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

This is the solution for 2nd rank in Kaggle competition: Feedback Prize - Evaluating Student Writing.

The reference baseline of final exam for XMU machine learning course

2.86% and 15.85% on CIFAR-10 and CIFAR-100

Video Instance Segmentation using Inter-Frame Communication Transformers (NeurIPS 2021)

AlphaBot2 Pi Core software for interfacing with the various components.

CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.

Differentiable architecture search for convolutional and recurrent networks

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

A way to store images in YAML.

The devkit of the nuPlan dataset.

EM-POSE 3D Human Pose Estimation from Sparse Electromagnetic Trackers.

AITUS - An atomatic notr maker for CYTUS

Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

Vision-and-Language Navigation in Continuous Environments using Habitat

This repository provides the official code for GeNER (an automated dataset Generation framework for NER).

Reproducing-BowNet: Learning Representations by Predicting Bags of Visual Words

This is Unofficial Repo. Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection (CVPR 2021)

A Python package for faster, safer, and simpler ML processes