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

DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene.

Sequence modeling benchmarks and temporal convolutional networks

Distributed Deep learning with Keras & Spark

U-Net Brain Tumor Segmentation

Clustergram - Visualization and diagnostics for cluster analysis in Python

The official implementation of the paper, "SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning"

code for paper -- "Seamless Satellite-image Synthesis"

One Million Scenes for Autonomous Driving

Using deep actor-critic model to learn best strategies in pair trading

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.

Multi-Task Deep Neural Networks for Natural Language Understanding

Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis

Repository for open research on optimizers.

U-2-Net: U Square Net - Modified for paired image training of style transfer

Full-featured Decision Trees and Random Forests learner.

Simulating Sycamore quantum circuits classically using tensor network algorithm.

A new video text spotting framework with Transformer

TorchX: A PyTorch Extension Library for More Efficient Deep Learning

Code for the Higgs Boson Machine Learning Challenge organised by CERN & EPFL