Programming Tutorials

  The "Grey Screen" of Replays You have a mature Event Sourced system. The  UserRegistered  event has been production-stable for two years. Today, you decided to refactor. The  fullName  string field in the payload is technically debt; you need structured data. You split it into  firstName  and  lastName , update your domain models, run the tests, and deploy. Ten minutes later, your projection replay service crashes. Error: Validation Failed. Path: ['firstName'] - Required Path: ['lastName'] - Required Source: { "fullName": "John Doe", ... } You just broke the cardinal rule of Event Sourcing:  The Event Store is an immutable ledger.  You cannot simply run an  UPDATE  SQL statement to migrate historical JSON blobs to the new schema because that corrupts the cryptographic or logical integrity of the log. Yet, your new code cannot understand the old language. The Root Cause: Immutable Facts vs. Mutable Code The core conflict lies...

  The "3:00 AM" Spike Your monitoring dashboard is all green. CPU usage is sitting at a comfortable 15%. Suddenly, without a corresponding increase in user traffic, your primary database CPU spikes to 100%, connections time out, and the API latency graph goes vertical. By the time you SSH in, the system has recovered. You just fell victim to the  Cache Stampede  (also known as the Thundering Herd). This isn't a traffic problem; it's a synchronization problem. A highly accessed cache key (e.g., a global configuration object or a trending leaderboard) expired. At that exact millisecond, 5,000 concurrent requests hit your backend. They all missed the cache simultaneously. They all decided to rebuild the expensive query simultaneously. Your database, unable to process 5,000 heavy aggregation queries in parallel, effectively DDoS'd itself. The Root Cause: The Gap of Doom The standard caching pattern is "Check Cache -> Miss -> Read DB -> Write Cache"....