How One Engineer Cut Memory Usage by 70 Percent
from The Software Engineering Podcast with Fexingo: Code, Architecture, and Engineering Best Practices Hosted by Fexingo
When a fintech startup's core service started OOM-killing pods every few hours, engineer Priya Patel didn't reach for a bigger instance. Instead, she profiled the Java heap and found that a single data structure — a ConcurrentHashMap holding cache entries — was responsible for over half the resident memory. In this episode, Lucas and Luna walk through how Priya replaced it with a custom, off-heap cache using sun.misc.Unsafe and direct ByteBuffers, cut memory usage by 70 percent, and kept latency flat. They discuss the trade-offs: garbage collection pauses dropped, but code complexity rose and they had to carefully manage native memory. The team also added a JFR-based memory profiler to catch regressions. By the end, you'll understand why off-heap caching can be a powerful tool in the right situation — and when it's just not worth the risk. This is a practical look at a real optimization that saved the company thousands in cloud costs and made the service far more stable. #OffHeapCaching #JavaPerformance #MemoryOptimization #GC #ConcurrentHashMap #Fintech #CloudCosts #JFR #Profiling #Engineering #Technology #SoftwareEngineering #FexingoBusiness #BusinessPodcast #TechPodcast #CodeOptimization #JVM #PerformanceTuning Keep every episode free: buymeacoffee.com/fexingo
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