DEPLOY.rst

Deploying

There is a Dockerfile for quick deployment. This section explains how to get the STAC service up and running with Docker. If you do not have Docker installed, take a look at this tutorial on how to install it in your system.

Requirements

Besides Docker, you will need an instance of a PostgreSQL DBMS with a database prepared using Brazil Data Cube Catalog Module.

Building the Docker Image

On the command line use the docker build command to create the docker image for the service::

docker build --no-cache -t bdc-stac:0.9.0-0 .

The above command will create a Docker image named bdc-stac and tag 0.9.0-0`, as one can see with the ``docker images command:

docker images

REPOSITORY                                          TAG                 IMAGE ID            CREATED             SIZE
bdc-stac                                            0.8.1-0             44651ac917e4        16 hours ago        333MB

Preparing the Network for Containers

If you have the PostgreSQL server running in a Docker container and you want to have it accessible to the STAC service, you can create a Docker network and attach your PostgreSQL container to it.

Note

If you have a valid address for the PostgreSQL DBMS you can skip this section.

To create a new network, you ca use the docker network command:

docker network create bdc_net

The above command will create a network named bdc_net. Now, it is possible to attach your database container in this network:

docker network connect bdc_net bdc_pg

In the previous command, we are supposing that your database container is named bdc_pg.

Launching the Docker Container with the STAC Service

The docker run command can be used to launch a container from the image bdc-stac:0.9.0-0. The command below shows an example on how to accomplish the launch of a container:

docker run --detach \
           --name bdc-stac \
           --publish 127.0.0.1:8080:5000 \
           --network=bdc_net \
           --env SQLALCHEMY_DATABASE_URI="postgresql://postgres:postgres@localhost:5432/bdc_catalog" \
           --env BDC_STAC_BASE_URL="http://localhost:8080" \
           --env BDC_STAC_FILE_ROOT="http://localhost:8081" \
           bdc-stac:0.8.1-0

Let's take a look at each parameter in the above command:

• --detach: tells Docker that the container will run in background (daemon).
• --name bdc-stac: names the container.
• --publish 127.0.0.1:8080:5000: by default the STAC service will be running on port 5000 of the container. You can bind a host port, such as 8080 to the container port 5000.
• --network=bdc_net: if the container should connect to the database server through a docker network, this parameter will automatically attach the container to the bdc_net. You can omit this parameter if the database server address can be resolved directly from a host address.
• --env SQLALCHEMY_DATABASE_URI="postgresql://postgres:postgres@localhost:5432/bdc_catalog": set the database URI.[1].
• --env BDC_STAC_BASE_URL="http://localhost:8080": Base URI of the service.
• --env BDC_STAC_FILE_ROOT="http://localhost:8081": File root for the image assets.
• bdc-stac:0.9.0-0: the name of the base Docker image used to create the container.

If you have launched the container, you can check if the service has initialized:

docker logs bdc-stac

* Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)

Finally, to test if it is listening, use the curl command:

$ curl localhost:8080

The output should be a JSON document similar to:

{
  "description": "Brazil Data Cube Catalog",
  "id": "bdc",
  "stac_version": "0.9.0",
  "links": [
    {
      "href": "http://localhost:5000/",
      "rel": "self",
      "type": "application/json",
      "title": "Link to this document"
    },
    {
      "href": "http://localhost:5000/docs",
      "rel": "service-doc",
      "type": "text/html",
      "title": "API documentation in HTML"
    },
    {
      "href": "http://localhost:5000/conformance",
      "rel": "conformance",
      "type": "application/json",
      "title": "OGC API conformance classes implemented by the server"
    },
    {
      "href": "http://localhost:5000/collections",
      "rel": "data",
      "type": "application/json",
      "title": "Information about image collections"
    },
    {
      "href": "http://localhost:5000/search",
      "rel": "search",
      "type": "application/json",
      "title": "STAC-Search endpoint"
    },
    {
      "href": "http://localhost:5000/collections/CB4_64_16D_STK-1",
      "rel": "child",
      "type": "application/json",
      "title": "Cbers-4 WFI 16 days"
    },
    {
      "href": "http://localhost:5000/collections/CB4_64-1",
      "rel": "child",
      "type": "application/json",
      "title": "Cbers-4 WFI Irregular"
    },
    {
      "href": "http://localhost:5000/collections/CB4-MUX-L4-SR-CMPAC-1",
      "rel": "child",
      "type": "application/json",
      "title": "CBERS-4 MUX surface reflectance product (Level-5)"
    },
    {
      "href": "http://localhost:5000/collections/RF_ANL-1",
      "rel": "child",
      "type": "application/json",
      "title": "Risco de Fogo - Anual"
    }
  ]
}

Footnotes

[1]	See the Brazil Data Cube Catalog Module.