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pprof | evcxr | |
---|---|---|
12 | 75 | |
7,399 | 5,168 | |
1.8% | 1.9% | |
7.6 | 8.7 | |
9 days ago | about 1 month ago | |
Go | Rust | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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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
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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
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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...
Go has pprof (https://github.com/google/pprof), which I've heard good things about --- and, the pprof data model was one of the influences I looked at when designing the Tokio console's wire format. But, I'm not sure if pprof has any similar UIs to the one we've implemented for the Tokio console; and I haven't actually used it all that much.
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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.
Would pprof's visualizations (especially the tree graph) work for you?
evcxr
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Scriptisto: "Shebang interpreter" that enables writing scripts in compiled langs
Emacs didn't invent REPL, and it's common everywhere. For Rust: https://github.com/evcxr/evcxr/blob/main/evcxr_repl/README.m.... But heck, the compiler is reasonably fast enough that any IDE can REPL by compiling the code.
The value here is more in being able to read a script before you run it, then have it run fast, maybe tweaking something here and there. And a compiled script will run 10,000 times faster than LISP, which can be important.
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Go: What We Got Right, What We Got Wrong
https://github.com/evcxr/evcxr can run Rust in a Jupyter notebook. It's not Golang but close enough.
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The Hallucinated Rows Incident
The engine uses rust_decimal::Decimal to represent high precision decimal numbers, like the weight property. Serialization of RocksDB keys is done by the storekey crate. To know how Yumi's machine stores diffs, we can now ask- How does storekey serialize rust_decimal? Well, using evcxr to run Rust in Jupyter, the answer is as a null-terminated string:
- TermiC: Terminal C, Interactive C/C++ REPL shell created with BASH
- Exploring Options for Dynamic Code Changes in Rust without Recompilation (hot reloading)
- Go 1.21 will (likely) have a static toolchain on Linux
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What’s an actual use case for Rust
In theory you should be able to create Rust notebooks (Jupyter notebook) using evcxr so maybe some AI, data analysis, prototyping make sense if you aim for good performance in final application (protype in evcxr and use notebook as reference to implement final application in Rust for speed and safety).
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would you use rust for scripting?
You should check out evcxr
- Nannou – An open-source creative-coding framework for Rust
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A Case for Rust in Deep Learning
I think you might like this project: https://github.com/google/evcxr . It brings the REPL workflow to Rust, so having fast iteration should not be an issue.
What are some alternatives?
vscode-jupyter - VS Code Jupyter extension
gperftools - Main gperftools repository
prometheus - The Prometheus monitoring system and time series database.
jaeger - CNCF Jaeger, a Distributed Tracing Platform
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
jupyter-rust - a docker container for jupyter notebooks for rust
tracy - Frame profiler
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.
rust-script - Run Rust files and expressions as scripts without any setup or compilation step.
bincode - A binary encoder / decoder implementation in Rust.
cargo-script - Cargo script subcommand
iron.nvim - Interactive Repl Over Neovim