Update Connection
bigquery_connection.update_connection
ActionGoogle Cloudconnection required
Calls bigqueryconnection v1 PATCH /v1/projects/{projectsId}/locations/{locationsId}/connections/{connectionsId}.
Updates the specified connection.
Connection
- Required
- Yes — the node will not save without one
- Auth type
- OAuth 2.0
- Providers
- Google BigQuery Connection
- Secret shape
- {"client_id", "client_secret", "refresh_token"}
- Config it needs
- project_id, location
- Resolved by
- config.connection_id, else node.connection_id
Configuration
4 fields, 2 required. A dotted path is a nested object and name[] an array of objects — the editor renders the same tree.
| Field | Type | Required | Default | Constraints | Example |
|---|---|---|---|---|---|
location Location or region; taken from the connection when unset. | string | No | — | — | — |
connection Connection name or id. | string | Yes | — | — | — |
body The Connection resource, as the API defines it. | object | Yes | — | — | — |
update_mask Update mask for the connection fields to be updated. Optional. | string | No | — | — | — |
Output
One shape.
result
Extra keys beyond the ones listed can appear.
| Field | Type | Example |
|---|---|---|
aws | object | — |
azure | object | — |
cloudResource | object | — |
cloudSpanner | object | — |
cloudSql | object | — |
configuration | object | — |
creationTime | string | — |
description | string | — |
friendlyName | string | — |
hasCredential | boolean | — |
kmsKeyName | string | — |
lastModifiedTime | string | — |
name | string | — |
salesforceDataCloud | object | — |
spark | object | — |
{
"aws": {},
"azure": {},
"cloudResource": {},
"cloudSpanner": {},
"cloudSql": {},
"configuration": {},
"creationTime": "",
"description": "",
"friendlyName": "",
"hasCredential": false,
"kmsKeyName": "",
"lastModifiedTime": "",
"name": "",
"salesforceDataCloud": {},
"spark": {}
}Related Google BigQuery Connection nodes
See Update Connection run in a workflow.
In a technical demo we add the Google BigQuery Connection connection, drop the node on the canvas and let Workflow Health pre-flight it before the first real run.