Go pprof Web

A developer-focused tool that analyzes Go pprof web data to surface CPU and memory hotspots, top functions, and call graphs for performance tuning.

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About This Tool

The tool analyzes Go pprof web profiling data to expose performance characteristics of Go applications. It targets developers, performance engineers, and SREs who need repeatable, machine-friendly analysis from CPU, memory, and blocking profiles. Use cases include identifying CPU hotspots in a web service, memory churn in batch jobs, and latency-sensitive goroutine contention. It is designed to produce structured outputs suitable for automation, reporting, or feeding AI agents.
The tool implements a domain-focused parsing and aggregation pipeline for pprof data. Required features include: ingesting profile data from local files or remote URLs; parsing CPU, memory, and blocking profiles; normalizing units (ns for time; bytes for memory); computing per-function metrics (total time, samples, allocations); building a lightweight call-graph representation with parent-child contributions; producing summary metrics and structured data for downstream tooling. Optional/advanced features include: filtering by function name, time window, or sampling rate; exporting results as JSON or CSV; supporting multiple profiles in a single run; and detecting outliers.
Inputs include profile_type (CPU, memory, block, mutex), data_source (path or URL), time_window (start-end), and optional filters (focus_function, ignore_noise). Outputs include a structured report with total_samples, total_time_ns, top_functions by time, allocation bytes, and a call-graph data set; timeseries-ready data can be consumed by charts. Validation ensures profile format compatibility and unit consistency.
Parses pprof proto/binary; aggregates samples by function; sums self-time and cumulated time; computes percentages of totals; ranks top contributors; derives simple call paths; outputs JSON-friendly structures for automation. Edge cases and external data failures are handled gracefully. The tool assumes input is valid Go pprof data or a supported dump; excludes UI rendering, editing of profiling data, and non-Go profiler formats. It is not a replacement for Go toolchain installations.

How to Use

  1. Provide inputs: profile_type (CPU, memory, block, mutex) and data_source (path or URL).
  2. Optional: set focus_function, time_window, and filters.
  3. Run analysis: execute parsing and aggregation.
  4. Review outputs: inspect top functions, total time, allocations, and call-graph data.
  5. Export results: download as JSON or CSV for automation or reporting.
How to use go tool pprof web

Frequently Asked Questions

Find Quick Answers

What data formats are supported?
The tool accepts standard Go pprof data, including CPU, memory, block, and mutex profiles. It parses binary and protobuf formats commonly produced by Go tooling and compatible profiling runs.
Can I analyze remote pprof endpoints?
Yes. The data_source can be a URL pointing to a pprof dump or endpoint. The tool fetches the data, validates it, and performs the same parsing and aggregation as local files, with network error handling.
Can I focus on specific functions or time ranges?
Yes. Optional filters allow focusing on a named function, restricting to a time window, or adjusting aggregation granularity. This helps isolate hotspots and compare different intervals.
What outputs are produced for automation?
The tool emits structured, machine-readable outputs including total_time_ns, total_samples, top_functions, and call-graph data. Outputs are designed for JSON/CSV ingestion in CI pipelines or dashboards.

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