eBPF: The Catalyst Behind Kubernetes Monitoring Evolution

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In Kubernetes, observability typically involves tracking via logs, metrics, tracing, and events. These various recorded logs offer perceptions into application activity, including how requests are handled, how long it takes to handle those requests, and other details. Events provide information about changes in the cluster and tracing aids in tracking requests between microservices. However, these conventional approaches have drawbacks in terms of deep visibility and effectiveness, such as the inability to spot bottlenecks in systems that use numerous different microservices.

The Kubernetes observability is changing as a result of eBPF (extended Berkeley Packet Filter). The Linux kernel may be dynamically and effectively instrumented, enabling real-time insights with no performance impact. System calls, network activity, and file I/O are just a few examples of the activities that can be monitored at the kernel-level with eBPF. eBPF improves performance optimization, security monitoring, and troubleshooting, revolutionizing how we see and handle Kubernetes systems. Let’s discuss the benefits of leveraging eBPF in Kubernetes in detail.

Benefits of Leveraging eBPF for Observability in Kubernetes


When eBPF observability is used in Kubernetes, monitoring and troubleshooting capabilities significantly improve. With kernel-level insights, customized metrics and many other features come to the forefront. Let’s get into further detail about why it makes sense to leverage eBPF for Kubernetes observability:

Kernel-Level Insights

By tracking and examining system calls, network activity, and file I/O operations at the kernel-level, eBPF enables you to delve deeply into the core of your containerized applications. Understanding how your applications interact with the underlying infrastructure is made possible by this fine-grained visibility. For instance, you can learn more about the behavior and performance of your programs as they interact with the kernel by tracing system calls. This information can be used to pinpoint bottlenecks and minimize resource utilization.

Customized Metrics

eBPF gives you the ability to generate unique metrics that cater to the particular requirements of your application. It is necessary for organizations to take care of a variety of needs, some of which may include searching for requests that have already been processed, maximizing CPU use and memory utilization, and more. Therefore, with the help of these custom metrics, you can monitor application-specific performance indicators or gain insights into Kubernetes environment-specific patterns of resource usage, resulting in excellent metrics and monitoring.

Integration with Existing Tools

The eBPF data can be connected with well-known observability tools like Prometheus, Grafana, and Jaeger because organizations may already be using a variety of observability technologies. This means that eBPF can use or incorporate the insights obtained by these techniques. It essentially integrates all the analytics. This will support your current monitoring and tracing infrastructure, giving you a comprehensive understanding of the functionality and behavior of your Kubernetes cluster.

Real-Time Insights 

When monitoring a very large architecture that uses either Kubernetes or microservices, it is absolutely necessary to obtain real-time insights into the system. This allows addressing the bottlenecks as soon as they begin to cause issues. eBPF offers real-time insights into the actions taken by containers, pods, and services that are managed by Kubernetes. This real-time data is extremely helpful in spotting issues, such as performance bottlenecks, security breaches, or anomalies, and devising appropriate solutions in a short amount of time. As a result, troubleshooting may be completed more quickly, which also results in improved response times.


Kubernetes observability requires multiple tools and methods to fully understand the clusters. Many monitoring and observability solutions are good, but not all provide kernel-level monitoring, which is necessary to understand the operating system’s kernel, including network activity and system calls.

eBPF fills gaps with insights other technologies overlook. Organizations can ensure the reliability and performance of their Kubernetes containerized workloads by combining the strengths of various monitoring and observability tools to uncover performance bottlenecks, security threats, and network anomalies.

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