lldb-mi VS autocannon

I'm on the record of loving the VSCode experience with Rust. And I do think that it's amazing that a "non-IDE" can feel so much like an IDE. However, I've recently pivoted off of that stance. I know it's still in EAP, but Rust Rover gives me all of the things that I get from VSCode plus an easier integration with LLDB.

Fortunately, we can use this same technique with our Node.js applications! This is possible through llnode: a LLDB plugin which enables us to inspect Node.js core dumps. With llnode, we can inspect objects in the memory and look at the complete backtrace of the program, including native (C++) frames and JavaScript frames. It can be used on a running Node.js application or through a core dump.

An alternative to gdb is lldb. But I like gdb.

The CMake-based, itk-wasm build system tooling enables the same C++ build system configuration and code to be reused when building a native system binary or a WebAssembly binary. As a result, native binary debugging tools, such as GDB, LLDB, or the Visual Studio debugger can be utilized.

The debugger component of the LLVM project. It’s what you’re typing into when you type po someExpression. https://lldb.llvm.org/ Web searches could help explain a lot of this for you 😊

If you really don't want to touch Visual Studio/MSVC then you can try to compile with clang and use lldb: https://lldb.llvm.org/

LLDB - https://lldb.llvm.org/ - Debugger from the LLVM project

Well, there's LLDB (https://lldb.llvm.org/) - I've heard it's got some nifty architectural features (e.g. having access to the Clang framework for handling C/C++ expressions).

I've done some minimal poking about in the code; I found its object-orientation a bit hard to grok (just for me personally) but it seemed to be quite uniformly applied so it might well be easier to work with.

The article does mention lldb as a future target.

check if lldb-mi comes with lldb in your package manager. if not build it form here: https://github.com/lldb-tools/lldb-mi.

Autocannon GitHub Repository

To make things interesting, let’s send some requests to this server with autocannon:

Using autocannon, I did the following script to simulate 500 concurrent connections over 30 seconds:

We then used autocannon to measure the throughput (requests per second) of each runtime server-rendering our React app.

You could also use tools like autocannon to see how many requests/second you can achieve with various methods. : https://github.com/mcollina/autocannon

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.

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.

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.

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.

When I test this with autocannon making three simultaneous requests (autocannon --connections 3 --amount 3 --timeout 10000 --no-progress http://localhost:5678/):