What is the eBPF, and why is it essential for observability?

What Is eBPF and Why Does It Matter for Observability?

Working within the Linux kernel is ideal for integrating security, networking, and observability features. It is not, however, without challenges. When changing kernel source code or adding modules, developers have usually dealt with complex architecture and abstraction layers that are difficult to diagnose. The Extended BPF addresses both of these difficulties (eBPF).

The Extended Berkeley Packet Filter (eBPF) is a kernel technology that enables programs to run without requiring changes to the kernel source code or the addition of new modules (beginning with Linux 4. x). It’s a sandbox virtual machine (VM) inside the Linux kernel where programmers can run BPF bytecode that uses specified kernel resources.

eBPF reduces the need to alter kernel source code and simplifies software’s ability to exploit existing layers. As a result, it’s a robust technology that has the potential to change the way services like networking, observability, and security are offered forever.

Here’s a closer look at what it is, how it works, and when you should use it.

How eBPF works

Event-driven eBPF programs are linked to a coded route. When any attached eBPF programs are provided, the code path contains particular triggers, known as hooks, that run them. Network events, system calls, function entry, and kernel tracepoints are all examples of themes.

When the code is triggered, it is initially compiled to BPF bytecode. As a result, the bytecode is checked before executing it to verify that it does not generate a loop. This step prevents the program from inadvertently or intentionally compromising the Linux kernel.

A program makes helper calls when a hook triggers it. These helper cells are functions that provide eBPF with a variety of memory-accessing capabilities. The kernel must pre-define helper calls, yet the set of available parts continues to grow.

When filtering network packets, eBPF was designed to promote observability and security. However, through time, it evolved into a means of making the use of user-supplied code safer, more convenient, and more performant.

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The advantages of eBPF

The benefits of using eBPF, commonly used to track userspace processes, are apparent. It’s a quick and easy technique to ensure:

• Performance and speed are important factors. Can now relocate packet processing out of kernel space and into userspace thanks to eBPF. eBPF is also a JIT (just-in-time) compiler. eBPF is called after the bytecode has been compiled, rather than creating a new bytecode interpretation for each method.
• The amount of intrusiveness is kept as low as possible. When used as a debugger, eBPF eliminates the need to halt a program to inspect its state.
• Security. Programs are effectively sandboxed, ensuring that kernel source code remains safe and intact. The verification phase ensures that resources aren’t overloaded by indefinitely running programs.
• Convenience. Writing code that hooks kernel functions takes less time and effort than creating and maintaining kernel modules.
• I was tracing consistently. eBPF is a single process tracing framework that is strong and simple to use. It enhances both visibility and security.
• Programmability. With eBPF can expand an environment’s feature set without the need for new layers. Because code is executed directly in the kernel, it can preserve data between eBPF events rather than dumped like other tracers.
• Expressiveness. Because eBPF is expressive, it can do tasks usually reserved for high-level languages.

eBPF best practices

Many aspects of eBPF remain unexplored because it is such a young technology. As the technology becomes more popular, best practices for eBPF are continually improving. While there is no set of best practices, you can do a few things to guarantee that your programs are practical and efficient.

We propose that if you’re utilizing eBPF for your ecosystem, you:

• To convert C to bytecode, use LLVM Clang. It was essential to code and constructed eBPF by hand when it initially appeared on the scene. The bytecode was then generated using the kernel’s assembler. Fortunately, this isn’t necessary any longer. Clang is a frontend and tooling framework for C languages.
• When writing BPF programs, use the BCC toolset. The BPF Compiler Collection (BCC) is a toolbox for writing kernel tracing and manipulation programs. It’s especially well-suited to activities like performance analysis and network traffic control.

The pitfalls of eBPF

Although it’s powerful, eBPF is not a silver bullet that suits every project or ecosystem. eBPF does have some notable disadvantages, which can make it frustrating to work within certain instances. Some developers might find eBPF inadequate to use because:

• It’s restricted to Linux and a recent kernel. eBPF was developed in the Linux kernel and is completely oriented around it. That makes it less portable than other tracers. Additionally, you need a fairly recent kernel. If you’re running anything older than v 4.13, you won’t be able to use it.
• Sandboxed programs are limited. eBPF derives increased security by limiting what resources programs can access. However, by limiting what parts of the OS a program can access, functionality is also potentially limited.

When eBPF typically works well

In cloud-native apps, eBPF is quickly gaining acceptance. As a result, eBPF is often employed in two scenarios:

• Observability is required while employing kernel tracing. eBPF is faster and more accurate in this case. There are no context changes, and eBPF programs are event-based, so nothing happens unless a specified trigger is triggered—and you won’t miss anything. 
• Security monitoring as we know it doesn’t work. In distributed and container-based setups, such as Kubernetes, eBPF is finding use. Because it can give visibility into HTTP traffic, eBPF can bridge the visibility gap in these contexts. 

You might also see eBPF used for other security purposes, such as:

• Device drivers Firewalls 
• Monitoring the performance of the network

New Relic and eBPF

Pixie is a Kubernetes-native-in-cluster observability technology that enables fast visibility into Kubernetes workloads without manual instrumentation. The majority of the magic underlying the Pixie platform is provided by eBPF. As previously mentioned, eBPF allows you to run limited code when an event occurs. This event could be a userspace (kprobes) or kernel space (kprobes) function call (uprobes). To provide observability across services and applications, Pixie employs both uprobes and kprobes.

Pixie uses eBPF to capture telemetry data automatically, and its edge-machine intelligence connects it to Kubernetes metadata to enable visibility while keeping data local. This visibility adds to the observability solution provided by Kubernetes. You’ll also be able to send Pixie-generated telemetry data starting in late May, gaining scalable retention, sophisticated visualizations, advanced correlation, and intelligent alerting features.

eBPF is observability made efficient

eBPF is a new Linux kernel technology that increases observability, networking, and security. It avoids the need to modify kernel source code or add modules, allowing you to build a more robust infrastructure to support your system without adding unnecessary complexity.

We looked at eBPF, how it works, and why it’s so valuable for distributed systems. Many of the problems related to cloud observability can be overcome by monitoring from the kernel layer. You may get a better understanding of your data and greater context and accuracy.

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