autocannon
FlameGraph
autocannon | FlameGraph | |
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14 | 53 | |
7,586 | 16,438 | |
- | - | |
6.5 | 4.5 | |
3 days ago | 19 days ago | |
JavaScript | Perl | |
MIT License | - |
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autocannon
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Optimize Your Node.js API with Clustering, Load Testing, and Advanced Caching
Autocannon GitHub Repository
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Taming the dragon: using llnode to debug your Node.js application
To make things interesting, let’s send some requests to this server with autocannon:
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Benchmarking Deno vs Node with GraphQL
Using autocannon, I did the following script to simulate 500 concurrent connections over 30 seconds:
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A first look at Bun: is it really 3x faster than Node.js and Deno?
We then used autocannon to measure the throughput (requests per second) of each runtime server-rendering our React app.
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Can we use Pydantic models (Basemodel) directly inside model.predict using FastAPI, if not why?
You could also use tools like autocannon to see how many requests/second you can achieve with various methods. : https://github.com/mcollina/autocannon
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How to Use Source Maps in TypeScript Lambda Functions (with Benchmarks)
I used autocannon to test the function at 100 concurrent executions for 30 seconds. I also used Lambda Power Tuning to find the ideal memory configuration, which proved to be 512MB. All the results are available.
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Find bottlenecks in Node.js apps with Clinic Flame
Moreover, if your blocking issue is appearing only on heavy load, you can easily test it using the very nice --autocannon CLI param (see it with clinic flame --help) where you can specificy autocannon options to generate some HTTP load on your web service.
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Created a URL shortener in Node (Fastify) and in Go (net/http). Why isn't Go faster?
I packaged them both with Docker and deployed them to an EC2 instance, each behind an Nginx reverse proxy I setup in docker-compose. I'm currently testing performance using autocannon from my laptop like this: `autocannon -a 5000 -w 10 URL` (5000 requests with 10 workers), and both apps complete in around 40 seconds. The EC2 instance is in Oregon and I'm testing from Toronto.
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DB query performance options.
You can test it by yourself using console.time(). You can use autocannon to stress-test your http server to see what is really the best options.
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Experiments in concurrency 3: Event loops
When I test this with autocannon making three simultaneous requests (autocannon --connections 3 --amount 3 --timeout 10000 --no-progress http://localhost:5678/):
FlameGraph
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JVM Profiling in Action
We'll use async-profiler and flame graphs for profiling. To simplify the process, we'll run the code using JBang.
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Memray – A Memory Profiler for Python
And flame graphs excel and this kind of thing
https://www.brendangregg.com/flamegraphs.html
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All my favorite tracing tools: eBPF, QEMU, Perfetto, new ones I built and more
which can output in a format understood by Brendan Gregg's flame frames (https://www.brendangregg.com/flamegraphs.html)
But that's not quite the kind of tracing you're talking about. We also built a printf-style interface to our recording files, which seems closer:
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Recap of Werner Vogels' Keynote at re:Invent 2023
Strategies included discontinuing or resizing underutilized services, transitioning to more cost-effective solutions, reducing the current resources to the amount of resources that we need for our application, and conducting detailed analyses of computing resource utilization through tools like flamegraphs. This detailed scrutiny helped identify and rectify significant cost-driving areas, such as garbage collection and application configurations.
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Pinpoint performance regressions with CI-Integrated differential profiling
Flame Graphs by Brendan Gregg
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Flameshow: A Terminal Flamegraph Viewer
Historically brendangregg's since AIUI he basically invented flamegraphs
https://www.brendangregg.com/flamegraphs.html
So if you can make your tool eat whatever https://github.com/brendangregg/FlameGraph is fed with you're going to support a lot of existing tooling across OSes and languages.
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Introducing Flame graphs: It’s getting hot in here
“Flame graphs are a visualization of hierarchical data, created to visualize stack traces of profiled software so that the most frequent code-paths to be identified quickly and accurately.”
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Using SVG to create simple sparkline charts
SVGs are amazing for interactive visualisation too. Like Flamegraphs: https://www.brendangregg.com/flamegraphs.html
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Good example of using flame graphs to speed up java code (50x improvement)
This may be a good example of the application of a flame graph but it is not a good demonstration of flame graphs; the graph is nearly incidental. The source has an actual explanation.
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Intro to PostGraphile V5 (Part 1): Replacing the Foundations
A profiling flame graph from Graphile Crystal (a precursor to Grafast) using GraphQL.js' executor (each tick is 1ms, total: 29ms). As we removed more and more responsibilities from GraphQL.js, we ended up only using it for output. Replacing this final responsibility with a custom implementation in Graphile Crystal itself, we reduced execution time for this query down to 15.5ms (effectively removing the majority of the yellow portion of the flame graph).
What are some alternatives?
node-clinic - Clinic.js diagnoses your Node.js performance issues
hotspot - The Linux perf GUI for performance analysis.
octane - Supercharge your Laravel application's performance.
benchmark - A microbenchmark support library
serverless-graphql - Serverless GraphQL Examples for AWS AppSync and Apollo
tracing-bunyan-formatter - A Layer implementation for tokio-rs/tracing providing Bunyan formatting for events and spans.
aws-sam-cli - CLI tool to build, test, debug, and deploy Serverless applications using AWS SAM
HeatMap - Heat map generation tools
lambda-sourcemaps
Swoole - 🚀 Coroutine-based concurrency library for PHP
pmu-tools - Intel PMU profiling tools