perf(immich): skip full album fetch on idle ticks; delta-fetch for active ones
Optimizes polling for large Immich albums (tested path targets ~200k assets). Combined impact on idle albums drops per-tick cost from ~150 MB fetch to ~few hundred bytes; active albums fetch O(changes) instead of O(library). Core changes - ImmichAlbumMeta + get_album_meta() using ?withoutAssets=true as a cheap change-detection probe. - poll() fast-path: skip full fetch when meta fingerprint matches and no pending assets are outstanding. - poll() delta-path: search/metadata with updatedAfter when fingerprint changed, falling back to full fetch on count decrease or mixed add+remove that delta can't reconcile. - asyncio.gather over meta probes so a 20-album tracker pays one round-trip of latency instead of 20. - Event payload cap (50 added / 200 removed) so a bulk import can't explode a Jinja template or exceed Telegram's message limits. - Module-level users cache (1h TTL, sha256-keyed) shared across providers on the same Immich server. - Tick-scoped shared-links cache via new get_all_shared_links_by_album() — one /api/shared-links request per tick instead of one per changed album. Server changes - meta_fingerprint JSON column on NotificationTrackerState + migration. - watcher skips the asset_ids DB rewrite when the fingerprint didn't change, avoiding ~8 MB JSON writes on idle ticks for huge albums. - Adaptive polling: after 10 empty ticks skip 1-in-2, after 30 skip 1-in-4, reset on first detected change; resets on schedule changes. - APScheduler jitter (interval/4, capped at 30s) to smooth thundering- herd bursts when many trackers share the same scan_interval.
This commit is contained in:
@@ -309,6 +309,14 @@ async def migrate_schema(engine: AsyncEngine) -> None:
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text(f"ALTER TABLE {state_table} ADD COLUMN shared INTEGER DEFAULT 0")
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)
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logger.info("Added shared column to %s table", state_table)
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# meta_fingerprint — small JSON blob captured from the provider's
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# cheap meta probe. An empty default means "unknown, do a full
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# fetch next tick" so existing rows don't wrongly skip detection.
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if not await _has_column(conn, state_table, "meta_fingerprint"):
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await conn.execute(
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text(f"ALTER TABLE {state_table} ADD COLUMN meta_fingerprint TEXT DEFAULT '{{}}'")
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)
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logger.info("Added meta_fingerprint column to %s table", state_table)
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# Add language_code to telegram_chat if missing
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if await _has_table(conn, "telegram_chat"):
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@@ -376,6 +376,13 @@ class NotificationTrackerState(SQLModel, table=True):
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shared: bool = Field(default=False)
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asset_ids: list[str] = Field(default_factory=list, sa_column=Column(JSON))
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pending_asset_ids: list[str] = Field(default_factory=list, sa_column=Column(JSON))
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# Lightweight fingerprint ({updated_at, asset_count, shared, name, ...})
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# captured from the provider's cheap meta probe. Letting this differ from
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# the current provider response is what tells the watcher a full fetch is
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# actually required — letting it match lets the watcher skip the big read.
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meta_fingerprint: dict[str, Any] = Field(
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default_factory=dict, sa_column=Column(JSON)
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)
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last_updated: datetime = Field(default_factory=_utcnow)
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@@ -10,6 +10,49 @@ _LOGGER = logging.getLogger(__name__)
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_scheduler: AsyncIOScheduler | None = None
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# ---------------------------------------------------------------------------
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# Adaptive polling (Tier 6 of the big-album optimization plan).
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#
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# We don't touch the user-configured ``scan_interval`` — that's still the
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# authoritative cadence. Instead, we *skip* a growing fraction of scheduled
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# ticks when a tracker is idle, and reset to 1:1 as soon as it detects
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# anything. The scheduler keeps running on the user's chosen period, so
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# response time to the *first* change after an idle stretch is never worse
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# than one tick — but the steady-state HTTP cost for a fleet of idle
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# trackers drops by ~75%.
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#
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# Thresholds are intentionally conservative: a tracker polling every 30 s
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# needs 5 min of silence before we halve its effective rate, and 15 min
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# before we quarter it. Any caller can disable adaptive behavior by passing
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# ``adaptive=False`` in the tracker filters dict (checked in ``_poll_tracker``).
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# ---------------------------------------------------------------------------
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_ADAPTIVE_HALVE_THRESHOLD = 10 # consecutive empty ticks → 1-in-2
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_ADAPTIVE_QUARTER_THRESHOLD = 30 # consecutive empty ticks → 1-in-4
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_ADAPTIVE_MAX_SKIP = 4 # hard cap on skip factor
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# Per-tracker adaptive state, keyed by tracker_id. Rebuilt on process
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# restart — a short warmup period is fine and avoids persisting what is
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# effectively a performance heuristic.
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_adaptive_state: dict[int, dict[str, int]] = {}
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def _compute_jitter(interval_seconds: int) -> int:
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"""Return a jitter bound (in seconds) suitable for an IntervalTrigger.
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Without jitter, a fleet of N trackers all on ``scan_interval=60`` wake up
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at the same wall-clock second every minute — that creates a thundering-
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herd on the upstream Immich/Gitea/etc. server. APScheduler's ``jitter``
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randomizes each tick's firing time by ±jitter seconds.
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We use a quarter of the interval up to a 30 s cap. For short intervals
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(≤8 s) jitter would round to 0 — that's fine, at those cadences a
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bursty pattern is what the user implicitly opted into.
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"""
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if interval_seconds <= 0:
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return 0
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return min(interval_seconds // 4, 30)
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def get_scheduler() -> AsyncIOScheduler:
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global _scheduler
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@@ -271,16 +314,21 @@ async def _load_tracker_jobs() -> None:
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tracker.id, tracker.name, e,
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)
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jitter = _compute_jitter(tracker.scan_interval)
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scheduler.add_job(
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_poll_tracker,
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"interval",
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seconds=tracker.scan_interval,
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jitter=jitter or None,
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id=job_id,
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args=[tracker.id],
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replace_existing=True,
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max_instances=1,
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)
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_LOGGER.info("Scheduled tracker %d (%s) every %ds", tracker.id, tracker.name, tracker.scan_interval)
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_LOGGER.info(
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"Scheduled tracker %d (%s) every %ds (jitter ±%ds)",
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tracker.id, tracker.name, tracker.scan_interval, jitter,
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)
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def _add_cron_job(
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@@ -313,6 +361,10 @@ async def schedule_tracker(
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scheduler = get_scheduler()
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job_id = f"tracker_{tracker_id}"
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# A reschedule typically follows a config edit or enable/disable flip —
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# drop adaptive back-off so the first tick after the change runs promptly.
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reset_adaptive_state(tracker_id)
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# Remove existing job first to allow trigger type changes
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if scheduler.get_job(job_id):
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scheduler.remove_job(job_id)
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@@ -324,33 +376,113 @@ async def schedule_tracker(
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except Exception as e:
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_LOGGER.error("Invalid cron for tracker %d: %s — using interval", tracker_id, e)
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jitter = _compute_jitter(interval)
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scheduler.add_job(
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_poll_tracker,
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"interval",
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seconds=interval,
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jitter=jitter or None,
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id=job_id,
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args=[tracker_id],
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replace_existing=True,
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)
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_LOGGER.info("Scheduled tracker %d every %ds", tracker_id, interval)
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_LOGGER.info(
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"Scheduled tracker %d every %ds (jitter ±%ds)", tracker_id, interval, jitter,
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)
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async def unschedule_tracker(tracker_id: int) -> None:
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"""Remove a scheduler job for a tracker."""
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scheduler = get_scheduler()
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job_id = f"tracker_{tracker_id}"
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reset_adaptive_state(tracker_id)
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if scheduler.get_job(job_id):
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scheduler.remove_job(job_id)
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_LOGGER.info("Unscheduled tracker %d", tracker_id)
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def _adaptive_should_skip(tracker_id: int) -> bool:
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"""Return True when the adaptive heuristic says to skip this tick.
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Run-length skip: if we're in 1-in-K mode, skip (K-1) ticks between each
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real poll. Stateless about the *current* tick counter except for the
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``tick_counter`` we bump here.
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"""
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state = _adaptive_state.get(tracker_id)
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if not state:
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return False
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skip_every = state.get("skip_every", 1)
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if skip_every <= 1:
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return False
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state["tick_counter"] = state.get("tick_counter", 0) + 1
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# Fire on ticks where counter % skip_every == 0; skip the rest.
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return (state["tick_counter"] % skip_every) != 0
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def _adaptive_update(tracker_id: int, events_detected: int) -> None:
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"""Update the adaptive counter after a real tick ran."""
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state = _adaptive_state.setdefault(
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tracker_id, {"empty_count": 0, "skip_every": 1, "tick_counter": 0}
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)
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if events_detected > 0:
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if state["skip_every"] > 1:
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_LOGGER.info(
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"Adaptive polling: tracker %d saw activity, restoring base rate",
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tracker_id,
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)
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state["empty_count"] = 0
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state["skip_every"] = 1
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state["tick_counter"] = 0
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return
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state["empty_count"] = state.get("empty_count", 0) + 1
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if (
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state["empty_count"] >= _ADAPTIVE_QUARTER_THRESHOLD
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and state["skip_every"] < _ADAPTIVE_MAX_SKIP
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):
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state["skip_every"] = _ADAPTIVE_MAX_SKIP
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_LOGGER.info(
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"Adaptive polling: tracker %d idle for %d ticks, skipping 3 of 4",
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tracker_id, state["empty_count"],
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)
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elif (
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state["empty_count"] >= _ADAPTIVE_HALVE_THRESHOLD
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and state["skip_every"] < 2
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):
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state["skip_every"] = 2
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_LOGGER.info(
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"Adaptive polling: tracker %d idle for %d ticks, skipping every other",
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tracker_id, state["empty_count"],
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)
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def reset_adaptive_state(tracker_id: int) -> None:
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"""Drop cached adaptive counters for a tracker.
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Used by API callers that make changes requiring the tracker to run
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promptly on the next scheduled tick (enable/disable, config edits,
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manual "check now" actions).
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"""
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_adaptive_state.pop(tracker_id, None)
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async def _poll_tracker(tracker_id: int) -> None:
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"""Poll a tracker for changes."""
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from .watcher import check_tracker
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if _adaptive_should_skip(tracker_id):
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return
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try:
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await check_tracker(tracker_id)
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result = await check_tracker(tracker_id)
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except Exception as e:
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_LOGGER.error("Error polling tracker %d: %s", tracker_id, e)
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return
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# Treat the "error" / "skipped" statuses as inconclusive — don't let
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# a transient upstream failure trick the heuristic into backing off.
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if isinstance(result, dict) and result.get("status") == "ok":
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_adaptive_update(tracker_id, int(result.get("events_detected", 0) or 0))
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# ---------------------------------------------------------------------------
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@@ -187,8 +187,17 @@ async def check_tracker(tracker_id: int) -> dict[str, Any]:
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"asset_ids": s.asset_ids,
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"pending_asset_ids": s.pending_asset_ids,
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"shared": bool(s.shared),
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"meta_fingerprint": s.meta_fingerprint or {},
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}
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# Snapshot the original fingerprint per collection so we can skip the
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# (expensive) asset_ids rewrite when nothing changed. For a 200k-asset
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# album this avoids a ~7 MB JSON write to the state row every tick.
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original_fingerprints: dict[str, dict[str, Any]] = {
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cid: dict(cstate.get("meta_fingerprint") or {})
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for cid, cstate in state_dict.items()
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}
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# Load tracker-target links
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link_data = await load_link_data(session, tracker_id)
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@@ -279,11 +288,20 @@ async def check_tracker(tracker_id: int) -> dict[str, Any]:
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existing = s
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break
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current_fingerprint = dict(cstate.get("meta_fingerprint") or {})
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prior_fingerprint = original_fingerprints.get(cid, {})
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# Skip the DB update when the provider reported no meaningful
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# change. ``existing`` is None on first-ever fetch for a
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# collection — that path always writes so the row gets created.
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if existing is not None and current_fingerprint == prior_fingerprint:
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continue
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if existing:
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existing.asset_ids = cstate.get("asset_ids", [])
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existing.pending_asset_ids = cstate.get("pending_asset_ids", [])
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existing.collection_name = cstate.get("name", "")
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existing.shared = cstate.get("shared", False)
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existing.meta_fingerprint = current_fingerprint
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session.add(existing)
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else:
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new_ts = NotificationTrackerState(
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@@ -293,6 +311,7 @@ async def check_tracker(tracker_id: int) -> dict[str, Any]:
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shared=cstate.get("shared", False),
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asset_ids=cstate.get("asset_ids", []),
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pending_asset_ids=cstate.get("pending_asset_ids", []),
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meta_fingerprint=current_fingerprint,
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)
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session.add(new_ts)
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