This article covers how Pipeline Performance Profiling helps teams analyze and optimize CI/CD pipeline performance. It breaks down execution into measurable phases and provides insights on resource usage, bottlenecks, and cost efficiency. The tool integrates with existing observability tools, making it easier to track performance trends and identify areas for improvement.

This article discusses how Ruby applications often consume a lot of CPU time, primarily due to library choices. It highlights key libraries impacting performance, the benefits of upgrading to Ruby 3, and the expected improvements with Ruby 4.0.

This article explores the concept of cursor profiling, highlighting its role in identifying performance bottlenecks in web applications. It discusses the missing layer in traditional profiling methods and offers insights into how developers can implement more effective profiling techniques.

Doltgres, a version-controlled SQL database, faced a performance regression due to the inclusion of system tables in its pg_catalog. The author utilized Go's pprof tool to visualize performance differences between code branches, ultimately identifying and fixing a bug that was causing unnecessary overhead in fetching table names. This fix not only resolved the slowdown but also improved overall performance benchmarks.

Flame graphs visually represent where a program consumes processing time, utilizing sampled call stack data collected by a profiler. This blog post discusses the creation and use of flame graphs for diagnosing performance bottlenecks in PostgreSQL, detailing methods for data collection and processing, and highlighting the importance of build types in profiling.

Understanding software performance often requires profiling to determine where code execution time is spent. Go offers built-in profiling tools and the article explains how to use flame graphs to visualize profiling data, helping developers identify performance bottlenecks effectively.