quant
twitterperf
quant | twitterperf | |
---|---|---|
1 | 2 | |
284 | 62 | |
1.8% | - | |
4.7 | 0.0 | |
9 months ago | over 1 year ago | |
C | Rust | |
BSD 2-clause "Simplified" License | - |
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quant
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Production Twitter on One Machine: 100Gbps NICs and NVMe Are Fast
https://github.com/NTAP/quant
"Quant uses the warpcore zero-copy userspace UDP/IP stack, which in addition to running on on top of the standard Socket API has support for the netmap fast packet I/O framework, as well as the Particle and RIOT IoT stacks. Quant hence supports traditional POSIX platforms (Linux, MacOS, FreeBSD, etc.) as well as embedded systems."
twitterperf
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Production Twitter on One Machine: 100Gbps NICs and NVMe are fast
Thought this was interesting, author provided the source code behind it (rust obviously) https://github.com/trishume/twitterperf.
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Production Twitter on One Machine: 100Gbps NICs and NVMe Are Fast
Very cool exercise. I enjoyed reading it.
I see a lot of comments here assuming that this proves something about Twitter being inefficient. Before you jump to conclusions, take a look at the author’s code: https://github.com/trishume/twitterperf
Notably absent are things like serving HTTP, not to even mention HTTPS. This was a fun exercise in algorithms, I/O, and benchmarking. It wasn’t actually imitating anything that resembles actual Twitter or even a usable website.
What are some alternatives?
f-stack - F-Stack is an user space network development kit with high performance based on DPDK, FreeBSD TCP/IP stack and coroutine API.
ghz - Simple gRPC benchmarking and load testing tool
NanoSDK - NanoSDK - MQTT 5.0-compliant SDK with QUIC support in NNG flavor
fair-queuing-aware-congestion-control - Fair Queuing Aware Congestion Control – based on picoquic
k3s - Lightweight Kubernetes
nghttp3 - HTTP/3 library written in C
netapp-dataops-toolkit - The NetApp DataOps Toolkit is a Python library that makes it simple for developers, data scientists, DevOps engineers, and data engineers to perform various data management tasks, such as near-instantaneously provisioning, cloning, or snapshotting a data volume or JupyterLab workspace.