The development and delivery of software has altered in the cloud-native age. Understanding your application’s behavior across multiple tiers of the stack can be difficult as applications get more sophisticated. This attempt to improve visibility into your program and help in troubleshooting capability is led by Extended Berkeley Packet Filter (eBPF), a technology that runs at the kernel level. Asking questions of the system rather than merely compiling monitoring data and attempting to correlate it is the essence of true next-generation observability.
In this article we’ll take a look at what eBPF is, its use cases and advantages and disadvantages of using eBPF.
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What is eBPF?
With the help of the eBPF Linux kernel feature, you can develop kernel-level applications for a variety of purposes, including networking, security, tracing, and observability, all without having to change any kernel modules or source code. When used with Kubernetes, eBPF significantly enhances observability, which is essential for application debugging.
You can create independent applications using eBPF that run when particular kernel-level events take place. Before being loaded and run inside the kernel, these applications are first compiled into eBPF bytecode and reviewed by a verifier to make sure they don’t cause kernel instability. To achieve excellent speed, this bytecode is further JIT-compiled into effective machine code.
Use Cases for eBPF
Operating eBPF at the kernel level has several benefits. For networking, especially routing, comes first. The kernel-level packet forwarding mechanism used by some high-performance routers, firewalls, and load balancers today may be programmed using eBPF. Since we are essentially routing in hardware at line-rate, programming the forwarding mechanism at the kernel level yields considerable performance advantages. Here are four instances where it would be the greatest choice for obtaining deep visibility:
Kubernetes is an excellent platform for demonstrating eBPF’s observability capabilities. It scales a workload up or down by adjusting the number of pods, as is common knowledge. The lifetime of the pods is unreliable and erratic in nature. As a result, installing instrumentation agents within each pod or container may cause performance difficulties with the workload and be ineffective because pods are arbitrarily produced and destroyed. You may use it to set up your monitoring camp at the OS level and keep an eye on every action that your Kubernetes configuration makes.
Dynamic Network Performance Monitoring
I’ve previously made several references to how simple it is for eBPF to track and monitor core Linux subsystem functions like CPU use and network performance. This feature of eBPF may be used to build up a network performance monitoring system. However, because the rules used to monitor the network are preset constants, such a system would be static. They can still be changed manually, but it takes time.
eBPF lets you execute custom code inside the Linux kernels, to reiterate how it operates. Since the kernel handles every activity that takes place in a system, it is simple to track and trace everything from one location. The eBPF applications can also be configured to run in response to system events. You may follow everything that happens in and around such events thanks to this. Additionally, because all eBPF programs go through a verification step in which they are examined for endless loops and other potential errors, it is one of the safest ways to implement kernel tracing. As a result, you can trust it to meet your needs for kernel tracing.
Pod-Level Network Monitoring
In a Kubernetes-based system, monitoring the network surrounding the pods is one of the most common uses of eBPF. It can be difficult to instrument a Kubernetes-based setup with conventional techniques since it can host a number of operating apps, each with its own distinct base image. It’s possible that various base operating systems, cloud servers, or coding standards call for various monitoring agents.
The Advantages of eBPF for Observability
So far, we’ve explored what eBPF is and what it can mean for your system observability. It can be a great tool when utilized in the right way when compared to more conventional observability solutions that allows deeper insights. Here are some of its advantages:
- Efficiency: eBPF programs are more effective than sidecar containers because they operate at the kernel level instead of in user space. Ops teams can easily keep an eye on kernel-level activity.
- Secure: You can keep your access control rules in place for modifications to the code because an eBPF application doesn’t affect the kernel at all. Using a kernel module is an alternative, although doing so raises a number of security issues. A verification step is another feature of eBPF applications that guards against resource overuse.
- Centralized: Compared to previous methods, using an eBPF software allows you to monitor and trace standards with more precise information and kernel context. This is simply exportable into the user space and may be consumed for visualization by an observability platform.
The Disadvantages of eBPF for Observability
Now that we’ve covered every possible way eBPF might boost observability, it’s time to discuss potential drawbacks:
- Novel and Unfamiliar: Even though eBPF has been around since 2017, it hasn’t been put to the test for more demanding criteria. This is a goal for the open-source project, but there is still work to be done.
- Linux Constraints: Only the most recent Linux kernel versions support eBPF, which may be prohibitive for a company that is a little behind on version upgrades. eBPF isn’t suitable for anyone who isn’t using Linux kernels, to put it simply.
When compared to more conventional observability solutions, eBPF is a remarkable observability tool that allows deeper insights. In the past, gathering telemetry data from the complete system in a safe, non-intrusive manner has required a number of products, application-level agents, and quite complicated processes. eBPF is a tool and approach that helps users get deep, intrinsic data access to produce low-overhead observability for a variety of application contexts; it is not the final destination.
Also Read: How To Maintain Kubernetes Security