I was recently reading the “Global Microservices Trends” report by Dimensional Research and found myself thinking, “A service mesh could help with that.” Let’s look at three key challenges listed in the report.
A couple key points in the report make it clear microservices are seeing widespread adoption. It’s also clear that along with the myriad benefits they bring, there are also tough challenges that come as part of the package. From the report:
- 91 percent of enterprises are using microservices or have plans to.
- 99 percent of users report challenges with using microservices.
Major Microservices Challenges
The report identifies a range of challenges companies are facing.
Companies are seeing a mix of technology and organizational challenges. We’ll focus on the technological challenges a service mesh solves, but it’s worth noting that one thing a service mesh does is bring uniformity so it’s possible to for all teams to have the same view, reducing the need for certain skills.
Each Additional Microservice Increases the Operational Challenges
Not with a service mesh! A service mesh provides monitoring, scalability and high availability through APIs instead of using discrete appliances. This flexible framework removes the operational complexity associated with modern applications. Infrastructure services were traditionally implemented as discrete appliances, which meant going to the actual appliance to get the service. Each appliance is unique, which makes monitoring, scaling and providing high availability difficult. A service mesh delivers these services inside the compute cluster itself through APIs and doesn’t require any additional appliances. Implementing a service mesh means adding new microservices without adding complexity.
It is More Difficult to Identify the Root Cause of Performance Issues
The service mesh toolbox gives you a couple of things that help solve this problem:
Distributed Tracing: Tracing provides service dependency analysis for different microservices and tracking for requests as they are traced through multiple microservices. It’s also a powerful way to identify performance bottlenecks and zoom into a particular request to identify which microservice contributed to the latency of a request or which service created an error.
Metrics Collection: With service mesh you also get the ability to collect consistent metrics across your mesh. Metrics are key to understanding what has happened in your applications, and when they were healthy compared to when they were not. A service mesh can gather telemetry data from across the mesh and produce consistent metrics for every hop. This makes it easier to quickly solve problems and build more resilient applications.
 
Differing Development Languages and Frameworks
Another major challenge that report respondents noted facing was the challenge of maintaining a distributed architecture in a polyglot world. When making the move from monolith to microservices, many companies struggle with the reality that, to make things work, they have to use different languages and tools. Large enterprises can be especially affected by this, as they have many large, distributed teams. Service mesh provides uniformity by providing programming language agnosticism, which addresses inconsistencies in a polyglot world where different teams, each with its own microservice, are likely to be using different programming languages and frameworks. A mesh also provides a uniform, applicationwide point for introducing visibility and control into the application runtime, moving service communication out of the realm of implied infrastructure, to where it can be easily seen, monitored, managed and controlled.
Microservices are cool, but service mesh makes them ice cold. If you’re on the microservices journey and are finding it difficult to manage the infrastructure challenges, a service mesh may be the right answer. Let me know if you have any questions on how to get the most out of service mesh, I’m happy to talk and you can find me @zjory.
