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https://github.com/GSA/notifications-admin.git
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56d9c29e91
> When we have jobs that have over 3% failure rates we should highlight > those so that peoples attention is drawn to deal with the failure. > > They would then go to the job view to see what the details are where > they could filter by failure, but that's a different story... > > This is just about calculating and highlighting those that need their > attention. — https://www.pivotaltracker.com/story/show/121206123 This commit: - calculates the failure rate for each job - makes jobs with a failure rate of > 3% go red on the dashboard
90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
from datetime import datetime
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from dateutil import parser
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from functools import reduce
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def sum_of_statistics(delivery_statistics):
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statistics_keys = (
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'emails_delivered',
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'emails_requested',
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'emails_failed',
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'sms_requested',
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'sms_delivered',
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'sms_failed'
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)
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if not delivery_statistics or not delivery_statistics[0]:
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return {
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key: 0 for key in statistics_keys
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}
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return reduce(
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lambda x, y: {
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key: x.get(key, 0) + y.get(key, 0)
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for key in statistics_keys
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},
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delivery_statistics
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)
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def add_rates_to(delivery_statistics):
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return dict(
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emails_failure_rate=(
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"{0:.1f}".format(
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float(delivery_statistics['emails_failed']) / delivery_statistics['emails_requested'] * 100
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)
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if delivery_statistics['emails_requested'] else 0
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),
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sms_failure_rate=(
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"{0:.1f}".format(
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float(delivery_statistics['sms_failed']) / delivery_statistics['sms_requested'] * 100
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)
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if delivery_statistics['sms_requested'] else 0
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),
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week_end_datetime=parser.parse(
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delivery_statistics.get('week_end', str(datetime.utcnow()))
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),
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**delivery_statistics
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)
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def statistics_by_state(statistics):
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return {
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'sms': {
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'processed': statistics['sms_requested'],
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'sending': (
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statistics['sms_requested'] - statistics['sms_failed'] - statistics['sms_delivered']
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),
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'delivered': statistics['sms_delivered'],
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'failed': statistics['sms_failed']
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},
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'email': {
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'processed': statistics['emails_requested'],
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'sending': (
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statistics['emails_requested'] - statistics['emails_failed'] - statistics['emails_delivered']
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),
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'delivered': statistics['emails_delivered'],
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'failed': statistics['emails_failed']
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}
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}
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def get_failure_rate_for_job(job):
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if not job.get('notifications_delivered'):
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if job.get('notifications_failed'):
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return 1
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return 0
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return (
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job.get('notifications_failed', 0) /
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(job.get('notifications_failed', 0) + job.get('notifications_delivered', 0))
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)
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def add_rate_to_jobs(jobs):
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return [dict(
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**job,
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failure_rate=(get_failure_rate_for_job(job)) * 100
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) for job in jobs]
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