Fundamentals of microservices architecture testing

The importance of microservices architecture testing

Increased adoption of digital has pushed the need for speed to the forefront. The need to conceptualize, develop, launch new products and iterate to make them better, much ahead of the competition and gain customer mindshare, has become the critical driver of growth. Adoption of agile principles or movement towards scrum teams for increased agility are all steps in this direction. Disruptive changes have also taken place on the application front with the 3-tier architecture of the late 90s and subsequent 2 tier monolithic architecture giving way to one that is based on microservices.

Having a single codebase made a monolithic architecture less risky but slow to adopt changes, the exact opposite of a services-based architecture. Microservices architecture makes it easier for multiple development teams to make changes to the codebase in parallel. By transforming an application into a distributed set of services that are highly independent, yet interdependent, provides the ability to create new services/functionalities and modify services without impacting the overall application. These changes can be achieved by teams cutting across geographies or locations and makes it easier for them to understand functional modules rather than the humongous application codebase. However, the highly distributed nature of services also gives them a heightened ability to fail.

Breaking it down – Testing strategies in a microservice architecture

At the core, a microservices architecture comprises of 3 layers – a REST layer that allows the service to expose APIs, the database layer, and the service layer. A robust testing strategy needs to cover all these layers and ensure that issues are not leaked to production. The further an issue moves across stages, the impact increases on account of multiple teams getting affected. Hence the test plan must cover multiple types of testing like service testing, subsystem testing, client acceptance testing, performance testing, etc. Subsequent paragraphs outline key aspects of service level testing and integration testing in a microservices architecture-based application landscape.

In service level testing, each service forming a part of the application architecture needs to be validated. Each service has dependencies on other services and transmits information to others based on need. In a monolith architecture, since connections are being established from one class to the other within the same Java Virtual machine (JVM), chances of failure are far lower. However, in a services architecture, these are distributed, driving the need for network calls to access other services and makes it more complex.

Functional Validation: The primary goal in services testing is the functionality validation of a service. Key to this is the need to understand all events the service handles through both internal as well as external APIs. At times this calls for simulating certain events to ensure that they are being handled properly by the service. Collaboration with the development team is key to understand incoming events being handled by the service as part of its functionality. A key element of functional validation – API contract testing, tests the request and response payload along with a host of areas like pagination and sorting behaviors, metadata, etc.

Compatibility: Another important aspect is recognizing and negating backward compatibility issues. This happens during the launch of a changed version of the service that breaks existing clients running in production. Changes that happen to API contracts need to be evaluated in detail to understand if they are mandatory and capable of breaking clients in production. An addition of a new attribute or a parameter may not classify as a breaking change; however, changes to response payload, behavior, error codes, or datatypes have the ability to break. A change in value typically changes the logic behind it as well. They need to be uncovered much earlier in the service testing lifecycle.

Dependencies: Another aspect of focus is external dependencies, where one would test both incoming as well as outgoing API calls. Since these are heavily dependent on the availability of other services and hence other teams, there is a strong need to obviate dependency through the usage of mocks. Having conversations with developers and getting them to insert mocks while creating individual services will enable testing dependencies without waiting for the service to be available. It is imperative to make sure the mocks are easily configurable without needing access to the codebase. Usage of mocks also drives ease in automation giving teams the ability to run independently with no configuration.

Understanding Microservices Architecture Testing

Once each service is tested for its functionality, the next step is to move onto validate how the various collaborating services work together end to end. Known as subsystem testing or integration testing, it tests the whole functionality exposed together. Understanding the architecture or application blueprint by discussions with the development team is paramount in this stage. Further, there is a strong need to use real services deployed in the integration environment rather than mocks used for external dependencies.

As part of integration testing, there is a need to validate if the services are wired very closely and talking to each other. The event stream and inter-service API calls need to be configured properly so inter-service communication channels are proper. If the service functionality level testing is proper, the chances of finding errors are minimal in this stage, since the required mocks created in the functionality testing stage would have ensured that the services function properly.

Looking in-depth, we find that the testing strategies in a microservices architecture are not extremely different from those adopted for a monolith application architecture. The fundamental difference comes in the way the interdependencies and communication between multiple services forming a part of the larger application are tested to ensure that the application as a whole function in line with expectations.

Understanding the Scope of AIOps and Its Role in Hyperautomation

The rapid acceleration of digital transformation initiatives in the modern business landscape has brought emerging technologies such as artificial intelligence, machine learning, and automation to the fore. Integrating AI into IT operations or AIOps has empowered IT teams to perform complex tasks with ease and resolve agility issues in complex settings.

Gartner sees great potential in AIOps and forecasts the global AIOps market to touch
US$3127.44 million by 2025 at a CAGR of 43.7% during the period 2020 to 2025. From just US$ 510.12 million in 2019, it is expected to touch a whopping US$ 3127.44 million in 2025. It believes 50% of organizations will use AIOps with application performance monitoring to deliver business impact while providing precise and intelligent solutions to complex problems.

A global survey of CIOs iterates why AIOps is so critical for IT enterprises. The survey pointed out that despite investing in 10 different monitoring tools on average, IT teams had full observability into just 11% of the environments. Those who needed those tools didn’t have access to them. 74% of CIOs were reportedly using cloud-native technologies, including microservices, containers, and Kubernetes, and 61% said these environments changed every minute or less.

In comparison, 89% reported their digital transformation had accelerated in the past 12 months despite a rather difficult 2020.
70% felt manual tasks could be automated though only 19% of repeatable IT processes were automated, and 93% believed AI assistance is critical in helping teams cope with increasing workloads.

AIOps offers IT companies the operational capability and the business value crucial for a robust digital economy. But AIOps adoption must be consistent across processes as it would fail to serve its purpose if it merely highlights another area that is a bottleneck. AIOps capabilities must therefore be such that the processes are perfectly aligned and automated to meet business objectives.

Now that we understand how crucial AIOps is let’s dive deeper to understand its scope.

Artificial intelligence, machine learning, big data have all been spoken about in great length and form the very backbone of Artificial Intelligence for IT operations or AIOps. AIOps are multi-layered technology platforms that collate data from multiple tools and devices within the IT environment to spot and resolve real-time issues while providing historical analytics.

What is AIOps?

Artificial intelligence, machine learning, big data have all been spoken about extensively and form the very backbone of AIOps. AIOps comprises multi-layered technology platforms that collate data from multiple tools and devices within the IT environment to spot and resolve real-time issues while providing historical analytics.

It is easier to understand its importance if we realize the extremely high cost of downtime. As per an IDC study, an infrastructure failure’s average hourly cost is $100,000 per hour, while the average total cost of unplanned application downtime per year is $1.25 – 2 billion.

The trends and factors driving AIOps include:

  • Complex IT environments are exceeding human scale, and monitoring them manually is no longer feasible.
  • IoT devices, APIs, mobile applications, and digital users have increased, generating an exponentially large amount of data that is impossible to track manually.
  • Even a small, unresolved issue can impact user experience, which means infrastructure problems should be addressed immediately.
  • Control and budget have shifted from IT’s core to the edge as enterprises continue to adopt cloud infrastructure and third-party services.
  • Accountability for the IT ecosystem’s overall well-being still rests with core IT teams. They are expected to take on more responsibility as networks and architectures continue to get more complex.

Why AIOps?

With 45% of businesses already using AIOps for root cause analysis and potential forecasting problems, its role is evident in several use cases, as mentioned below.

Detection of anomalies and incidents – IT teams can leverage AIOps to see when anomalies, incidents, and events have been detected to follow up on them and resolve them. The thing with anomalies is that they can occur in any part of the technology stack and hence necessitate constant processing of a massive amount of IT data. AIOps leverages machine learning algorithms to detect actual triggers in almost real-time to prevent them. Full-stack visibility into applications and infrastructure helps isolate the root cause of issues, accelerate incident response, streamline operations, improve teams’ efficiency, and ensure customer service quality.

Security analysis – AI-powered algorithms can help identify data breaches and violations by analyzing various sources, including log files and network & event logs, and assess their links with external malicious IP and domain information to uncover negative behaviors inside the infrastructure. AIOps thus bridges the gap between IT operations and security operations, improving security, efficiency, and system uptime.

Resource consumption and planning – AIOps ensures that the system availability levels remain optimal by assessing the changes in usage and adapting the capacity accordingly. Through AI-powered recommendations, AIOps helps decrease workload and ensure proper resource planning. AIOps can be effectively leveraged to manage routine tasks like reconfigurations and recalibration for network and storage management. Predictive analytics can have a dynamic impact on available storage space, and capacity can be added as required based on disk utilization to prevent outages that arise due to capacity issues.

AIOps will drive the new normal.

With virtually everyone forced to work from home, data collected from different locations varies considerably. Thanks to AIOps, disparate data streams can be analyzed despite the high volumes.

AIOps has helped data centers and computer environments operate flawlessly despite the pandemic through unforeseen labor shortages. It allows data center administrators to mitigate operational skills, reduce system noise and incidents, provide actionable insights into the performance of services and related networks and infrastructure, get historical and real-time visibility into distributed system topologies, and execute guided or fully automated responses to resolve issues quickly.

As such, AIOps has been instrumental in helping enterprises improve efficiency and lighten workloads for remote workers. AIOps can reduce event volumes, predict outages in the future, and apply automation to reduce staff downtime and workload. As Travis Greene, director of strategy for IT operations with software company Micro Focus explains, “The end goal is to tie in service management aspects of what’s happening in the environment.”
AIOps and hyperautomation

A term coined by Gartner in 2019, hyper-automation is next-level automation that transcends individual process automation. IDC calls it digital process automation, while Forrester calls it digital process automation. As per Gartner’s Hyper-automation, organizations rapidly identify and automate as many business processes as possible. It involves using a combination of technology tools, including but not limited to machine learning, packaged software, and automation tools to deliver work’.

Irrespective of what it’s called, hyper-automation combines artificial intelligence (AI) tools and next-level technologies like robotic process automation (RPA) to automate complex and repetitive tasks rapidly and efficiently to augment human capabilities. Simply put, it automates automation and creates bots to enable it. It’s a convergence of multiple interoperable technologies such as AI, RPA, Advanced Analytics, Intelligent Business Management, etc.

Hyperautomation can dramatically boost the speed of digital transformation and seems like a natural progression for AIOps. It helps build digital resilience as ‘humans plus machines’ become the norm. It allows organizations to create their digital twin (DTO) or a digital replica of their physical assets and processes. DTO provides them real-time intelligence to help them visualize and understand how different processes, functions, and KPIs interact to create value and how these interactions can be leveraged to drive business opportunities and make informed decisions.

With sensors and devices monitoring these digital twins, enterprises tend to get more data that gives an accurate picture of their health and performance. Hyperautomation helps organizations track the exact ROI while attaining digital agility and flexibility at scale.

Those on a hyper-automation drive are keen on making collaborative IT operations a reality and regard AIOps as an important area of hyper-automation for breakthrough IT operations. When AIOps meets hyper-automation, businesses can rise above human limits and move with agility towards becoming completely autonomous digital enterprises. Concludes John-David Lovelock, distinguished research vice-president at Gartner, “Optimization initiatives, such as hyper-automation, will continue, and the focus of these projects will remain on returning cash and eliminating work from processes, not just tasks.”

It’s time you adopted AIOps too and deliver better business outcomes.

Accelerate AIOps solutions with Trigent

Trigent offers a range of capabilities to enterprises with diverse needs and complexities. As the key to managing multi-cloud, multi-geo, multi-vendor heterogeneous environments, AIOps need organizations to rethink their automation strategies. Our consulting team can look into your organizational maturity to know at what stage of AIOps adoption you are and ensure that AIOps initiatives are optimized to maximize your business value and opportunity.

Our solutions are intuitive and easy to use. Call us today for a business consultation. We would be happy to partner with you on your way to AIOps adoption.

Evolve into a Cloud-Native culture

Why go Cloud Native?

Cloud-Native is one of the biggest trends in the software industry today. The cloud-native approach works for modernizing existing applications and building new applications.

Cloud-native application takes advantage of cloud computing models to increase speed, flexibility, and quality and reduce deployment risks. The key factor to consider here is how applications are built, deployed, and managed.

As a platform-agnostic application, it is easy to manage iterative improvements using Agile & DevOps processes.

1. From a legacy system into the cloud
Organizations who moved from the legacy system into the cloud may face certain challenges. The legacy backup and disaster recovery tools used in old-school data centers do not work in cloud-native environments. Considering that the responsibility for data, processes, data management, maintenance, troubling shooting rests with the business, and not the cloud service provider, cloud-native is the way to go.

2. Rebuild technology foundation
Organizations that wish to make technological changes but do not have the luxury of rebuilding their technology foundation can adopt the Cloud Native approach. They stand to gain significantly by making gradual and fundamental shifts in their culture, processes, and technology to become cloud-native.

3. Innovation & Speed
As software is key to how consumers engage with businesses, innovation and speed have become imperative to their survival and growth. Businesses benefit from the cloud-native approach that gives them the ability to improve the quality of applications, reduces deployment risks, and improves the time to market.

Benefits of Cloud Native

Benefits of Cloud-Native

The building blocks of Cloud Native apps

Whether the challenge is in creating a new Cloud Native app or upgrading an existing one, organizations need to consider these essential building blocks of a Cloud Native ecosystem.

1. Microservices architecture for continuous improvement
The process breaks applications down to single-function services called microservices. Microservices are loosely coupled but remain independent. They allow incremental, automated, and continuous improvement of an application without causing downtime.

2. Containers for flexibility and scalability
Containers package software with all its code and dependencies in one place allowing the software to run anywhere – on a desktop, traditional IT, or the cloud. This allows maximum flexibility and portability in a multi-cloud environment. Containers allow fast scaling up or down with Kubernetes orchestration defined by the user.

3. Kubernetes for cost-effective Cloud Native development
The container orchestration platform enables scheduling and automating the deployment, management, and scaling of containerized applications. Kubernetes is versatile and offers a breadth of functionality, vast open-source of supporting tools, and portability across leading cloud service providers.

4. Agile methods in DevOps processes
Application development for the Cloud-Native approach follows Agile methods and DevOps principles with a focus on building and delivering apps collaboratively by development, quality assurance, security, IT operations, and delivery teams.

Are you ready for the Cloud Native journey?

The path to Cloud Native is unique to each organization depending on their stage in cloud maturity and business goals. Before beginning the Cloud-Native journey, consider these factors.

Cloud applications

1. Cloud-enabled
A cloud-enabled application was developed for deployment in a traditional data center but it was later changed so that it could run in a cloud environment.
Cloud-Native applications are designed to be platform-agnostic and are scalable.

2. Cloud-ready
The cloud-ready application works in the cloud environment or a traditional app that has been reconfigured for a cloud environment.
Cloud-Native apps are developed from the beginning to work only in the cloud and take advantage of cloud architecture.

Business objectives

1. Develop new Cloud Native apps – Organizations can quickly respond to new opportunities with the Cloud Native approach to building new applications.

2. Modernize existing apps – Many valuable applications are critical to business operations and revenue. They may not be easily replaceable. Applications are portable from on-premise infrastructure to the cloud and re-architected to become Cloud Native.

3. Improve app delivery – Container-based automation can accelerate the app delivery cycle.

4. Drive business innovation – For businesses whose success depends on constant innovation, introducing new features, Cloud Native tools support innovation, new ways to deliver solutions faster.

As Cloud Native technologies grow, businesses that wish to keep pace with competition and stay relevant in the future need to start right now. Evolution towards cloud-native effects design, implementation, deployment, operation of applications. Being prepared for the next big technological wave by making the shift today is essential.

Trigent Cloud Services team handholds businesses to leverage the advantages of the cloud for next-gen business requirements. Our experts help in building scalable, reliable, secure, flexible cloud-based apps in the native environment by leveraging Cloud-Native features of AWS, Microsoft Azure, and Google Cloud Platform.

Among other Cloud Services, our portfolio includes Cloud Architecture and Cloud Managed Services with a key focus on Cloud Native applications.

Take the next step in the cloud journey – get in touch with our experts for a business consultation.


Sources:
https://www.ibm.com/cloud/learn/cloud-native
Red Hat – the path to cloud-native applications