Datacube-OWS provides a way to serve data indexed in an Open Data Cube as visualisations, through open web services (OGC WMS, WMTS and WCS).

• 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.
• Fully supports WMS v1.3.0. Partial support (GetMap requests only) for v1.1.1.
• Supports WMTS 1.0.0
• Supports WCS versions 1.0.0, 2.0.0 and 2.1.0.
• Richly featured styling engine for serving data visualisations via WMS and WMTS.

This project welcomes community participation.

Join the ODC Slack if you need help setting up or using this project, or the Open Data Cube more generally. Conversation about datacube-ows is mostly concentrated in the Slack channel #wms.

Please help us to keep the Open Data Cube community open and inclusive by reading and following our Code of Conduct.

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.

flake8 . --exclude Dockerfile --ignore=E501 --select=F401,E201,E202,E203,E502,E241,E225,E306,E231,E226,E123,F811
isort --check --diff **/*.py
autopep8  -r  --diff . --select F401,E201,E202,E203,E502,E241,E225,E306,E231,E226,E123,F811

The configuration file format for OWS is fully documented here.

And example configuration file datacube_ows/ows_cfg_example.py is also provided, but may not be as up-to-date as the formal documentation.

Environment variables that directly or indirectly affect the running of OWS are documented here.

We use docker-compose to make development and testing of the containerised ows images easier.

Set up your environment by creating a .env file (see below).

To start OWS with flask connected to a pre-existing database on your local machine: :

docker-compose up

The first time you run docker-compose, you will need to add the --build option: :

docker-compose up --build

To start ows with a pre-indexed database: :

docker-compose -f docker-compose.yaml -f docker-compose.db.yaml up

To start ows with db and gunicorn instead of flask (production) :

docker-compose -f docker-compose.yaml -f docker-compose.db.yaml -f docker-compose.prod.yaml up

The default environment variables (in .env file) can be overriden by setting local environment variables :

# Enable pydev for pycharm (needs rebuild to install python libs)
# hot reload is not supported, so we need to set FLASK_DEV to production
export PYDEV_DEBUG=yes
export FLASK_DEV=production
docker-compose -f docker-compose.yaml -f docker-compose.db.yaml up --build

cp .env_simple .env # for a single ows config file setup
cp .env_ows_root .env # for multi-file ows config with ows_root_cfg.py
docker-compose up

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 \
      -e DATACUBE_OWS_CFG=datacube_ows.config.test_cfg.ows_cfg   # Location of config object
      -e AWS_NO_SIGN_REQUEST=yes                                 # Allowing access to AWS S3 buckets
      -e AWS_DEFAULT_REGION=ap-southeast-2 \                     # AWS Default Region (supply even if NOT accessing files on S3! See Issue #151)
      -e SENTRY_DSN=https://key@sentry.local/projid \            # Key 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
      -e PYTHONPATH=/code                                        # The default PATH is under env, change this to target /code
      -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.

Installation with Conda ------------

The following instructions are for installing on a clean Linux system.

• Create a conda python 3.8 and activate conda environment:

  conda create -n ows -c conda-forge python=3.8 datacube pre_commit postgis
  conda activate ows

• install the latest release using pip install:

  pip install datacube-ows[all]

• setup a database:

  pgdata=$(pwd)/.dbdata
  initdb -D ${pgdata} --auth-host=md5 --encoding=UTF8 --username=ubuntu
  pg_ctl -D ${pgdata} -l "${pgdata}/pg.log" start # if this step fails, check log in ${pgdata}/pg.log
  
  createdb ows -U ubuntu

• enable postgis extension:

  psql -d ows
  create extension postgis;
  \q

• init datacube and ows schema:

  export DATACUBE_DB_URL=postgresql:///ows
  datacube system init
  
  # to create schema, tables and materialised views used by datacube-ows.
  
  export DATACUBE_OWS_CFG=datacube_ows.ows_cfg_example.ows_cfg
  datacube-ows-update --role ubuntu --schema

• Create a configuration file for your service, and all data products you wish to publish in it. Detailed documentation of the configuration format can be found here.
• Set environment variables as required. Environment variables that directly or indirectly affect the running of OWS are documented here.
• Run datacube-ows-update (in the Datacube virtual environment).

• When additional datasets are added to the datacube, the following steps will need to be run:

  datacube-ows-update --views
  datacube-ows-update

• 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_ows/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

1. create an empty database and db_user
2. run datacube system init after creating a datacube config file
3. A product added to your datacube datacube product add url some examples are here: https://github.com/GeoscienceAustralia/dea-config/tree/master/products

4. Index datasets into your product for example refer to https://datacube-ows.readthedocs.io/en/latest/usage.html

   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/{}

5. 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
6. Run datacube-ows-update --schema --role <db_read_role> to create ows specific tables
7. Run datacube-ows-update to generate ows extents.

Getting things working with Apache2 mod_wsgi is not trivial and probably not the best approach in most circumstances, but it may make sense for you.

If you use the pip install approach described above, your OS's pre-packaged python3 apache2-mod-wsgi package should suffice.

• Activate the wsgi module:

cd /etc/apache2/mods-enabled
ln -s ../mods-available/wsgi.load .
ln -s ../mods-available/wsgi.conf .

• 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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