eBPF observability is a robust approach that involves implementing programs to monitor kernel events, and enhances the Linux kernel’s observability, networking, and security. It eliminates the need to edit kernel source code or add modules, allowing you to create a more robust infrastructure to support your system without complicating it. In this article, we will discuss eBPF, how it works, and the advantages and disadvantages of using eBPF for observability.

What is eBPF?

Let’s first understand what eBPF is, and why it is important. 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 securely and effectively enhances the functionality of the kernel without you having to load kernel modules or modify the source code.

Given the kernel’s capability to monitor and manage the whole system, operating systems have typically been the ideal 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 development is lagging behind improvements provided outside through eBPF.

What is eBPF Observability?

eBPF observability refers to a simple but complete implementation of programs meant for observing kernel events. This is done mostly in Linux kernels. eBPF stands out as an observability tool because it can run programs within the kernel to exfiltrate monitoring data without altering the source code. Observability with eBPF is extremely safe, isolated, and unobtrusive, and it may be transferred to centralized systems. It increases observability by boosting infrastructure and network event visibility, context, and accuracy.

How Does eBPF Work?

In Kubernetes clusters, containers are frequently used to install services that continue to operate as long as the application is up and running, and eBPF offers a dependable method 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 and system calls.

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.

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How Does eBPF Observability Work?

To completely comprehend the underlying mechanics enabling eBPF observability, we must first comprehend the idea of hooks. eBPF programs are typically event-driven, meaning they are activated once a specified event happens. For example, whenever a function call is performed, an eBPF application can be invoked to collect data for observability.

First, these hooks might exist in either kernel or user space. As a result, eBPF may be used to monitor both user space apps and kernel-level events. Second, these hooks can be pre-determined/static or dynamically introduced into a running system (without restarts!). Each of them is enabled by four unique eBPF processes:

• Kprobes (Kernel Probes): Utilized at runtime to dynamically hook into any area of the kernel code.

• Uprobes (User Probes): Used at runtime to dynamically hook into any portion of a user-space program.

• Kernel tracepoints: Used to hook onto kernel-defined events.

• USDT: Used to hook into developer-defined tracepoints in application code.

There are various predefined hooks in the kernel space to which an eBPF application may be readily attached (e.g., system calls, function entry/exit, network events, kernel tracepoints). Similarly, many language runtimes, database systems, and software stacks have preconfigured hooks for Linux BCC utilities that eBPF programs may hook into in the user space.

Who Should Use eBPF Observability?

The use of eBPF in cloud-native apps is growing. In Kubernetes and other distributed and container-based settings, eBPF is frequently employed when traditional security monitoring fails. eBPF may assist in bridging the visibility gap in such scenarios since it gives visibility into HTTP traffic.

Advantages of Using eBPF for Observability

The capacity to adapt to different demands and settings is crucial for system observability. This is where this technology excels, offering developers with incomparable amounts of freedom. Here are a few advantages to using eBPF for observability.

Security

Programs are essentially sandboxed, which implies that kernel source code remains untouched and safe. The verification process ensures that programs that conduct endless loops do not abuse resources.

Centralized

eBPF programs are centralized and give greater granular data about kernel events. This gives a wealth of information on container and network activities, which can then be transferred to centralized platforms for aggregation and visibility.

Convenience

Writing code that hooks into kernel routines requires less effort than building and maintaining kernel modules.

Unified Tracing

eBPF provides a unified, robust, and user-friendly framework for tracing processes. This improves both visibility and security.

Programmability

Using eBPF increases an environment’s feature-richness without adding extra layers. Similarly, because code is performed directly in the kernel, eBPF can store the data between eBPF events rather than dumping it like other tracers do.

Disadvantages of Using eBPF for Observability

Using eBPF for observability has its benefits, however, all of these superior abilities come at a cost. Let us look at some of the limitations of eBPF and what is being done about it.

Linux Restrictions

eBPF is now mostly utilized in Linux kernels. This provides a problem for organizations trying to use eBPF technologies for Kubernetes and container workloads running in non-Linux systems. eBPF for Windows, on the other hand, is under active development.

Market Adoption

There is a lot of enthusiasm and buzz about the technology, and support from big tech companies has helped to strengthen its case in the cloud-native arena. However, it is still in its early stages and necessitates significant subject expertise. As a result, market uptake has been slow.

Conclusion

In the next few years, this technology has the potential to transform the current approach to observability. As new eBPF applications in observability emerge, it is evident that this transformative technology will drive innovation and influence the future of observability. eBPF is a robust observability technology that assists us in capturing vital data points with low overhead. Its potential to deliver a safer and more straightforward observability implementation will be important for enhancing system performance, safety, and overall operational efficiency.

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