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datacube-ows

Datacube Web Map Service

Features

  • Leverages the power of the Open Data Cube, including support for COGs on S3.
  • Supports WMS and WMTS.
  • Experimental support for WCS (1.0, 2.0, 2.1).

Note on Naming

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.

Setup

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.

Docker-Compose

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

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

export DB_USERNAME=username
export DB_PASSWORD=password
export DB_DATABASE=opendatacube
export DB_HOSTNAME=localhost
export DB_PORT=5432
OWS_CFG_FILE=/path/to/ows_cfg.py
docker-compose up

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

# Change location of default config file (good for testing config changes on a local db)
OWS_CFG_FILE=/path/to/ows_cfg.py
docker-compose -f docker-compose.yaml

Docker

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/ows \
    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_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_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
      -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.

Manual installation

At the time of writing, pre-built pip-installed configurations also work fairly seemlessly:

The folllowing instructions are for installing on a clean Linux system with established ODC environment.

  • We currently recommend using pip with pre-built binary packages. Create a new python 3.6 or 3.7 virtualenv and run pip install against the supplied requirements.txt:

    pip install --pre -r requirements.txt
  • Run ::

    python update_ranges.py --role datacube_owner_role --schema

    to create schema, tables and materialised views 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:

    PYTHONPATH=.
DATACUBE_OWS_CFG=ows_cfg_filename.ows_cfg
AWS_NO_SIGN_REQUEST=yes
AWS_DEFAULT_REGION=ap-southeast-2

  • Run python update_ranges.py (in the Datacube virtual environment).

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

    python update_ranges.py --views --blocking
python update_ranges.py

  • 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:

Flask Dev Server

  • 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

Local Postgres database

  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/dev/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/{}
  1. 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
  2. Run python3 https://github.com/opendatacube/datacube-ows/blob/master/update_ranges.py --schema to create ows specific tables
  3. Run update_ranges.py to generate ows extents python3 update_ranges.py PRODUCT

Apache2 mod_wsgi

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 --pre 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

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.