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Darkroom - Yet Another Image Proxy
Introduction
Darkroom combines the storage backend and the image processor and acts as an
Image Proxyon your image source.
You may implement your own
Storageand
Processorinterfaces 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:///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=flushbytesThese 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.