To implement an efficient caching system to reduce the load on the bank, we need to consider key factors such as data consistency, cache invalidation, and choosing the right caching strategy. ## <br>1. Define What to Cache - Frequent queries: Store results of frequently accessed queries like account balances, recent transactions, and user authentication details. - Computed results: Cache computationally expensive operations like interest calculations. - API responses: Store responses of commonly accessed APIs to reduce repeated database hits. ## <br>2. Choose the Right Caching Strategy - Write-through Cache: Writes data to both cache and database simultaneously (useful for frequently read data). - Write-back Cache: Writes data to cache first and later syncs it with the database (improves performance but has a risk of data loss). - Time-to-Live (TTL) Cache: Automatically expires data after a set period (good for transient data like session tokens). - Least Recently Used (LRU) Cache: Evicts the least used entries when cache memory is full. ## <br>3. Select the Appropriate Caching Technology - Redis: Fast, in-memory key-value store, supports TTL, LRU eviction, and pub/sub messaging. - Memcached: Simple key-value store, good for caching raw query results. - Database Query Caching: Use built-in database caching mechanisms such as MySQL Query Cache or PostgreSQL Materialized Views. - Edge Caching/CDN: If applicable, use a CDN like Cloudflare to cache static responses. ## <br>4. Implement Cache in Code (Example with Redis in Python) Using Redis to cache account balances: ```py import redis import time # Connect to Redis cache = redis.Redis(host='localhost', port=6379, db=0) def get_account_balance(account_id): cache_key = f"balance:{account_id}" # Check if balance is in cache balance = cache.get(cache_key) if balance: print("Cache hit") return float(balance) print("Cache miss, fetching from DB") balance = fetch_from_database(account_id) # Simulated DB function # Store in cache with TTL of 5 minutes cache.setex(cache_key, 300, balance) return balance def fetch_from_database(account_id): time.sleep(2) # Simulate database query delay return 1000.50 # Example balance # Test the caching system print(get_account_balance(123)) # First time (Cache miss) print(get_account_balance(123)) # Second time (Cache hit) ``` ## <br>5. Cache Invalidation Strategies - Time-based Expiry (TTL): Use for data that changes over time. - Event-based Invalidation: Update or delete cache when a database update occurs. - Manual Invalidation: Allow manual cache clearing in case of inconsistencies. ## <br>6. Monitor Cache Performance - Redis INFO command: Check memory usage, cache hits/misses. - Prometheus/Grafana: Monitor cache performance with alerts. - Log slow queries: Identify database queries that should be cached. Help me implement this in a language other than Python. Join our community