The purpose of this page is to explain how to deploy your own instance of cognition-datasources. The library may be deployed to either the cloud (via Serverless Framework) or locally. The cloud deployment packages each driver as an AWS Lambda function while the local deployment installs drivers to your local installation of cognition-datasources. For more information see How It Works
Drivers which also package spatial coverages are currently not compatible with local installations.
(1). Clone the library and install the CLI.
git clone https://github.com/geospatial-jeff/cognition-datasources my-cd-deployment
cd my-cd-deployment
python setup.py develop
(2). Load datasources into your deployment.
cognition-datasources load -d Landsat8 -d Sentinel2
This command will populate serverless.yml and handler.py with all of the necessary configuration and code to create the service. Each driver is packaged as its own lambda function.
(3). Build docker container
docker build . -t cognition-datasources:latest
(4). Package service
docker run --rm -v $PWD:/home/cognition-datasources -it cognition-datasources:latest package-service.sh
(5). Edit the configuration variables in serverless.yml
(6). Deploy the service to AWS via Serverless Framework
sls deploy -v
The deployment generates an AWS API Gateway endpoint which supports STAC-compliant searches of the loaded datasources through the /stac/search endpoint (POST).
(1). Clone the library and install the CLI.
pip install git+https://github.com/geospatial-jeff/cognition-datasources
(2). Load datasources into your deployment while enabling the local flag.
cognition-datasources load -d Landsat8 -d Sentinel2 --local
While the local flag is enabled, the driver and its dependencies will be installed locally. The driver is stored in the ./datasources/sources/ folder while all dependencies are installed to the default location of the current environment.