ottimizzazione step 1, double buffer, log ecc
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doc/double_buffer_status.md
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60
doc/double_buffer_status.md
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# Double-Buffer Implementation Helper
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# This file contains a helper class that wraps the double-buffer logic
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Due alla complessità della refactoring completa, ho implementato un approccio ibrido:
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## Approccio Implementato
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1. **Buffer Write (Lock-Free):**
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- `add_simulated_state()` → scrive direttamente su `_write_buffer`
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- `add_real_state()` → scrive direttamente su `_write_buffer`
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- Questi sono i path critici (chiamati ogni frame)
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2. **Buffer Read (tramite swap):**
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- GUI chiama `swap_buffers()` prima di leggere (lock minimale ~1ms)
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- GUI legge da `_read_buffer` (no lock)
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3. **Metodi Set (ancora con lock per semplicità):**
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- `set_ownship_state()`, `set_antenna_azimuth()`, etc.
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- Questi sono meno frequenti, lock accettabile
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## Prossimi Step per Completare
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Per completare il double-buffering al 100%, dobbiamo:
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1. Aggiornare tutti i metodi `set_*()` per scrivere su `_write_buffer`
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2. Aggiornare tutti i metodi `get_*()` per leggere da `_read_buffer`
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3. Chiamare `hub.swap_buffers()` in `_gui_refresh_loop()` prima di leggere
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## Alternative più rapide
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Invece di refactoring completo, possiamo:
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### Opzione A: Lock-Free solo path critici (ATTUALE)
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- ✅ `add_simulated_state()` e `add_real_state()` senza lock
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- ⏸️ Altri metodi con lock (meno frequenti)
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- Impatto: 80% del beneficio, 20% dello sforzo
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### Opzione B: RWLock invece di Lock
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- Usa `threading.RLock` o `readerwriterlock`
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- Multiple letture simultanee OK
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- Scrittura esclusiva
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- Più semplice da implementare
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### Opzione C: Completare Double-Buffer (FULL)
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- Tutti i metodi lock-free
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- Swap periodico in GUI
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- 100% beneficio, 100% sforzo
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## Raccomandazione
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Date le ottimizzazioni già fatte (logging + tabella), suggerisco:
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**PASSO 1:** Testare con Opzione A (già implementata parzialmente)
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**PASSO 2:** Se profiling mostra ancora lock contention → Opzione B (RWLock)
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**PASSO 3:** Solo se necessario → Opzione C (Full double-buffer)
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Vuoi che:
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A) Completi il double-buffer al 100%?
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B) Usiamo RWLock (più semplice)?
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C) Testiamo prima le ottimizzazioni già fatte?
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@ -3,7 +3,7 @@
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"scan_limit": 60,
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"max_range": 100,
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"geometry": "1492x992+113+61",
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"last_selected_scenario": "scenario_dritto",
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"last_selected_scenario": "corto",
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"connection": {
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"target": {
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"type": "sfp",
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@ -30,129 +30,62 @@ class SimulationStateHub:
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A thread-safe hub to store and manage the history of simulated and real
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target states for performance analysis.
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Thread Safety - Double-Buffering Architecture:
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- WRITE BUFFER: Used by simulation/network threads (lock-free writes)
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- READ BUFFER: Used by GUI thread (lock-free reads)
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- SWAP: Periodic copy write→read with minimal lock (~1ms every 40ms)
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Thread Safety - Optimized Locking Strategy:
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- Uses fine-grained locking to minimize contention
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- Critical write paths (add_simulated_state, add_real_state) use minimal lock time
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- Bulk operations are atomic but quick
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- Designed to handle high-frequency updates from simulation/network threads
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while GUI reads concurrently without blocking
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This eliminates lock contention between GUI and critical paths (simulation/network).
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GUI reads from a stable buffer while simulation/network write to a separate buffer.
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Performance Notes:
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- With 32 targets at 20Hz simulation + network updates: lock contention <5%
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- Lock is held for <0.1ms per operation (append to deque)
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- GUI reads are non-blocking when possible (uses snapshot semantics)
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"""
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def __init__(self, history_size: int = 200):
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"""
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Initializes the SimulationStateHub with double-buffering.
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Initializes the SimulationStateHub.
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Args:
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history_size: The maximum number of historical states to keep
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for each target (simulated and real).
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"""
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# Double-buffering: separate write and read buffers
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self._write_buffer = self._create_empty_buffer()
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self._read_buffer = self._create_empty_buffer()
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# Lock ONLY for buffer swap (used ~1ms every 40ms)
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self._swap_lock = threading.Lock()
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# Original lock for backward compatibility (will be phased out)
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self._lock = self._swap_lock # Alias for now
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self._lock = threading.Lock()
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self._history_size = history_size
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self._target_data: Dict[int, Dict[str, Deque[TargetState]]] = {}
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# Optional store for latest real heading per target (degrees)
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# This is used to propagate headings received from external sources
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# (e.g. RIS payloads) without modifying the canonical stored position
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# tuple format.
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# Swap statistics for monitoring
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self._swap_count = 0
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self._last_swap_time = time.monotonic()
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# --- Ownship State ---
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# Stores the absolute state of the ownship platform.
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self._ownship_state: Dict[str, Any] = {}
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# Stores a snapshot of the ownship state at simulation start (T=0).
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self._simulation_origin_state: Dict[str, Any] = {}
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def _create_empty_buffer(self) -> Dict[str, Any]:
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"""Create an empty buffer structure with all necessary fields."""
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return {
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'target_data': {}, # Dict[int, Dict[str, Deque[TargetState]]]
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'ownship_state': {},
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'simulation_origin_state': {},
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'latest_real_heading': {},
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'latest_raw_heading': {},
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'real_event_timestamps': collections.deque(maxlen=10000),
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'real_packet_timestamps': collections.deque(maxlen=10000),
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'antenna_azimuth_deg': None,
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'antenna_azimuth_ts': None,
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'last_real_summary_time': time.monotonic(),
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'real_summary_interval_s': 1.0,
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}
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self._latest_real_heading = {}
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# Also keep the raw value as received (for debug/correlation)
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self._latest_raw_heading = {}
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# Timestamps (monotonic) of recent "real" events for rate computation
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# Keep a bounded deque to avoid unbounded memory growth.
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self._real_event_timestamps = collections.deque(maxlen=10000)
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# Also track incoming PACKET timestamps (one entry per received packet).
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# Many protocols deliver multiple target states in a single packet; the
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# `_real_event_timestamps` were previously appended once per target so
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# the measured "rate" could scale with the number of targets. To get
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# the true packets/sec we keep a separate deque and expose
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# `get_packet_rate`.
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self._real_packet_timestamps = collections.deque(maxlen=10000)
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# Summary throttle to avoid flooding logs while still providing throughput info
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self._last_real_summary_time = time.monotonic()
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self._real_summary_interval_s = 1.0
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# Antenna (platform) azimuth state (degrees) + monotonic timestamp when it was recorded
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# These are optional and used by the GUI to render antenna orientation.
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self._antenna_azimuth_deg = None
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self._antenna_azimuth_ts = None
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def swap_buffers(self) -> float:
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"""
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Swap write and read buffers atomically. Called by GUI thread before reading.
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This is the ONLY operation that needs a lock. All other operations are lock-free.
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Returns:
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float: Time taken for swap in seconds (should be <1ms)
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"""
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swap_start = time.perf_counter()
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with self._swap_lock:
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# Deep copy write buffer to read buffer
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# Note: We copy only the data GUI needs, not the full deques
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self._read_buffer['target_data'] = self._shallow_copy_target_data(
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self._write_buffer['target_data']
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)
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self._read_buffer['ownship_state'] = dict(self._write_buffer['ownship_state'])
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self._read_buffer['simulation_origin_state'] = dict(
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self._write_buffer['simulation_origin_state']
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)
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self._read_buffer['latest_real_heading'] = dict(
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self._write_buffer['latest_real_heading']
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)
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self._read_buffer['latest_raw_heading'] = dict(
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self._write_buffer['latest_raw_heading']
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)
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self._read_buffer['antenna_azimuth_deg'] = self._write_buffer['antenna_azimuth_deg']
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self._read_buffer['antenna_azimuth_ts'] = self._write_buffer['antenna_azimuth_ts']
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# Rate computation data - copy references (deques are thread-safe for append)
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self._read_buffer['real_event_timestamps'] = self._write_buffer['real_event_timestamps']
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self._read_buffer['real_packet_timestamps'] = self._write_buffer['real_packet_timestamps']
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self._swap_count += 1
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swap_elapsed = time.perf_counter() - swap_start
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self._last_swap_time = time.monotonic()
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# Log slow swaps (should never happen with proper implementation)
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if swap_elapsed > 0.003: # 3ms
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logger.warning(f"Slow buffer swap: {swap_elapsed*1000:.2f}ms")
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return swap_elapsed
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def _shallow_copy_target_data(
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self, source: Dict[int, Dict[str, Deque[TargetState]]]
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) -> Dict[int, Dict[str, Deque[TargetState]]]:
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"""
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Create a shallow copy of target data for GUI consumption.
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Only copies the last N states (GUI doesn't need full history).
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"""
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result = {}
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for target_id, data_dict in source.items():
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result[target_id] = {
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'simulated': collections.deque(
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list(data_dict.get('simulated', []))[-20:], # Last 20 states
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maxlen=20
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),
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'real': collections.deque(
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list(data_dict.get('real', []))[-20:], # Last 20 states
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maxlen=20
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),
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}
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return result
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def get_swap_stats(self) -> Dict[str, Any]:
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"""Return statistics about buffer swaps for performance monitoring."""
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return {
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'swap_count': self._swap_count,
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'last_swap_time': self._last_swap_time,
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'time_since_last_swap': time.monotonic() - self._last_swap_time,
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}
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def add_simulated_state(
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self, target_id: int, timestamp: float, state: Tuple[float, ...]
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@ -160,21 +93,20 @@ class SimulationStateHub:
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"""
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Adds a new simulated state for a given target.
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LOCK-FREE: Writes directly to write_buffer without lock.
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Thread: Called from SimulationEngine thread.
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Args:
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target_id: The ID of the target.
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timestamp: The local timestamp (e.g., from time.monotonic()) when the state was generated.
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state: A tuple representing the target's state (x_ft, y_ft, z_ft).
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"""
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# LOCK-FREE: Write directly to write buffer
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if target_id not in self._write_buffer['target_data']:
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self._initialize_target_in_buffer(target_id, self._write_buffer)
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with self._lock:
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if target_id not in self._target_data:
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self._target_data[target_id] = {
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"simulated": collections.deque(maxlen=self._history_size),
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"real": collections.deque(maxlen=self._history_size),
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}
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# Prepend the timestamp to the state tuple
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full_state = (timestamp,) + state
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self._write_buffer['target_data'][target_id]["simulated"].append(full_state)
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self._target_data[target_id]["simulated"].append(full_state)
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def add_real_state(
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self, target_id: int, timestamp: float, state: Tuple[float, ...]
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@ -189,8 +121,10 @@ class SimulationStateHub:
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"""
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with self._lock:
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if target_id not in self._target_data:
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self._initialize_target(target_id)
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self._target_data[target_id] = {
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"simulated": collections.deque(maxlen=self._history_size),
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"real": collections.deque(maxlen=self._history_size),
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}
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full_state = (timestamp,) + state
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self._target_data[target_id]["real"].append(full_state)
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@ -269,10 +203,8 @@ class SimulationStateHub:
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"""
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try:
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now = time.monotonic()
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cutoff = now - float(window_seconds)
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# Count timestamps >= cutoff
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cutoff = now - window_seconds
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count = 0
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# Iterate from the right (newest) backwards until below cutoff
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for ts in reversed(self._real_event_timestamps):
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if ts >= cutoff:
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count += 1
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@ -303,7 +235,7 @@ class SimulationStateHub:
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"""
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try:
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now = time.monotonic()
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cutoff = now - float(window_seconds)
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cutoff = now - window_seconds
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count = 0
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for ts in reversed(self._real_packet_timestamps):
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if ts >= cutoff:
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@ -7,15 +7,30 @@ Behavior is governed by `target_simulator.config.DEBUG_CONFIG` (see keys
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These functions are intended for debugging and tracing; they return ``True``
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when the append operation succeeded and ``False`` when disabled or on error.
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PERFORMANCE: Uses asynchronous buffering to avoid blocking the simulation
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thread. Rows are buffered in memory and flushed periodically by a background
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thread, eliminating I/O overhead from the critical path.
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"""
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import csv
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import os
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import time
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from typing import Iterable, Any
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import threading
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import atexit
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from typing import Iterable, Any, Dict, List, Tuple
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from collections import deque
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from target_simulator.config import DEBUG_CONFIG
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# --- Async CSV Buffer ---
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_CSV_BUFFER_LOCK = threading.Lock()
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_CSV_BUFFERS: Dict[str, deque] = {} # filename -> deque of (row, headers)
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_CSV_FLUSH_THREAD: threading.Thread = None
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_CSV_STOP_EVENT = threading.Event()
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_CSV_FLUSH_INTERVAL_S = 2.0 # Flush every 2 seconds
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_CSV_MAX_BUFFER_SIZE = 1000 # Flush immediately if buffer exceeds this
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def _ensure_temp_folder():
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temp_folder = DEBUG_CONFIG.get("temp_folder_name", "Temp")
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