What Are Engineers Saying About Cloud-Native's Impact on Observability?
January 06, 2021

Dotan Horovits

For the last few years, cloud-native technologies like microservices, serverless, and Kubernetes have been increasingly dominant in the technology stack of modern engineering teams. Why? First of all, they work seamlessly with modern cloud environments, they're scalable, resilient, and enable continuous delivery of software in an agile fashion.

But all these benefits don't come without drawbacks — and as over 1000 engineers report in Logz.io's DevOps Pulse 2020, many of these drawbacks relate to their complexity and their tendency to hinder observability into system performance.

While there's still some confusion in the industry about observability and it's distinction from more popular terms such as monitoring and APM, we're seeing increased adoption of many observability technologies, primarily tracing in 2020.

Could this be to combat the complexity of cloud-native and microservices architectures? Let's take a deeper dive into the data!

The Rise of Cloud-Native Tech

According to the DevOps Pulse, while the majority of workloads today run on containers and cloud computing infrastructure. However, many organizations are still in the process of transitioning to a cloud-native architecture.

56% of respondents store more than half of their infrastructure in the public cloud, while 42% store three quarters or more, an 8% increase from the survey's results in 2019. AWS maintains its dominance with over 66% of the respondents reported using it, but Google Cloud (11%) is catching up with Azure (12%) in the race to the second runner.

Not surprisingly, containers have also become a mature technology, with 50% of respondents having half of their applications or more containerized.

Although much younger, serverless adoption is growing, with only 25% currently not utilizing this technology. This is a 33% increase from 2019, in which 58% had not yet adopted serverless frameworks.

As we can see, cloud-native technology has become a cornerstone of modern architecture and is not going anywhere anytime soon. Open source plays an important role in this movement, with prominent open source projects such as Docker, Kubernetes and Prometheus becoming de-facto standards.

But as more engineers jump on the cloud-native bandwagon, the challenges associated with managing and monitoring these technologies are becoming increasingly apparent. Approximately 87% of respondents report their main observability challenges lie in Kubernetes, microservices, and serverless frameworks.

Kubernetes in particular still raises challenges in multiple areas. Among the top Kubernetes challenges are monitoring and troubleshooting (44%), security (35%), networking (34%), and cluster management (30%). This is due to the fact that adopting Kubernetes involves adapting new operational and security practices, as well as the fact that OSS community projects typically fall short on enterprise grade capabilities, documentation and support. This is primarily why managed Kubernetes services are so popular, as they reduce the complexity for the user while providing the flexibility of orchestrating containerized workloads with Kubernetes.

Is Distributed Tracing Our Next Silver Bullet?

While no technology is a perfect solution in and of itself, the rise of distributed tracing as the third telemetry data type for observability may help engineering teams tackle issues presented by microservice architecture..

Survey respondents indicated their top reasons for implementing distributed tracing are identifying faulty services during troubleshooting (34%), understanding request sequence and critical paths (24%), identifying request latency sources (22%), and understanding service dependencies (20%).

According to the DevOps Pulse, tracing solutions have increased in popularity this year. In 2019, only 19% of respondents had adopted distributed tracing solutions, but in this year's survey, the number rose to 26%. In addition, 68% of respondents say that distributed tracing plays an important role in their observability strategy. Among those who have not yet implemented distributed tracing, 64% plan to implement it in the next two years.

Jaeger is an open source project, and backed by the Cloud Native Computing Foundation (CNCF). Jaeger's growing adoption is a clear indication of the prominence of open source in observability. About 44% of the survey respondents indicated that over half of their observability tools are open source. Respondents indicated the clear benefits of open source in easing integrating with DevOps and cloud-native tech, the low cost of ownership and the virtue of being a part of, and benefiting from, the community.

Combined with logs and metrics for a complete observability solution, tracing can help crack performance issues in production, better understand our distributed system and accelerate troubleshooting and root-cause analysis.

There's still a ways to go until we get there as only 30% of respondents currently combine telemetry data from logs, metrics and traces to achieve full observability and improve production monitoring and analytics.

But we expect that in the next few years tracing adoption will continue to increase significantly until it, too, becomes a mature and widely used technology, and a unified approach to observability will be established.

Dotan Horovits is a Product Evangelist at Logz.io
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