Wzprof, a cross-language profiler using WebAssembly

Hacker News

We are excited to present wzprof: a pprof-based profiler for WebAssembly modules. With it we can collect CPU and memory profiles during their execution. You execute your program compiled to WebAssembly under wzprof, and you get profiling metrics that can be consumed by anything that understands the pprof format (e.g. go pprof, Pyroscope, Parca). A few things motivated us to build this tool. WebAssembly runtimes typically allow profiling guest code via an external profiler such as perf, but in many cases, the recording and analysis of profiles remain a difficult task, especially due to features like JIT compilation. pprof is the de-facto standard profiling tool for Go programs and offers some of the simplest and quickest ways to gather insight into the performance of an application. wzprof aims the combine the capabilities and user experience of pprof, enabling the profiling of any application compiled to WebAssembly.

Visit product View on Hacker NewsMay 11, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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