamental principle of REST architecture is to give the resources, which are part of the application. It uses some standard HTTP verbs to interact with them.
A standard microservices deployment may take a decentralized data management approach. It may serve a different resource for various services. For example, one service for the user resources, another for the text messaging resources, and one for relationships. Each service may have its own subsidiary database. However, this does not mean that there should be multiple database servers for each database. A single SQL server hosts all these databases under an application. They may all have the same logical distinction.
However, creating this logical distinction may set the base for quick and easy physical scaling over time. If the platform gains massive adoption, admins can also split it into different logical databases. They can then host those databases on multiple physical servers.
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Avoiding SQL JOIN – Decentralized Data Management
An ideal decentralized data management approach should drop the need for SQL JOIN. This is a significant characteristic of the approach. Making the APIs easier for the clients may cause using JOINs. Say, for example, a messaging app we use may have a timeline view. This timeline may need to have the latest messages from each of the users. It should show the name of the user and an image beside the message.
With a basic REST API, which we define, the client may need to make some API calls to get this detailed view. You need to make multiple API requests. For example, you need to request the list of users. You also need to retrieve the name and image for each user. And you need to fetch the messages from each user. This may be unacceptable from the performance viewpoint. There is not much roundtrip latency between client and server. This happens before this view is displayed.
An ideal solution for this is to add a more logical route to the API. With this, the client will be able to fetch a timeline resource for getting the data to be rendered in the timeline view. This is the significant difference between the approach of centralized data management and the decentralized data management approach.
In monolith databases, this route may be coded as SQL JOIN offloaded to the database server, which will access all the tables and generate the results. For SQL database management solutions, you can take the assistance of relational database services offered by RemoteDBA.
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In decentralized data management, using an SQL JOIN may not just be advisable. It may also be impossible if the data has proper physical separation. Timeline services can request backing the microservices in just milliseconds as timeline services.
Other microservices may be hosted in the same data center or the same containers hosted in a machine. To reduce the roundtrip latency, timeline service may also leverage the “bulk fetch” endpoints. The user microservice may have a particular endpoint that will accept a given list of user IDs to return matching objects. With this, the timeline service needs to make only one request for each service to get the output.
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In decentralized data management, timeline services will function as a central point to define the logic for the timelines. The businesses’ changing requirements and the clients’ need to display the latest messages from each user can quickly change in the timeline service. This can happen without modifying the other microservices that host the primary resources.
The separation between how data is stored and manipulated also needs to be refactored. This is necessary because they continue to adhere to the resources as expected by the timeline services. Each service maintainer may also rewrite how they store the relationships. This won’t break the timeline services. However, in JOIN queries, you must view all the joins against the given table. Update the table structures if you want to achieve the same.
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Side Effects of Decentralized Data Management in Microservices Applications
The significant side effect of a decentralized data management is the additional need to deal with eventual consistency. In centralized data stores, the developers may use transactional capabilities to ensure that the data remains consistent among various tables. Although, this may not be the case if data is kept separately in different physical or logical DBs.
Say, for example, a user tries to fetch the timeline exactly when another user deletes the account. The timeline services may first fetch the list of the second user from the related services. The second user deletes the account, with which the user object is deleted from the related service.
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The timeline service requests the related service to turn the ID into user details. It receives a 404 Not Found errors response. Decentralized data modeling may also need conditional handling for detection. This is necessary in cases where the data changes between two consecutive requests.
Decentralized data management has its own advantages and disadvantages. You can appropriately deploy them by starting with the REST basics. But, doing this may need careful handling of the challenges of eventual consistency. You may also use polyglot persistence to store various types of data in storage and handle the data best.
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