eBPF is a powerful tool that can be used to monitor Kubernetes in a variety of ways. Despite being a relatively new technology, it is quickly gaining popularity due to its flexibility and performance. With eBPF, developers can write and load custom programs into the kernel, allowing them to efficiently intercept and analyze events at various points in the kernel execution.
This functionality is particularly beneficial in monitoring Kubernetes as it enables fine-grained visibility into the system’s behavior and performance. In this article, we’ll learn about what eBPF is, how it works, its architecture, and how to monitor Kubernetes with the help of eBPF.
What is eBPF?
The Extended Berkeley Packet Filter (eBPF) is a cutting-edge Linux kernel feature that allows sandboxed programs to operate within the operating system kernel. It can securely and effectively enhance the functionality of the kernel without requiring the loading of kernel modules or modifying the source code.
Given the kernel’s capability to monitor and manage the whole system, operating systems have typically been the optimal area to incorporate security, observability, and networking capabilities. Because of its critical purpose and stringent security and stability requirements, the operating system kernel is resistant to change. As a result, operating system-level innovation has lagged behind advancements provided outside through eBPF.
How eBPF Works
Containers are often used in Kubernetes clusters to deploy services that run as long as the application is up and running, and eBPF provides a reliable approach for monitoring services in the cluster. eBPF may also be used to audit Kubernetes cluster commands, offering a granular view of each command. It provides information on the initiator, timing, location, and other relevant facts.
Event-driven eBPF applications are linked to a code route. Hooks are particular triggers in the code path that run any associated eBPF programs when they are provided. Hooks can be found in network events, system calls, function entries, and kernel tracepoints. When the code is activated, it is first compiled to BPF bytecode. In turn, before execution, the bytecode is examined to ensure that it does not produce a loop.
This step protects the software from inadvertently or intentionally compromising the Linux kernel. When a program is called at a hook, it performs assistance calls. These utility calls are functions that provide eBPF with several memory-accessing functionalities. The kernel must specify helper calls ahead of time, yet the number of accessible functions is continually growing.
Let’s explore the key components of the eBPF architecture, and how each element contributes to the robustness and extensibility of this framework. By harnessing these components, developers can address critical challenges in networking, monitoring, security, and performance optimization.
eBPF programs execute in response to events that activate them. A hook point is reached when a program crosses a certain threshold. Network events, system calls, and function calls are examples of preconfigured hooks.
An eBPF software must be able to save its state and distribute the data it collects. eBPF maps can assist applications in retrieving and storing information based on a variety of data types. eBPF maps can be accessed via system calls from both eBPF programs and apps.
An eBPF application cannot invoke a kernel function at will. This is because eBPF applications must retain compatibility and avoid being tied to certain kernel versions. As a result, eBPF applications perform function calls using helper functions. Helper functions are kernel-provided APIs that may be readily customized.
These calls enable eBPF programs to be composed. In a program, function calls allow functions to be defined and invoked. Other eBPF applications can be run via tail calls. They can also modify the context of execution.
How to Monitor Kubernetes with the Help of eBPF?
In this section of the article we would be monitoring the clusters. The eBPF can capture detailed information at the kernel level across the Kubernetes cluster, making it less complicated for teams to establish new clusters.
Deploying in Kubernetes
We must ensure that kernel headers are installed on the node to which we are distributing. To do this, we could build an Init container that runs the pre-checks and, if the headers aren’t found, downloads and installs them before starting up the agent.
When everything is finished, we can bundle it all together in a single deployment YAML file and send it to the Kubernetes API. This will generate a Daemon Set that will connect to each node’s Kernel and begin scraping the scrapeable Prometheus endpoint.
Finally, we may set up a Prometheus server with a scrape target using a pod.
kubectl apply -f “yaml file.yaml”
We can examine the TCP data flow inside the cluster by running it for a few minutes and connecting to its dashboard through a port-forward.
kubectl port-forward prometheus-<POD-ID> -n monitoring 9090:9090
4 Reasons Why eBPF Is Useful for Kubernetes Monitoring
Monitoring and scaling Kubernetes clusters has typically been difficult owing to the networked design of Kubernetes. Monitoring is the activity of collecting understanding of a system’s behavior using various forms of data. Using eBPF for Kubernetes service offers multiple benefits that guarantee that everything will run smoothly. Here are some reasons why you should use eBPF for Kubernetes monitoring.
Small-Scale Tracking of Performance
eBPF enables developers to track performance measurements, identify errors, and debug code in real time without incurring substantial performance penalties.
Monitoring Kubernetes Services
eBPF may be utilized by users to track services deployed as containers in a Kubernetes cluster. Because containers are basically processes that continue as long as the application within them is active, eBPF provides a consistent and trustworthy solution for monitoring services functioning in a cluster.
Enhanced System Visibility
eBPF also provides a uniform platform for tracking processes, which improves system visibility. It also allows for the extraction of vital kernel metrics, giving developers insights into kernel-level activity that may be utilized for performance optimization, debugging, and security audits.
eBPF is a safe way to execute code within the kernel of an operating system. Unlike Linux kernel modules, eBPF applications are subjected to a series of security tests before being loaded into kernel spaces to verify the code’s safety and security.
eBPF is a solution that can dramatically improve the observability, networking, and security of Kubernetes. You don’t need to update kernel source code or add modules when using eBPF, which streamlines the process of building a more resilient infrastructure. By monitoring at the kernel level, eBPF can ease many of the challenges associated with observability in a distributed system like Kubernetes. This gives you additional insight, context, and accuracy in your data, allowing you to manage and improve your Kubernetes infrastructure more effectively.