I'm excited to share that my new book, Performance Engineering in Practice, is now available through Manning's Early Access Program! 🎉
For 25 years I've seen the same pattern: performance problems surface after users complain, teams panic, patch, and repeat. I call it the Performance Decay Cycle.
This book is the practical guide to breaking that cycle. First 5 chapters available now with 50% off at launch 🚀
https://hubs.la/Q044cvlc0
Are you a senior software developer, architect, or engineering leader wrestling with performance bottlenecks and increasing system complexity?
This #InfoQ talk, based on Aleksey Shipilev's performance work phase diagram, reveals the critical "Performance - Complexity Curve" and how it impacts your engineering decisions and costs.
Watch the video now: https://youtu.be/0tSLRAPC_0M
#CaseStudy: Uber & Google teamed up: they integrated #ProfileGuidedOptimization into #GoLang.
The result?
➡️ Significant performance improvements
➡️ Resource savings across their service fleet
Read the full story on #InfoQ 👉 https://bit.ly/4iI3Rh1
#SoftwarePerformance & #SoftwareComplexity are related! It's common for refactoring to introduce unexpected regressions, and performance optimizations often face tough code reviews.
In this #InfoQ video, Richard Startin explores the recent enhancements to the Java String class and the intricate relationship between Complexity & Performance.
Have you faced this trade-off? Watch now: https://bit.ly/4hhpGmq
📄 #transcript included
Performance regressions have a tremendous impact on the quality of software. One way to catch regressions before they reach production is executing performance tests before deployment, e.g., using microbenchmarks, which measure performance at subroutine level. In projects with many microbenchmarks, this may take several hours due to repeated execution to get accurate results, disqualifying them from frequent use in CI/CD pipelines. We propose $μ$OpTime, a static approach to reduce the execution time of microbenchmark suites by configuring the number of repetitions for each microbenchmark. Based on the results of a full, previous microbenchmark suite run, $μ$OpTime determines the minimal number of (measurement) repetitions with statistical stability metrics that still lead to accurate results. We evaluate $μ$OpTime with an experimental study on 14 open-source projects written in two programming languages and five stability metrics. Our results show that (i) $μ$OpTime reduces the total suite execution time (measurement phase) by up to 95.83% (Go) and 94.17% (Java), (ii) the choice of stability metric depends on the project and programming language, (iii) microbenchmark warmup phases have to be considered for Java projects (potentially leading to higher reductions), and (iv) $μ$OpTime can be used to reliably detect performance regressions in CI/CD pipelines.
Den Odell
RubikChat
InfoQ
Jenetics GA