Synthesizing Custom Event Streams
In this lesson, you will learn how to synthesize high-fidelity observability data by correlating disparate kernel tracepoints and kprobes into unified custom event streams.
Beyond Single-Source Tracing
When investigating complex performance regressions, looking at a single probe (like sys_read or a block I/O tracepoint) is often insufficient. Real-world performance issues—such as a specific application stall—often emerge from the interaction between different kernel subsystems. Custom Perf Event Analysis involves capturing disparate data points across the kernel and "stitching" them together into a meaningful sequence of events.
By combining kprobes for entry/exit points in proprietary or non-instrumented code with standard tracepoints in the networking or scheduler subsystems, you can build a causal chain of events.
The Synthesis Workflow
The goal is to maintain a shared correlation ID (like a process ID, thread ID, or a custom request ID) across your probes. By storing state in a BPF_HASH or BPF_PERCPU_HASH, you can link the start of an operation at one probe site with its eventual completion at another.
Consider this logic for tracking request latency across two distinct kernel boundaries:
struct val_t { u64 start_ts; };
BPF_HASH(start_map, u32, struct val_t);
// Probe 1: Start of operation (e.g., request entry)
int kprobe__entry_func(struct pt_regs *ctx) {
u32 pid = bpf_get_current_pid_tgid();
struct val_t val = { .start_ts = bpf_ktime_get_ns() };
start_map.update(&pid, &val);
return 0;
}
// Probe 2: End of operation (e.g., a tracepoint for resource completion)
int tracepoint__exit_func(void *ctx) {
u32 pid = bpf_get_current_pid_tgid();
struct val_t *val = start_map.lookup(&pid);
if (val) {
u64 delta = bpf_ktime_get_ns() - val->start_ts;
// Emit custom event stream data here
start_map.delete(&pid);
}
return 0;
}
By synthesizing these streams, you transform isolated kernel observations into a cohesive timeline of how a specific task traverses the kernel's internal state machine. This allows you to ignore the noise of system-wide activity and focus strictly on the lifecycle of a single request type, effectively creating your own custom performance events.
Key Takeaway: Custom perf event analysis allows you to correlate disjointed kernel events by using eBPF maps to bridge state between multiple probes, transforming fragmented logs into a unified narrative of system performance.