Strategies for Low-overhead Tracing

In this lesson, you will learn how to implement adaptive sampling in eBPF to keep your observability overhead predictably low during normal operations while still capturing critical anomalies.

The Problem: The "Observability Tax"

Continuous tracing is powerful, but capturing every single event—like every packet processed or every file descriptor opened—can saturate the CPU and blow out your memory cache. While static sampling (e.g., "capture every 100th event") is easy to implement, it often misses the "needle in the haystack" spikes that occur during system degradation. Adaptive sampling solves this by dynamically shifting the sampling rate based on system state.

Implementing Adaptive Sampling

Adaptive sampling works by using a feedback loop. You define a baseline sampling rate (e.g., 0.1%) that runs during normal operation. When an eBPF program detects an anomaly—such as a latency threshold breach or a surge in error codes—it pushes a signal to user-space to increase the sampling frequency to 100% for a short, defined window.

Consider a simple map-based approach where your eBPF program checks a sampling configuration:

struct {
    __uint(type, BPF_MAP_TYPE_ARRAY);
    __type(key, u32);
    __type(value, u32);
} config_map SEC(".maps");

SEC("kprobe/sys_execve")
int trace_exec(struct pt_regs *ctx) {
    u32 key = 0;
    u32 *sample_rate = bpf_map_lookup_elem(&config_map, &key);
    
    // Only process event if random roll passes the current threshold
    if (sample_rate && (bpf_get_prandom_u32() % 1000 > *sample_rate)) {
        return 0;
    }
    
    // Proceed to capture telemetry...
    return 0;
}

Orchestrating the Logic

In your user-space controller, you monitor the output of your traces. If the error rate rises or latency spikes in your metrics pipeline, your agent updates the config_map value to a higher integer. Once the spike subsides and the system returns to a stable state, the agent reverts the map value to the low-overhead baseline. This ensures you spend the majority of your CPU cycles performing actual work, only "paying the tax" when the system is behaving unusually and requires deep diagnostic insight.

Key Takeaway: Adaptive sampling preserves system performance by keeping instrumentation overhead negligible during baseline operation and dynamically scaling trace granularity only when an anomaly is detected.