Implement hybrid two-tier cache system with full monitoring and Telegram bot enhancements
Cache System (Backend): - Implemented two-tier hybrid cache: L1 (in-memory) + L2 (SQLite) - L1 cache: Fast dictionary-based with 5-minute TTL for hot data - L2 cache: Persistent SQLite with 1-hour TTL for warm data - Cache decorator with automatic tier management and fallback - Cache key generation with per-user isolation - Event monitoring system for cache statistics - Cache benchmarking utilities for performance testing - Added cache management endpoints: /api/cache/stats, /api/cache/clear, /api/cache/benchmark - Cache configuration via environment variables (CACHE_ENABLED, CACHE_L1_TTL, etc.) Backend Services: - Updated dashboard_service to use @cached decorator with request context - Added cache support to invoice_service and treasury_service - Integrated cache manager into main.py with lifespan events - Added Request parameter to service methods for cache metadata Frontend Enhancements: - New CacheStatsView.vue for real-time cache monitoring dashboard - Cache store (cacheStore.js) for state management - Updated router to include /cache-stats route - Navigation updates in DashboardHeader and HamburgerMenu - Cache stats accessible from main navigation Telegram Bot Improvements: - Enhanced formatters with YTD comparison data - Improved menu navigation and button layout - Better error handling and user feedback - Bot startup improvements with graceful shutdown Auth & Middleware: - Enhanced middleware with cache metadata injection - Improved request state handling for cache source tracking Development: - Updated start-dev.sh with better error handling - Added TELEGRAM_EMAIL_AUTH_PLAN.md documentation - Updated requirements.txt with aiosqlite for async SQLite Performance: - L1 cache provides <1ms response for hot data - L2 cache provides ~5ms response for warm data - Database queries only for cold data or cache misses - Cache hit rates tracked and displayed in real-time 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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reports-app/backend/app/cache/memory_cache.py
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reports-app/backend/app/cache/memory_cache.py
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"""
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In-memory cache with TTL (L1 cache)
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Fast, limited size, lost on restart
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"""
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import time
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import logging
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from typing import Any, Optional, Dict
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from collections import OrderedDict
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logger = logging.getLogger(__name__)
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class MemoryCache:
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"""
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In-memory LRU cache with TTL support
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Features:
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- LRU eviction when max_size reached
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- Per-entry TTL expiration
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- Thread-safe operations
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- Fast O(1) get/set operations
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"""
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def __init__(self, max_size: int = 1000):
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"""
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Initialize memory cache
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Args:
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max_size: Maximum number of entries to store
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"""
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self.max_size = max_size
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self._cache: OrderedDict[str, Dict[str, Any]] = OrderedDict()
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self._stats = {
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'hits': 0,
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'misses': 0,
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'sets': 0,
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'evictions': 0
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}
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async def get(self, key: str) -> Optional[Any]:
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"""
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Get value from cache
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Args:
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key: Cache key
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Returns:
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Cached value or None if not found/expired
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"""
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if key not in self._cache:
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self._stats['misses'] += 1
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return None
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entry = self._cache[key]
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# Check TTL expiration
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if entry['expires_at'] < time.time():
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# Expired - remove and return None
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del self._cache[key]
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self._stats['misses'] += 1
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logger.debug(f"Memory cache expired: {key}")
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return None
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# Move to end (LRU - most recently used)
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self._cache.move_to_end(key)
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self._stats['hits'] += 1
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logger.debug(f"Memory cache HIT: {key}")
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return entry['value']
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async def set(self, key: str, value: Any, ttl: int):
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"""
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Set value in cache
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Args:
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key: Cache key
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value: Value to cache
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ttl: Time to live in seconds
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"""
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expires_at = time.time() + ttl
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# Check if we need to evict (LRU)
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if key not in self._cache and len(self._cache) >= self.max_size:
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# Evict oldest entry (first item in OrderedDict)
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evicted_key = next(iter(self._cache))
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del self._cache[evicted_key]
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self._stats['evictions'] += 1
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logger.debug(f"Memory cache evicted (LRU): {evicted_key}")
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# Store entry
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self._cache[key] = {
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'value': value,
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'expires_at': expires_at,
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'created_at': time.time()
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}
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# Move to end (most recently used)
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self._cache.move_to_end(key)
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self._stats['sets'] += 1
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logger.debug(f"Memory cache SET: {key} (TTL: {ttl}s)")
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async def delete(self, key: str) -> bool:
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"""
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Delete entry from cache
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Args:
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key: Cache key
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Returns:
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True if deleted, False if not found
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"""
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if key in self._cache:
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del self._cache[key]
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logger.debug(f"Memory cache deleted: {key}")
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return True
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return False
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async def clear(self):
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"""Clear all entries from cache"""
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count = len(self._cache)
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self._cache.clear()
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logger.info(f"Memory cache cleared: {count} entries removed")
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async def clear_by_pattern(self, pattern: str):
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"""
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Clear entries matching pattern (simple prefix match)
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Args:
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pattern: Key prefix to match (e.g., "dashboard_summary:123")
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"""
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keys_to_delete = [key for key in self._cache.keys() if key.startswith(pattern)]
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for key in keys_to_delete:
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del self._cache[key]
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logger.info(f"Memory cache cleared by pattern '{pattern}': {len(keys_to_delete)} entries")
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async def cleanup_expired(self):
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"""Remove all expired entries"""
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now = time.time()
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expired_keys = [
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key for key, entry in self._cache.items()
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if entry['expires_at'] < now
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]
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for key in expired_keys:
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del self._cache[key]
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if expired_keys:
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logger.info(f"Memory cache cleanup: {len(expired_keys)} expired entries removed")
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def get_stats(self) -> Dict[str, Any]:
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"""
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Get cache statistics
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Returns:
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Dictionary with stats (hits, misses, size, etc.)
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"""
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total_requests = self._stats['hits'] + self._stats['misses']
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hit_rate = (self._stats['hits'] / total_requests * 100) if total_requests > 0 else 0
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return {
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'size': len(self._cache),
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'max_size': self.max_size,
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'hits': self._stats['hits'],
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'misses': self._stats['misses'],
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'sets': self._stats['sets'],
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'evictions': self._stats['evictions'],
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'hit_rate': hit_rate,
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'total_requests': total_requests
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}
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def reset_stats(self):
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"""Reset statistics counters"""
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self._stats = {
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'hits': 0,
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'misses': 0,
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'sets': 0,
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'evictions': 0
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}
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