Skip to content

Latest commit

 

History

History
87 lines (60 loc) · 3.19 KB

tutorial.md

File metadata and controls

87 lines (60 loc) · 3.19 KB
Raw

Jira to BigQuery [JigQuery]

Select a Project

Prepare Cloud Shell

Press this button, to open the Cloud Shell:

Then run the following snippet, to set your Project ID:

gcloud config set project {{project-id}}

Enable the required APIs

gcloud services --project smedia-business-intelligence enable \
iam.googleapis.com \
cloudkms.googleapis.com \
cloudscheduler.googleapis.com \
cloudfunctions.googleapis.com \
storage_component storage_api \
bigquery \
pubsub

Create your Schema

First, create a basic schema file from the example:

cp ./.example.schema.json ./.schema.json && cloudshell_open --open_in_editor ./.schema.json

This should copy the example and open the resulting file in your Cloud Shell Editor.

Edit your Schema

The schema defines which fields from your Jira issues get stored in BigQuery. It consists of a JSON Array containing field definitions, that look like this:

{
  "name": "issue", // name of the field in bigquery
  "type": "string", // data type of the field in bigquery (see Types section)
  "path": "key", // path inside the jira issue in dot-annotation. E.g. fields.updated
  "required": true, // if the field is required in the bigquery schema (optional)
  "repeated": false // if the field is repeated in the bigquery schema (optional)
}

You can read more about the schema in the README.md:

Note: Comments are not allowed in JSON, so please do not copy this example without removing them first. A comment is defined like // comment.

Deploy your Function

The following command will ask you several questions regarding your target environment. Those include:

  • Your Jira Project,
  • the BigQuery Dataset and Table to store Issues in
  • and if not present from a previous execution, your Jira login credentials.

The BigQuery Dataset and Table should not exist, as the Function will create them on it's first run.

Note: Your Jira credentials will be encrypted with Google Cloud KMS and eventually deployed to the Cloud Function environment. They get stored in the same encrypted form in your Cloud Shell environment but can only be decrypted by actors with the respective access to your KMS KeyRing and Key.

For encrypting credentials, a KMS Keyring and Key are required. Those will be created if they don't exist (jigquery.jigquery). If you want to provide existing ones, add --keyring [yourKeyRing] --key [yourKey] to the call below.

After it was provided with all required information, the program will encrypt your Jira credentials, deploy the Cloud Function and create the respective Pub/Sub and Cloud Scheduler instances to run it once a day.

GOOGLE_CLOUD_PROJECT={{project-id}} ./cli -mode deploy -i