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A Comparison of eBPF Observability vs. Agents and Sidecars

The observability landscape is witnessing a radical transformation today. The central driver of this shift is eBPF (extended Berkeley Packet Filter), a technology that is revolutionizing how we observe and monitor systems. In an earlier post, we took a detailed look at the technology of eBPF and its implications for observability.

In this article, we will compare eBPF-based instrumentation with other instrumentation methods like code agents and sidecars and see which best suits the needs of observability today.

Before we dive in, let’s briefly revisit eBPF.

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Distributed Tracing: Past, Present, and Future

Distributed Tracing is a divisive topic. Once the doyen of every KubeCon, the technology was expected to revolutionize observability.

Fast forward five years, and the hype has subsided somewhat. There's a lot more talk about the pain, and adoption is moderate. Meanwhile, there continues to be steady activity around expanding and standardizing the technology — Open Telemetry (based on OpenTracing) is the 2nd largest CNCF project after Kubernetes. So, what is the deal with Distributed Tracing? Should one implement it right away or wait and watch? In this article, let's explore Distributed Tracing in depth:

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Decoding eBPF Observability: How eBPF Transforms Observability as We Know It

There has been a lot of chatter about eBPF in cloud-native communities over the last 2 years. eBPF was a mainstay at KubeCon, eBPF days and eBPF summits are rapidly growing in popularity, companies like Google and Netflix have been using eBPF for years, and new use cases are emerging all the time. Especially in observability, eBPF is expected to be a game changer.

So let’s look at eBPF — what is the technology, how is it impacting observability, how does it compare with existing observability practices, and what might the future hold?