delete_batch_job
Remove a Dataproc batch job from Google Cloud by specifying project ID, region, and batch ID to manage serverless batch operations.
Instructions
Delete a batch job.
Args:
project_id: Google Cloud project ID
region: Dataproc region
batch_id: Batch job ID to deleteInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | ||
| region | Yes | ||
| batch_id | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- MCP tool handler and registration for 'delete_batch_job'. Decorated with @mcp.tool(), delegates to DataprocBatchClient.delete_batch_job.
@mcp.tool() async def delete_batch_job(project_id: str, region: str, batch_id: str) -> str: """Delete a batch job. Args: project_id: Google Cloud project ID region: Dataproc region batch_id: Batch job ID to delete """ batch_client = DataprocBatchClient() try: result = await batch_client.delete_batch_job(project_id, region, batch_id) return str(result) except Exception as e: logger.error("Failed to delete batch job", error=str(e)) return f"Error: {str(e)}" - Core implementation of delete_batch_job in DataprocBatchClient. Calls Google Cloud Dataproc API to delete the batch job.
async def delete_batch_job( self, project_id: str, region: str, batch_id: str ) -> dict[str, Any]: """Delete a batch job.""" try: loop = asyncio.get_event_loop() client = self._get_batch_client(region) request = types.DeleteBatchRequest( name=f"projects/{project_id}/locations/{region}/batches/{batch_id}" ) await loop.run_in_executor(None, client.delete_batch, request) return { "batch_id": batch_id, "status": "DELETED", "message": f"Batch job {batch_id} deletion initiated", } except Exception as e: logger.error("Failed to delete batch job", error=str(e)) raise