def run_manual_code(study_id):
"""
Create an AWS Batch job for the Study specified
:param study_id: Primary key of a Study
"""
# we assume that the cluster is configured only in one region.
pipeline_region = get_current_region()
# Get the object ID of the study, used in the pipeline
query = Study.objects.filter(pk=study_id)
if not query.exists():
return abort(404)
object_id = query.get().object_id
error_sentry = make_error_sentry("data",
tags={"pipeline_frequency": "manually"})
# Get new data access credentials for the manual user, submit a manual job, display message
# Report all errors to sentry including DataPipelineNotConfigured errors.
with error_sentry:
ssm_client = get_boto_client('ssm', pipeline_region)
refresh_data_access_credentials('manually',
ssm_client=ssm_client,
webserver=True)
batch_client = get_boto_client('batch', pipeline_region)
create_one_job('manually', object_id, batch_client, webserver=True)
flash('Data pipeline code successfully initiated!', 'success')
if error_sentry.errors:
flash('An unknown error occurred when trying to run this task.',
category='danger')
print error_sentry
return redirect('/data-pipeline/{:s}'.format(study_id))
def run_manual_code(study_id):
"""
Create an AWS Batch job for the Study specified
:param study_id: Primary key of a Study
"""
# Get the object ID of the study, used in the pipeline
object_id = Study.objects.get(pk=study_id).object_id
error_sentry = make_error_sentry("data",
tags={"pipeline_frequency": "manually"})
with error_sentry:
# Get new data access credentials for the manual user
aws_object_names = get_aws_object_names()
refresh_data_access_credentials('manually', aws_object_names)
# Submit a manual job
create_one_job('manually', object_id)
# The success message gets displayed to the user upon redirect
flash('Data pipeline code successfully initiated!', 'success')
if error_sentry.errors:
flash('An unknown error occurred when trying to run this task.',
'danger')
return redirect('/data-pipeline/{:s}'.format(study_id))
def run_manual_code(study_id):
"""
Create an AWS Batch job for the Study specified
:param study_id: ObjectId of a Study
"""
# Get new data access credentials for the manual user
aws_object_names = get_aws_object_names()
refresh_data_access_credentials('manually', aws_object_names)
# Submit a manual job
create_one_job('manually', study_id)
# The success message gets displayed to the user upon redirect
flash('Data pipeline code successfully initiated!', 'success')
return redirect('/data-pipeline/{:s}'.format(study_id))
def run_manual_code(study_id):
"""
Create an AWS Batch job for the Study specified
:param study_id: Primary key of a Study
"""
username = session["admin_username"]
destination_email_addresses_string = ''
if 'destination_email_addresses' in request.values:
destination_email_addresses_string = request.values[
'destination_email_addresses']
destination_email_addresses = [
d.strip() for d in filter(
None, re.split("[, \?:;]+",
destination_email_addresses_string))
]
for email_address in destination_email_addresses:
if not validate_email(email_address):
flash(
'Email address {0} in ({1}) does not appear to be a valid email address.'
.format(email_address, destination_email_addresses_string),
category='danger')
return redirect('/data-pipeline/{:s}'.format(study_id))
destination_email_addresses_string = ','.join(
destination_email_addresses)
participants_string = ''
if 'participants' in request.values:
participants_string = request.form.getlist('participants')
participants_string = ','.join(participants_string)
data_start_time = ''
if 'time_start' in request.values:
data_start_time = request.values['time_start']
data_end_time = ''
if 'time_end' in request.values:
data_end_time = request.values['time_end']
# Get the object ID of the study, used in the pipeline
query = Study.objects.filter(pk=study_id)
if not query.exists():
flash('Could not find study corresponding to study id {0}'.format(
study_id),
category='danger')
return redirect('/data-pipeline/{:s}'.format(study_id))
#return abort(404)
object_id = query.get().object_id
pipeline_region = os.getenv("pipeline_region", None)
if not pipeline_region:
pipeline_region = 'us-east-1'
flash('Pipeline region not configured, choosing default ({})'.format(
pipeline_region),
category='info')
# return redirect('/data-pipeline/{:s}'.format(study_id))
error_sentry = make_error_sentry("data",
tags={"pipeline_frequency": "manually"})
# Get new data access credentials for the manual user, submit a manual job, display message
# Report all errors to sentry including DataPipelineNotConfigured errors.
with error_sentry:
ssm_client = get_boto_client('ssm', pipeline_region)
refresh_data_access_credentials('manually', ssm_client=ssm_client)
batch_client = get_boto_client('batch', pipeline_region)
create_one_job('manually', object_id, username,
destination_email_addresses_string, data_start_time,
data_end_time, participants_string, batch_client)
if data_start_time and data_end_time:
flash(
'Data pipeline successfully initiated on data collected between {0} and {1}! Email(s) will be sent to {2} on completion.'
.format(data_start_time, data_end_time,
destination_email_addresses), 'success')
elif data_start_time:
flash(
'Data pipeline successfully initiated on data collected after {0}! Email(s) will be sent to {1} on completion.'
.format(data_start_time,
destination_email_addresses), 'success')
elif data_end_time:
flash(
'Data pipeline successfully initiated on data collected before {0}! Email(s) will be sent to {1} on completion.'
.format(data_start_time,
destination_email_addresses), 'success')
else:
flash(
'Data pipeline successfully initiated! Email(s) will be sent to {0} on completion.'
.format(destination_email_addresses), 'success')
if error_sentry.errors:
flash('An error occurred when trying to execute the pipeline: {0}'.
format(error_sentry),
category='danger')
print(error_sentry)
return redirect('/data-pipeline/{:s}'.format(study_id))
pipeline_region = get_current_region()
ssm_client = get_boto_client('ssm', pipeline_region)
error_sentry = make_error_sentry("data",
tags={"pipeline_frequency": "manually"})
batch_client = get_boto_client('batch', pipeline_region)
yesterday = timezone.now() - timedelta(days=1)
refresh_data_access_credentials('manually',
ssm_client=ssm_client,
webserver=False)
################################################################################################
# if you are running this on an ubuntu machine you have to sudo apt-get -y install cloud-utils #
################################################################################################
for study in Study.objects.all():
with error_sentry:
# we only want to run the pipeline for data that has been uploaded, Report all errors to sentry
for patient_id in ChunkRegistry.get_updated_users_for_study(
study, yesterday):
create_one_job('manually',
study,
patient_id,
batch_client,
webserver=False)
print("creating job for", study.name)
# raise errors
if error_sentry.errors:
error_sentry.raise_errors()