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Deploying

lccs-ws implementation depends essentially on Flask, SQLAlchemy and in the LCCS Database Module.

There is a Dockerfile for quick deployment. This section explains how to get the LCCS 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

Make sure you have a database prepared with the schema for LCSS-WS from the LCCS-DB. You can have an instance of a PostgreSQL DBMS with a database prepared from the LCCS-DB.

Building the Docker Image

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

docker build -t lccs-ws:0.8.0 . --no-cache

The above command will create a Docker image named lccs-ws and tag 0.8.0, as one can see with the docker images command:

docker images

REPOSITORY      TAG       IMAGE ID         CREATED         SIZE
lccs-ws         0.8.0     ce2ba6a67896     16 hours ago    752MB

Preparing the Network for Containers

If you have the PostgreSQL server running in a Docker container and you want to have it accesible to the LCCS-WS, you can create a Docker network and attach it to your PostgreSQL container.

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 above command, we are supposing that your database container is named bdc_pg.

Launching the Docker Container with the LCCS-WS

The docker run command can be used to launch a container from the image lccs-ws:0.8.0. The command below shows an example on how to accomplish the launch of a container:

$ docker run --detach \
     --name lccs-ws \
     --publish 127.0.0.1:5000:5000 \
     --network=bdc_net \
     --env LCCS_URL="http://localhost:5000" \
     --env SQLALCHEMY_DATABASE_URI="postgresql://user:password@localhost:5432/dbname" \
     --env LCCSWS_ENVIRONMENT="ProductionConfig" \
     lccs-ws:0.8.0

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

  • --detach: tells Docker that the container will run in background (daemon).
  • --name lccs-ws: names the container.
  • --publish 127.0.0.1:5000:5000: by default the LCCS-WS 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 ommit this parameter if the database server address can be resolved directly from a host address.
  • --env SQLALCHEMY_DATABASE_URI="postgresql://user:password@localhost:5432/dbname": The database URI to be used [1].
  • --env LCCS_URL="http://localhost:5000": Base URI of the service.
  • --env LCCSWS_ENVIRONMENT="ProductionConfig": execution mode (ProductionConfig, DevelopmentConfig, TestingConfig).
  • lccs-ws:0.8.0: 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 lccs-ws

 * 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:5000/

[[{
    "href": "http://localhost:5000/",
    "rel": "self",
    "title": "Link to this document",
    "type": "application/json"
  },
  {
    "href": "http://localhost:5000/classification_systems",
    "rel": "classification_systems",
    "title": "List classification_systems",
    "type": "application/json"
  }
]]

Footnotes

[1]Make sure you have a database prepared with the schema for LCSS-WS from the LCCS-DB