def put_metric_data(self, metric_key, timestamp, value, unit=None):
def get_stats(tmp_stat):
try:
ret = dict(zip(self.COLUMNS, map(lambda x: x.values()[0], tmp_stat)))
for v in ret:
if v == None:
v = float("nan")
except IndexError:
storm.log("index %s is not in DB." % time_idx)
ret = {
"SampleCount": float("nan"),
"Sum": float("nan"),
"Average": float("nan"),
"Minimum": float("nan"),
"Maximum": float("nan"),
}
return ret
time_idx = timestamp.replace(second=0, microsecond=0)
time_diff = utils.utcnow() - time_idx
if timedelta(seconds=self.STATISTICS_TTL) < time_diff:
msg = "index %s is older than TTL. It doesn't need to insert DB"
storm.log(msg % time_idx)
return
if time_idx not in self.df.index:
self._reindex()
value = utils.to_default_unit(value, unit)
try:
stat = self.df.ix[time_idx]
for v in stat:
if v == None:
v = float("nan")
except KeyError:
stat = self.cass.get_metric_statistics_for_key(metric_key, time_idx)
stat = get_stats(stat)
stat["SampleCount"] = 1.0 if isnull(stat["SampleCount"]) else stat["SampleCount"] + 1.0
stat["Sum"] = value if isnull(stat["Sum"]) else stat["Sum"] + value
stat["Average"] = stat["Sum"] / stat["SampleCount"]
stat["Minimum"] = value if (isnull(stat["Minimum"]) or stat["Minimum"] > value) else stat["Minimum"]
stat["Maximum"] = value if (isnull(stat["Maximum"]) or stat["Maximum"] < value) else stat["Maximum"]
# insert into DB
stat_dict = {
"SampleCount": {time_idx: stat["SampleCount"]},
"Sum": {time_idx: stat["Sum"]},
"Average": {time_idx: stat["Average"]},
"Minimum": {time_idx: stat["Minimum"]},
"Maximum": {time_idx: stat["Maximum"]},
}
ttl = self.STATISTICS_TTL - time_diff.total_seconds()
self.cass.insert_stat(self.metric_key, stat_dict, ttl)
storm.log("metric data inserted %s" % (self.metric_key))
def put_metric_data(self, metric_key, timestamp, value, unit=None):
time_idx = timestamp.replace(second=0, microsecond=0)
if timedelta(seconds=self.cass.STATISTICS_TTL) < (utils.utcnow() - time_idx):
msg = "index %s is older than TTL. It doesn't need to insert DB"
storm.log(msg % time_idx)
return
if time_idx not in self.df.index:
self._reindex()
value = utils.to_default_unit(value, unit)
try:
stat = self.df.ix[time_idx]
for v in stat:
if v == None:
v = float("nan")
except KeyError:
stat = self.cass.get_metric_statistics_for_key(metric_key, time_idx)
if [{}, {}, {}, {}, {}] == stat:
storm.log("index %s is not in DB." % time_idx)
stat = {
"SampleCount": float("nan"),
"Sum": float("nan"),
"Average": float("nan"),
"Minimum": float("nan"),
"Maximum": float("nan"),
}
else:
stat = dict(zip(self.cass.STATISTICS, map(lambda x: x.values()[0], stat)))
for v in stat:
if v == None:
v = float("nan")
stat["SampleCount"] = 1.0 if isnan(stat["SampleCount"]) else stat["SampleCount"] + 1.0
stat["Sum"] = value if isnan(stat["Sum"]) else stat["Sum"] + value
stat["Average"] = stat["Sum"] / stat["SampleCount"]
stat["Minimum"] = value if isnan(stat["Minimum"]) or stat["Minimum"] > value else stat["Minimum"]
stat["Maximum"] = value if isnan(stat["Maximum"]) or stat["Maximum"] < value else stat["Maximum"]
# insert into DB
stat_dict = {
"SampleCount": {time_idx: stat["SampleCount"]},
"Sum": {time_idx: stat["Sum"]},
"Average": {time_idx: stat["Average"]},
"Minimum": {time_idx: stat["Minimum"]},
"Maximum": {time_idx: stat["Maximum"]},
}
self.cass.insert_stat(self.metric_key, stat_dict)
storm.log("metric data inserted %s" % (self.metric_key))
# self.df.ix[time_idx] = stat
now = utils.utcnow().replace(second=0, microsecond=0)
timedelta_buf = now - time_idx
if timedelta_buf <= timedelta(seconds=self.MAX_START_PERIOD):
# check alarms
self.check_alarms()
def put_metric_data(self, metric_key, timestamp, value, unit=None):
def get_stats(tmp_stat):
try:
ret = dict(zip(self.cass.STATISTICS,
map(lambda x: x.values()[0], tmp_stat)))
for v in ret:
if v == None: v = float('nan')
except IndexError:
LOG.debug("index %s is not in DB.", time_idx)
ret = {'SampleCount' : float('nan'),
'Sum' : float('nan'),
'Average' : float('nan'),
'Minimum' : float('nan'),
'Maximum' : float('nan') }
return ret
time_idx = timestamp.replace(second=0, microsecond=0)
time_diff = utils.utcnow() - time_idx
if timedelta(seconds=self.cass.statistics_ttl) < time_diff:
msg = "index %s is older than TTL. It doesn't need to insert DB"
LOG.debug(msg, time_idx)
return
if time_idx not in self.df.index:
self._reindex()
if value == None:
LOG.info("metric inputted without value")
return
else:
value = utils.to_default_unit(value, unit)
try:
stat = self.df.ix[time_idx]
for v in stat:
if v == None: v = float('nan')
except KeyError:
stat = self.cass.get_metric_statistics_for_key(metric_key,
time_idx)
stat = get_stats(stat)
stat['SampleCount'] = 1.0 if isnull(stat['SampleCount']) \
else stat['SampleCount'] + 1.0
stat['Sum'] = value if isnull(stat['Sum']) \
else stat['Sum'] + value
stat['Average'] = stat['Sum'] / stat['SampleCount']
stat['Minimum'] = value \
if (isnull(stat['Minimum']) or
stat['Minimum'] > value) \
else stat['Minimum']
stat['Maximum'] = value \
if (isnull(stat['Maximum']) or
stat['Maximum'] < value) \
else stat['Maximum']
# insert into DB
stat_dict = {
'SampleCount':{time_idx: stat['SampleCount']},
'Sum':{time_idx: stat['Sum']},
'Average':{time_idx: stat['Average']},
'Minimum':{time_idx: stat['Minimum']},
'Maximum':{time_idx: stat['Maximum']}
}
ttl = self.cass.statistics_ttl - time_diff.total_seconds()
self.updated_timestamp = utils.utcnow()
if ttl > 0.1:
self.cass.insert_stat(self.metric_key, stat_dict, ttl)
else:
LOG.debug("ttl must be positive, ttl %s", ttl)
self.cass.update_metric(self.metric_key, {'updated_timestamp':
self.updated_timestamp})
LOG.info("metric data inserted %s, time_idx %s", str(self), time_idx)
def put_metric_data(self, metric_key, timestamp, value, unit=None):
def get_stats(tmp_stat):
try:
ret = dict(
zip(self.cass.STATISTICS,
map(lambda x: x.values()[0], tmp_stat)))
for v in ret:
if v == None: v = float('nan')
except IndexError:
LOG.debug("index %s is not in DB.", time_idx)
ret = {
'SampleCount': float('nan'),
'Sum': float('nan'),
'Average': float('nan'),
'Minimum': float('nan'),
'Maximum': float('nan')
}
return ret
time_idx = timestamp.replace(second=0, microsecond=0)
time_diff = utils.utcnow() - time_idx
if timedelta(seconds=self.cass.statistics_ttl) < time_diff:
msg = "index %s is older than TTL. It doesn't need to insert DB"
LOG.debug(msg, time_idx)
return
if time_idx not in self.df.index:
self._reindex()
if value == None:
LOG.info("metric inputted without value")
return
else:
value = utils.to_default_unit(value, unit)
try:
stat = self.df.ix[time_idx]
for v in stat:
if v == None: v = float('nan')
except KeyError:
stat = self.cass.get_metric_statistics_for_key(
metric_key, time_idx)
stat = get_stats(stat)
stat['SampleCount'] = 1.0 if isnull(stat['SampleCount']) \
else stat['SampleCount'] + 1.0
stat['Sum'] = value if isnull(stat['Sum']) \
else stat['Sum'] + value
stat['Average'] = stat['Sum'] / stat['SampleCount']
stat['Minimum'] = value \
if (isnull(stat['Minimum']) or
stat['Minimum'] > value) \
else stat['Minimum']
stat['Maximum'] = value \
if (isnull(stat['Maximum']) or
stat['Maximum'] < value) \
else stat['Maximum']
# insert into DB
stat_dict = {
'SampleCount': {
time_idx: stat['SampleCount']
},
'Sum': {
time_idx: stat['Sum']
},
'Average': {
time_idx: stat['Average']
},
'Minimum': {
time_idx: stat['Minimum']
},
'Maximum': {
time_idx: stat['Maximum']
}
}
ttl = self.cass.statistics_ttl - time_diff.total_seconds()
self.updated_timestamp = utils.utcnow()
if ttl > 0.1:
self.cass.insert_stat(self.metric_key, stat_dict, ttl)
else:
LOG.debug("ttl must be positive, ttl %s", ttl)
self.cass.update_metric(self.metric_key,
{'updated_timestamp': self.updated_timestamp})
LOG.info("metric data inserted %s, time_idx %s", str(self), time_idx)
def put_metric_data(self, metric_key, timestamp, value, unit=None):
def get_stats(tmp_stat):
try:
ret = dict(zip(self.cass.STATISTICS,
map(lambda x: x.values()[0], tmp_stat)))
for v in ret:
if v == None: v = float('nan')
except IndexError:
storm.log("index %s is not in DB." % time_idx)
ret = {'SampleCount' : float('nan'),
'Sum' : float('nan'),
'Average' : float('nan'),
'Minimum' : float('nan'),
'Maximum' : float('nan') }
return ret
time_idx = timestamp.replace(second=0, microsecond=0)
if timedelta(seconds=self.cass.STATISTICS_TTL) < (utils.utcnow() -
time_idx):
msg = "index %s is older than TTL. It doesn't need to insert DB"
storm.log(msg % time_idx)
return
if time_idx not in self.df.index:
self._reindex()
value = utils.to_default_unit(value, unit)
try:
stat = self.df.ix[time_idx]
for v in stat:
if v == None: v = float('nan')
except KeyError:
stat = self.cass.get_metric_statistics_for_key(metric_key, time_idx)
stat = get_stats(stat)
stat['SampleCount'] = 1.0 if isnan(stat['SampleCount']) \
else stat['SampleCount'] + 1.0
stat['Sum'] = value if isnan(stat['Sum']) \
else stat['Sum'] + value
stat['Average'] = stat['Sum'] / stat['SampleCount']
stat['Minimum'] = value \
if isnan(stat['Minimum']) or stat['Minimum'] > value \
else stat['Minimum']
stat['Maximum'] = value \
if isnan(stat['Maximum']) or stat['Maximum'] < value \
else stat['Maximum']
# insert into DB
stat_dict = {
'SampleCount':{time_idx: stat['SampleCount']},
'Sum':{time_idx: stat['Sum']},
'Average':{time_idx: stat['Average']},
'Minimum':{time_idx: stat['Minimum']},
'Maximum':{time_idx: stat['Maximum']}
}
self.cass.insert_stat(self.metric_key, stat_dict)
storm.log("metric data inserted %s" % (self.metric_key))
#self.df.ix[time_idx] = stat
now = utils.utcnow().replace(second=0, microsecond=0)
timedelta_buf = now - time_idx
if(timedelta_buf <= timedelta(seconds=self.MAX_START_PERIOD)):
# check alarms
self.check_alarms()
