Cloud-native monitoring is the process of instrumenting a cloud-native application to collect, aggregate, and analyze logs, metrics, distributed traces and other telemetry. The goal is to better understand application behavior. Logs, metrics and distributed traces are often necessary to get a full picture of a cloud native system. Distributed tracing becomes more important as an application becomes more distributed.
Cloud-native monitoring is often referred to as microservices monitoring, because cloud-native applications are commonly built in a microservices architecture, with each component operating as an independent, decoupled microservice that interacts with others over the network and shared services.
Monitoring can involve a broad range of activities, from keeping track of specific system properties on a host, such as CPU utilization, storage space, and memory consumption, to detailed analysis of distributed requests served by multiple components and how failures spread among them.
One of the main differentiations between cloud-native environments and more traditional environments that affect monitoring is that many cloud-native components are ephemeral—they are frequently created and destroyed. Therefore, it is not always possible to tie monitoring to specific resource names, and monitoring systems must have a strategy for collection of logs from distributed components to perform central storage and analysis.
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IT environments have steadily become more complex. The growth of cloud computing and hybrid environments, the proliferation of nodes, endpoints, and technology stacks, additional levels of abstractions used in architectures and the growing use of containerized and serverless architectures. Visibility over IT resources has become a major challenge, and debugging complex distributed applications is time-consuming and frustrating, especially while an outage is ongoing.
According to Google’s SRE handbook, the following four key metrics are the most important to evaluate system performance and health: latency, traffic, error rate, and saturation.
Latency is the time it takes for a service or system to respond to a request. It covers the journey of sending a request through the network, processing it, and returning a response. Pay attention to error latency—failed responses can be unpredictably time-consuming, both in terms of longer-than-expected responses, e.g., when timeouts are involved, as well as fail-fast responses, e.g., in case of malformed input.
Traffic is a measure of the load served by a system. There are several ways to define and measure traffic depending on the system: for example, traffic in a database-specific system is the number of database transactions per second, while the amount of requests served is a good measure of traffic for web server-like applications.
The error rate is the number of requests that fail—there are various types of failure, including explicit failures, undesired responses, and slow responses. Monitoring errors is often challenging, given the complexity of different failure types. Error tracking is a form of monitoring that collects environmental data to identify the causes of errors. Understanding errors is important for maintaining an adequate level of service to end-users.
Saturation is the extent to which a system is full. This metric measures the fraction of memory or CPU used, indicating the proportion of processing bandwidth consumed continuously. Setting a saturation target is important because system performance depends on changing resource utilization patterns. Monitoring saturation helps determine workload targets that reflect real-world demands.
Here are some important best practices for monitoring your cloud-native deployments.
Cloud-native architectures are more complex than traditional application environments, consisting of distributed systems made of many moving parts, often from multiple teams and written in a variety of languages. Being able to pinpoint quickly and accurately where errors originate and how they spread to the end users is key to detecting and solving issues quickly.
Distributed tracing is a monitoring technique that has come to the forefront with cloud-native applications due to their innate distribution and the complexity therein. In a nutshell, distributing tracing consists of collecting across all components, a “trace” that describes what each component does to serve a specific request. Think of it as a distributed log ledger, with each of the components of your application adding to the history of a request.
OpenTelemetry, a project under the umbrella of the Cloud Native Computing Foundation (CNCF), is quickly rising as the de-facto standard for distributed tracing, being increasingly integrated in open-source and commercial projects alike.
Related content: Read our guide to OpenTelemetry
Automate all tasks possible, as this will help you monitor a dynamic, distributed environment. Automation is especially employment for deployment and baselining. Relying on a team to manually implement monitoring configuration and instrumentation tasks is time-consuming and expensive. It also makes it harder to keep the monitoring tools updated. Even better, select a monitoring tool that is inherently automated and frees you from the toil of maintaining monitoring configurations as your code evolves.
Automated monitoring also helps minimize blind spots and increase observability, enabling more contextual, accurate insights. You can use a CI/CD tool to store environment-specific parameters packaged with every delivery. It can execute processes such as making service calls.
Implement continuous testing by automating regression and performance tests. CI/CD pipelines usually incorporate various forms of automation to improve code quality and accelerate delivery processes.
Take the time to outline the types of alerts required by various teams to help them identify problems quickly. Proper alert configuration is important for preventing alert fatigue and ensuring alert specificity to minimize false positives. An effective alert strategy helps reduce response times so teams can solve issues faster. You can automate baseline creation to facilitate alert configuration, automate root-cause-analysis, and prioritize alerts.
Group alerts based on their business impact to help teams prioritize high-risk alerts. Risk classification and prioritization are important for focusing efforts on relevant issues, saving time, and preventing the worst damage. Different alert groups can generate alerts sent to different teams for specialized treatment.
Create custom dashboards to provide specific teams and analysts with the relevant monitoring data. You can have a different role-specific dashboard for each team to prevent team members from viewing sensitive or irrelevant data. There should be a unifying, coherent data model underlying the data across your specialized dashboards.
Lumigo is a cloud native observability tool, purpose-built to navigate the complexities of microservices. Through automated distributed tracing, Lumigo is able to stitch together the distributed components of an application in one complete view, and track every service of every request. Taking an agentless approach to monitoring, Lumigo sees through the black boxes of third parties, APIs and managed services.
With Lumigo users can:
Get started with a free trial of Lumigo for your microservice applications.
