The digital revolution has helped transform multiple spheres of business. Microservices-based architecture is perhaps the most impactful digital transformation yet, with its rapid deployment capabilities and specific functionalities. It has enabled various departments of a business to create and own apps that cater to tasks or process-specific operations and help enhance productivity.
In fact, certain associated trends like serverless architecture and Kubernetes have made microservices even more popular and useful than before. Businesses can scale up their operations easily and execute complicated tasks with relative simplicity in processes.
With that said, all the benefits that come with microservices can also be the downfall of an organization. The fact that microservices can be quickly created according to requirements initiates a snowball that can lead to a potential multiplicity of buggy apps.
How to solve it? The one-word answer is ‘observability.’
Driving observability and more visibility into the deployment of microservices by your organization helps to create a more efficient ecosystem that functions optimally. Observability also helps you identify the problematic areas in the process.
But achieving observability in Microservices-based apps is a stand-alone challenge that can be tackled by Distributed tracing, which we will discuss in detail in this article.
Why are Microservices So Popular?
Microservices are emerging as a popular solution, an alternative to full-suite software packages, because of their compact, service-specific nature. Imagine your mobile phone – you have an application for everything, viewing pictures, creating documents, entertainment, etc. Whenever you need a “Service,” you just access the respective application, launch the service, use it, and close it.
Microservices also function with a similar concept. Instead of packaging everything in a software-like architecture (where you may or may not need certain services but would get them anyway as part of the package), each service is treated like an individual application that can be used when needed. Not only does this help to optimize resource use, but it also helps to control expenses when requesting a service.
Two factors are at play in making microservices so popular today.
1. Cloud Computing
Cloud computing has enabled businesses to avail of software and services over the network as SaaS. Many cloud service providers offer needs-based solutions to their clients, which plays a big role in furthering microservices. Its popularity has also increased because of cloud-based services being online 24×7 and available on-demand.
Containerization can be considered as a cheat code for faster app development where the developers can create an “illusion” of a dedicated OS instance for each app. Thus the word “Container” – an executable package that contains an app bundled with the specific OS libraries and dependencies it needs to run well.
Digitalization is entering the pacing phase now, where faster and more accurate development holds the key to growth. Businesses compete in terms of quickness and efficiency in processes, which microservices can deliver seamlessly. However, haste makes waste, and there are bound to be problems with output qualities and management of countless internal apps. It necessitates observability to ensure that the output is high-quality and the internal execution is neat.
What is Observability in Microservices?
Observability is a tooling setup that allows your development teams to continually debug the errors and problems that arise with your business’s microservices. It has been derived from the concept that the developer can logically draw on the system’s internal state by observing the output.
Simply put, observability is the ability of your developers to draw insight into the internal working, health, and quality of a microservices system merely by observing the system’s outputs.
A common misconception has made Observability interchangeable with Monitoring. In concept, both activities are inherently different. While monitoring does allow the development teams to get a bigger, more comprehensive, and complete picture of the performance of an application, it leaves out the deeper details. Observability lets the developers see what exactly is happening inside the application that is making it work the way it is – faulty or not – and augments the data with actionable suggestions.
Observability is, with microservices, an essential tool to ensure that bugs are fixed as soon as they arise and that this process remains continuous and sustained with optimal resource use. DevOps get access to enriched insight, debugging capabilities, and visualized information pivotal to addressing even the smallest of issues with a microservice.
Why is Observability Important in a Microservices Architecture?
Every app must evolve to its perfect, efficient, and usable self before it can be released or put to work internally at an organization. However, merely prescribing changes without gauging their impact on the app development process and the production environment is going to set you several steps back. Feedback is thus necessary and must be gathered at each error or bug encountered.
Observability is necessary in a microservices architecture as it helps with the following aspects:
- Monitors the performance of applications in-depth with a granular approach
- Provides real-time data and insights on a comprehensive dashboard that lets you see what’s happening with your app right now
- Observability is a self-healing setup
- The information is readily accessible and actionable
- Allows visibility inside the application’s working and mechanism for error and bug fixing at the root cause
- Is better for troubleshooting than monitoring because observability is highly specific and detailed
Challenges in Implementing Observability for Microservices Architecture
It is because of their disparate, scattered nature that microservices can be challenging to “observe.” When a user initiates a request that requires traveling through multiple services, and each of these services has a limited success rate, then the result would ultimately compound the errors, in the end, should they occur.
This is why observability is necessary for a Continuous Integration/Continuous Delivery (CI/CD) process to ensure that errors are kept at a minimum.
However, challenges still remain to be scaled. Here are the four major challenges that microservices pose to observability.
Orchestration Setup and Infrastructure
Resource scheduling, deployment of containers, scaling, and managing Kubernetes isn’t feasible with manual processes. The challenge arises when an orchestration platform needs to be set up to handle containerization and observability automatically and at scale. Some organizations may be looking at a major IT overhaul to achieve this.
Challenges with Remote Procedure Calls
There is inherent complexity with microservices observability because these components need to know how to communicate with each other when a request is initiated. Observability may run into a wall when there is no robust RPC framework, server proxies, or network meshes in place that facilitate it.
The Disparity Issue
Working with full-suite or monolith software reduces the touchpoints for requests running into errors. However, distributed microservices that could be scattered over multiple clouds and networks may decrease reliability and make observability extremely challenging.
Network Response and Latencies
Mathematically, network latencies have the nature to get compounded with each increment in touchpoints that a request makes. In short, a request with a single touchpoint in microservices may have low latency and better observability.
However, a request with 100 or 150 touchpoints will compound the latency encountered at each one and may result in downstream problems. Observability may be difficult for monitoring such multiple touchpoints.
Distributed Tracing for Microservices Observability
Observability is important in a microservices architecture, the challenges notwithstanding. Whether a request has 1 touchpoint or 100, there is a way to effectively improve observability through Distributed Tracing.
In this method, the process follows a request across each touchpoint it creates with a microservice across its entire path. From input to output, a distributed trace records everything that a request encounters along the way, relaying this observability data to the DevOps dashboard. This way, instead of monitoring an entire microservices ecosystem with a bird’s eye view, distributed tracing follows each app across its journey through the microservices, observing only what needs to be observed.
Here’s a small walk-through about how to achieve observability for Microservices-based apps using Middleware:
Middleware is a full-stack observability platform that follows standards defined by open-source project named OpenTelemetry.
In order to record tracing data for your application, all you need to do is install one of the Middleware APMs.
For example, If you are running a Node.js Application inside your infrastructure, all you need to do is install the Middleware npm package into your project and enable tracking data by copy-pasting a small code snippet.
The APM will now monitor all the HTTP & GRPC APIs running inside your project, and you will be able to monitor the data collected on Middleware APM Dashboard.
1. The APM Dashboard has a precise summary of all the projects which is connected with APM:
A user can also see a list of projects, highlighting important charts in the list view itself.
2. Inside each project, you get a service list along with all the important metrics for services:
3. You can click on one service and check its entire path, helping you achieve observability for microservices-based apps:
Note: Middleware is free for community use for a limited time. Sign up on the platform to achieve complete observability.
With the increase in the number of apps in a microservices architecture, there is also an increase in complexity. The user request can initiate endless permutation-combination of possible touchpoints, making observability a nightmare. However, there are methods to improve the management and observability of such systems.
With the right platform, like Middleware, to orchestrate distributed tracing, observability can be enhanced significantly.
Savan Kharod is a growth marketer at Middleware. He is an engineer turned marketer and a tech enthusiast. When not solving dev marketing issues at middleware, he likes to read novels. Say hello to him on LinkedIn.