This is the back-end component of the Fractalis project. It is a computational node that is responsible for the MicroETL process and the execution of analytical tasks. See https://fractalis.lcsb.uni.lu/
Please have a look at this playlist to see a demo of the visual aspects of Fractalis: Playlist.
The easiest and most convenient way to deploy Fractalis is using Docker. All necessary information can be found here.
If you do not want to use docker or want a higher level of control of the several components, that's fine. In fact it isn't difficult to setup Fractalis manually:
- Install and run Redis, which is available on most Linux distributions. This instance must be accessible by the web service and the workers.
- Install and run RabbitMQ, which is available on most Linux distributions. This instance must be accessible by the web service and the workers.
- Install Fractalis via
pip3 install fractalis. Please note that Fractalis requires Python3.4 or higher. This must be installed on all machines that will run the web service or the workers. - Install required all required R packages. We won't list these packages excplicitely, as they can change frequently. Please refer instead to the Dockerfile, which is always up-to-date, as a new version of Fractalis is only released when the Docker image passes all tests. This must be installed on all machines that will run the web service or the workers.
- Run and expose the Fractalis web service with whatever tools you want. We recommend gunicorn and nginx, but others should work, too.
- Run the celery workers on any machine that you want within the same network. (For a simple setup this can be the very same machine that the web service runs on).
Note: The docker-compose.yml describes how the different services are started and how they connect with each other.
Use the environment variable FRACTALIS_CONFIG to define the configuration file path.
This variable must be a) a valid python file (.py) and b) be available on all instances that host a Fractalis web service or a Fractalis worker.
Tip: Use the default settings as an example for your own configuration file. Please note, that all this files combines Flask settings, Celery settings, and Fractalis settings, which are all listed and documented within this file. Please don't overwrite default settings if you don't know what you are doing. This might have severe implications for security or might cause Fractalis to not work correctly.
See a sample configuration in tests directory.
Required part of the configuration is a list of all supported data sources, together with a mapping to a handler type:
DATA_SERVICES = {
'data_services': {
'<name-of-service-1>': {
'handler': '<handler-type-1>',
'server': '<service-1-url>'
},
'<name-of-service-2>': {
'handler': '<handler-type-2>',
'server': '<service-2-url>'
}
}
}
Data services config model is defined in data_services_config.py. Name of the service is a one of the required parameters of data request (see the swagger API documentation).
Configuration options for authorization, currently specific to the transmart handler:
| Configuration | Default value | Description |
|---|---|---|
| AUTHORIZATION_DISABLED | False | Disable validation of an access token from a request. Disabling is not recommended! |
| OIDC_OFFLINE_TOKEN | None | OIDC refresh token enabling an offline access. Used to refresh user tokens to prevent early expiration. |
| OIDC_SERVER_URL | OIDC server URL, including the realm e.g. https://keycloak-example.com/auth/realms/transmart-realm/ | |
| OIDC_CLIENT_ID | ID of the OIDC client |
The identity provider configured here will be used for all data services.
Please refer to this document.
Manuscript is in preparation.