When Good Locks Go Bad: Diagnosing a System Meltdown Under Load Introduction: The Backbone of BBD Readiness At Flipkart, preparing for our annual Big Billion Days (BBD) sale is an epic undertaking …

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Engineers at Flipkart diagnosed a critical system failure during load testing for their Big Billion Days sale. Their Mirana service crashed under load due to excessive contention on a Redis distributed lock. Initial solutions using queuing failed because they violated the 'fail fast' principle. The team ultimately solved the problem by implementing an AtomicInteger-based semaphore to limit concurrent threads attempting lock acquisition. The key insight was optimizing for actual service performance (200-300ms per request) rather than downstream resource limits, reducing allowed concurrency from 128 to 5 threads per pod and achieving stable throughput of 90 QPS across 9 pods.

When Good Locks Go Bad: Diagnosing a System Meltdown Under Load

Introduction: The Backbone of BBD Readiness

The Investigation: Following the Trail of Clues

Solution Two: Embracing the “Fail Fast” Principle

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The Final Twist: When the Math Is Right, but the Logic Is Wrong