datadog-raygun

An app to help you pipe Raygun metrics into DataDog:

• raygun.error_count: The total error count for a particular period of time and Raygun application.
• raygun.new_error_count: The new error count for a particular period of time and Raygun application.

Because of the way this information is made available, results might not always be reliable or up-to-date. While working on this project, I experienced both delays and missing callbacks when dealing with the Raygun webhooks. Suggestions for improvement and pull requests are welcome.

Installation

The app is provided as a Docker image: https://hub.docker.com/r/lipanski/datadog-raygun

Alternatively you can build it from source, which requires Crystal:

shards install --production
crystal build --release src/datadog_raygun.cr

Usage

The app consists of a web server and a background job processor. The web server exposes an endpoint POST /webhook/[SECRET] which is to be used with Raygun's webhook integration. The background job processor records all the error details coming from Raygun, computes the metrics and sends them periodically to DataDog.

The application is configured via environment variables:

• WEBHOOK_SECRET: The secret used inside the POST /webhook/[SECRET] endpoint for authentication.
• DATADOG_API_KEY: A DataDog API key, used to push the metrics to DataDog.
• QUEUE_DEADLINE: A value in seconds, which determines how often the metrics will be delivered to DataDog (defaults to 60 seconds).
• LOG_LEVEL: The log level (defaults to INFO).

Here's an example of how to run the app:

# The webhook endpoint will be available at http://localhost:3000/webhook/my-secret
WEBHOOK_SECRET=my-secret \
  DATADOG_API_KEY=your-datadog-api-key \
  QUEUE_DEADLINE=60 \
  LOG_LEVEL=DEBUG \
  datadog_raygun

By default the server will run on port 3000, but you can override this via the --port argument.

Once you've installed the app and ensured it's running, go over to Raygun and enable the Webhook integration for every Raygun application you are interested in collecting metrics from.

DataDog tags are produced automatically by splitting the Raygun application name into words. A Raygun application called Hello/World [Production] will be tagged in DataDog with hello, world, production and hello_world_production.

That's it, enjoy your new & shiny metrics!

Development

Get a copy of the .env file and fill in the gaps:

cp .env.example .env

Install dependencies:

shards install

Run the thing:

crystal src/datadog_raygun.cr

...or use sentry to rebuild the thing on every change:

# Build sentry
crystal build lib/sentry/src/sentry_cli.cr

# Run sentry
./sentry_cli

The easiest way to test your local build in integration with Raygun would be via ngrok:

ngrok http 3000

Then you can configure your Raygun project to point to the ngrok host and trigger an error:

curl -XPOST https://api.raygun.com/entries -H "X-ApiKey: <RAYGUN API KEY>" -H "Content-Type: application/json" -d @examples/full.json -i

If you want to trigger a new error, change the groupingKey in examples/full.json.

If you don't want to expose your local server, you can simulate the Raygun callback to localhost:

curl -XPOST http://localhost:3000/webhook\?secret\=secret -H "Content-Type: application/json" -d @examples/error_reoccurred.json -i

Docker

Build the image:

docker build -t datadog-raygun .

Run a container:

docker run -it \
  -e WEBHOOK_SECRET=secret \
  -e DATADOG_API_KEY=your-datadog-api-key \
  -e QUEUE_DEADLINE=60 \
  -e LOG_LEVEL=DEBUG \
  -p 3000:3000 \
  datadog-raygun

Your container will be reachable over port 3000.