def _aggregate(where, delta, is_base, now,
old, new, comments = None):
global _INPUT_DB
_INPUT_DB = _INPUT_DB if _INPUT_DB else inputDb('input_mozilla_org_new')
sql = '''
SELECT
description,
id
FROM feedback_response fr
WHERE
%s
AND locale = "en-US"
AND happy = 0
AND (campaign IS NULL OR campaign = "")
AND (source IS NULL OR source = "")
AND (version NOT RLIKE "[^a.0-9]")
;
''' % where
try:
results = _INPUT_DB.execute_sql(sql, old = old, new = new,
now = now)
except (OperationalError):
#TODO(rrayborn): raise an alert instead of just warning.
warn('Database timed out executing base sql.')
return
total = 0
for row in results:
# Tokenize the row into delta and store comments for retreival in comments
(word_dict, value) = tokenize(row.description, input_id = row.id)
if comments is not None:
comments[row.id] = row.description
if value > 0:
for (key, word_set) in word_dict.iteritems():
if (key is None) or not re.match('\S', key):
continue
data_set = delta[key].base if is_base else delta[key].after
data_set.insert(key = key, link = (row.id, value),
meta = word_set)
total += 1
return total
def _aggregate(where, delta, is_base, now, old, new, comments=None):
global _INPUT_DB
_INPUT_DB = _INPUT_DB if _INPUT_DB else inputDb('input_mozilla_org_new')
sql = '''
SELECT
description,
id
FROM feedback_response fr
WHERE
%s
AND locale = "en-US"
AND happy = 0
AND (campaign IS NULL OR campaign = "")
AND (source IS NULL OR source = "")
AND (version NOT RLIKE "[^a.0-9]")
;
''' % where
try:
results = _INPUT_DB.execute_sql(sql, old=old, new=new, now=now)
except (OperationalError):
#TODO(rrayborn): raise an alert instead of just warning.
warn('Database timed out executing base sql.')
return
total = 0
for row in results:
# Tokenize the row into delta and store comments for retreival in comments
(word_dict, value) = tokenize(row.description, input_id=row.id)
if comments is not None:
comments[row.id] = row.description
if value > 0:
for (key, word_set) in word_dict.iteritems():
if (key is None) or not re.match('\S', key):
continue
data_set = delta[key].base if is_base else delta[key].after
data_set.insert(key=key, link=(row.id, value), meta=word_set)
total += 1
return total
def process_alerts(product,
now = datetime.now(), old = _PAST_TIMEFRAME, new = None,
debug = False, debug_file = sys.stdout,
send_email = True, address = None):
delta = defaultdict(WordDeltaCounter)
# Resolve date
if not isinstance(now, datetime) and isinstance(now, date):
now = datetime.combine(now, time(0,0,0))
#if not isinstance(now, datetime):
# I don't feel like checking this. It's not a likely exception.
#raise Exception('"now" must me of type datetime or date.')
now_string = now.strftime('%Y-%m-%d %H:%M:%S')
now = tz('US/Pacific').localize(now)
# Product related vars
if product.lower() == 'desktop':
new = new if new else _DESKTOP_TIMEFRAME
where_product = ('product = "firefox"' +
'\nAND LEFT(platform,7) IN("Windows","OS X","Linux")')
flavor = 'word-based'
subject = 'Desktop Input Alert'
address = address if address else _DESKTOP_EMAIL
elif product.lower() == 'android':
new = new if new else _ANDROID_TIMEFRAME
where_product = 'product = "Firefox for Android"'
flavor = 'android-word-based'
subject = 'Android Input Alert'
address = address if address else _ANDROID_EMAIL
else:
raise Exception('product must be "desktop" or "android".')
# Resolve debug info
if debug and not isinstance(debug_file, file):
warn('Debug file should be type <file>, outputting to stdout.')
debug_file = sys.stdout
is_print = not debug or debug_file != sys.stdout
if is_print:
print 'Generating %s for %s' % (subject, now_string)
# Retrieve old timeframe
where = (where_product +
'\nAND created > DATE_SUB(:now, INTERVAL :old WEEK)' +
'\nAND created < DATE_SUB(:now, INTERVAL :new HOUR)')
base_total = _aggregate(where, delta, True, now_string, old, new)
# Retrieve new timeframe
after_comments = {}
where = (where_product +
'\nAND created > DATE_SUB(:now, INTERVAL :new HOUR)' +
'\nAND created < :now')
after_total = _aggregate(where, delta, False, now_string, old, new,
comments = after_comments)
if (after_total < _MIN_DENOM_THRESHOLD or
base_total < _MIN_DENOM_THRESHOLD):
warn('NOT ENOUGH FEEDBACK %d before and %d after' % (base_total,
after_total))
return
#Generate alerts
alerted_feedback = {}
# Determine if we should alert for each word and add the alert feedback to a
# dict for spam detection
for (k,v) in delta.iteritems():
v.set_thresholds(diff_pct = _DIFF_PCT_MIN, diff_abs = _DIFF_ABS_MIN)
v.set_potentials(base = base_total, after = after_total)
v.end_time = tz('UTC').normalize(now)
if (v.is_significant and v.severity >= _ALERT_SEV_MIN
and v.after.count >= _MIN_COUNT_THRESHOLD):
for link_item in v.after.link_list:
alerted_feedback[link_item[0]] = link_item[1]
v.alert = True
# Find spam
test_spam = { x: after_comments[x] for x in alerted_feedback.keys() }
spam = SpamDetector().check_entries_for_spam(test_spam)
# Remove spam
after_total -= len(spam.keys())
for (k,v) in delta.iteritems():
if (v.alert):
for s in spam.keys():
if s in v.after.link_list:
v.after.remove(link = (s, alerted_feedback[s]))
v.alert = False
# Reprocess alerts while removing spam
has_alerts = False
email_list = set()
for (k,v) in delta.iteritems():
v.set_potentials(base = base_total, after = after_total)
if v.is_significant and v.after.count >= _MIN_COUNT_THRESHOLD:
if v.severity >= _ALERT_SEV_MIN:
if is_print:
print 'Emitting alert for %s' % v.after.sorted_metadata[0]
v.emit(timeframe = new, flavor = flavor,
debug = debug, debug_file = debug_file)
has_alerts = True
if send_email and v.severity >= _EMAIL_SEV_MIN:
email_list.add(v)
if not has_alerts:
# This is super fishy but technically valid usecase.
# Might alert on this in the future
if is_print:
print 'No alerts today'
return
# Now send an email, looking up each piece of feedback.
if email_list:
_email_results(email_list, subject, address, after_comments)
def emit(self, timeframe = None, flavor = 'unknown',
debug = False, debug_file = sys.stdout):
if debug:
if isinstance(debug_file, file):
self.log_to_csv(debug_file)
else:
warn('Debug file should be type <file>, outputting to stdout')
self.log_to_csv(sys.stdout)
return
headers = {
'content-type': 'application/json',
'accept': 'application/json; indent=4',
'Fjord-Authorization': 'Token ' + _ALERT_TOKEN,
}
timediff = timedelta(hours = timeframe)
start_time = self.end_time - timediff
link_list = list(self.after.link_list)
link_list.sort(key = lambda x:(x[1], x[0]), reverse=True)
link_list = link_list[:_MAX_ALERT_LINKS]
links = []
for link in link_list:
links.append({
'name': 'Input Link',
'url' : 'http://input.mozilla.org/dashboard/response/' + \
str(link[0])
})
description = dedent('''
Trending words: %s
Before: %.2f/1000
After %.2f/1000
Absolute Difference: %.2f %%age points
Percent Difference: %.2f %%
Total feedback in the past %d hours: %d
'''%(
', '.join(self.after.sorted_metadata),
self.base_pct * 10,
self.after_pct * 10,
self.diff_abs * 10,
self.diff_pct,
timeframe,
len(self.after.link_list)
)).strip()
payload = {
'severity': self.severity,
'summary': '%s is trending up by %.2f'%\
(self.after.sorted_metadata[0], self.diff_pct),
'description': description,
'flavor': flavor,
'emitter_name': 'input_word_alert',
'emitter_version': _VERSION,
'links': links,
'start_time': start_time.isoformat(),
'end_time': self.end_time.isoformat()
}
resp = requests.post(
'https://input.mozilla.org/api/v1/alerts/alert/',
data=json.dumps(payload),
headers=headers
)
if resp.status_code == 201:
print 'All systems good. Submitted alert for %s' % \
(self.after.sorted_metadata[0])
else:
print 'Failed to submit alert for %s' % \
(self.after.sorted_metadata[0])
print resp.json()['detail']
def process_alerts(product,
now=datetime.now(),
old=_PAST_TIMEFRAME,
new=None,
debug=False,
debug_file=sys.stdout,
send_email=True,
address=None):
delta = defaultdict(WordDeltaCounter)
# Resolve date
if not isinstance(now, datetime) and isinstance(now, date):
now = datetime.combine(now, time(0, 0, 0))
#if not isinstance(now, datetime):
# I don't feel like checking this. It's not a likely exception.
#raise Exception('"now" must me of type datetime or date.')
now_string = now.strftime('%Y-%m-%d %H:%M:%S')
now = tz('US/Pacific').localize(now)
# Product related vars
if product.lower() == 'desktop':
new = new if new else _DESKTOP_TIMEFRAME
where_product = ('product = "firefox"' +
'\nAND LEFT(platform,7) IN("Windows","OS X","Linux")')
flavor = 'word-based'
subject = 'Desktop Input Alert'
address = address if address else _DESKTOP_EMAIL
elif product.lower() == 'android':
new = new if new else _ANDROID_TIMEFRAME
where_product = 'product = "Firefox for Android"'
flavor = 'android-word-based'
subject = 'Android Input Alert'
address = address if address else _ANDROID_EMAIL
else:
raise Exception('product must be "desktop" or "android".')
# Resolve debug info
if debug and not isinstance(debug_file, file):
warn('Debug file should be type <file>, outputting to stdout.')
debug_file = sys.stdout
is_print = not debug or debug_file != sys.stdout
if is_print:
print 'Generating %s for %s' % (subject, now_string)
# Retrieve old timeframe
where = (where_product +
'\nAND created > DATE_SUB(:now, INTERVAL :old WEEK)' +
'\nAND created < DATE_SUB(:now, INTERVAL :new HOUR)')
base_total = _aggregate(where, delta, True, now_string, old, new)
# Retrieve new timeframe
after_comments = {}
where = (where_product +
'\nAND created > DATE_SUB(:now, INTERVAL :new HOUR)' +
'\nAND created < :now')
after_total = _aggregate(where,
delta,
False,
now_string,
old,
new,
comments=after_comments)
if (after_total < _MIN_DENOM_THRESHOLD
or base_total < _MIN_DENOM_THRESHOLD):
warn('NOT ENOUGH FEEDBACK %d before and %d after' %
(base_total, after_total))
return
#Generate alerts
alerted_feedback = {}
# Determine if we should alert for each word and add the alert feedback to a
# dict for spam detection
for (k, v) in delta.iteritems():
v.set_thresholds(diff_pct=_DIFF_PCT_MIN, diff_abs=_DIFF_ABS_MIN)
v.set_potentials(base=base_total, after=after_total)
v.end_time = tz('UTC').normalize(now)
if (v.is_significant and v.severity >= _ALERT_SEV_MIN
and v.after.count >= _MIN_COUNT_THRESHOLD):
for link_item in v.after.link_list:
alerted_feedback[link_item[0]] = link_item[1]
v.alert = True
# Find spam
test_spam = {x: after_comments[x] for x in alerted_feedback.keys()}
spam = SpamDetector().check_entries_for_spam(test_spam)
# Remove spam
after_total -= len(spam.keys())
for (k, v) in delta.iteritems():
if (v.alert):
for s in spam.keys():
if s in v.after.link_list:
v.after.remove(link=(s, alerted_feedback[s]))
v.alert = False
# Reprocess alerts while removing spam
has_alerts = False
email_list = set()
for (k, v) in delta.iteritems():
v.set_potentials(base=base_total, after=after_total)
if v.is_significant and v.after.count >= _MIN_COUNT_THRESHOLD:
if v.severity >= _ALERT_SEV_MIN:
if is_print:
print 'Emitting alert for %s' % v.after.sorted_metadata[0]
v.emit(timeframe=new,
flavor=flavor,
debug=debug,
debug_file=debug_file)
has_alerts = True
if send_email and v.severity >= _EMAIL_SEV_MIN:
email_list.add(v)
if not has_alerts:
# This is super fishy but technically valid usecase.
# Might alert on this in the future
if is_print:
print 'No alerts today'
return
# Now send an email, looking up each piece of feedback.
if email_list:
_email_results(email_list, subject, address, after_comments)
def emit(self,
timeframe=None,
flavor='unknown',
debug=False,
debug_file=sys.stdout):
if debug:
if isinstance(debug_file, file):
self.log_to_csv(debug_file)
else:
warn('Debug file should be type <file>, outputting to stdout')
self.log_to_csv(sys.stdout)
return
headers = {
'content-type': 'application/json',
'accept': 'application/json; indent=4',
'Fjord-Authorization': 'Token ' + _ALERT_TOKEN,
}
timediff = timedelta(hours=timeframe)
start_time = self.end_time - timediff
link_list = list(self.after.link_list)
link_list.sort(key=lambda x: (x[1], x[0]), reverse=True)
link_list = link_list[:_MAX_ALERT_LINKS]
links = []
for link in link_list:
links.append({
'name': 'Input Link',
'url' : 'http://input.mozilla.org/dashboard/response/' + \
str(link[0])
})
description = dedent('''
Trending words: %s
Before: %.2f/1000
After %.2f/1000
Absolute Difference: %.2f %%age points
Percent Difference: %.2f %%
Total feedback in the past %d hours: %d
''' % (', '.join(self.after.sorted_metadata), self.base_pct * 10,
self.after_pct * 10, self.diff_abs * 10, self.diff_pct,
timeframe, len(self.after.link_list))).strip()
payload = {
'severity': self.severity,
'summary': '%s is trending up by %.2f'%\
(self.after.sorted_metadata[0], self.diff_pct),
'description': description,
'flavor': flavor,
'emitter_name': 'input_word_alert',
'emitter_version': _VERSION,
'links': links,
'start_time': start_time.isoformat(),
'end_time': self.end_time.isoformat()
}
resp = requests.post('https://input.mozilla.org/api/v1/alerts/alert/',
data=json.dumps(payload),
headers=headers)
if resp.status_code == 201:
print 'All systems good. Submitted alert for %s' % \
(self.after.sorted_metadata[0])
else:
print 'Failed to submit alert for %s' % \
(self.after.sorted_metadata[0])
print resp.json()['detail']
def process_alerts(date = None, debug = False, debug_file = sys.stdout, email = True):
input_db = inputDb('input_mozilla_org_new')
delta = defaultdict(WordDeltaCounter)
base_total = 0
after_total = 0
if (date is None) :
date = dt.datetime.now()
if (isinstance(date, dt.datetime)):
pass
elif (isinstance(date, dt.date)):
date = dt.datetime.combine(date, dt.time(0,0,0))
date_string = date.strftime('%Y-%m-%d %H:%M:%S')
date = tz('US/Pacific').localize(date)
if debug:
if not isinstance(debug_file, file):
warn("Debug file should be type <file>, outputting to stdout instead")
debug_file = sys.stdout
if (not debug or debug_file != sys.stdout):
print "Generating alerts for " + date_string
old_data_sql = """
SELECT description, MIN(id) as id
FROM feedback_response fr
WHERE
created > DATE_SUB(:now, INTERVAL :old WEEK) AND
created < DATE_SUB(:now, INTERVAL :new HOUR)
AND product LIKE 'firefox'
AND locale = 'en-US'
AND happy = 0
AND (campaign IS NULL or campaign = '')
AND (source IS NULL or source = '')
AND (version NOT RLIKE '[^a.0-9]')
AND (platform LIKE 'Windows%' OR platform LIKE 'OS X' OR platform LIKE 'Linux')
GROUP BY 1
"""
try:
results = input_db.execute_sql(old_data_sql, old=_PAST_TIMEFRAME, new=_TIMEFRAME, now = date_string)
except (OperationalError):
warn("Database timed out executing base sql.")
#TODO: raise an alert instead of just printing.
return
for row in results:
(word_dict, value) = tokenize(row.description)
if value == 0:
continue
for (key, word_set) in word_dict.iteritems():
if (key is None) or not re.match('\S', key):
continue
delta[key].base.insert(key = key, link = (row.id, value), meta = word_set)
base_total += 1
new_data_sql = """
SELECT description, MIN(id) as id
FROM feedback_response fr
WHERE
created > DATE_SUB(:now, INTERVAL :new HOUR) AND
created < :now
AND product LIKE 'firefox'
AND locale = 'en-US'
AND happy = 0
AND (campaign IS NULL or campaign = '')
AND (source IS NULL or source = '')
AND (version NOT RLIKE '[^a.0-9]')
AND (platform LIKE 'Windows%' OR platform LIKE 'OS X' OR platform LIKE 'Linux')
GROUP BY 1
"""
try:
results = input_db.execute_sql(new_data_sql, new=_TIMEFRAME, now = date_string)
except (OperationalError):
warn("Database timed out executing after sql.")
return
for row in results:
(word_dict, value) = tokenize(row.description)
if value == 0:
continue
for (key, word_set) in word_dict.iteritems():
if (key is None) or not re.match('\S', key):
continue
delta[key].after.insert(key = key, link = (row.id, value), meta = word_set)
after_total += 1
if (after_total < _MIN_DENOM_THRESHOLD or base_total < _MIN_DENOM_THRESHOLD):
warn("NOT ENOUGH FEEDBACK %d before and %d after" % (base_total, after_total))
return
#Generate alerts
alert_count = 0
for (k,v) in delta.iteritems():
v.set_thresholds(diff_pct = _DIFF_PCT_MIN, diff_abs = _DIFF_ABS_MIN)
v.set_potentials(base = base_total, after = after_total)
v.end_time = tz('UTC').normalize(date)
if (v.is_significant and v.severity >= _ALERT_SEV_MIN
and v.after.count >= _MIN_COUNT_THRESHOLD):
alert_count += 1
if (not debug or debug_file != sys.stdout):
print "Emitting alert for %s" % v.after.sorted_metadata[0]
v.emit(debug = debug, debug_file = debug_file)
if alert_count <= 0:
print "No alerts today"
#This is super fishy but technically valid usecase. I guess leave it for now.
# Now send an email, looking up each piece of feedback.
if (email):
email_list = set()
for (k,v) in delta.iteritems():
v.set_thresholds(diff_pct = _DIFF_PCT_MIN, diff_abs = _DIFF_ABS_MIN)
if (v.is_significant and v.severity >= _EMAIL_SEV_MIN
and v.after.count >= _MIN_COUNT_THRESHOLD):
email_list.add(v)
email_results(email_list)
