def main(cl_args=None):
# parser command-line args
arg_parser = _make_arg_parser()
options = arg_parser.parse_args(cl_args)
MRJob.set_up_logging(quiet=options.quiet, verbose=options.verbose)
# create the persistent job
runner = EMRJobRunner(**_runner_kwargs(options))
log.debug('Terminating cluster %s' % options.cluster_id)
runner.make_emr_client().terminate_job_flows(
JobFlowIds=[options.cluster_id])
log.info('Terminated cluster %s' % options.cluster_id)
def main(cl_args=None):
# parser command-line args
arg_parser = _make_arg_parser()
options = arg_parser.parse_args(cl_args)
MRJob.set_up_logging(quiet=options.quiet, verbose=options.verbose)
# create the persistent job
runner = EMRJobRunner(**_runner_kwargs(options))
log.debug('Terminating cluster %s' % options.cluster_id)
runner.make_emr_client().terminate_job_flows(
JobFlowIds=[options.cluster_id])
log.info('Terminated cluster %s' % options.cluster_id)
def main(cl_args=None):
# parser command-line args
option_parser = _make_option_parser()
options, args = option_parser.parse_args(cl_args)
if len(args) != 1:
option_parser.error('This tool takes exactly one argument.')
cluster_id = args[0]
MRJob.set_up_logging(quiet=options.quiet, verbose=options.verbose)
# create the persistent job
runner = EMRJobRunner(**_runner_kwargs(options))
log.debug('Terminating cluster %s' % cluster_id)
runner.make_emr_client().terminate_job_flows(
JobFlowIds=[cluster_id])
log.info('Terminated cluster %s' % cluster_id)
def main(cl_args=None):
arg_parser = _make_arg_parser()
options = arg_parser.parse_args(cl_args)
MRJob.set_up_logging(quiet=options.quiet, verbose=options.verbose)
runner_kwargs = {
k: v
for k, v in options.__dict__.items()
if k not in ('quiet', 'verbose', 'step_id')
}
runner = EMRJobRunner(**runner_kwargs)
emr_client = runner.make_emr_client()
# pick step
step = _get_step(emr_client, options.cluster_id, options.step_id)
if not step:
raise SystemExit(1)
if step['Status']['State'] != 'FAILED':
log.warning('step %s has state %s, not FAILED' %
(step['Id'], step['Status']['State']))
# interpret logs
log.info('Diagnosing step %s (%s)' % (step['Id'], step['Name']))
log_interpretation = dict(step_id=step['Id'])
step_type = _infer_step_type(step)
error = runner._pick_error(log_interpretation, step_type)
# print error
if error:
log.error('Probable cause of failure:\n\n%s\n\n' %
_format_error(error))
else:
log.warning('No error detected')
def main(cl_args=None):
arg_parser = _make_arg_parser()
options = arg_parser.parse_args(cl_args)
MRJob.set_up_logging(quiet=options.quiet, verbose=options.verbose)
runner_kwargs = {k: v for k, v in options.__dict__.items()
if k not in ('quiet', 'verbose', 'step_id')}
runner = EMRJobRunner(**runner_kwargs)
emr_client = runner.make_emr_client()
# pick step
step = _get_step(emr_client, options.cluster_id, options.step_id)
if not step:
raise SystemExit(1)
if step['Status']['State'] != 'FAILED':
log.warning('step %s has state %s, not FAILED' %
(step['Id'], step['Status']['State']))
# interpret logs
log.info('Diagnosing step %s (%s)' % (step['Id'], step['Name']))
log_interpretation = dict(step_id=step['Id'])
step_type = _infer_step_type(step)
error = runner._pick_error(log_interpretation, step_type)
# print error
if error:
log.error('Probable cause of failure:\n\n%s\n\n' %
_format_error(error))
else:
log.warning('No error detected')
def _maybe_terminate_clusters(dry_run=False,
max_mins_idle=None,
now=None,
pool_name=None,
pooled_only=False,
unpooled_only=False,
max_mins_locked=None,
quiet=False,
**kwargs):
if now is None:
now = _boto3_now()
# old default behavior
if max_mins_idle is None:
max_mins_idle = _DEFAULT_MAX_MINS_IDLE
runner = EMRJobRunner(**kwargs)
emr_client = runner.make_emr_client()
num_starting = 0
num_bootstrapping = 0
num_done = 0
num_idle = 0
num_pending = 0
num_running = 0
# include RUNNING to catch clusters with PENDING jobs that
# never ran (see #365).
for cluster_summary in _boto3_paginate(
'Clusters',
emr_client,
'list_clusters',
ClusterStates=['WAITING', 'RUNNING']):
cluster_id = cluster_summary['Id']
# check if cluster is done
if _is_cluster_done(cluster_summary):
num_done += 1
continue
# check if cluster is starting
if _is_cluster_starting(cluster_summary):
num_starting += 1
continue
# check if cluster is bootstrapping
if _is_cluster_bootstrapping(cluster_summary):
num_bootstrapping += 1
continue
# need steps to learn more about cluster
steps = list(
reversed(
list(
_boto3_paginate('Steps',
emr_client,
'list_steps',
ClusterId=cluster_id))))
if any(_is_step_running(step) for step in steps):
num_running += 1
continue
# cluster is idle
time_idle = now - _time_last_active(cluster_summary, steps)
is_pending = _cluster_has_pending_steps(steps)
# need to get actual cluster to see tags
cluster = emr_client.describe_cluster(ClusterId=cluster_id)['Cluster']
_, pool = _pool_hash_and_name(cluster)
if is_pending:
num_pending += 1
else:
num_idle += 1
log.debug('cluster %s %s for %s, %s (%s) - %s' % (
cluster_id,
'pending' if is_pending else 'idle',
strip_microseconds(time_idle),
('unpooled' if pool is None else 'in %s pool' % pool),
cluster_summary['Name'],
'protected' if cluster['TerminationProtected'] else 'unprotected',
))
# filter out clusters that don't meet our criteria
if (max_mins_idle is not None
and time_idle <= timedelta(minutes=max_mins_idle)):
continue
if (pooled_only and pool is None):
continue
if (unpooled_only and pool is not None):
continue
if (pool_name is not None and pool != pool_name):
continue
if cluster['TerminationProtected']:
continue
# terminate idle cluster
_terminate_and_notify(runner=runner,
cluster_id=cluster_id,
cluster_name=cluster_summary['Name'],
num_steps=len(steps),
is_pending=is_pending,
time_idle=time_idle,
dry_run=dry_run,
max_mins_locked=max_mins_locked,
quiet=quiet)
log.info('Cluster statuses: %d starting, %d bootstrapping, %d running,'
' %d pending, %d idle, %d done' %
(num_starting, num_bootstrapping, num_running, num_pending,
num_idle, num_done))
def _maybe_terminate_clusters(dry_run=False,
max_hours_idle=None,
mins_to_end_of_hour=None,
now=None,
pool_name=None,
pooled_only=False,
unpooled_only=False,
max_mins_locked=None,
quiet=False,
**kwargs):
if now is None:
now = _boto3_now()
# old default behavior
if max_hours_idle is None and mins_to_end_of_hour is None:
max_hours_idle = _DEFAULT_MAX_HOURS_IDLE
runner = EMRJobRunner(**kwargs)
emr_client = runner.make_emr_client()
num_starting = 0
num_bootstrapping = 0
num_done = 0
num_idle = 0
num_pending = 0
num_running = 0
# We don't filter by cluster state because we want this to work even
# if Amazon adds another kind of idle state.
for cluster_summary in _boto3_paginate('Clusters', emr_client,
'list_clusters'):
cluster_id = cluster_summary['Id']
# check if cluster is done
if _is_cluster_done(cluster_summary):
num_done += 1
continue
# check if cluster is starting
if _is_cluster_starting(cluster_summary):
num_starting += 1
continue
# check if cluster is bootstrapping
if _is_cluster_bootstrapping(cluster_summary):
num_bootstrapping += 1
continue
# need steps to learn more about cluster
steps = list(
reversed(
list(
_boto3_paginate('Steps',
emr_client,
'list_steps',
ClusterId=cluster_id))))
if any(_is_step_running(step) for step in steps):
num_running += 1
continue
# cluster is idle
time_idle = now - _time_last_active(cluster_summary, steps)
time_to_end_of_hour = _est_time_to_hour(cluster_summary, now=now)
is_pending = _cluster_has_pending_steps(steps)
bootstrap_actions = list(
_boto3_paginate('BootstrapActions',
emr_client,
'list_bootstrap_actions',
ClusterId=cluster_id))
_, pool = _pool_hash_and_name(bootstrap_actions)
if is_pending:
num_pending += 1
else:
num_idle += 1
log.debug('cluster %s %s for %s, %s to end of hour, %s (%s)' %
(cluster_id, 'pending' if is_pending else 'idle',
strip_microseconds(time_idle),
strip_microseconds(time_to_end_of_hour),
('unpooled' if pool is None else 'in %s pool' % pool),
cluster_summary['Name']))
# filter out clusters that don't meet our criteria
if (max_hours_idle is not None
and time_idle <= timedelta(hours=max_hours_idle)):
continue
# mins_to_end_of_hour doesn't apply to jobs with pending steps
if (mins_to_end_of_hour is not None and
(is_pending or
time_to_end_of_hour >= timedelta(minutes=mins_to_end_of_hour))):
continue
if (pooled_only and pool is None):
continue
if (unpooled_only and pool is not None):
continue
if (pool_name is not None and pool != pool_name):
continue
# terminate idle cluster
_terminate_and_notify(runner=runner,
cluster_id=cluster_id,
cluster_name=cluster_summary['Name'],
num_steps=len(steps),
is_pending=is_pending,
time_idle=time_idle,
time_to_end_of_hour=time_to_end_of_hour,
dry_run=dry_run,
max_mins_locked=max_mins_locked,
quiet=quiet)
log.info('Cluster statuses: %d starting, %d bootstrapping, %d running,'
' %d pending, %d idle, %d done' %
(num_starting, num_bootstrapping, num_running, num_pending,
num_idle, num_done))
def _maybe_terminate_clusters(dry_run=False,
max_mins_idle=None,
now=None,
pool_name=None,
pooled_only=False,
unpooled_only=False,
max_mins_locked=None,
quiet=False,
**kwargs):
if now is None:
now = _boto3_now()
# old default behavior
if max_mins_idle is None:
max_mins_idle = _DEFAULT_MAX_MINS_IDLE
runner = EMRJobRunner(**kwargs)
emr_client = runner.make_emr_client()
num_starting = 0
num_bootstrapping = 0
num_done = 0
num_idle = 0
num_pending = 0
num_running = 0
# We don't filter by cluster state because we want this to work even
# if Amazon adds another kind of idle state.
for cluster_summary in _boto3_paginate(
'Clusters', emr_client, 'list_clusters'):
cluster_id = cluster_summary['Id']
# check if cluster is done
if _is_cluster_done(cluster_summary):
num_done += 1
continue
# check if cluster is starting
if _is_cluster_starting(cluster_summary):
num_starting += 1
continue
# check if cluster is bootstrapping
if _is_cluster_bootstrapping(cluster_summary):
num_bootstrapping += 1
continue
# need steps to learn more about cluster
steps = list(reversed(list(_boto3_paginate(
'Steps', emr_client, 'list_steps',
ClusterId=cluster_id))))
if any(_is_step_running(step) for step in steps):
num_running += 1
continue
# cluster is idle
time_idle = now - _time_last_active(cluster_summary, steps)
is_pending = _cluster_has_pending_steps(steps)
# need to get actual cluster to see tags
cluster = emr_client.describe_cluster(ClusterId=cluster_id)['Cluster']
_, pool = _pool_hash_and_name(cluster)
if is_pending:
num_pending += 1
else:
num_idle += 1
log.debug(
'cluster %s %s for %s, %s (%s)' %
(cluster_id,
'pending' if is_pending else 'idle',
strip_microseconds(time_idle),
('unpooled' if pool is None else 'in %s pool' % pool),
cluster_summary['Name']))
# filter out clusters that don't meet our criteria
if (max_mins_idle is not None and
time_idle <= timedelta(minutes=max_mins_idle)):
continue
if (pooled_only and pool is None):
continue
if (unpooled_only and pool is not None):
continue
if (pool_name is not None and pool != pool_name):
continue
# terminate idle cluster
_terminate_and_notify(
runner=runner,
cluster_id=cluster_id,
cluster_name=cluster_summary['Name'],
num_steps=len(steps),
is_pending=is_pending,
time_idle=time_idle,
dry_run=dry_run,
max_mins_locked=max_mins_locked,
quiet=quiet)
log.info(
'Cluster statuses: %d starting, %d bootstrapping, %d running,'
' %d pending, %d idle, %d done' % (
num_starting, num_bootstrapping, num_running,
num_pending, num_idle, num_done))
