Correlating Logs, Metrics, and Traces

In this lesson, you will learn how to bridge the gap between low-level kernel performance telemetry and high-level application behavior by propagating correlation IDs across the user-kernel boundary.

The Observability Gap

In high-performance environments, we often encounter a "correlation gap." Your metrics might show a spike in CPU usage or kernel wait times, and your logs might show a timeout error, but linking these two events requires a shared piece of context. By injecting a unique Correlation ID into the kernel’s execution path, we can tie specific request flows to the underlying Linux kernel activity.

Unifying Telemetry with Correlation IDs

The strategy is to propagate a request ID (often a Trace ID from your distributed tracing header) into a thread-local variable or a task-specific metadata field that eBPF programs can access.

When your application initiates a system call, it marks the current thread with the active Correlation ID. An eBPF program, triggered by a tracepoint or kprobe, can then capture this ID alongside kernel telemetry like scheduler latency or disk I/O wait.

Consider a simple eBPF probe capturing disk latency:

SEC("kprobe/blk_account_io_done")
int BPF_KPROBE(trace_io_done) {
    u64 pid = bpf_get_current_pid_tgid();
    u32 tid = pid; 
    // Retrieve the Correlation ID stored by the app in a BPF map
    u64 *corr_id = bpf_map_lookup_elem(&active_requests, &tid);
    
    if (corr_id) {
        // Output the latency event tagged with the Correlation ID
        bpf_printk("Request %llu I/O latency: %llu ns", *corr_id, latency);
    }
    return 0;
}

By standardizing this approach, your log aggregator receives a log line containing the correlation_id, your time-series database receives a metric labeled with that ID, and your tracing backend receives the corresponding span. When an anomaly occurs, you no longer search for "slow requests"; you query your telemetry store for a specific correlation_id to see the complete lifecycle from the application logic down to the kernel’s thread scheduling decisions.

Key Takeaway: By injecting Correlation IDs into task metadata, you unify disparate signals, allowing you to reconstruct a request’s journey across both application threads and kernel subsystems.