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Detailed information on devloping in thewebrtc github repo can be found in theWebRTC GitHub repo developer's guide.

The development AppRTC server can be accessed by visitinghttp://localhost:8080.

Running AppRTC locally requires theGoogle App Engine SDK for Python andGrunt.

Detailed instructions for running on Ubuntu Linux are provided below.

Install grunt by first installingnpm. npm isdistributed as part of nodejs.

sudo apt-get install nodejssudo npm install -g npm

On Ubuntu 14.04 the default packages installs/usr/bin/nodejs but the/usr/bin/node executable is required for grunt. This is installed on some Ubuntu package sets; if it is missing, you can add this by installing thenodejs-legacy package,

sudo apt-get install nodejs-legacy

It is easiest to install a shared version ofgrunt-cli fromnpm using the-g flag. This will allow you access thegrunt command from/usr/local/bin. More information can be found ongruntjs Getting Started.

sudo npm -g install grunt-cli

Omitting the-g flag will installgrunt-cli to the current directory under thenode_modules directory.

Finally, you will want to install grunt and required grunt dependencies.This can be done from any directory under your checkout of thewebrtc/apprtc repository.

npm install

On Ubuntu, you will also need to install the webtest package:

sudo apt-get install python-webtest

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 theout/app_engine directory by running the Google App Engine SDK dev server,

<path to sdk>/dev_appserver.py ./out/app_engine

All tests by runninggrunt.

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 asecrets.json containing Service Account credentials to a Google Developer project where BigQuery is enabled. DO NOT COMMITsecrets.json TO THE REPOSITORY.

To generate asecrets.json file in the Google Developers Console for your project:

1. Go to the project page.
2. UnderAPIs & auth selectCredentials.
3. Confirm aService Account already exists or create it by selectingCreate new Client ID.
4. SelectGenerate new JSON key from theService Account area to create and download JSON credentials.
5. Rename the downloaded file tosecrets.json and place in the directory containinganalytics.py.

When theAnalytics class detects that AppRTC is running locally, all data is logged toanalytics table in thedev dataset. You can bootstrap thedev dataset by following the instructions in theBootstrapping/Updating BigQuery.

When running on App Engine theAnalytics class will log toanalytics table in theprod dataset for whatever project is defined inapp.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, fieldscannot 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 prodbq mk -t prod.analytics bigquery/analytics_schema.json