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

Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

This was initially the repo for the project of [email protected] of Asaf Mazar, Millad Kassaie and Georgios Chochlakis named "Powered by the Will? Exploring Lay Theories of Behavior Change through Social Media"

ML powered analytics engine for outlier detection and root cause analysis.

A High-Performance Distributed Library for Large-Scale Bundle Adjustment

Auto grind btdb2 exp for tower

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

This is a TensorFlow implementation for C2-Rec

This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch]

Bunch of different tools which helps visualizing and annotating images for semantic/instance segmentation tasks

The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

PyTorch implementation of a collections of scalable Video Transformer Benchmarks.

Activity tragle - Google is tracking everything, we just look at it

DISTIL: Deep dIverSified inTeractIve Learning.

Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Memoized coduals - Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers