Cloud Computing, DevOps

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Dive into eBPF Observability on Kubernetes – Part 1

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As Kubernetes continues to dominate the container orchestration landscape, observability becomes increasingly crucial for monitoring and troubleshooting applications running on this platform. Traditional monitoring tools often struggle to provide deep insights into containerized environments due to their dynamic and transient nature. This is where eBPF (extended Berkeley Packet Filter) comes into play, offering a powerful and efficient way to observe, trace, and analyze activities within Kubernetes clusters.


eBPF, originally developed as an extension of the Berkeley Packet Filter within the Linux kernel, has evolved into a versatile technology capable of executing custom code within the kernel without requiring modifications to the kernel itself.

This allows developers to implement highly efficient tracing and observability tools that can capture detailed insights into the system’s behavior with minimal overhead.

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Capabilities of eBPF

eBPF provides a wide range of capabilities for observability in Kubernetes environments:

  1. Dynamic Tracing: eBPF enables dynamic tracing of kernel functions, system calls, and other events without the need for recompilation or kernel modules. This allows for real-time monitoring of system activities without impacting performance.
  2. Efficient Packet Processing: With eBPF, developers can create custom packet filters and classifiers, enabling precise packet inspection and manipulation at high speeds. This is particularly useful for network observability within Kubernetes clusters.
  3. Custom Probes: eBPF allows the insertion of custom probes (kprobes, uprobes) into user-space and kernel-space applications, enabling detailed tracing of application behavior without modifying the application code.
  4. Low Overhead: eBPF programs are designed to execute with minimal overhead, making them suitable for production environments where performance is critical. This low overhead makes eBPF well-suited for observability tasks in Kubernetes clusters.
  5. Security Observability: eBPF can also be leveraged for security observability, allowing administrators to monitor suspicious activities, detect intrusions, and enforce security policies within Kubernetes environments.


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How does eBPF Work?

eBPF, or extended Berkeley Packet Filter, serves as a powerful mechanism for enhancing observability within Kubernetes clusters. Leveraging a series of programmable hooks triggered by specific events such as system calls, network traffic, or function executions, eBPF enables the execution of custom logic directly within the Linux kernel.

In the context of Kubernetes, these hooks provide deep insights into the behavior of containerized workloads, allowing for real-time monitoring, tracing, and analysis. eBPF programs, written in a restricted C-like language, are compiled into efficient bytecode and loaded into the kernel, where they operate within a secure and sandboxed environment. By interacting with kernel data structures and leveraging predefined helper functions, eBPF programs can track resource utilization, monitor network traffic, and capture detailed telemetry data from individual containers and pods. This low-overhead, high-performance approach to observability makes eBPF an invaluable tool for understanding and optimizing the performance of Kubernetes deployments in production environments.


Kubernetes Observability via eBPF

In Kubernetes, eBPF can be used to gain deep insights into various aspects of cluster operations:

  • Network Visibility: eBPF-powered tools like Cilium and Calico leverage eBPF to provide comprehensive network visibility, allowing operators to monitor network traffic, enforce policies, and detect anomalies within Kubernetes clusters.
  • Application Performance Monitoring (APM): eBPF-based APM solutions such as Falco and Sysdig provide detailed insights into application performance and behavior, helping developers identify performance bottlenecks, debug issues, and optimize resource utilization.
  • Security Monitoring: eBPF enables fine-grained security monitoring within Kubernetes clusters, allowing operators to detect and respond to security threats in real-time. Tools like Falco leverage eBPF to monitor suspicious activities and enforce security policies based on predefined rules.
  • Resource Utilization: eBPF can be used to monitor resource utilization within Kubernetes clusters, providing visibility into CPU, memory, and disk usage across nodes and pods. This information is invaluable for optimizing resource allocation and scaling applications effectively.


eBPF offers a powerful and efficient way to observe, trace, and analyze activities within Kubernetes clusters. Its dynamic tracing capabilities, low overhead, and versatility make it an ideal tool for observability tasks in modern containerized environments. In the next part of this series, we will explore a practical demonstration of eBPF observability in action within a Kubernetes cluster.

Drop a query if you have any questions regarding eBPF and we will get back to you quickly.

Stay tuned for Part 2, where we will dive deeper into a hands-on demonstration of eBPF observability on Kubernetes.

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1. How does eBPF differ from traditional kernel modules?

ANS: – Unlike traditional kernel modules, eBPF programs can be loaded and executed within the kernel without requiring modifications to the kernel itself. This makes eBPF more flexible, secure, and efficient compared to traditional kernel modules.

2. How does eBPF ensure security and stability within the kernel?

ANS: – eBPF programs undergo rigorous security checks before being loaded into the kernel, ensuring that they do not compromise the security or stability of the system. Additionally, eBPF operates within a secure and sandboxed environment, limiting its impact on the underlying kernel.

3. What programming languages can be used to write eBPF programs?

ANS: – eBPF programs are typically written in a restricted C-like language, although there are higher-level frameworks and tools available that provide abstractions and APIs for writing eBPF programs in languages such as Go, Rust, and Python.

WRITTEN BY Harikrishnan S

Harikrishnan Seetharaman is a Research Associate (DevOps) at CloudThat. He completed his Bachelor of Engineering degree in Electronics and Communication, and he achieved AWS solution architect-Associate certification. His area of interest is implementing a cloud-native solution for customers and helping them by proving robust and reliable solutions for their complex problems, DevOps, and SaaS. Apart from his professional interest he likes to spend time in farming and learning new DevOps tools.



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