def auto_delete_size_is_exceeded():
logger = logging.getLogger('limitation')
model_list = [('crash', 'Crash'), ('feedback', 'Feedback')]
for model in model_list:
result = delete_size_is_exceeded(*model)
if result.get('count', 0):
result['size'] /= 1024.0 * 1024
log_id = str(uuid.uuid4())
params = dict(log_id=log_id)
splunk_url = get_splunk_url(params)
splunk_filter = 'log_id=%s' % log_id if splunk_url else None
raven_extra = dict(id=log_id,
splunk_url=splunk_url,
splunk_filter=splunk_filter)
raven.captureMessage(
"[Limitation]Periodic task 'Size is exceeded' cleaned up %d %s, total size of cleaned space is %.2f Mb[%d]"
% (result['count'], model[1], result['size'], time.time()),
data=dict(level=20, logger='limitation'),
extra=raven_extra)
extra = dict(log_id=log_id,
meta=True,
count=result['count'],
size=result['size'],
model=model[1],
reason='size_is_exceeded')
logger.info(
add_extra_to_log_message('Automatic cleanup', extra=extra))
for element in result['elements']:
element.update(dict(log_id=log_id))
logger.info(
add_extra_to_log_message('Automatic cleanup element',
extra=element))
def test_crashes(self):
gpm['Crash__limit_size'] = 1
crash_size = 10 * 1024 * 1023
CrashFactory.create_batch(200,
archive_size=crash_size,
minidump_size=0)
self.assertEqual(Crash.objects.all().count(), 200)
del_count = 98
deleted = list(
Crash.objects.values_list('id', 'created', 'signature', 'userid',
'appid'))[:del_count]
deleted = map(
lambda x: dict(id=x[0],
element_created=x[1].strftime("%d. %B %Y %I:%M%p"),
signature=x[2],
userid=x[3],
appid=x[4]), deleted)
result = delete_size_is_exceeded('crash', 'Crash')
self.assertDictEqual(
result,
dict(count=del_count,
size=del_count * crash_size,
elements=deleted))
self.assertEqual(Crash.objects.all().count(), 102)
def deferred_manual_cleanup(model, limit_size=None, limit_days=None, limit_duplicated=None):
full_result = dict(count=0, size=0, elements=[], signatures={})
if limit_duplicated:
result = delete_duplicate_crashes(limit=limit_duplicated)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
full_result['signatures'].update(result['signatures'])
if limit_days:
result = delete_older_than(*model, limit=limit_days)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_size:
result = delete_size_is_exceeded(*model, limit=limit_size)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
full_result['size'] /= 1024.0 * 1024
extra = dict(elements=full_result['elements'])
extra.update(full_result['signatures'])
raven.captureMessage("[Limitation]Manual cleanup freed %d %s, total size of cleaned space is %.2f Mb[%d]" %
(full_result['count'], model[1], full_result['size'], time.time()),
data=dict(level=20, logger='limitation'), extra=extra)
def deferred_manual_cleanup(model,
limit_size=None,
limit_days=None,
limit_duplicated=None):
logger = logging.getLogger('limitation')
full_result = dict(count=0, size=0, elements=[])
if limit_duplicated:
result = delete_duplicate_crashes(limit=limit_duplicated)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_days:
result = delete_older_than(*model, limit=limit_days)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_size:
result = delete_size_is_exceeded(*model, limit=limit_size)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
full_result['size'] /= 1024.0 * 1024
log_id = str(uuid.uuid4())
params = dict(log_id=log_id)
splunk_url = get_splunk_url(params)
splunk_filter = 'log_id=%s' % log_id if splunk_url else None
raven_extra = dict(id=log_id,
splunk_url=splunk_url,
splunk_filter=splunk_filter)
raven.captureMessage(
"[Limitation]Manual cleanup freed %d %s, total size of cleaned space is %.2f Mb[%s]"
% (full_result['count'], model[1], full_result['size'], log_id),
data=dict(level=20, logger='limitation'),
extra=raven_extra)
extra = dict(log_id=log_id,
meta=True,
count=full_result['count'],
size=full_result['size'],
model=model[1],
limit_duplicated=limit_duplicated,
limit_size=limit_size,
limit_days=limit_days,
reason='manual')
logger.info(add_extra_to_log_message('Manual cleanup', extra=extra))
for element in full_result['elements']:
element.update(dict(log_id=log_id))
logger.info(
add_extra_to_log_message('Manual cleanup element', extra=element))
def test_feedbacks(self):
gpm['Feedback__limit_size'] = 1
feedback_size = 10*1024*1023
FeedbackFactory.create_batch(200, screenshot_size=feedback_size, system_logs_size=0, attached_file_size=0, blackbox_size=0)
self.assertEqual(Feedback.objects.all().count(), 200)
del_count = 98
deleted = list(Feedback.objects.values_list('id', 'created'))
deleted = map(lambda x: dict(id=x[0], element_created=x[1].strftime("%d. %B %Y %I:%M%p")), deleted)[:del_count]
result = delete_size_is_exceeded('feedback', 'Feedback')
self.assertDictEqual(result, dict(count=del_count, size=del_count * feedback_size, elements=deleted))
self.assertEqual(Feedback.objects.all().count(), 102)
def auto_delete_size_is_exceeded():
model_list = [
('crash', 'Crash'),
('feedback', 'Feedback')
]
for model in model_list:
result = delete_size_is_exceeded(*model)
if result.get('count', 0):
extra = dict(elements=result['elements'])
result['size'] /= 1024.0 * 1024
raven.captureMessage("[Limitation]Periodic task 'Size is exceeded' cleaned up %d %s, total size of cleaned space is %.2f Mb[%d]" %
(result['count'], model[1], result['size'], time.time()),
data=dict(level=20, logger='limitation'), extra=extra)
def test_crashes(self):
gpm['Crash__limit_size'] = 1
crash_size = 10*1024*1023
CrashFactory.create_batch(200, archive_size=crash_size, minidump_size=0)
self.assertEqual(Crash.objects.all().count(), 200)
del_count = 98
deleted = list(Crash.objects.values_list('id', 'created', 'signature', 'userid', 'appid'))[:del_count]
deleted = map(lambda x: dict(id=x[0], element_created=x[1].strftime("%d. %B %Y %I:%M%p"), signature=x[2],
userid=x[3], appid=x[4]), deleted)
result = delete_size_is_exceeded('crash', 'Crash')
self.assertDictEqual(result, dict(count=del_count, size=del_count * crash_size, elements=deleted))
self.assertEqual(Crash.objects.all().count(), 102)
def test_crashes(self):
gpm["Crash__limit_size"] = 1
crash_size = 10 * 1024 * 1023
CrashFactory.create_batch(500, archive_size=crash_size, minidump_size=0)
self.assertEqual(Crash.objects.all().count(), 500)
del_count = 398
created_to_string = lambda x: "Created: %s" % x.strftime("%d. %B %Y %I:%M%p")
signature_to_string = lambda x: "Signature: %s" % x
deleted = list(Crash.objects.values_list("id", "created", "signature"))[:del_count]
deleted = map(lambda x: (x[0], created_to_string(x[1]), signature_to_string(x[2])), deleted)
result = delete_size_is_exceeded("crash", "Crash")
self.assertDictEqual(result, dict(count=del_count, size=del_count * crash_size, elements=deleted))
self.assertEqual(Crash.objects.all().count(), 102)
def test_feedbacks(self):
gpm["Feedback__limit_size"] = 1
feedback_size = 10 * 1024 * 1023
FeedbackFactory.create_batch(
500, screenshot_size=feedback_size, system_logs_size=0, attached_file_size=0, blackbox_size=0
)
self.assertEqual(Feedback.objects.all().count(), 500)
del_count = 398
created_to_string = lambda x: "Created: %s" % x.strftime("%d. %B %Y %I:%M%p")
deleted = list(Feedback.objects.values_list("id", "created"))[:del_count]
deleted = map(lambda x: (x[0], created_to_string(x[1])), deleted)
result = delete_size_is_exceeded("feedback", "Feedback")
self.assertDictEqual(result, dict(count=del_count, size=del_count * feedback_size, elements=deleted))
self.assertEqual(Feedback.objects.all().count(), 102)
def auto_delete_size_is_exceeded():
logger = logging.getLogger('limitation')
model_list = [('crash', 'Crash'), ('feedback', 'Feedback')]
for model in model_list:
result = delete_size_is_exceeded(*model)
if result.get('count', 0):
log_id = str(uuid.uuid4())
params = dict(log_id=log_id)
splunk_url = get_splunk_url(params)
splunk_filter = 'log_id=%s' % log_id if splunk_url else None
ids_list = sorted(
[element['id'] for element in result['elements']])
raven_extra = {
"id": log_id,
"splunk_url": splunk_url,
"splunk_filter": splunk_filter,
"%s_list" % (model[1]): ids_list
}
raven.captureMessage(
"[Limitation]Periodic task 'Size is exceeded' cleaned up %d %s, total size of cleaned space is %s [%d]"
% (result['count'], model[1],
filters.filesizeformat(result['size']).replace(
u'\xa0', u' '), time.time()),
data=dict(level=20, logger='limitation'),
extra=raven_extra)
extra = dict(log_id=log_id,
meta=True,
count=result['count'],
size=filters.filesizeformat(result['size']).replace(
u'\xa0', u' '),
model=model[1],
reason='size_is_exceeded')
logger.info(
add_extra_to_log_message('Automatic cleanup', extra=extra))
for element in result['elements']:
element.update({
"log_id": log_id,
"%s_id" % (model[1]): element.pop('id')
})
logger.info(
add_extra_to_log_message('Automatic cleanup element',
extra=element))
def deferred_manual_cleanup(model, limit_size=None, limit_days=None, limit_duplicated=None):
logger = logging.getLogger('limitation')
full_result = dict(count=0, size=0, elements=[])
if limit_duplicated:
result = delete_duplicate_crashes(limit=limit_duplicated)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_days:
result = delete_older_than(*model, limit=limit_days)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_size:
result = delete_size_is_exceeded(*model, limit=limit_size)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
full_result['size'] /= 1024.0 * 1024
log_id = str(uuid.uuid4())
params = dict(log_id=log_id)
splunk_url = get_splunk_url(params)
splunk_filter = 'log_id=%s' % log_id if splunk_url else None
raven_extra = dict(id=log_id, splunk_url=splunk_url, splunk_filter=splunk_filter)
raven.captureMessage("[Limitation]Manual cleanup freed %d %s, total size of cleaned space is %.2f Mb[%s]" %
(full_result['count'], model[1], full_result['size'], log_id),
data=dict(level=20, logger='limitation'), extra=raven_extra)
extra = dict(log_id=log_id, meta=True, count=full_result['count'], size=full_result['size'], model=model[1],
limit_duplicated=limit_duplicated, limit_size=limit_size, limit_days=limit_days, reason='manual')
logger.info(add_extra_to_log_message('Manual cleanup', extra=extra))
for element in full_result['elements']:
element.update(dict(log_id=log_id))
logger.info(add_extra_to_log_message('Manual cleanup element', extra=element))
def deferred_manual_cleanup(model, limit_size=None, limit_days=None, limit_duplicated=None):
logger = logging.getLogger('limitation')
full_result = dict(count=0, size=0, elements=[])
if limit_duplicated:
result = delete_duplicate_crashes(limit=limit_duplicated)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_days:
result = delete_older_than(*model, limit=limit_days)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
if limit_size:
result = delete_size_is_exceeded(*model, limit=limit_size)
if result.get('count', 0):
full_result['count'] += result['count']
full_result['size'] += result['size']
full_result['elements'] += result['elements']
log_id = str(uuid.uuid4())
params = dict(log_id=log_id)
splunk_url = get_splunk_url(params)
splunk_filter = 'log_id=%s' % log_id if splunk_url else None
ids_list = sorted([element['id'] for element in full_result['elements']])
raven_extra = {"id": log_id, "splunk_url": splunk_url, "splunk_filter": splunk_filter, "%s_list" % (model[1]): ids_list}
raven.captureMessage("[Limitation]Manual cleanup freed %d %s, total size of cleaned space is %s [%s]" %
(full_result['count'], model[1], filters.filesizeformat(full_result['size']).replace(u'\xa0', u' '), log_id),
data=dict(level=20, logger='limitation'), extra=raven_extra)
extra = dict(log_id=log_id, meta=True, count=full_result['count'], size=filters.filesizeformat(full_result['size']).replace(u'\xa0', u' '), model=model[1],
limit_duplicated=limit_duplicated, limit_size=limit_size, limit_days=limit_days, reason='manual')
logger.info(add_extra_to_log_message('Manual cleanup', extra=extra))
for element in full_result['elements']:
element.update({"log_id": log_id, "%s_id" % (model[1]): element.pop('id')})
logger.info(add_extra_to_log_message('Manual cleanup element', extra=element))
def auto_delete_size_is_exceeded():
logger = logging.getLogger('limitation')
model_list = [
('crash', 'Crash'),
('feedback', 'Feedback')
]
for model in model_list:
result = delete_size_is_exceeded(*model)
if result.get('count', 0):
log_id = str(uuid.uuid4())
params = dict(log_id=log_id)
splunk_url = get_splunk_url(params)
splunk_filter = 'log_id=%s' % log_id if splunk_url else None
raven_extra = dict(id=log_id, splunk_url=splunk_url, splunk_filter=splunk_filter)
raven.captureMessage("[Limitation]Periodic task 'Size is exceeded' cleaned up %d %s, total size of cleaned space is %s [%d]" %
(result['count'], model[1], filters.filesizeformat(result['size']).replace(u'\xa0', u' '), time.time()),
data=dict(level=20, logger='limitation'), extra=raven_extra)
extra = dict(log_id=log_id, meta=True, count=result['count'], size=filters.filesizeformat(result['size']).replace(u'\xa0', u' '), model=model[1], reason='size_is_exceeded')
logger.info(add_extra_to_log_message('Automatic cleanup', extra=extra))
for element in result['elements']:
element.update(dict(log_id=log_id))
logger.info(add_extra_to_log_message('Automatic cleanup element', extra=element))
def test_feedbacks(self):
gpm['Feedback__limit_size'] = 1
feedback_size = 10 * 1024 * 1023
FeedbackFactory.create_batch(200,
screenshot_size=feedback_size,
system_logs_size=0,
attached_file_size=0,
blackbox_size=0)
self.assertEqual(Feedback.objects.all().count(), 200)
del_count = 98
deleted = list(Feedback.objects.values_list('id', 'created'))
deleted = map(
lambda x: dict(id=x[0],
element_created=x[1].strftime("%d. %B %Y %I:%M%p")),
deleted)[:del_count]
result = delete_size_is_exceeded('feedback', 'Feedback')
self.assertDictEqual(
result,
dict(count=del_count,
size=del_count * feedback_size,
elements=deleted))
self.assertEqual(Feedback.objects.all().count(), 102)