gistfile1.txt

"""
You need to design and implement a **Rate-Limited Cache System** that serves as
a middleware layer for expensive API calls. The system should provide caching
functionality while preventing abuse through rate limiting and collecting
analytics.

### Core Functionality

1. **Cache Operations**: Implement `get(key)` and `put(key, value, ttl)`
operations
2. **Rate Limiting**: Limit requests per user (configurable rate limit)
3. **TTL Support**: Cache entries should expire after their time-to-live
4. **Analytics**: Track cache hits, misses, and rate limit violations

### Technical Constraints

- Cache should have a maximum capacity with LRU eviction
least recently used
- Rate limiting should use a sliding window approach
- Include proper error handling and logging

"""

from typing import Optional, Dict, Any
from threading import Lock
from datetime import datetime, timedelta
import time
import logging

rate_limit = 2
lru_limit = 5000

class RateLimitedCache:
	storage = {}

	def __init__(self, max_capacity: int = 500, default_rate_limit: int = 60):
		"""
		Initialize the rate-limited cache system.

		Args:
			max_capacity: Maximum number of items in cache
			default_rate_limit: Default requests per minute per user
		"""
		pass

	def get(self, key: str, user_id: str) -> Optional[Any]:
		"""
		Retrieve a value from the cache with rate limiting.

		Args:
			key: The cache key
			user_id: Identifier for rate limiting

		Returns:
			The cached value if found and rate limit not exceeded, None otherwise

		Raises:
			RateLimitExceeded: If user has exceeded their rate limit
		"""

		"""
		if cache_data["user_id"] <= rate_limit:

		else:
			return

		"""
		cache_data = self.storage.get(key)
		ttl = timedelta(seconds=cache_data["ttl"])

		if cache_data["created_at"] + ttl < datetime.now():
			return None
		return cache_data


	def put(self, key: str, value: Any, ttl: int, user_id: str) -> bool:
		"""
		Store a value in the cache with rate limiting.

		Args:
			key: The cache key
			value: The value to store
			ttl: Time to live in seconds
			user_id: Identifier for rate limiting

		Returns:
			True if stored successfully, False if rate limited
		"""
		# remove the oldest records when the cache overflows
		if len(self.storage.keys()) < lru_limit:
			self.storage[key] = {"value": value, "user_id": user_id, "created_at": datetime.now(), "ttl": ttl}
			return True
		else:
			# clean
			# remove the oldest record
			self.storage.
			# add the key/value
			self.storage[key] = {"value": value, "user_id": user_id, "created_at": datetime.now(), "ttl": ttl}

	def get_analytics(self) -> Dict[str, Any]:
		"""
		Get system analytics.

		Returns:
			Dictionary with cache hits, misses, rate limit violations, etc.
		"""
		pass

	def set_user_rate_limit(self, user_id: str, rate_limit: int):
		"""Set custom rate limit for a specific user."""
		pass


def test_put():
	value = {}
	ttl = 50
	user_id = 1
	cache_storage = RateLimitedCache()
	assert cache_storage.put("foo-1-key", value, ttl, user_id) == True

def test_get_retrieval():
	value = {}
	ttl = 50
	user_id = 1
	cache_storage = RateLimitedCache()
	cache_storage.put("foo-1-key", value, ttl, user_id)
	cache_data = cache_storage.get("foo-1-key", user_id)
	assert cache_data["value"] == value

def test_get_retrieval_with_ttl_passing():
	value = {}
	ttl = 50
	user_id = 1
	cache_storage = RateLimitedCache()
	cache_storage.put("foo-1-key", value, ttl, user_id) # now
	time.sleep(4)
	cache_data = cache_storage.get("foo-1-key", user_id)
	assert cache_data == None


#test_put()
#test_get_retrieval()
test_get_retrieval_with_ttl()