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Redis Cache Service for Gapura AI
Provides caching layer for Google Sheets data and predictions
"""
import os
import time
import json
import logging
from typing import Optional, Any, Callable
from datetime import timedelta
from functools import wraps
import redis
from redis.exceptions import RedisError
logger = logging.getLogger(__name__)
class CacheService:
"""Redis-based caching service with L1 In-Memory and L2 Redis layers"""
def __init__(self):
self.redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0")
self.client = None
# In-memory cache stores: key -> (value, expiry_timestamp)
self.in_memory_cache = {}
self.enabled = os.getenv("CACHE_ENABLED", "true").lower() == "true"
self.backend = os.getenv("CACHE_BACKEND", "").lower() # 'redis' | 'memory' | ''
# Auto-detect HF Spaces and prefer memory backend if REDIS_URL is not set
if (os.getenv("SPACE_ID") or os.getenv("HF_TOKEN")) and not os.getenv("REDIS_URL"):
if self.backend == "":
self.backend = "memory"
if self.enabled and self.backend != "memory":
self._connect()
def _connect(self):
"""Connect to Redis with in-memory fallback"""
if not self.enabled:
logger.info("Cache disabled via environment variable")
return
if self.backend == "memory":
logger.info("Cache backend set to memory; skipping Redis connection")
self.client = None
return
try:
self.client = redis.from_url(
self.redis_url,
decode_responses=True,
socket_connect_timeout=2,
socket_timeout=2,
)
self.client.ping()
logger.info(f"Connected to Redis at {self.redis_url}")
except RedisError as e:
logger.warning(f"Failed to connect to Redis: {e}. Falling back to in-memory cache.")
self.client = None
def get(self, key: str) -> Optional[Any]:
"""Get value from cache (L1 Memory -> L2 Redis)"""
if not self.enabled:
return None
# 1. Try L1 In-Memory Cache first
if key in self.in_memory_cache:
entry = self.in_memory_cache[key]
# Check for tuple (value, expiry)
if isinstance(entry, tuple) and len(entry) == 2:
value, expiry = entry
if expiry > time.time():
logger.debug(f"L1 Memory HIT: {key}")
return value
else:
# Expired, remove it
del self.in_memory_cache[key]
else:
# Legacy format (just value), return it
logger.debug(f"L1 Memory HIT (Legacy): {key}")
return entry
# 2. Try L2 Redis Cache
if self.client:
try:
value_str = self.client.get(key)
if value_str:
logger.debug(f"L2 Redis HIT: {key}")
data = json.loads(value_str)
# Backfill L1 Memory
# Try to get TTL from Redis to sync expiration
try:
ttl = self.client.ttl(key)
if ttl > 0:
self.in_memory_cache[key] = (data, time.time() + ttl)
else:
self.in_memory_cache[key] = (data, time.time() + 300) # Default 5m
except:
self.in_memory_cache[key] = (data, time.time() + 300)
return data
except (RedisError, json.JSONDecodeError) as e:
logger.warning(f"Redis get error for {key}: {e}")
logger.debug(f"Cache MISS: {key}")
return None
def set(self, key: str, value: Any, ttl_seconds: int = 300) -> bool:
"""Set value in cache (L1 Memory + L2 Redis)"""
if not self.enabled:
return False
success = False
# 1. Set L2 Redis
if self.client:
try:
serialized = json.dumps(value, default=str)
self.client.setex(key, ttl_seconds, serialized)
logger.debug(f"L2 Redis SET: {key}")
success = True
except (RedisError, TypeError) as e:
logger.warning(f"Redis set error for {key}: {e}")
# 2. Set L1 Memory
expiry = time.time() + ttl_seconds
self.in_memory_cache[key] = (value, expiry)
# Memory Management: Simple FIFO if too large
if len(self.in_memory_cache) > 1000:
# Cleanup expired items first
now = time.time()
expired_keys = [k for k, v in self.in_memory_cache.items()
if isinstance(v, tuple) and v[1] < now]
for k in expired_keys:
del self.in_memory_cache[k]
# If still too big, remove oldest inserted
if len(self.in_memory_cache) > 1000:
first_key = next(iter(self.in_memory_cache))
self.in_memory_cache.pop(first_key)
success = True
return success
def delete(self, key: str) -> bool:
"""Delete key from cache"""
if not self.enabled:
return False
# Delete from Redis
if self.client:
try:
self.client.delete(key)
except RedisError:
pass
# Delete from In-Memory
if key in self.in_memory_cache:
del self.in_memory_cache[key]
return True
def delete_pattern(self, pattern: str) -> int:
"""Delete all keys matching pattern"""
if not self.enabled:
return 0
deleted_count = 0
# 1. Delete from Redis
if self.client:
try:
keys = self.client.keys(pattern)
if keys:
deleted_count = self.client.delete(*keys)
logger.debug(f"Redis DELETE pattern {pattern}: {deleted_count} keys")
except RedisError as e:
logger.warning(f"Cache delete pattern error for {pattern}: {e}")
# 2. Delete from In-Memory (using simple string matching)
try:
mem_deleted = 0
if not self.in_memory_cache:
return deleted_count
# Simple wildcard matching
token = pattern.replace("*", "")
keys_to_check = list(self.in_memory_cache.keys())
for k in keys_to_check:
if token in k: # Simple substring match for now
del self.in_memory_cache[k]
mem_deleted += 1
if mem_deleted > 0:
logger.debug(f"In-memory DELETE pattern {pattern}: {mem_deleted} keys")
# Return max of both (approximate)
return max(deleted_count, mem_deleted)
except Exception as e:
logger.warning(f"In-memory delete pattern error for {pattern}: {e}")
return deleted_count
def health_check(self) -> dict:
"""Check cache health"""
if not self.enabled:
return {"status": "disabled", "message": "Caching is disabled"}
status = {
"backend": self.backend if self.backend else ("redis" if self.client else "memory"),
"l1_items": len(self.in_memory_cache),
}
if self.client:
try:
self.client.ping()
info = self.client.info("memory")
status.update({
"redis_status": "connected",
"redis_used_memory": info.get("used_memory_human", "unknown"),
"redis_clients": self.client.client_list().__len__(),
})
except RedisError as e:
status["redis_status"] = f"error: {str(e)}"
else:
status["redis_status"] = "not_configured"
return status
def cached(key_prefix: str, ttl_seconds: int = 300):
"""
Decorator for caching function results
Usage: @cached("my_prefix", ttl_seconds=300)
"""
def decorator(func: Callable) -> Callable:
@wraps(func)
def wrapper(*args, **kwargs):
bypass_cache = kwargs.pop("bypass_cache", False)
# Use singleton instance!
cache = get_cache()
if bypass_cache:
logger.debug(f"Cache bypassed for {key_prefix}")
return func(*args, **kwargs)
# Create a consistent cache key
# Filter out authentication related args if present to avoid caching user-specifics if not needed
# For now, just hash everything
arg_str = str(args) + str(sorted(kwargs.items()))
cache_key = f"{key_prefix}:{hash(arg_str)}"
cached_result = cache.get(cache_key)
if cached_result is not None:
return cached_result
result = func(*args, **kwargs)
if result is not None:
cache.set(cache_key, result, ttl_seconds)
return result
return wrapper
return decorator
_cache_instance: Optional[CacheService] = None
def get_cache() -> CacheService:
"""Get singleton cache instance"""
global _cache_instance
if _cache_instance is None:
_cache_instance = CacheService()
return _cache_instance
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