Avoiding The Microservices Antipatterns

Microservices Antipatterns

Microservices are today one of the most sought-out after trends in application architecture. As the digital economy booms, studies estimate that the global market for cloud microservices will hit nearly USD 2. Billion by 2027. Enterprises are racing to leverage its key advantages of independent deployment, wider technology stack options, loosely coupled architecture that help in bringing benefits such as faster time to market, lower cost of maintenance, and better digital agility. However, the growth story of microservices also faces challenges. One of the biggest amongst these challenges is microservice antipatterns.

In its simplest definition, a microservice antipattern is a behavioral pattern wherein the architecture chosen to run an application as microservices results in creating more problems than the solution due to the inherent architectural mistakes that developers or service owners make during development. 

Let us take a closer look at the top 5 microservice antipatterns and how enterprises can avoid them:

Distributed Monolith

The most common of antipatterns that enterprise tech teams make is not taking into consideration the extent of dependency of services when a large monolithic application is split into several microservices. If a microservice requires all other microservices or their library and other components to run, then the entire application architecture becomes a distributed monolith. This effectively cancels out the advantages and benefits of a microservice transition. To avoid this antipattern, it is important to build de-coupled services that require minimal communication and can operate on their own even when other services are down or unavailable due to some reason.

Poor Versioning

While implementing a microservices architecture, there will be a collection of independent services that work to provide the result or feature of the application. However, over time, these independent services may get upgrades and customizations to optimize their working. This could lead to situations where code becomes extremely hard to manage due to poor versioning. The end application could see multiple services trying to work together with code that is not interoperable. Having a well-thought-out versioning strategy, especially focusing on making the API version transparently accessible to all services in a route, can help avoid this antipattern to a great extent.

Interservice Dependency Disorder

While splitting a large application into numerous microservices, there may be scenarios where all or some of the microservices need to operate and collaborate in a particular order to ensure the right output for the application. When the tech team fails to address the order requirement, then the microservice architecture for that application will face the antipattern called dependency disorder. 

Lack of cohesion on the functional dependencies of the services is a major reason for this antipattern. A bigger issue would be circular service dependency when services enter a never-ending loop wherein all stakeholders are stuck for resources from the preceding one in a circular fashion. To avoid this antipattern, the best option is to leverage an API gateway along with an orchestration management platform like Kubernetes which helps define service boundaries, identify dependencies, and align deployment to match the functional cohesiveness of the application.

Distributed Transactions

When large monolithic applications are decomposed into microservices, there are chances where transactions may become distributed or, in other words, distributed across different microservices. The execution of the transaction would put multiple microservices into action but any failure in one microservice may not be able to put the transaction on hold or reverted by another microservice. This is against the atomicity feature required by transactional systems. To avoid this antipattern, the system can be designed to run on a two-phase prepare and commit approach for transaction fulfilment when transactions are shared among different microservices. A global transaction coordinator needs to be deployed that directs different microservices to prepare and commit on demand.

Insufficient Metric Logging

A major problem in identifying the failure of microservices is often the lack of quantifiable metrics not being logged in. Finding and debugging the reasons for a microservice’s failure is critical to help prevent future disruptions and improve the overall architectural efficiency of the microservice. To prevent this antipattern, enterprise tech leaders need to enforce observability practices within their development philosophies. It encourages developers to build microservices with observable metrics or traces and events that can help diagnose the reasons for failure more effectively and faster. 

Limited Interservice Communication Mechanisms

For smaller applications, developers often use a queue system to help manage communication between services and thus keep the application in its optimal performance. However, as the number of services increases, along with the increasing scale of growth amongst individual microservices, this dependency on a single interservice communication channel will result in chaos. To avoid this issue, it is important to create multiple pathways for interservice communication within an application. By strategically using a mix of HTTP protocols and message queues, various facets of the application can be managed depending on their needs for synchronous or asynchronous communication without impacting the scalability and stability of the larger app.

Shared Databases and Entangled Data

In monolithic application architecture, different components within the application share the same database or data source. Enterprises that replicate this behavior with a microservice-oriented architecture will face a new antipattern resulting in heavy compromise of application performance and scalability. Different microservices would have to fight it out for their share of the data source and can create deployment issues as well. 

Another data-related problem in a different setting is entangled data which occurs when different services have access to the same data resources as, for instance, a database object. The data may become tangled in the queue thereby delaying its access to a service that may need it urgently. When a service is unable to retrieve the right data it needs on time, it could become a cascading problem if that service has a dependency on other services for their operation. To avoid this data antipattern, the best solution is to design microservice architectures that ensure each service has access to its own database or datastore. As for entangled data, it can be eliminated by ensuring that critical data is isolated and providing access control that allows only designated services to have access when needed.

Microservice architecture is a puzzle that enterprises need to strategically approach. It is not wise to transition every application into a microservice just because everyone else is doing it or talking about it. You need to identify benefits that outweigh the risks, gain confidence from all stakeholders, prepare a detailed prioritized roadmap that incrementally transitions features or components into microservices rather than an end-to-end single transition or refactoring of the monolith application. For this, enterprises need to have a sound advisory on picking the best practices and ensuring that their investments in microservices are able to reap benefits faster and sustainably. Get in touch with us to know more.

This article was originally published at https://www.itvoice.in/avoiding-the-microservices-antipatterns

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