Get Model
bigquery.get_model
ActionDatabases & warehousesconnection required
Calls bigquery v2 GET /projects/{projectsId}/datasets/{datasetsId}/models/{modelsId}.
Gets the specified model resource by model ID.
Connection
- Required
- Yes — the node will not save without one
- Auth type
- OAuth 2.0
- Providers
- Google BigQuery
- Secret shape
- {"client_id", "client_secret", "refresh_token"}
- Config it needs
- project_id
- Resolved by
- config.connection_id, else node.connection_id
Configuration
2 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 |
|---|---|---|---|---|---|
dataset Dataset name or id. | string | Yes | — | — | — |
model Model name or id. | string | Yes | — | — | — |
Output
One shape.
result
Extra keys beyond the ones listed can appear.
| Field | Type | Example |
|---|---|---|
bestTrialId | string | — |
creationTime | string | — |
defaultTrialId | string | — |
description | string | — |
encryptionConfiguration | object | — |
etag | string | — |
expirationTime | string | — |
featureColumns | array | — |
friendlyName | string | — |
hparamSearchSpaces | object | — |
hparamTrials | array | — |
labelColumns | array | — |
labels | object | — |
lastModifiedTime | string | — |
location | string | — |
modelReference | object | — |
modelType | string | — |
optimalTrialIds | array | — |
remoteModelInfo | object | — |
trainingRuns | array | — |
transformColumns | array | — |
{
"bestTrialId": "",
"creationTime": "",
"defaultTrialId": "",
"description": "",
"encryptionConfiguration": {},
"etag": "",
"expirationTime": "",
"featureColumns": [],
"friendlyName": "",
"hparamSearchSpaces": {},
"hparamTrials": [],
"labelColumns": [],
"labels": {},
"lastModifiedTime": "",
"location": "",
"modelReference": {},
"modelType": "",
"optimalTrialIds": [],
"remoteModelInfo": {},
"trainingRuns": [],
"transformColumns": []
}See Get Model run in a workflow.
In a technical demo we add the Google BigQuery connection, drop the node on the canvas and let Workflow Health pre-flight it before the first real run.