Jack Nagy
709fdf444d
State freshness now comes from a tiered PollScheduler over the persistent DTLS session; OBSERVE registrations are kept as an opportunistic acceleration layer. Behaviour is identical online vs air-gapped except for worst-case freshness latency. Adds three modules: - StateCache: single source of truth, source-tagged change events - PollScheduler: hot/warm/cold + sweep tiers, write-defer past the fetchback-revert window, per-window RTT/slow-poll tracking - KeepaliveTask: CoAP empty-CON ping with consecutive-fail detection driving MQTT availability Bridge publishes per-appliance diagnostic entities (Push Active, Last Update Source, Poll Max RTT, Slow Polls, Poll Errors, Stalest Resource Age, Last OBSERVE Age) under HA's Diagnostic section. Tier cadences are descriptor-declared, calibrated against measured per-firmware ceilings (dryer ~14 req/s, oven ~8 req/s via probe_poll_rate_combined.py). Drops HEARTBEAT_INTERVAL_S in favour of the descriptor-declared sweep tier; PING_INTERVAL_S now consumed by KeepaliveTask inside the bridge rather than driven from main.py. README explains the push/poll split and what happens when the appliance is blocked from internet.
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""Single source of truth for one appliance's state.
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All writers (OBSERVE notify, poll, seed, optimistic) call apply_rep().
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A registered on_change callback fires after any apply that mutated the
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cache, which the bridge wires to its MQTT publish gate.
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"""
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from __future__ import annotations
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import threading
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import time
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from typing import Callable, Optional, TYPE_CHECKING
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if TYPE_CHECKING:
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from .appliances.base import ApplianceDescriptor
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class StateCache:
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def __init__(self, descriptor: 'ApplianceDescriptor'):
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self.descriptor = descriptor
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self.links: dict[str, dict] = {}
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self.last_updated: dict[str, float] = {}
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self.source: dict[str, str] = {}
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self.descriptor_state: dict = {}
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self._on_change: Optional[Callable[[bool, str], None]] = None
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self._lock = threading.RLock()
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def set_on_change(self, cb: Callable[[bool, str], None]) -> None:
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self._on_change = cb
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def apply_rep(self, href: str, rep: dict, source: str) -> bool:
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if not isinstance(rep, dict):
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return False
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with self._lock:
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prior = self.links.get(href)
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changed = prior != rep
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self.links[href] = rep
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self.last_updated[href] = time.time()
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self.source[href] = source
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hook = self.descriptor.on_observation
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if hook is not None:
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try:
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hook(self.descriptor_state, href, rep)
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except Exception:
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pass
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if self._on_change is not None:
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try:
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self._on_change(changed, source)
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except Exception:
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pass
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return changed
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def apply_optimistic(self, href: str, body: dict) -> bool:
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if not isinstance(body, dict):
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return False
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with self._lock:
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merged = dict(self.links.get(href) or {})
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merged.update(body)
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return self.apply_rep(href, merged, source='optimistic')
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def get(self, href: str) -> Optional[dict]:
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with self._lock:
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return self.links.get(href)
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def snapshot(self) -> dict[str, dict]:
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with self._lock:
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return dict(self.links)
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def freshness_s(self, href: str) -> Optional[float]:
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ts = self.last_updated.get(href)
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return None if ts is None else (time.time() - ts)
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def stalest(self) -> Optional[tuple[str, float]]:
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with self._lock:
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if not self.last_updated:
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return None
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href = min(self.last_updated, key=self.last_updated.get)
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return href, time.time() - self.last_updated[href]
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