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  There are few log entries as frustrating to a backend engineer as   ERROR 1213 (40001): Deadlock found when trying to get lock; try restarting transaction . It often appears sporadically under high load, vanishing when you attempt to reproduce it locally. While the error message suggests a simple retry, treating Error 1213 merely as a signal to "try again" is a mistake. In high-throughput systems—like payment gateways or inventory management systems—deadlocks are symptoms of conflicting access patterns that degrade database performance and user experience. This guide moves beyond generic advice. We will analyze how InnoDB handles locking, dissect a real-world eCommerce deadlock scenario, and implement an architectural solution to resolve it. The Anatomy of an InnoDB Deadlock To fix a deadlock, you must first understand what InnoDB is actually locking. A common misconception is that MySQL locks specific rows of data. In reality,  InnoDB locks index records. If you execut...

  You know the pattern. Your monitoring dashboard looks healthy: 99% cache hit ratio, database CPU at 15%. Suddenly, a single "hot" key—perhaps the global configuration object or the homepage personalization metadata—expires. In the 200 milliseconds it takes to fetch the data from the database and repopulate Redis, 5,000 concurrent requests miss the cache. They all rush the database simultaneously. The database CPU spikes to 100%, connections time out, and the backend enters a crash loop because the database never recovers enough to serve the first query that would repopulate the cache. This is the  Cache Stampede  (or Thundering Herd). This post details the two architectural patterns to solve it:  Distributed Locking  and  Probabilistic Early Expiration (PER) . The Root Cause: The Latency Gap The stampede occurs because there is a non-zero time gap ($\Delta$) between detecting a cache miss and writing the new value. If your system throughput is $R$ request...