def Run(self, args):
client = self.context['dataproc_client']
messages = self.context['dataproc_messages']
job_ref = util.ParseJob(args.id, self.context)
request = messages.DataprocProjectsRegionsJobsCancelRequest(
projectId=job_ref.projectId,
region=job_ref.region,
jobId=job_ref.jobId,
cancelJobRequest=messages.CancelJobRequest())
# TODO(user) Check if Job is still running and fail or handle 401.
if not console_io.PromptContinue(
message="The job '{0}' will be killed.".format(args.id)):
raise exceptions.ToolException('Cancellation aborted by user.')
job = client.projects_regions_jobs.Cancel(request)
log.status.Print('Job cancellation initiated for [{0}].'.format(
job_ref.jobId))
job = util.WaitForJobTermination(
job,
self.context,
message='Waiting for job cancellation',
goal_state=messages.JobStatus.StateValueValuesEnum.CANCELLED)
log.status.Print('Killed [{0}].'.format(job_ref))
return job
def Run(self, args):
dataproc = dp.Dataproc(self.ReleaseTrack())
job_ref = args.CONCEPTS.job.Parse()
request = dataproc.messages.DataprocProjectsRegionsJobsCancelRequest(
projectId=job_ref.projectId,
region=job_ref.region,
jobId=job_ref.jobId,
cancelJobRequest=dataproc.messages.CancelJobRequest())
# TODO(b/36049788) Check if Job is still running and fail or handle 401.
console_io.PromptContinue(
message="The job '{0}' will be killed.".format(args.job),
cancel_on_no=True,
cancel_string='Cancellation aborted by user.')
job = dataproc.client.projects_regions_jobs.Cancel(request)
log.status.Print(
'Job cancellation initiated for [{0}].'.format(job_ref.jobId))
job = util.WaitForJobTermination(
dataproc,
job,
job_ref,
message='Waiting for job cancellation',
goal_state=dataproc.messages.JobStatus.StateValueValuesEnum.CANCELLED)
log.status.Print('Killed [{0}].'.format(job_ref))
return job
def Run(self, args):
"""This is what gets called when the user runs this command."""
dataproc = dp.Dataproc(self.ReleaseTrack())
request_id = util.GetUniqueId()
job_id = args.id if args.id else request_id
# Don't use ResourceArgument, because --id is hidden by default
job_ref = util.ParseJob(job_id, dataproc)
self.PopulateFilesByType(args)
cluster_ref = util.ParseCluster(args.cluster, dataproc)
request = dataproc.messages.DataprocProjectsRegionsClustersGetRequest(
projectId=cluster_ref.projectId,
region=cluster_ref.region,
clusterName=cluster_ref.clusterName)
cluster = dataproc.client.projects_regions_clusters.Get(request)
self._staging_dir = self.GetStagingDir(cluster,
job_ref.jobId,
bucket=args.bucket)
self.ValidateAndStageFiles()
job = dataproc.messages.Job(
reference=dataproc.messages.JobReference(
projectId=job_ref.projectId, jobId=job_ref.jobId),
placement=dataproc.messages.JobPlacement(clusterName=args.cluster))
self.ConfigureJob(dataproc.messages, job, args)
if args.max_failures_per_hour:
scheduling = dataproc.messages.JobScheduling(
maxFailuresPerHour=args.max_failures_per_hour)
job.scheduling = scheduling
request = dataproc.messages.DataprocProjectsRegionsJobsSubmitRequest(
projectId=job_ref.projectId,
region=job_ref.region,
submitJobRequest=dataproc.messages.SubmitJobRequest(
job=job, requestId=request_id))
job = dataproc.client.projects_regions_jobs.Submit(request)
log.status.Print('Job [{0}] submitted.'.format(job_id))
if not args.async_:
job = util.WaitForJobTermination(
dataproc,
job,
job_ref,
message='Waiting for job completion',
goal_state=dataproc.messages.JobStatus.StateValueValuesEnum.
DONE,
error_state=dataproc.messages.JobStatus.StateValueValuesEnum.
ERROR,
stream_driver_log=True)
log.status.Print('Job [{0}] finished successfully.'.format(job_id))
return job
def Run(self, args):
dataproc = dp.Dataproc(self.ReleaseTrack())
job_ref = args.CONCEPTS.job.Parse()
job = dataproc.client.projects_regions_jobs.Get(
dataproc.messages.DataprocProjectsRegionsJobsGetRequest(
projectId=job_ref.projectId,
region=job_ref.region,
jobId=job_ref.jobId))
# TODO(b/36050945) Check if Job is still running and fail or handle 401.
job = util.WaitForJobTermination(
dataproc,
job,
job_ref,
message='Waiting for job completion',
goal_state=dataproc.messages.JobStatus.StateValueValuesEnum.DONE,
error_state=dataproc.messages.JobStatus.StateValueValuesEnum.ERROR,
stream_driver_log=True)
log.status.Print('Job [{0}] finished successfully.'.format(args.job))
return job
def Run(self, args):
"""This is what gets called when the user runs this command."""
client = self.context['dataproc_client']
messages = self.context['dataproc_messages']
job_id = util.GetJobId(args.id)
job_ref = util.ParseJob(job_id, self.context)
self.PopulateFilesByType(args)
cluster_ref = util.ParseCluster(args.cluster, self.context)
request = messages.DataprocProjectsRegionsClustersGetRequest(
projectId=cluster_ref.projectId,
region=cluster_ref.region,
clusterName=cluster_ref.clusterName)
try:
cluster = client.projects_regions_clusters.Get(request)
except apitools_exceptions.HttpError as error:
raise exceptions.HttpException(error)
self._staging_dir = self.GetStagingDir(cluster, job_ref.jobId)
self.ValidateAndStageFiles()
job = messages.Job(
reference=messages.JobReference(projectId=job_ref.projectId,
jobId=job_ref.jobId),
placement=messages.JobPlacement(clusterName=args.cluster))
self.ConfigureJob(job, args)
request = messages.DataprocProjectsRegionsJobsSubmitRequest(
projectId=job_ref.projectId,
region=job_ref.region,
submitJobRequest=messages.SubmitJobRequest(job=job))
try:
job = client.projects_regions_jobs.Submit(request)
except apitools_exceptions.HttpError as error:
raise exceptions.HttpException(error)
log.status.Print('Job [{0}] submitted.'.format(job_id))
if not args. async:
job = util.WaitForJobTermination(
job,
self.context,
message='Waiting for job completion',
goal_state=messages.JobStatus.StateValueValuesEnum.DONE,
stream_driver_log=True)
log.status.Print('Job [{0}] finished successfully.'.format(job_id))
return job
def Run(self, args):
"""This is what gets called when the user runs this command."""
dataproc = dp.Dataproc(self.ReleaseTrack())
job_id = util.GetJobId(args.id)
job_ref = util.ParseJob(job_id, dataproc)
self.PopulateFilesByType(args)
cluster_ref = util.ParseCluster(args.cluster, dataproc)
request = dataproc.messages.DataprocProjectsRegionsClustersGetRequest(
projectId=cluster_ref.projectId,
region=cluster_ref.region,
clusterName=cluster_ref.clusterName)
cluster = dataproc.client.projects_regions_clusters.Get(request)
self._staging_dir = self.GetStagingDir(
cluster, job_ref.jobId, bucket=args.bucket)
self.ValidateAndStageFiles()
job = dataproc.messages.Job(
reference=dataproc.messages.JobReference(
projectId=job_ref.projectId,
jobId=job_ref.jobId),
placement=dataproc.messages.JobPlacement(
clusterName=args.cluster))
self.ConfigureJob(dataproc.messages, job, args)
request = dataproc.messages.DataprocProjectsRegionsJobsSubmitRequest(
projectId=job_ref.projectId,
region=job_ref.region,
submitJobRequest=dataproc.messages.SubmitJobRequest(
job=job))
job = dataproc.client.projects_regions_jobs.Submit(request)
log.status.Print('Job [{0}] submitted.'.format(job_id))
if not args.async:
job = util.WaitForJobTermination(
dataproc,
job,
message='Waiting for job completion',
goal_state=dataproc.messages.JobStatus.StateValueValuesEnum.DONE,
stream_driver_log=True)
log.status.Print('Job [{0}] finished successfully.'.format(job_id))
return job
def Run(self, args):
client = self.context['dataproc_client']
messages = self.context['dataproc_messages']
job_ref = util.ParseJob(args.id, self.context)
request = job_ref.Request()
job = client.projects_regions_jobs.Get(request)
# TODO(user) Check if Job is still running and fail or handle 401.
job = util.WaitForJobTermination(
job,
self.context,
message='Waiting for job completion',
goal_state=messages.JobStatus.StateValueValuesEnum.DONE,
stream_driver_log=True)
log.status.Print('Job [{0}] finished successfully.'.format(args.id))
return job