Ever since cloud applications became a dominant force, enterprises have been on a transition path in moving their core technology architecture into a flexible, scalable, and highly resilient one. The biggest destination in this journey was to transition into a microservices architecture for enterprise applications.
Read: How to build scale with microservices
Gartner, in a recent survey of IT leaders, has observed that nearly 74% of organizations leverage microservices architecture in their technology stack.
Developing and deploying large enterprise applications as a set of modular constituents is helping companies achieve seamless efficiency in their technology management.
However, there has been an exponential rise in the scale of instances being operationalized in microservices in tandem with business growth. This eventually escalated management complexity for applications. With more business functions transitioning into digital-first experiences, the tech powering the same must be well-positioned to support the scale of growth. From a monitoring perspective, two important concepts are finding critical importance in enterprises that rely on large-scale applications with microservices architecture. They are observability and distributed tracing.
Leaders need to understand why these concepts are important. They must also know how they can be jointly strategized to ensure disruption-free performance for their microservice-oriented enterprise applications.
Observability In the Microservice Landscape
Experts have devised the trait of observability as a vital parameter for technology leaders to ensure high performance and resilience in their microservices architecture. In simple terms, observability is a measure of how well internal application components can provide transparent insights into their system health.
There is a slight difference between observability and monitoring as the former allows us to better understand the beginning of an issue and act before it occurs. In other words, observability is a more proactive approach than monitoring, and it lets respective teams discover granular insights into how inner processes and workflows are executed within different services in a microservice environment. Observability accelerates decision-making on defects or issues as it sheds light on the deepest elements of each microservice and these may even include hidden issues.
Observability works primarily on 3 key pillars – metrics, logs, and traces.
- Metrics are parameters that help users identify deviations from actual or intended outputs demonstrated historically by a service.
- Logs are nothing but records of historical occurrences of each issue and details on how and why it occurred.
- The third pillar is perhaps one of the most critical elements that power observability – traces. Traces comprise of information that aids in the easy location of even the most granular issues that may occur in a microservice.
Generically, these three pillars are referred to as telemetry data. Today there are standards like OpenTelemetry that focus on simplifying the end-to-end management of telemetry data. The steps involved in the OpenTelemetry framework are -
- Adding code to a microservice that collects data about its functioning.
- Acquiring the necessary data and then building a span for specific request journeys. For example, a request for the login module to initiate a login process might be considered one span of the larger application.
- Analyzing the data to learn more about how requests navigate each microservice and this helps in troubleshooting in the future.
- Setting alerts to inform respective stakeholders when alerts are triggered by pre-defined conditions in a microservice. Replacements must be quickly made to minimize damage.
OpenTelemetry paves the way for an enhanced understanding of observability without traditional hindrances.
Observability plays a vital role in incident management as it is directly connected to your ability to understand and mitigate risky incidents. It offers developers the power to confidently work on their deployments knowing that there are measures in place to detect any issues and in the event of an incident, they can safely roll back changes to prevent any major mishap in the application.
The World of Distributed Tracing
We have seen how traces are a major pillar of observability and how they contribute to faster issue resolution. When it comes to large microservice-oriented applications, however, simple tracing efforts will not work as efficiently as in the case of monolithic applications. For observability to sustain momentum in complex microservice applications, there is a need for distributed tracing.
An application built on microservice architecture will experience multiple simultaneous service calls from different entities. Getting a snapshot of system health for observability in this complex context would involve additional challenges. This is where distributed tracing can help make a huge difference. With distributed tracing, insights into each request are collected at the origin of the request itself. Tools and processes used for distributed tracing will ensure that the data is captured at the very instant like for example, when a user clicks a button on a web form that triggers an application workflow. The entire pathway of the request in its execution journey will be traced with further sub-spans created as the request enters each microservice.
Depending on the type of request, all critical insights needed for tracing are captured at every point and tagged to the original request ID. With such a nested and distributed tracing system, engineers get visibility into every aspect of a user request. For example, they can identify how much time a user request was being handled in a specific microservice, the unique function calls made from the request like a database entry or change within a microservice, and much more. This aids in improving the observability of the application as it becomes easier to pinpoint the root location of any issue.
Observability in a Microservice Architecture is Powered by Distributed Tracing
As we can see above, the degree of observability within a complex microservice architecture-oriented application is directly related to how well-distributed tracing works with it. What enterprises need are the right tools, strategies, and inclusive processes to build efficient workflows that support both observability and distributed tracing. This is where a knowledgeable partner like Wissen can make for a great asset. Get in touch with us to know more.
