gojek / darkroom

Darkroom - Yet Another Image Proxy

Introduction

Darkroom combines the storage backend and the image processor and acts as an Image Proxy on your image source.
You may implement your own Storage and Processor interfaces to gain custom functionality while still keeping other Darkroom Server functionality.
The native implementations focus on speed and resiliency.

Features

Darkroom supports several image operations which are documented here.

Installation

go get -u github.com/gojek/darkroom

Other ways to run can be found here.

Metrics Support

Darkroom supports Prometheus and StatsD for tracking and monitoring metrics. You need to specify the metrics system by adding an environment variable, METRICS_SYSTEM=prometheus/statsd

Prometheus

The application exposes the metrics at "http://<application_url>/metrics" endpoint. Since it's a pull based system, Prometheus server that is set up from docker-compose scrapes metrics from the application endpoint and its configuration can be changed in prometheus.yml.

StatsD

In order to use StatsD as your metrics system, you also need to add the following env variables,

METRICS_STATSD_STATSDADDR=hostname:port
METRICS_STATSD_PREFIX=client-prefix
METRICS_STATSD_SAMPLERATE=sample-rate
METRICS_STATSD_FLUSHBYTES=flushbytes

These are used to set up the StatsD client.

Grafana

Darkroom currently supports grafana provisioning for Prometheus based metrics.

Grafana is preconfigured with dashboards and Prometheus as the default data source:

• Name: Prometheus
• Type: Prometheus
• Url: http://prometheus:9090
• Access: proxy

Visualization of Darkroom metrics(prometheus) on Grafana:

Contributing Guide

Read our contributing guide to learn about our development process, how to propose bugfixes and improvements, and how to build and test your changes to Darkroom.

License

Darkroom is MIT licensed.