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Generates color-coded differential flame graphs that highlight code paths with increased (red) or decreased (green) resource consumption between two snapshots.
Distinct from Flame Graph Generators: Distinct from Flame Graph Generators: adds the capability to compare two profiles and visually highlight differences, not just generate a single flame graph.
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This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on
Provides methods for generating differential flame graphs to isolate performance regressions by comparing two CPU sampling snapshots.
Parca is an always-on continuous profiling platform that captures CPU and memory usage from running applications without any code modifications. It uses eBPF kernel-level tracing to automatically discover and sample stack traces across infrastructure, and provides a web-based flame graph dashboard for interactive performance analysis. Its label-based query engine lets users slice and aggregate profiling data across dimensions such as service, container, or region, using a Prometheus-style selector syntax. Unlike basic profilers, Parca stores profile samples in a columnar format using Apache A
Parca generates a color-coded differential flame graph that highlights code paths with increased (red) or decreased (green) resource consumption between two snapshots.