Network Visibility with eBPF

Network Visibility with eBPF

In this lesson, you will learn how to design custom observability agents that extract granular network telemetry directly from the kernel’s networking stack.

Traditional monitoring tools often rely on netstat or ss, which query the kernel for snapshots of connection states. However, these tools suffer from high overhead when polling frequently and often miss transient, short-lived connections common in microservices. By building a custom eBPF observability agent, you can hook directly into the kernel's execution path—such as tcp_connect or tcp_rcv_established—to capture telemetry in real-time as events occur.

Architecting the Agent

An effective network observability agent consists of two parts: the BPF program (running in the kernel) and the userspace controller.

1. Kernel-side: You define a BPF program that attaches to tracepoints or kprobes within the networking stack. Every time a packet hits a specific state (like a TCP state transition), the program extracts relevant metadata—such as source/destination IP, port, and latency metrics—and pushes this data into a BPF_MAP_TYPE_PERF_EVENT_ARRAY or a hash map.
2. Userspace-side: Your agent polls these shared maps. Because the BPF program performs the heavy lifting (filtering and data aggregation) inside the kernel, your userspace agent only needs to process relevant, pre-formatted events.

Practical Implementation

Consider a simple scenario where you want to log every connection attempt. Your BPF code would look conceptually like this:

SEC("kprobe/tcp_v4_connect")
int BPF_KPROBE(trace_connect, struct sock *sk) {
    struct event_t event = {};
    // Extract metadata from the socket structure
    event.saddr = sk->__sk_common.skc_rcv_saddr;
    event.daddr = sk->__sk_common.skc_daddr;
    
    // Push the event to a ring buffer for userspace to read
    bpf_perf_event_output(ctx, &events, BPF_F_CURRENT_CPU, &event, sizeof(event));
    return 0;
}

By leveraging BPF_MAPS for data transport, you create a low-latency feedback loop. This allows you to build a custom agent that understands the specific context of your cloud-native services, such as identifying traffic patterns between pods or measuring the exact time spent in the kernel’s protocol stack versus the application.

Key Takeaway: Custom eBPF observability agents leverage kernel-level hooks to transform raw network events into actionable telemetry, providing high-fidelity visibility that polling-based tools cannot match.