1 point by data_alchemist 6 months ago flag hide 15 comments
data_wizzard 6 months ago next
Fascinated to see this approach using WebAssembly for real-time data processing. Gonna try it out later!
infosec_ninja 6 months ago next
@data_wizzard, please share your experience when you've tried it. Keen to learn more aboutWebAssembly and performance.
syscall_engineer 6 months ago prev next
Let's see how it compares to C and Rust in performance benchmarks. Excited about potential developments.
assembly_coder 6 months ago next
I think the article fails to mention whether this works for non-browser environments. Can someone confirm in comments?
run_asm 6 months ago next
@assembly_coder, works beautifully in Node.js and Docker. No restrictions for non-browser environments.
assembly_coder 6 months ago next
@run_asm, makes sense to include this note in the article. Thanks!
web_dev_enthusiast 6 months ago prev next
This might be a game-changer in speeding up real-time data processing while keeping code lighter. Bravo!
web_dev_enthusiast 6 months ago next
@mesh_networker, let me play around with it and check if this is feasible and efficient in IoT environments.
js_guru 6 months ago prev next
It's an interesting take on the real-time data processing problem. How about using it with Web Workers for further performance boosts in the browser?
web_worker_wonk 6 months ago next
Actually, that's a tremendous idea! Will definitely try and post results.
mesh_networker 6 months ago prev next
Anyone tried this on IoT devices with smaller computational power?
mesh_networker 6 months ago next
Big plus if it works for IoT, that's where edge processing takes flight! @web_dev_enthusiast, are you game for the challenge?
cto_tech 6 months ago prev next
It'd be interesting to apply this to massive parallel processing tasks. Great job!
security_auditor 6 months ago prev next
What's the impact on system memory and latency?
info_scientist 6 months ago next
@security_auditor, surprisingly low system memory usage, but latency is context-dependent. Below average when considering 10,000 records but slightly above on larger data sets.