Kubernetes (K8s) containers and environments are the leading approach to packaging, deploying and managing containerized applications at scale. The dynamic, open-source, microservices-based configuration of Kubernetes can be a great fit for businesses that are looking to maximize infrastructure agility. However, the distributed flexibility that makes Kubernetes appealing can also make implementing Kubernetes monitoring and observability practices challenging.
Observability comprises a range of processes and metrics that help teams gain actionable insights into a system’s internal state by examining system outputs. It’s an essential part of maintaining any IT infrastructure. But managing the sheer volume of data, nodes, pods, services and endpoints that comprise Kubernetes environments requires observability practices that are appropriate for the job.
In this blog, we discuss how Kubernetes observability works, and how organizations can use it to optimize cloud-native IT architectures.
How does observability work?
Broadly speaking, observability describes how well internal system states can be inferred from external outputs. It’s the ability to diagnose and understand why a system is behaving in a particular way, which is vital to troubleshooting, deciphering performance issues and improving system design.
In DevOps, the concept of observability has evolved to refer to the end-to-end visibility of a system state as dictated by telemetry data. The primary data classes used—known as the three pillars of observability—are logs, metrics and traces.
Logs
Logs include discrete events recorded every time something occurs in the system, such as status or error messages, or transaction details. Kubernetes logs can be written in both structured and unstructured text.
Metrics
CPU usage, memory consumption, network I/O, request latency or any business-specific indicators. Kubernetes metrics are often aggregated to create time-series observability data that can help teams spot trends and identify patterns.
Traces
Traces help teams follow a request or transaction through the various services and components of a distributed system. They also help teams visualize the dependencies between different components of an infrastructure so that delays and errors can be located quickly.
Achieving successful observability requires the deployment of appropriate Kubernetes monitoring tools and the implementation of effective processes for collecting, storing and analyzing the three primary outputs. This might include setting up and maintaining monitoring systems, application log aggregators, application performance management (APM) tools or other observability platforms.
However, Kubernetes environments also necessitate a more thorough examination of standard metrics. Kubernetes systems comprise a vast environment of interconnected containers, microservices and other components, all of which generate large amounts of data. Kubernetes schedules and automates container-related tasks throughout the application lifecycle, including:
Deployment
Kubernetes can deploy a specific number of containers to a specific host and keep them running in their desired state.
Rollouts
A rollout is a Kubernetes deployment modification. Kubernetes enables teams to initiate, pause, resume and roll back rollouts.
Service discovery
Kubernetes can automatically expose a container to the internet or other containers using a DNS name or IP address.
Autoscaling
When traffic spikes, Kubernetes can automatically spin up new clusters to handle the additional workload.
Storage provisioning
Teams can set up Kubernetes to mount persistent local or cloud storage for containers.
Load balancing
Based on CPU utilization or custom metrics, Kubernetes load balancing features can distribute workloads across the network to maintain performance and stability.
Self-healing for high availability
Kubernetes can automatically debug, restart or replace a failed container to prevent downtime. It can also decommission containers that don’t meet health check requirements.
With so many shifting, interacting and layered components comes as many potential issues and failure points, therefore lots of areas where real-time monitoring becomes a necessity. It also means that a conventional approach to monitoring logs, metrics and traces might prove insufficient for observability in a Kubernetes environment.
Kubernetes observability principles
Because every component in a Kubernetes architecture is interdependent on other components, observability requires a more holistic approach.
Kubernetes observability requires organizations to go beyond collecting and analyzing cluster-level data from logs, traces and metrics; connecting data points to better understand relationships and events within Kubernetes clusters is central to the process. This means that organizations must rely on a tailored, cloud-native observability strategy and scrutinize every available data source within the system.
Observability in a K8s environment involves:
1. Moving beyond metrics, logs and apps. Much like virtual machine (VM) monitoring, Kubernetes observability must account for all log data (from containers, master and worker nodes, and the underlying infrastructure) and app-level metrics. However, unlike VMs, Kubernetes orchestrates container interactions that transcend apps and clusters. As such, Kubernetes environments house enormous amounts of valuable data both outside and within network clusters and apps. This includes data in CI/CD pipelines (which feed into K8s clusters) and GitOps workflows (which power K8s clusters).
Kubernetes also doesn’t expose metrics, logs and trace data in the same way traditional apps and VMs do. Kubernetes tends to capture data “snapshots,” or information captured at a specific point in the lifecycle. In a system where each component within every cluster records different types of data in different formats at different speeds, it can be difficult—or impossible—to establish observability by simply analyzing discrete data points.
What’s more, Kubernetes doesn’t create master log files at either the app or cluster level. Every app and cluster records data in its respective environment, so users must aggregate and export data manually to see it all in one place. And since containers can spin up, spin down or altogether disappear within seconds, even manually aggregated data can provide an incomplete picture without proper context.
2. Prioritizing context and data correlation. Both monitoring and observability are key parts of maintaining an efficient Kubernetes infrastructure. What differentiates them is a matter of objective. Whereas monitoring helps clarify what’s going on in a system, observability aims to clarify why the system is behaving the way that it is. To that end, effective Kubernetes observability prioritizes connecting the dots between data points to get to the root cause of performance bottlenecks and functionality issues.
To understand Kubernetes cluster behavior, you must understand each individual event in a cluster within the context of all other cluster events, the general behavior of the cluster, and any events that led up to the event in question.
For instance, if a pod starts in one worker node and terminates in another, you need to understand all the events that are happening simultaneously in the other Kubernetes nodes, and all the events that are happening across your other Kubernetes services, API servers and namespaces to get a clear understanding of the change, its root cause, and its potential consequences.
In other words, merely monitoring tasks is often inadequate in a Kubernetes environment. To achieve Kubernetes observability, get relevant system insights or conduct accurate accurate root cause analyses, IT teams must be able to aggregate data from across the network and contextualize it.
3. Using Kubernetes observability tools. Implementing and maintaining Kubernetes observability is a large, complex undertaking. However, using the right frameworks and tools can simplify the process and improve overall data visualization and transparency.
Businesses can choose from a range of observability solutions, including programs that automate metrics aggregation and analysis (like Prometheus and Grafana), programs that automate logging (like ELK, Fluentd and Elasticsearch) and programs that facilitate tracing visibility (like Jaeger). Integrated solutions, like OpenTelemetry, can manage all three major observability practices. And customized, cloud-native solutions, like Google Cloud Operations, AWS X-Ray, Azure Monitor and and IBM Instana Observability, offer observability tools and Kubernetes dashboards optimized for clusters that are running on their infrastructure.
Best practices for optimizing Kubernetes observability
• Define your KPIs. Figure out which key performance indicators, like app performance, system health and resource usage, give you the most useful insights into your infrastructure’s behavior. Revise them as needed.
• Centralize logging. K8s environments generate massive amounts of data. Aggregating and storing it using a centralized logging solution is integral to data management.
• Monitor resource usage. Collect real-time data on memory, CPU and network usage so you can proactively scale resources when necessary.
• Set up alerts and alarms. Use established KPI thresholds to configure alerts and alarms. This practice allows teams to receive timely notifications when issues arise.
Establish Kubernetes observability with IBM® Instana® Observability
Kubernetes is the industry-standard container orchestration platform, managing containerized workloads with remarkable efficiency. However, the distributed, multi-layered microservices architecture of Kubernetes demands robust observability mechanisms and advanced solutions, like IBM Instana Observability.
Instana Observability provides automated Kubernetes observability and APM capabilities that are designed to monitor your entire Kubernetes application stack—from nodes and pods to containers and applications—for all Kubernetes distributions.
Observability in Kubernetes is not just a technical implementation; it’s a strategic approach that requires attentive planning and an organizational culture that values data transparency.
Instana Observability helps teams gain a comprehensive understanding of their Kubernetes environments and deliver robust, high-performing applications in an increasingly cloud-based world.
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