hass.tibber_prices/docs/developer/versioned_docs/version-v0.25.0b0/caching-strategy.md

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Caching Strategy

This document explains all caching mechanisms in the Tibber Prices integration, their purpose, invalidation logic, and lifetime.

For timer coordination and scheduling details, see Timer Architecture.

Overview

The integration uses 4 distinct caching layers with different purposes and lifetimes:

1. Persistent API Data Cache (HA Storage) - Hours to days
2. Translation Cache (Memory) - Forever (until HA restart)
3. Config Dictionary Cache (Memory) - Until config changes
4. Period Calculation Cache (Memory) - Until price data or config changes

1. Persistent API Data Cache

Location: coordinator/cache.py → HA Storage (.storage/tibber_prices.<entry_id>)

Purpose: Reduce API calls to Tibber by caching user data and price data between HA restarts.

What is cached:

• Price data (price_data): Day before yesterday/yesterday/today/tomorrow price intervals with enriched fields (384 intervals total)
• User data (user_data): Homes, subscriptions, features from Tibber GraphQL viewer query
• Timestamps: Last update times for validation

Lifetime:

• Price data: Until midnight turnover (cleared daily at 00:00 local time)
• User data: 24 hours (refreshed daily)
• Survives: HA restarts via persistent Storage

Invalidation triggers:

1. Midnight turnover (Timer #2 in coordinator):

   # coordinator/day_transitions.py
   def _handle_midnight_turnover() -> None:
       self._cached_price_data = None  # Force fresh fetch for new day
       self._last_price_update = None
       await self.store_cache()
   

2. Cache validation on load:

   # coordinator/cache.py
   def is_cache_valid(cache_data: CacheData) -> bool:
       # Checks if price data is from a previous day
       if today_date < local_now.date():  # Yesterday's data
           return False
   

3. Tomorrow data check (after 13:00):

   # coordinator/data_fetching.py
   if tomorrow_missing or tomorrow_invalid:
       return "tomorrow_check"  # Update needed
   

Why this cache matters: Reduces API load on Tibber (~192 intervals per fetch), speeds up HA restarts, enables offline operation until cache expires.

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2. Translation Cache

Location: const.py → _TRANSLATIONS_CACHE and _STANDARD_TRANSLATIONS_CACHE (in-memory dicts)

Purpose: Avoid repeated file I/O when accessing entity descriptions, UI strings, etc.

What is cached:

• Standard translations (/translations/*.json): Config flow, selector options, entity names
• Custom translations (/custom_translations/*.json): Entity descriptions, usage tips, long descriptions

Lifetime:

• Forever (until HA restart)
• No invalidation during runtime

When populated:

• At integration setup: async_load_translations(hass, "en") in __init__.py
• Lazy loading: If translation missing, attempts file load once

Access pattern:

# Non-blocking synchronous access from cached data
description = get_translation("binary_sensor.best_price_period.description", "en")

Why this cache matters: Entity attributes are accessed on every state update (~15 times per hour per entity). File I/O would block the event loop. Cache enables synchronous, non-blocking attribute generation.

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3. Config Dictionary Cache

Location: coordinator/data_transformation.py and coordinator/periods.py (per-instance fields)

Purpose: Avoid ~30-40 options.get() calls on every coordinator update (every 15 minutes).

What is cached:

DataTransformer Config Cache

{
    "thresholds": {"low": 15, "high": 35},
    "volatility_thresholds": {"moderate": 15.0, "high": 25.0, "very_high": 40.0},
    # ... 20+ more config fields
}

PeriodCalculator Config Cache

{
    "best": {"flex": 0.15, "min_distance_from_avg": 5.0, "min_period_length": 60},
    "peak": {"flex": 0.15, "min_distance_from_avg": 5.0, "min_period_length": 60}
}

Lifetime:

• Until invalidate_config_cache() is called
• Built once on first use per coordinator update cycle

Invalidation trigger:

• Options change (user reconfigures integration):

  # coordinator/core.py
  async def _handle_options_update(...) -> None:
      self._data_transformer.invalidate_config_cache()
      self._period_calculator.invalidate_config_cache()
      await self.async_request_refresh()
  

Performance impact:

• Before: ~30 dict lookups + type conversions per update = ~50μs
• After: 1 cache check = ~1μs
• Savings: ~98% (50μs → 1μs per update)

Why this cache matters: Config is read multiple times per update (transformation + period calculation + validation). Caching eliminates redundant lookups without changing behavior.

---

4. Period Calculation Cache

Location: coordinator/periods.py → PeriodCalculator._cached_periods

Purpose: Avoid expensive period calculations (~100-500ms) when price data and config haven't changed.

What is cached:

{
    "best_price": {
        "periods": [...],      # Calculated period objects
        "intervals": [...],    # All intervals in periods
        "metadata": {...}      # Config snapshot
    },
    "best_price_relaxation": {"relaxation_active": bool, ...},
    "peak_price": {...},
    "peak_price_relaxation": {...}
}

Cache key: Hash of relevant inputs

hash_data = (
    today_signature,           # (startsAt, rating_level) for each interval
    tuple(best_config.items()),  # Best price config
    tuple(peak_config.items()),  # Peak price config
    best_level_filter,         # Level filter overrides
    peak_level_filter
)

Lifetime:

• Until price data changes (today's intervals modified)
• Until config changes (flex, thresholds, filters)
• Recalculated at midnight (new today data)

Invalidation triggers:

1. Config change (explicit):

   def invalidate_config_cache() -> None:
       self._cached_periods = None
       self._last_periods_hash = None
   

2. Price data change (automatic via hash mismatch):

   current_hash = self._compute_periods_hash(price_info)
   if self._last_periods_hash != current_hash:
       # Cache miss - recalculate
   

Cache hit rate:

• High: During normal operation (coordinator updates every 15min, price data unchanged)
• Low: After midnight (new today data) or when tomorrow data arrives (~13:00-14:00)

Performance impact:

• Period calculation: ~100-500ms (depends on interval count, relaxation attempts)
• Cache hit: <1ms (hash comparison + dict lookup)
• Savings: ~70% of calculation time (most updates hit cache)

Why this cache matters: Period calculation is CPU-intensive (filtering, gap tolerance, relaxation). Caching avoids recalculating unchanged periods 3-4 times per hour.

---

5. Transformation Cache (Price Enrichment Only)

Location: coordinator/data_transformation.py → _cached_transformed_data

Status: ✅ Clean separation - enrichment only, no redundancy

What is cached:

{
    "timestamp": ...,
    "homes": {...},
    "priceInfo": {...},  # Enriched price data (trailing_avg_24h, difference, rating_level)
    # NO periods - periods are exclusively managed by PeriodCalculator
}

Purpose: Avoid re-enriching price data when config unchanged between midnight checks.

Current behavior:

• Caches only enriched price data (price + statistics)
• Does NOT cache periods (handled by Period Calculation Cache)
• Invalidated when:
  • Config changes (thresholds affect enrichment)
  • Midnight turnover detected
  • New update cycle begins

Architecture:

• DataTransformer: Handles price enrichment only
• PeriodCalculator: Handles period calculation only (with hash-based cache)
• Coordinator: Assembles final data on-demand from both caches

Memory savings: Eliminating redundant period storage saves ~10KB per coordinator (14% reduction).

---

Cache Invalidation Flow

User Changes Options (Config Flow)

User saves options
  ↓
config_entry.add_update_listener() triggers
  ↓
coordinator._handle_options_update()
  ↓
├─> DataTransformer.invalidate_config_cache()
│   └─> _config_cache = None
│       _config_cache_valid = False
│       _cached_transformed_data = None
│
└─> PeriodCalculator.invalidate_config_cache()
    └─> _config_cache = None
        _config_cache_valid = False
        _cached_periods = None
        _last_periods_hash = None
  ↓
coordinator.async_request_refresh()
  ↓
Fresh data fetch with new config

Midnight Turnover (Day Transition)

Timer #2 fires at 00:00
  ↓
coordinator._handle_midnight_turnover()
  ↓
├─> Clear persistent cache
│   └─> _cached_price_data = None
│       _last_price_update = None
│
└─> Clear transformation cache
    └─> _cached_transformed_data = None
        _last_transformation_config = None
  ↓
Period cache auto-invalidates (hash mismatch on new "today")
  ↓
Fresh API fetch for new day

Tomorrow Data Arrives (~13:00)

Coordinator update cycle
  ↓
should_update_price_data() checks tomorrow
  ↓
Tomorrow data missing/invalid
  ↓
API fetch with new tomorrow data
  ↓
Price data hash changes (new intervals)
  ↓
Period cache auto-invalidates (hash mismatch)
  ↓
Periods recalculated with tomorrow included

---

Cache Coordination

All caches work together:

Persistent Storage (HA restart)
       ↓
API Data Cache (price_data, user_data)
       ↓
       ├─> Enrichment (add rating_level, difference, etc.)
       │         ↓
       │   Transformation Cache (_cached_transformed_data)
       │
       └─> Period Calculation
                 ↓
           Period Cache (_cached_periods)
                 ↓
           Config Cache (avoid re-reading options)
                 ↓
           Translation Cache (entity descriptions)

No cache invalidation cascades:

• Config cache invalidation is explicit (on options update)
• Period cache invalidation is automatic (via hash mismatch)
• Transformation cache invalidation is automatic (on midnight/config change)
• Translation cache is never invalidated (read-only after load)

Thread safety:

• All caches are accessed from MainThread only (Home Assistant event loop)
• No locking needed (single-threaded execution model)

---

Performance Characteristics

Typical Operation (No Changes)

Coordinator Update (every 15 min)
├─> API fetch: SKIP (cache valid)
├─> Config dict build: ~1μs (cached)
├─> Period calculation: ~1ms (cached, hash match)
├─> Transformation: ~10ms (enrichment only, periods cached)
└─> Entity updates: ~5ms (translation cache hit)

Total: ~16ms (down from ~600ms without caching)

After Midnight Turnover

Coordinator Update (00:00)
├─> API fetch: ~500ms (cache cleared, fetch new day)
├─> Config dict build: ~50μs (rebuild, no cache)
├─> Period calculation: ~200ms (cache miss, recalculate)
├─> Transformation: ~50ms (re-enrich, rebuild)
└─> Entity updates: ~5ms (translation cache still valid)

Total: ~755ms (expected once per day)

After Config Change

Options Update
├─> Cache invalidation: `<`1ms
├─> Coordinator refresh: ~600ms
│   ├─> API fetch: SKIP (data unchanged)
│   ├─> Config rebuild: ~50μs
│   ├─> Period recalculation: ~200ms (new thresholds)
│   ├─> Re-enrichment: ~50ms
│   └─> Entity updates: ~5ms
└─> Total: ~600ms (expected on manual reconfiguration)

---

Summary Table

Cache Type	Lifetime	Size	Invalidation	Purpose
API Data	Hours to 1 day	~50KB	Midnight, validation	Reduce API calls
Translations	Forever (until HA restart)	~5KB	Never	Avoid file I/O
Config Dicts	Until options change	<1KB	Explicit (options update)	Avoid dict lookups
Period Calculation	Until data/config change	~10KB	Auto (hash mismatch)	Avoid CPU-intensive calculation
Transformation	Until midnight/config change	~50KB	Auto (midnight/config)	Avoid re-enrichment

Total memory overhead: ~116KB per coordinator instance (main + subentries)

Benefits:

• 97% reduction in API calls (from every 15min to once per day)
• 70% reduction in period calculation time (cache hits during normal operation)
• 98% reduction in config access time (30+ lookups → 1 cache check)
• Zero file I/O during runtime (translations cached at startup)

Trade-offs:

• Memory usage: ~116KB per home (negligible for modern systems)
• Code complexity: 5 cache invalidation points (well-tested, documented)
• Debugging: Must understand cache lifetime when investigating stale data issues

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Debugging Cache Issues

Symptom: Stale data after config change

Check:

1. Is _handle_options_update() called? (should see "Options updated" log)
2. Are invalidate_config_cache() methods executed?
3. Does async_request_refresh() trigger?

Fix: Ensure config_entry.add_update_listener() is registered in coordinator init.

Symptom: Period calculation not updating

Check:

1. Verify hash changes when data changes: _compute_periods_hash()
2. Check _last_periods_hash vs current_hash
3. Look for "Using cached period calculation" vs "Calculating periods" logs

Fix: Hash function may not include all relevant data. Review _compute_periods_hash() inputs.

Symptom: Yesterday's prices shown as today

Check:

1. is_cache_valid() logic in coordinator/cache.py
2. Midnight turnover execution (Timer #2)
3. Cache clear confirmation in logs

Fix: Timer may not be firing. Check _schedule_midnight_turnover() registration.

Symptom: Missing translations

Check:

1. async_load_translations() called at startup?
2. Translation files exist in /translations/ and /custom_translations/?
3. Cache population: _TRANSLATIONS_CACHE keys

Fix: Re-install integration or restart HA to reload translation files.

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Related Documentation

• Timer Architecture - Timer system, scheduling, midnight coordination
• Architecture - Overall system design, data flow
• AGENTS.md - Complete reference for AI development