Python in-memory cache with time to live

Python in-memory cache with time to live

To implement an in-memory cache with a time-to-live (TTL) feature in Python, you can use a combination of a dictionary to store the cached items and a mechanism to track the time of insertion. You can achieve this using the time module to keep track of time and periodically check the TTL of cached items.

Here's a basic example of how you can create an in-memory cache with TTL:

import time class Cache: def __init__(self, ttl_seconds): self.cache = {} self.ttl = ttl_seconds def put(self, key, value): self.cache[key] = (value, time.time()) def get(self, key): if key in self.cache: value, timestamp = self.cache[key] if time.time() - timestamp <= self.ttl: return value else: del self.cache[key] return None def clear_expired(self): current_time = time.time() for key, (_, timestamp) in self.cache.items(): if current_time - timestamp > self.ttl: del self.cache[key] # Create a cache with a TTL of 5 seconds cache = Cache(ttl_seconds=5) # Add items to the cache cache.put('key1', 'value1') cache.put('key2', 'value2') # Retrieve items from the cache print(cache.get('key1')) # Output: value1 print(cache.get('key2')) # Output: value2 # Wait for TTL to expire time.sleep(6) # Check if items are expired and clear them cache.clear_expired() print(cache.get('key1')) # Output: None (expired) print(cache.get('key2')) # Output: None (expired)

In this example, the Cache class allows you to put and get items from the cache, and the clear_expired() method clears expired items based on their TTL.

Keep in mind that this is a simple example, and for more complex caching needs, you might want to consider using existing libraries like cachetools or redis as they provide more advanced caching features and better performance.

Examples

  1. How to implement an in-memory cache with Time to Live (TTL) in Python?

    • Description: An in-memory cache with TTL stores data in memory and automatically removes stale entries after a certain period. You can use a dictionary to store cache entries and a background thread to remove expired items.
    • Code:
      import time import threading class TTLCache: def __init__(self, ttl): self.cache = {} self.ttl = ttl self.lock = threading.Lock() threading.Thread(target=self._cleaner, daemon=True).start() def set(self, key, value): with self.lock: self.cache[key] = (value, time.time() + self.ttl) def get(self, key): with self.lock: value, expiry = self.cache.get(key, (None, 0)) if time.time() > expiry: return None return value def _cleaner(self): while True: time.sleep(self.ttl) with self.lock: now = time.time() keys_to_remove = [k for k, (_, expiry) in self.cache.items() if now > expiry] for key in keys_to_remove: del self.cache[key] cache = TTLCache(ttl=5) # TTL set to 5 seconds cache.set("test", "value") time.sleep(6) print(cache.get("test")) # Outputs: None (entry expired)

  2. How to implement a TTL-based in-memory cache using a third-party library in Python?

    • Description: Libraries like cachetools provide built-in TTL functionality for in-memory caching, making it easier to implement with less code.
    • Code:
      from cachetools import TTLCache import time cache = TTLCache(maxsize=10, ttl=5) # Cache with TTL of 5 seconds cache["test"] = "value" time.sleep(6) print("Key 'test' value:", cache.get("test", "Not found")) # Outputs: "Not found"

  3. How to update an in-memory cache with TTL in Python?

    • Description: To update a TTL-based cache, you can use the same method to set or update cache entries and ensure the TTL is refreshed.
    • Code:
      from cachetools import TTLCache cache = TTLCache(maxsize=10, ttl=5) # TTL set to 5 seconds cache["key"] = "initial_value" # Updating the value and resetting the TTL cache["key"] = "updated_value" print("Updated cache value:", cache["key"]) # Outputs: "updated_value"

  4. How to check if a key exists in a TTL-based in-memory cache in Python?

    • Description: You can check for the presence of a key in a TTL-based cache and handle expired keys accordingly.
    • Code:
      from cachetools import TTLCache import time cache = TTLCache(maxsize=10, ttl=5) # Cache with TTL of 5 seconds cache["key"] = "value" # Check if key exists before TTL expires exists_before_expiry = "key" in cache print("Exists before expiry:", exists_before_expiry) # Outputs: True time.sleep(6) # Check after TTL expiry exists_after_expiry = "key" in cache print("Exists after expiry:", exists_after_expiry) # Outputs: False

  5. How to implement TTL-based in-memory cache for a web application in Python?

    • Description: In web applications, caching can improve performance. A TTL-based in-memory cache can be used to store frequent responses or database query results, expiring old entries to maintain freshness.
    • Code:
      from cachetools import TTLCache import flask import time app = flask.Flask(__name__) cache = TTLCache(maxsize=50, ttl=10) # Cache with TTL of 10 seconds @app.route("/data") def get_data(): if "data" in cache: return cache["data"] else: # Simulate a database query data = "Database result" cache["data"] = data return data app.run()

  6. How to remove expired entries from a TTL-based in-memory cache in Python?

    • Description: In a TTL-based cache, expired entries can be removed automatically using a background thread or manually by checking the expiration times.
    • Code:
      import threading import time from cachetools import TTLCache cache = TTLCache(maxsize=10, ttl=5) # Cache with TTL of 5 seconds # Background cleaner to remove expired entries def cleaner(): while True: time.sleep(5) cache.expire() # Explicitly remove expired entries threading.Thread(target=cleaner, daemon=True).start() cache["key"] = "value" time.sleep(6) # Allow TTL to expire print("Cache after cleaning:", cache.get("key", "Not found")) # Outputs: "Not found"

  7. How to control the maximum size of a TTL-based in-memory cache in Python?

    • Description: Using third-party libraries like cachetools, you can set a maximum cache size along with the TTL to prevent the cache from growing indefinitely.
    • Code:
      from cachetools import TTLCache import time cache = TTLCache(maxsize=3, ttl=5) # Maximum size of 3 items, TTL 5 seconds cache["a"] = "value1" cache["b"] = "value2" cache["c"] = "value3" # Adding a new item, causing the oldest one to be evicted cache["d"] = "value4" print("Current cache:", list(cache.items())) # Outputs: [('b', ...), ('c', ...), ('d', ...)]

  8. How to use TTL-based in-memory cache to cache API responses in Python?

    • Description: A TTL-based cache can store API responses to reduce external API calls and improve performance by serving cached results within the TTL timeframe.
    • Code:
      import requests from cachetools import TTLCache cache = TTLCache(maxsize=10, ttl=30) # TTL of 30 seconds def get_weather(city): if city in cache: return cache[city] else: # Fetch from API if not in cache response = requests.get(f"https://api.weather.com/v3/weather/forecast?city={city}") result = response.json() cache[city] = result # Cache the response return result # Example usage print(get_weather("New York")) # Outputs: API response

  9. How to use a TTL-based in-memory cache with database queries in Python?

    • Description: Using a TTL-based cache, you can reduce the load on a database by caching frequently accessed data and reusing it within the TTL period.
    • Code:
      import sqlite3 from cachetools import TTLCache # Create in-memory cache with TTL of 60 seconds cache = TTLCache(maxsize=20, ttl=60) def get_user_details(user_id): if user_id in cache: return cache[user_id] else: # Simulate a database query conn = sqlite3.connect("users.db") cursor = conn.cursor() cursor.execute("SELECT * FROM users WHERE id=?", (user_id,)) result = cursor.fetchone() cache[user_id] = result # Cache the result return result print(get_user_details(1)) # Outputs: Database query result

  10. How to measure TTL-based cache performance in Python?

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