Would you better meet your DevOps goals, such as increased automation, reduced cost and effort, and operational efficiency, if you transitioned to a microservices framework? Or would you end up with a distributed monolith that has all the drawbacks of a monolith architecture, along with the added complexity of distribution, but none of the advantages of microservices? The answer lies in how your development team overcomes the challenges associated with microservices architecture, detailed below.
What Are Microservices?
As the name suggests, Microservices is a term used for the myriad of services that form part of an application. These services are organized around business capabilities, are independently deployable, and communicate with each other through well-defined and simple mechanisms to make applications functional. Microservices are also scalable, can be implemented using different programming languages, and are most suited for cloud-native applications, serverless computing, and applications that rely on container deployment.Â
According to Gartner, while Microservices architecture helps deliver agile, scalable software, it is more complex than traditional application architectures and therefore, demands more effort and discipline to design, build and manage it. “Although its design can be complex — and accompanied by its own set of challenges — when implemented properly, the payoff will offer minimized design time, build time and runtime dependencies between services and their consumers,†Gartner saysOpens a new window .
Challenges of Microservices Architecture
Here are the most common challenges encountered while using microservices for managing distributed systems and the best way to handle these challenges.
1. Overcoming Design Complexity
In the case of application design, taking a microservices approach simply means creating an application that is a bundle of loosely combined services that communicate with one another to achieve a predetermined business objective. Each microservice clarifies, encapsulates, and defines a specific business responsibility–not every aspect of the business. However, one should not attempt designing an enterprise-wide service.Â
Communication between microservices comes with a certain amount of overhead, making it difficult to design a proper means of communication between different services. You will need to handle requests moving back and forth between the services carefully. Although most developers follow a data-centric view to model a domain, ideally, you should start your design with context (business capability) and logic instead of data. Without the incorporation of logic, any amount of data would be rendered useless. You should also not use UI screens as guidelines to identify different data ownership and service boundaries.
Two of the biggest challenges that a designer faces are identifying the correct boundaries and minute details of a microservice. As long as these remain unclear, breaking down your business requirements into specific domains, and eventually creating microservices that are sized adequately, would be extremely difficult to achieve.
2. Achieving Data Consistency
In this system, with each service handling its data independently across data stores, the problem of data redundancy becomes real. Take, for example, data that might be stored for a particular transaction in one service. There is a high possibility that this same data would get stored in other services for reasons such as reporting, analytics, or archiving.
Traditional techniques of managing data cannot enforce data relationships that span a conglomeration of services. The overall system would be in a consistent state only after each microservice has completed its work. Multiphase commits would be required for the division of logical schemas in a bounded context of wider transaction boundaries unless intermediate states are kept using saga patterns. Any changes to state get stored as journaled business events. Hence, the current state is not known by retrieving data from a store but by navigating the history of the business events and calculating from there onward.Â
Suppose numerous microservices are tied to the same tables in a database. In that case, any alteration in the schema will lead to cascading changes in other microservices as well, thereby negating the purpose of having independent microservices. As such, each microservice would be required to have complete ownership of the data it requires. However, this does not amount to having a separate physical database but simply ownership of the data that it controls. All types of databases are required by microservices such as NoSQL, Graph, and in-memory. Simply put, having a relational database that is the default storage of all types of data would not give the desired results.
3. Need for Testing and Monitoring
If the required testing and monitoring tools are not in place, things can quickly escalate and go out of control. There are more services to monitor, but they also may have been developed using different programming languages. Also, debugging problems must consider that each service has its own set of logs, resulting in a plethora of distributed unstructured data with which to go through to find the source of a failure. When dealing with a microservices environment, there can be various reasons for a runtime failure, such as the microservice itself, its container, or even the network interconnecting the various services. Any failure would result in complex intermediate states, which would be difficult to recover from in most cases.Â
Interdependencies between services should be closely monitored. Any downtime of service due to service outages, service upgrades, etc., can all have cascading downstream effects. Due to the standalone nature of each microservice, every dependent service needs to be determined and confirmed before testing. One transaction can easily space across multiple services, and, as a result, any issue in one area can lead to a problem elsewhere. Besides independently testing individual services, you would also need to factor in integrating services and their interdependencies while devising your test plan.
Learn More: Here’s Why Microservices Desperately Need Service Mesh Anomaly DetectionÂ
4. Debugging Issues
It is safe to say that tracking and fixing error sources in microservice architectures is both a time-consuming and expensive affair. More often than not, failure data isn’t propagated in a useful manner immediately inside microservices, and there is an understandable stack trace. Instead, you would be required to work your way backward through status codes and vague error messages generated across the network. Also, logging formats are not the same for different microservices. This makes it challenging to acquire all the required information you would need to fix any error.
Because microservices are stateless, distributed, and independent, traditional logging techniques are ineffective. The only option in determining what caused a failure is to make sure that the state of the system is recorded when the failure occurs. The correct manner in which this can be done uses an application performance management (APM) tool that facilitates proper logging.
5. Compromised Security
Since data is distributed in a microservices-based framework, maintaining the confidentiality and integrity of user data is difficult. Besides the challenge organizations face when setting up access controls and administering secured authentication to individual services, there is also the challenge of increased attack surface vulnerability.
When deploying microservices across multi-cloud environments, there is heightened risk, besides loss of control and visibility of application components, resulting in more vulnerable points. Besides, it becomes extremely difficult to test for vulnerabilities since each microservice communicates with others through different infrastructure layers.
6. Increased Operational Complexity
A microservices-based application entails a multitude of independent services. Managing each service would require serious effort to ensure that the whole application is resilient and failovers can be avoided. This also requires sophisticated tooling for automated provisioning in a highly secure and resilient manner.Â
With each microservice team using its technology and independently deciding how to deploy the service and where and how to run it, the operating of each microservice would obviously differ. This would inadvertently lead to issues like hard-coded IP addresses or TLS keys getting stored in the code repository. As a result, traditional techniques of application monitoring would probably not work. For instance, when a request from the user interface travels through multiple services before it reaches the service that can actually fulfill the requirement, in such cases, identifying the underlying cause of the issue is more often than not impossible.
Since applications are broken down into smaller, independent services hosted and deployed on different servers, one would also be required to coordinate all these individual components to maintain seamless operations. However, this can become particularly challenging when there is a sudden spike in application usage.
Finally, in a microservice-based application, when one component fails, it can cascade the effect to the entire system. As such, you will need to ensure that each microservice is resilient enough by itself to withstand both internal and external failures. Therefore, a good approach to API management, messaging infrastructure, and monitoring is essential if you are looking to overcome these challenges.
Learn More: Service-Oriented Architecture (SOA) vs. Microservices: What Are the Differences?Â
7. Inter-Service Communication Breakdown
Microservices that rely on each other will need to communicate, which is done using well-defined APIs without sharing the same technology stacks, libraries, or frameworks. For such communication, you would need to configure infrastructure layers that facilitate resource sharing across various services. The ways that services communicate with each other need to be explicitly defined. This includes serialization, security, request options, error handling, and the list of expected responses. Poor configuration can easily lead to increased latency.Â
Managing communication between microservices can be hard without using automation and advanced methodologies such as Agile. You will need DevOps tools such as CI/CD servers, configuration management platforms, and application performance management (APM) tools to manage the network. According to Gartner and IDC, companies that look to deploy microservices to production would require some form of service mesh capabilities to scale. This architectural pattern is meant for microservices deployment that allows reliable, fast, and secure service-to-service communication.
8. Requires Team Expertise
When transitioning to a microservices architecture, most of the failures occur due to ill-prepared design and development teams. Switching to microservices will not be a successful exercise if the team does not have the required experience working with distributed architectures. It also won’t work if your team has not adopted a DevOps culture. Even if teams choose the best programming language and platform for their particular microservice, they should collaborate with other microservice teams to cover the whole cycle. They must be able to think in terms of distributed application design and interaction patterns. Teams need to apply concepts such as CQRS, functional interfaces, CAP, BASE, and sagas.
A good understanding of polyglot persistence, persistence ignorance, or event-driven messaging is necessary to achieve data persistence and integration. Developers need to be well-equipped in building and maintaining polyglot microservices through complex CI/CD pipelines.
9. Maintenance of Microservices
Microservice standards allow you to use different technological bases, such as programming languages, for each different service. When the application transitions into maintenance mode, more resources will be required to maintain all the tools and technologies rather than having teams scaled down. In such a case, the advantages gained through technical diversity may quickly get outweighed by increased maintenance costs.Â
To ensure the availability aspect, developers will have to ensure that a microservice failure should not bring down the entire system. They will need to be well aware of all the failure modes and have a backup in place if a failure occurs. Furthermore, updates to a service may break the services that depend on it, which is further complicated by the fact that multiple services can be updated at any one time. Design rules must be rigorously followed and checked with any new version of a microservice before it is deployed to prevent any breakdown of the entire application.Â
10. Network Management
What you gain from the simplicity of single-responsibility microservices can be lost with the complexity of the network they run on, which has less fault tolerance and needs more load balancing. What earlier was an in-memory call now becomes one that transits between processes on the network, bringing with it additional latency and performance delays.
Also, teams should avoid chatty applications that depend upon various services to satisfy a request or process. Since they need all dependent microservices to be available and operational at the same time, chatty applications not only end up without any advantages of microservices, but also become distributed monoliths with a slew of drawbacks of a monolith application.
Learn More: Moving to Microservices: 5 Ways to Recode Your Organization Â
In Conclusion
With the right kind of automation and tools such as deployment automation technologies and CI frameworks and a properly trained staff, organizations can address all of the challenges mentioned above. However, be aware that not all applications for managing distributed systems warrant a microservices architecture.Â
Microservices architecture is appropriate for large applications that require short release cycles, complex applications that must be highly scalable, and those with rich domains or many sub-domains. Implementing a microservices framework is to break down complex applications into many different and smaller services. This architecture should not be applied to break down applications that are small in the first place.
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