Commands below will help you set up and work with Ververica Platform.

scli platform api-token create \
  --vvp-url "https://vvp.example.com" \
  --vvp-namespace "default" \
  --name "cicd" \
  --role "editor" \
  --save-to-kubernetes-secret "vvp/secret" 

Sample response:

{
  "namespace": "default",
  "name": "cicd",
  "role": "editor",
  "secret": "x§11d091jd1jd9jasd0j"
}

scli platform api-token remove \
  --vvp-url "https://vvp.example.com" \
  --vvp-namespace "default" \
  --name "cicd"

You can set up your own profile, which will help you to connect to Ververica. Instead of providing common parameters to each command, you can just pass the profile name with --profile or export environmental variable as SCLI_PROFILE.

The command below will walk you through an interactive way of setting up a profile:

scli profile add sandbox

You can also set up a profile in a non-interactive way by providing all required parameters as arguments:

scli profile add sandbox \
  --vvp-url "https://vvp.streaming-platform.getindata.dev" \
  --vvp-namespace "default" \
  --vvp-deployment-target "vvp-team1" \
  --vvp-api-token "x§11d091jd1jd9jasd0j" \
  --docker-registry-url "registry.gitlab.com/flink-jobs"

scli platform deployment-target create \
  --vvp-url "https://vvp.example.com" \
  --vvp-namespace "default" \
  --vvp-api-token "x§11d091jd1jd9jasd0j" \
  --name "vvp-team1" \
  --profile "sandbox"

Parameters --vvp-url, --vvp-namespace, --vvp-api-token, --vvp-deployment-target are optional if they can be read from profile.

Sample response:

{
  "name": "vvp-team1"
}

scli project deploy \
  --vvp-url "https://vvp.example.com" \
  --vvp-namespace "default" \
  --vvp-api-token "x§11d091jd1jd9jasd0j" \
  --vvp-deployment-target "vvp-team1" \
  --docker_image_registry "${CI_REGISTRY_IMAGE}" \
  --docker-image-tag "${CI_COMMIT_TAG}" \
  --docker-image-repository tmp_project \
  --profile "sandbox"

Parameters --vvp-url, --vvp-namespace, --vvp-api-token, --vvp-deployment-target are optional if they can be read from profile.

• pipenv

• make setup - Install dependencies required to build a wheel package
• make package - Create a wheel package

• make install - Install a wheel package
• make install/force - Reinstall a wheel package

In order to actually use these magics, you must install our PIP package:

python3 -m pip install streamingcli --extra-index-url https://__token__:<your_personal_token>@gitlab.com/api/v4/projects/29597698/packages/pypi/simple

In local development, when streaming-cli repository is checkouted on local machine, you may install the most current version:

python3 -m pip install -e file:///{{path_to_your_repository}}/streaming-cli

The last thing to do is to register in Jupyter with a running IPython in the first cell:

%load_ext streamingcli.jupyter.integrations