Github Traffic Insights as Prometheus metrics.

Last update: Oct 27, 2022

Overview

github-traffic

Github Traffic collects your repository's traffic data and exposes it as Prometheus metrics.

Grafana dashboard that displays the metrics generated by Github Traffic.

Quickstart

Requirements:

Docker >= 20.10.3

To run github-traffic locally you've to create a .env file like this one:

$ cat .env
# Required
GITHUB_TOKEN=your-github-token-goes-here
ORG_NAME=the-name-of-your-organization-goes-here
# Optional
REPO_TYPE=public-or-private # Default: public
REPO_NAME_CONTAINS=string-to-match-repositories-with # Default: ""
CRONTAB_SCHEDULE=crontab-schedule-to-get-data-from-github # Default: "0 * * * *"

Run the image:

$ docker run --env-file .env -it -p 8001:8001 ghcr.io/grafana/github-traffic
level=INFO msg="Github traffic is running!" 
level=INFO msg="Gather insights" repo="k6" views=163 unique_views=90 clones=406 unique_clones=109 stars=13805
level=INFO msg="Gather insights" repo="postman-to-k6" views=3 unique_views=2 clones=1 unique_clones=1 stars=238
level=INFO msg="Gather insights" repo="jmeter-to-k6" views=1 unique_views=1 clones=2 unique_clones=2 stars=44
...
Go to http://localhost:8001/metrics

Profit!

Now you can collect those metrics as you would do with any other service. To visualize them, we provide an example/template Grafana dashboard: https://grafana.com/grafana/dashboards/15000

You might also like...

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in

5 Sep 26, 2022

Code for paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Decoupled Spatial-Temporal Graph Neural Networks Code for our paper: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

43 Jan 4, 2023

A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

About This repository contains the code of a PaddlePaddle implementation of STGCN based on the paper Spatio-Temporal Graph Convolutional Networks: A D

1 Jan 11, 2022

Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

Flow Flow is a computational framework for deep RL and control experiments for traffic microsimulation. See our website for more information on the ap

867 Jan 2, 2023

DI-smartcross - Decision Intelligence Platform for Traffic Crossing Signal Control

DI-smartcross DI-smartcross - Decision Intelligence Platform for Traffic Crossin

213 Jan 2, 2023

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Note: the current releases of this toolbox are a beta release, to test working with Haskell's, Python's, and R's code repositories. Metrics provides i

1.6k Dec 26, 2022

Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Hera Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser. Setting up Step 1. Plant the spy Install the package pip

495 Dec 10, 2022

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Image Crop Analysis This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post. If you plan to use this

239 Jan 2, 2023

Details about the wide minima density hypothesis and metrics to compute width of a minima

wide-minima-density-hypothesis Details about the wide minima density hypothesis and metrics to compute width of a minima This repo presents the wide m

9 Dec 27, 2022

Comments

Added top referrers and top paths to metrics
I extended the code to also collect the github traffic top paths and referrers.

For instance, for my open source project protoCURL, the web UI shows this:

With the changes in the commit, Icreated these panels in Prometheus:

I would like to integrate these changes, as I think that other users could also benefit from that.

The changes essentially just call these two python methods:

get_top_referrers()

get_top_paths()

What do you think?
opened by GollyTicker 3

Github Traffic Insights as Prometheus metrics.

Related tags

Overview

github-traffic

Quickstart

You might also like...

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

Code for paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

DI-smartcross - Decision Intelligence Platform for Traffic Crossing Signal Control

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Details about the wide minima density hypothesis and metrics to compute width of a minima

Comments

Added top referrers and top paths to metrics

Releases(v0.0.3)

v0.0.3(Aug 18, 2022)

v0.0.2(Apr 25, 2022)

v0.0.1(Sep 21, 2021)

v0.0.1rc2(Sep 12, 2021)

v0.0.1rc(Sep 12, 2021)

Owner

Grafana Labs

4th place solution to datafactory challenge by Intermarché.

[CVPR 2022] Unsupervised Image-to-Image Translation with Generative Prior

Create Own QR code with Python

RIM: Reliable Influence-based Active Learning on Graphs.

The code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

NP DRAW paper released code

This repository contains PyTorch models for SpecTr (Spectral Transformer).

DeepMReye: magnetic resonance-based eye tracking using deep neural networks

a project for 3D multi-object tracking

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors, CVPR 2021

Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.

A simple approach to emable dense segmentation with ViT.

Code for pre-training CharacterBERT models (as well as BERT models).

ML model to classify between cats and dogs

A really easy-to-use and powerful sudoku solver.