How to Achieve Kubernetes Observability: Principles and Best Practices

Kubernetes (K8s) containers and environments are the leading approach for packaging, deploying, and managing containerized applications at scale. Kubernetes’ dynamic, open-source, microservice-based composition can be a great fit for enterprises looking to maximize infrastructure agility. However, the distributed flexibility that makes Kubernetes attractive can also make Kubernetes monitoring and observability practices difficult to implement.

Observability consists of a variety of processes and metrics that help teams inspect system output to gain actionable insights into the internal state of the system. This is an essential part of maintaining your IT infrastructure. However, managing the massive amounts of data, nodes, pods, services, and endpoints that make up a Kubernetes environment requires observability practices that are right for the job.

In this blog, we discuss how Kubernetes observability works and how organizations can use it to optimize their cloud-native IT architecture.

How does observability work?

Broadly speaking, observability refers to how well the internal system state can be inferred from external output. This is the ability to diagnose and understand why a system behaves a certain way, which is essential for troubleshooting and deciphering performance problems and improving system design.

In DevOps, the concept of observability has evolved to refer to end-to-end visibility into system health based on telemetry data. The basic data classes, known as the three pillars of observability, are logs, metrics, and traces.

Log

Logs contain individual events recorded whenever a problem 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 business-specific metrics. Kubernetes metrics are often aggregated to produce time series observability data that can help teams spot trends and identify patterns.

trace

trace Helps teams follow requests or transactions through various services and components in a distributed system. It also helps teams visualize dependencies between various components of their infrastructure to quickly find delays and errors.

Achieving successful observability requires deploying appropriate Kubernetes monitoring tools and implementing effective processes to collect, store, and analyze the three primary outputs: This may include setting up and maintaining a monitoring system, application log collector, application performance management (APM) tool, or other observation platform.

However, Kubernetes environments also require a more thorough examination of standard metrics. Kubernetes systems consist of a vast environment of interconnected containers, microservices, and other components, all generating large amounts of data. Kubernetes schedules and automates container-related tasks throughout the application lifecycle, including:

deployment

Kubernetes allows you to deploy a specific number of containers to a specific host and keep them running in any desired state.

Release

A rollout is a Kubernetes deployment modification. Kubernetes allows teams to start, pause, resume, and roll back rollouts.

service discovery

Kubernetes can automatically expose containers to the internet or other containers using DNS names or IP addresses.

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 reliability.

Self-healing for high availability

Kubernetes can automatically debug, restart, or replace failed containers to prevent downtime. You can also discard containers that do not meet health check requirements.

With so many moving, interacting, layered components, many potential problems and points of failure, many areas require real-time monitoring. This also means that traditional approaches to monitoring logs, metrics, and traces may not provide sufficient observability in a Kubernetes environment.

Kubernetes Observability Principles

Because every component of the Kubernetes architecture is interdependent with other components, observability requires a more holistic approach.

Kubernetes observability requires organizations to do more than collect and analyze cluster-level data from logs, traces, and metrics. Connecting data points to better understand relationships and events within a Kubernetes cluster is central to the process. This means that organizations must rely on customized cloud-based observability strategies and scrutinize all available data sources within their systems.

Observability in the K8s environment includes:

One. Metrics, logs, apps and more. Similar to virtual machine (VM) monitoring, Kubernetes observability must account for all log data (from containers, master and worker nodes, and underlying infrastructure) and app-level metrics. However, unlike VMs, Kubernetes orchestrates container interactions beyond apps and clusters. Therefore, Kubernetes environments hold enormous amounts of valuable data both externally and internally to network clusters and apps. This includes data from CI/CD pipelines (which feed the K8s cluster) and GitOps workflows (which drive the K8s cluster).

Additionally, Kubernetes doesn’t expose metrics, logs, and trace data in the same way as traditional apps and VMs. Kubernetes tends to capture data “snapshots”, i.e. information captured at a specific point in its life cycle. In systems where each component within every cluster records different types of data in different formats and at different rates, it can be difficult or impossible to establish observability simply by analyzing individual data points.

Additionally, Kubernetes does not create master log files at the app or cluster level. Every app and cluster records data in its environment, so users must manually aggregate and export data to see it all in one place. And because containers can rotate, rotate, or disappear completely within seconds, even manually aggregated data can provide an incomplete picture without proper context.

2. Prioritize context and data correlation. Monitoring and observability are both important parts of maintaining an efficient Kubernetes infrastructure. Distinguishing them is an objective matter. Monitoring helps clarify what is happening in a system, while observability aims to clarify why the system behaves the way it does. To this end, effective Kubernetes observability prioritizes connecting the dots between data points to identify the root cause of performance bottlenecks and functionality issues.

To understand Kubernetes cluster behavior, you need to understand each individual event in the cluster within the context of all other cluster events, the general behavior of the cluster, and any events that caused the event in question.

For example, if a pod starts on one worker node and terminates on another, you need to understand all events that occur simultaneously on other Kubernetes nodes, as well as all events that occur in other Kubernetes services, API servers, and namespaces. Clearly understand the change, its root causes, and potential consequences.

In other words, simply monitoring tasks are often insufficient in a Kubernetes environment. To achieve Kubernetes observability, gain relevant system insights, or perform precise and accurate root cause analysis, IT teams need to be able to aggregate and contextualize data across the network.

three. Use Kubernetes Observation Tools. Implementing and maintaining Kubernetes visibility is a large and complex task. However, with the right framework and tools, you can simplify the process and improve overall data visualization and transparency.

Companies can choose from a variety of observability solutions, including programs that automate metric aggregation and analysis (such as Prometheus and Grafana), programs that automate logging (such as ELK, Fluentd, and Elasticsearch), and programs that facilitate trace visibility (such as Jaeger). You can choose. An integrated solution like OpenTelemetry can manage all three key observation cases. And custom cloud-based solutions like Google Cloud Operations, AWS X-Ray, Azure Monitor, and IBM Instana Observability provide observability tools and Kubernetes dashboards optimized for clusters running on your infrastructure.

Best Practices for Optimizing Kubernetes Observability

• KPI Definition. Find out which key performance indicators, such as app performance, system health, and resource usage, provide the most useful insight into infrastructure behavior. Modify as needed.
• Centralize logging. K8s environments generate enormous amounts of data. Aggregating and storing this using a centralized logging solution is essential for data management.
• Monitor resource usage. Collect real-time data on memory, CPU, and network usage so you can proactively scale resources when needed.
• Warning and alarm settings. Configure alerts and alerts using set KPI thresholds. This allows your team to receive timely notifications when issues arise.

Setting up Kubernetes observability using IBM® Instana® Observability

Kubernetes is the industry-standard container orchestration platform that manages containerized workloads with incredible efficiency. However, Kubernetes’ distributed, multi-tiered microservices architecture requires powerful observability mechanisms and advanced solutions such as IBM Instana Observability.

Instana Observability provides automated Kubernetes observability and APM capabilities designed to monitor the entire Kubernetes application stack, from nodes and pods to containers and applications, for any Kubernetes deployment.

Observability in Kubernetes is not just a technical implementation. This is a strategic approach that requires careful planning and an organizational culture that values ​​data transparency.

Instana Observability helps teams gain a comprehensive understanding of their Kubernetes environment and deliver robust, high-performance applications in an increasingly cloud-based world.

Explore Instana Observability