Datacube Web Map Service
- Free software: Apache Software License 2.0
- Documentation: https://datacube-ows.readthedocs.io.
- Leverages the power of the Open Data Cube, including support for COGs on S3.
- Supports WMS and WMTS.
- Experimental support for WCS.
This project originally supported WMS only and was known as "datacube_wms".
There are still a handful of file and object names in the codebase that include the substring "wms" although they are actually more general. These names will be updated to "ows" as time permits.
Datacube_ows (and datacube_core itself) has many complex dependencies on particular versions of geospatial libraries. Dependency conflicts are almost unavoidable in environments that also contain other large complex geospatial software packages. We therefore strongly recommend some kind of containerised solution and we supply scripts for building appropriate Docker containers.
To run the standard Docker image, create a docker volume containing your ows config files and use something like:
docker build --tag=name_of_built_container .
docker run \
--rm \
opendatacube/wms \
gunicorn -b '0.0.0.0:8000' -w 5 --timeout 300 datacube_ows:ogc
docker run --rm \
-e DATACUBE_OWS_CFG=datacube_ows.config.test_cfg.ows_cfg # Location of config object
-e AWS_ACCESS_KEY_ID=THISISNOTAREALAWSKEY \ # AWS ACCESS KEY (if accessing files on S3)
-e AWS_SECRET_ACCESS_KEY=THISisNOTaREALawsSECRETaccessKEY \# AWS SECRET ACCESS KEY (if accessing files on S3)
-e AWS_DEFAULT_REGION=ap-southeast-2 \ # AWS Default Region (supply even if NOT accessing files on S3! See Issue #151)
-e SENTRY_KEY=set5gstgw45gdfgw54t \ # Key for Sentry logging (optional)
-e SENTRY_PROJECT=my_datacube_ows_project \ # Project name for Sentry logging (optional)
-e DB_HOSTNAME=172.17.0.1 -e DB_PORT=5432 \ # Hostname/IP address and port of ODC postgres database
-e DB_DATABASE=datacube \ # Name of ODC postgres database
-e DB_USERNAME=cube -e DB_PASSWORD=DataCube \ # Username and password for ODC postgres database
-p 8080:8000 \ # Publish the gunicorn port (8000) on the Docker
\ # container at port 8008 on the host machine.
--mount source=test_cfg,target=/code/datacube_ows/config \ # Mount the docker volume where the config lives
name_of_built_container
The image is based on the standard ODC container.
The folllowing instructions are for installing on a clean Linux system.
Follow datacube installation instructions
- Make sure you are using the conda-forge channel.
Run the following commands:
conda config --prepend channels conda-forge conda update --all
Clone the repo public repository into your desired destination using git clone git://github.com/opendatacube/datacube-ows
Datacube OWS requires the scikit-image package: conda install scikit-image
Manually install dea-proto:
pip install 'git+https://github.com/opendatacube/dea-proto.git#egg=dea-proto[async]'
Datacube OWS has some dependencies that cannot be handled by conda. After doing the conda installs, run pip install against the supplied requirements.txt:
pip install -r requirements.txt
Run python update_ranges.py --role *datacube_user_role* --schema to create schema and tables used by datacube-ows.
Create a configuration file for your service, and all data products you wish to publish in it. See datacube_ows/ows_cfg_example.py for examples and documentation of the configuration format. The simplest approach is to make a copy of ows_cfg_example.py called ows_cfg.py and edit as required. But for production deployments other approaches such as importing config as json are possible.
Run python update_ranges.py -- product *product_name* --no-calculate-extent (in the Datacube Conda environment). This script will need to be re-run every time additional datasets are added to the Datacube.
If you are accessing data on AWS S3 and running datacube_ows on Ubuntu you may encounter errors with GetMap similar to: Unexpected server error: '/vsis3/bucket/path/image.tif' not recognized as a supported file format.. If this occurs run the following commands:
mkdir -p /etc/pki/tls/certs ln -s /etc/ssl/certs/ca-certificates.crt /etc/pki/tls/certs/ca-bundle.crt
Launch flask app using your favorite WSGI server. We recommend using Gunicorn with either nginx or a load balancer.
The following approaches have also been tested:
Good for initial dev work and testing. Not (remotely) suitable for production deployments.
cd to the directory containing this README file.
Set the FLASK_APP environment variable:
export FLASK_APP=datacube_wms/ogc.py
Run the Flask dev server:
flask run
If you want the dev server to listen to external requests (i.e. requests from other computers), use the --host option:
flask run --host=0.0.0.0
- create an empty database and db_user
- run datacube system init after creating a datacube config file
- A product added to your datacube datacube product add url some examples are here: https://github.com/GeoscienceAustralia/dea-config/tree/master/dev/products
- Index datasets into your product for example refer to https://github.com/opendatacube/datacube-ows/blob/master/docs/usage.rst
aws s3 ls s3://deafrica-data/jaxa/alos_palsar_mosaic/2017/ --recursive | grep yaml | awk '{print $4}' | xargs -n1 -I {} datacube dataset add s3://deafrica-data/{}
- Write an ows config file to identify the products you want available in ows, see example here: https://github.com/opendatacube/datacube-ows/blob/master/datacube_ows/ows_cfg_example.py
- Run python3 https://github.com/opendatacube/datacube-ows/blob/master/update_ranges.py --schema to create ows specific tables
- Run update_ranges.py to generate ows extents python3 update_ranges.py --product PRODUCT --no-calculate-extent
Getting things working with Apache2 mod_wsgi is not trivial and probably not the best approach in most circumstances, but if it makes sense for you, this how we have got it working in the past:
Getting mod_wsgi to work with a Conda virtual environment is not trivial. The following steps worked for me, but will not support connecting your web server to multiple web apps using different virtual environments.
Uninstall any previously installed mod_wsgi packages
(From the Datacube Conda environment) install mod_wsgi with pip. Take note of the name of the resulting module which is given to you at the end of the install process, you will need it later:
pip install mod_wsgi
Find the full path of mod_wsgi-express with which mod_wsgi-express
Install mod_wsgi into Apache:
sudo /full/path/to/installed/mod_wsgi-express install-module
Ensure the following lines appear somewhere in your Apache2 config (Note they must appear in the "root" of the config, they cannot appear inside a VirtualHost section):
LoadModule wsgi_module /full/path/to/wsgi/module.so WSGIPythonHome /path/to/your/conda/cubeenv
Add the following to your Apache config (inside the appropriate VirtualHost section):
WSGIDaemonProcess datacube_ows processes=20 threads=1 user=uuu group=ggg maximum-requests=10000 WSGIScriptAlias /datacube_ows /path/to/source_code/datacube-ows/datacube_ows/wsgi.py <Location /datacube_ows> WSGIProcessGroup datacube_ows </Location> <Directory /path/to/source_code/datacube-ows/datacube_ows> <Files wsgi.py> AllowOverride None Require all granted </Files> </Directory>Note that uuu and ggg above are the user and group of the owner of the Conda virtual environment.
Copy datacube_ows/wsgi.py to datacube_odc/local_wsgi.py and edit to suit your system.
Update the url in the configuration
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