pprof
flamegraph
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pprof | flamegraph | |
---|---|---|
12 | 47 | |
7,423 | 4,241 | |
2.1% | 2.4% | |
7.6 | 7.4 | |
3 days ago | 4 days ago | |
Go | Rust | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pprof
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Profiling Caddy
The pprof format is not tied to Go. From my understanding, it's used within Google across multiple languages. The format is defined in the pprof repository[0], and the visualization tool is source-language agnostic. I've seen libraries in numerous languages (e.g. Python, Java) to publish profiles in pprof format. This is an indicator the pprof format has become de-facto. Grafana Pyroscope[1] is a tool that's capable of parsing the pprof format, agnostic to the source programming language, and has instructions for Go, Java, Python, Ruby, node.js, Rust, and .NET.
My understanding is that you're searching for a combination of the profiles, metrics, and tracing. Caddy supports all 3.
[0] https://github.com/google/pprof/blob/main/doc/README.md
[1] https://grafana.com/docs/pyroscope/latest/
metrics and tracing need to be manually enabled (for now, perhaps)
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Why So Slow? Using Profilers to Pinpoint the Reasons of Performance Degradation
Because we couldn't identify the issue using the results we got from Callgrind, we reached for another profiler, gperftools. It's a sampling profiler and therefor it has a smaller impact on the application's performance in exchange for less accurate call statistics. After filtering out the unimportant parts and visualizing the rest with pprof, it was evident that something strange was happening with the send function. It took only 71 milliseconds with the previous implementation and more than 900 milliseconds with the new implementation of our Bolt server. It was very suspicious, but based on Callgrind, its cost was almost the same as before. We were confused as the two results seemed to conflict with each other.
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Improving the performance of your code starting with Go
github.com - google/pprof
- Proposal to Support Timestamps and Labels in Pprof Events
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A Generic Approach to Troubleshooting
The application performances in a specific code path (e.g. gdb, pprof, …).
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Does rust have a visual analysis tool for memory and performance like pprof of golang?
pprof is https://github.com/google/pprof, it's a very useful tool in golang , and really really really convenient
- pprof - tool for visualization and analysis of profiling data
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Tokio Console
Go also has pretty good out of the box profiling (pprof[0]) and third-party runtime debugging (delv[1]) that can be used both remotely and local.
These tools also have decent editor integration and can be use hand in hand:
https://blog.jetbrains.com/go/2019/04/03/profiling-go-applic...
https://blog.jetbrains.com/go/2020/03/03/how-to-find-gorouti...
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Cats and Clouds – There Are No Pillars in Observability with Yoshi Yamaguchi
And what we do in Google Cloud is that we still use the pprof. But it's a kind of forked version of the pprof because the visualization part is totally different. So we give that tool as the Cloud Profiler. So that is the product name. And then, the difference between the pprof and a Cloud Profiler is that Cloud Profiler provides the agent library for each famous programming language such as Java, Python, Node.js, and Go. And then what you need to do is to just write 5 to 10 lines of code in a new application. That launches the profile agent in your application as a subsidiary thread of the main thread. And then, that thread periodically collects the profile data of the application and then sends that data back to Google Cloud and the Cloud Profiler.
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Is there a way I can visualize all the function calls made while running the project(C++) in a graphical way?
gprftools (https://github.com/gperftools/gperftools) can be easily plugged in using LD_PRELOAD and signal, and has nice go implemented visualization tool https://github.com/google/pprof.
flamegraph
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Rust Tooling: 8 tools that will increase your productivity
You can install cargo-flamegraph with cargo install flamegraph. There are some underlying requirements to be able to use cargo-flamegraph; you will want to take a look at the repo here to make sure you have the right dependencies.
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Need help making sense of these benchmark results
I tried to diagnose the issue with flamegraph, but unfortunately the flamegraph didn't show anything beyond the next call for some reason
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Why is my code so slow ? advent of code 2022, day 16 (basic graph stuff)
having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go)
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why is my code so slow ? advent of code 2023, day 16 (basic graph stuff)
I'm currently implementing a solution for the first part of the day 16. It work but it is really slow... I'd like to : - understand why - having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go) - eventually have some clue/solution/idea - have general feedback on what in my "coding style" is not appropriate for rust (I come from java/kotlin/ts even if I've already coded a bit in c/c++) : for example I love iterator & sequence but i feel they are not really suited to overuse in rust (mostly because of async & result).
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how expensive is an operation?
Use a profiler. Flamegraph is a good way to visualise profiler output. This lets you identify which functions are taking up a large amount of time - and hence helps you identify where to focus your optimisation efforts.
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Slow Rust Redis
You tried trying to see what takes the most time under load via flames? https://github.com/flamegraph-rs/flamegraph
- making a virtual machine in rust
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Need help with rust performance
Well, in cases like that the answer is straight forward, use a profiler like https://github.com/flamegraph-rs/flamegraph
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superdiff - a way to find similar code blocks in projects (comments appreciated)
I don't see any obvious problems with your algorithm. I've had luck using cargo-flamegraph to identify the slow parts of my code. That's going to show you which parts to focus on improving the performance of!
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Data-driven performance optimization with Rust and Miri
From the readme of cargo flamegraph:
What are some alternatives?
gperftools - Main gperftools repository
cargo-flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
prometheus - The Prometheus monitoring system and time series database.
tracing - Application level tracing for Rust.
jaeger - CNCF Jaeger, a Distributed Tracing Platform
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
tracy - Frame profiler
heaptrack - A heap memory profiler for Linux
parca - Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability.
hashbrown - Rust port of Google's SwissTable hash map
massif-visualizer - Visualizer for Valgrind Massif data files
snmalloc-rs - rust bindings of snmalloc