func (r *RingRecorder) flush() // Snappy compress and write to offline file // No analysis happens here.
type RingRecorder struct buffer []Event head atomic.Uint64 fd *os.File compressor *snappy.Writer offline cross profiler
"timestamp_ns": 1732145678123456789, "core_id": 3, "pid_tid": "1337/1402", "domain": "CPU", "metric": "cycles_stalled_backend", "value": 42000, "stack_id": "0x7fffdeadbeef" func (r *RingRecorder) flush() // Snappy compress and
if idx%1024 == 0 // Flush every 1024 events r.flush() The "Cross" Advantage: Correlating Domains The magic of
func (r *RingRecorder) Record(domain string, key string, value uint64) idx := r.head.Add(1) % uint64(len(r.buffer)) e := &r.buffer[idx] e.Ts = uint64(time.Now().UnixNano()) e.Domain = domain e.Key = key e.Value = value // Capture stack (omit for brevity)
Running on a developer’s workstation (not the target), the analyzer loads the 10GB trace file, builds indexes on timestamp_ns and stack_id , and executes cross-domain queries. 3. The "Cross" Advantage: Correlating Domains The magic of the offline cross-profiler is not in a single flame graph, but in the intersection of graphs. Consider a real-world bug: High latency in a database.
"CPU usage is 50%. I don't see the problem."