quant
ghz
quant | ghz | |
---|---|---|
1 | 6 | |
284 | 2,887 | |
1.8% | - | |
4.7 | 5.8 | |
9 months ago | 8 days ago | |
C | Go | |
BSD 2-clause "Simplified" License | Apache License 2.0 |
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.
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."
ghz
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Production Twitter on One Machine: 100Gbps NICs and NVMe Are Fast
I once built a quick and dirty load testing tool for a public facing service we built. The tool was pretty simple - something like https://github.com/bojand/ghz but with traffic and data patterns closer to what we expected to see in the real world. We used argo-workflows to generate scale.
One thing which we noticed was that there was a considerable difference in performance characteristics based on how we parallelized the load testing tool (multiple threads, multiple processes, multiple kubernetes pods, pods forced to be distributed across nodes).
I think that when you run non-distrubuted load tests you benefit from bunch of cool things which happen with http2 and Linux (multiplexing, resource sharing etc) which might make applications seem much faster than they would be in the real world.
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GRPC Performance Testing , Load Testing
I'm not sure. Maybe you can write to the discussion section of the repo https://github.com/bojand/ghz/discussions
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Testing gRPC services - request collections and modern load testing
In part 1 we looked at ghz for load testing gRPC services, and now I want to cover k6, which claims to be a modern load testing tool built for developer happiness. After only a brief experience with it I can see why is that and why Grafana moved to acquire k6 earlier this year.
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grpc_bench: open-source, objective gRPC benchmark
It should be unbound, in this particular benchmark we set ghz concurrency to 50 and connections to 5 and we don't set the rps flag of ghz (e.g. --rps=2000, from this tool)
a second container running ghz makes unary requests to the server
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.
grpcurl - Like cURL, but for gRPC: Command-line tool for interacting with gRPC servers
NanoSDK - NanoSDK - MQTT 5.0-compliant SDK with QUIC support in NNG flavor
jmeter-grpc-plugin - A JMeter plugin supports load test gRPC
twitterperf - Prototyping the performance of various components of a theoretical faster Twitter
grpc-go - The Go language implementation of gRPC. HTTP/2 based RPC
fair-queuing-aware-congestion-control - Fair Queuing Aware Congestion Control – based on picoquic
grpc_bench - Various gRPC benchmarks
nghttp3 - HTTP/3 library written in C
gRPC - The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
k3s - Lightweight Kubernetes
k6 - A modern load testing tool, using Go and JavaScript - https://k6.io