​I built a project to truly understand what happens inside the JVM when pushed to the limit. 

The goal wasn't just "working code," but a deep understanding of memory utilization, CPU cycles, and runtime behavior.

​The task: Scanning a log file of one million lines and counting errors per hour.

​The process was divided into 4 versions, starting from the most naive implementation to optimization at the Bytecode and Assembly levels. 

At each stage, I identified a specific bottleneck and improved it for the next version.

​The tools I used:

​JMH: For accurate micro-benchmarking while neutralizing Warmup biases.

​JITWatch: For analyzing C2 Compiler decisions and examining the final Assembly.

​The results:

​I started with a standard implementation running at 872ms with a significant load on the GC, and ended with an optimal implementation running at 78ms with 0 allocations. (On a limited hardware environment, which emphasizes the execution efficiency).

​To keep the code readable and focused on single-core performance, I chose to work with only one thread. The difference in performance stems solely from improving code efficiency and adapting it to the JVM architecture.

​You are welcome to view the process, the measurement documentation, and the source code on GitHub:

https://github.com/Yosefnago/java-deep-dive

​#Java #JVM #PerformanceTuning #BackendDevelopment #SoftwareEngineering #CleanCode #JIT #LowLatency #SoftwareArchitecture #DeepDive #CodingLife #JavaDeveloper #TechWriting

Don't guess, measure.