Detailed information on devloping in the webrtc github repo can be found in the WebRTC GitHub repo developer's guide.
The development AppRTC server can be accessed by visiting http://localhost:8080.
Running AppRTC locally requires the Google App Engine SDK for Python and Grunt.
Detailed instructions for running on Ubuntu Linux are provided below.
Install grunt by first installing npm,
sudo apt-get install npm
On Ubuntu 14.04 the default packages installs /usr/bin/nodejs but the /usr/bin/node executable is required for grunt. You can add this by installing the nodejs-legacy package,
sudo apt-get install nodejs-legacy
It is easiest to install a shared version of grunt-cli from npm using the -g flag. This will allow you access the grunt command from /usr/local/bin. More information can be found on gruntjs Getting Started.
sudo npm -g install grunt-cli
Omitting the -g flag will install grunt-cli to the current directory under the node_modules directory.
Finally, you will want to install grunt and required grunt dependencies. This can be done from any directory under your checkout of the GoogleChrome/webrtc repository.
npm install
Before you start the AppRTC dev server and *everytime you update the source code you need to recompile the App Engine package by running,
grunt build
Start the AppRTC dev server from the out/app_engine directory by running the Google App Engine SDK dev server,
<path to sdk>/dev_appserver.py ./out/app_engine
All tests by running grunt.
To run only the Python tests you can call,
grunt runPythonTests
Note that logging is automatically enabled when running on Google App Engine using an implicit service account.
By default, logging to a BigQuery from the development server is disabled. Log information is presented on the console. Unless you are modifying the analytics API you will not need to enable remote logging.
Logging to BigQuery when running LOCALLY requires a secrets.json containing Service Account credentials to a Google Developer project where BigQuery is enabled. DO NOT COMMIT secrets.json TO THE REPOSITORY.
To generate a secrets.json file in the Google Developers Console for your project:
- Go to the project page.
- Under APIs & auth select Credentials.
- Confirm a Service Account already exists or create it by selecting Create new Client ID.
- Select Generate new JSON key from the Service Account area to create and download JSON credentials.
- Rename the downloaded file to
secrets.jsonand place in the directory containinganalytics.py.
When the Analytics class detects that AppRTC is running locally, all data is logged to analytics table in the dev dataset. You can bootstrap the dev dataset by following the instructions in the Bootstrapping/Updating BigQuery.
When running on App Engine the Analytics class will log to analytics table in the prod dataset for whatever project is defined in app.yaml.
bigquery/analytics_schema.json contains the fields used in the BigQuery table. New fields can be added to the schema and the table updated. However, fields cannot be renamed or removed. Caution should be taken when updating the production table as reverting schema updates is difficult.
Update the BigQuery table from the schema by running,
bq update -t prod.analytics bigquery/analytics_schema.json
Initialize the required BigQuery datasets and tables with the following,
bq mk prod
bq mk -t prod.analytics bigquery/analytics_schema.json