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Convert cumulative value to rate on publish. #69

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01d144f
Convert cumulative value to rate on publish.
komeda-s Jul 11, 2018
fc22c92
code cleanup
komeda-s Jul 11, 2018
653be14
remove debug code
komeda-s Jul 11, 2018
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39 changes: 35 additions & 4 deletions src/cloudwatch/modules/flusher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ def __init__(self, config_helper, dataset_resolver):
self.client = None
self.config = config_helper
self.metric_map = {}
self.last_value = {}
self.last_flush_time = time.time()
self.nan_key_set = set()
self.enable_high_resolution_metrics = config_helper.enable_high_resolution_metrics
Expand Down Expand Up @@ -56,12 +57,24 @@ def _resolve_ds_names(self, value_list):

return ds_names

def _resolve_ds_types(self, value_list):
ds_types = self._dataset_resolver.get_dataset_types(value_list.type)
if not ds_types:
return ['gauge' for i in range(len(value_list.values))]

return ds_types

def _expand_value_list(self, value_list):
if len(value_list.values) == 1:
ds_type = self._resolve_ds_types(value_list)
if value_list.meta:
value_list.meta['ds_type'] = ds_type[0]
else:
value_list.meta = {'ds_type': ds_type[0]}
return [value_list]

expanded = []
for ds_name, value in zip(self._resolve_ds_names(value_list), value_list.values):
for ds_name, ds_type, value in zip(self._resolve_ds_names(value_list), self._resolve_ds_types(value_list), value_list.values):
new_value = value_list.__class__(
host=value_list.host,
plugin=value_list.plugin,
Expand All @@ -73,6 +86,10 @@ def _expand_value_list(self, value_list):
meta=value_list.meta,
values=[value]
)
if new_value.meta:
new_value.meta['ds_type'] = ds_type
else:
new_value.meta = {'ds_type': ds_type}
expanded.append(new_value)

return expanded
Expand Down Expand Up @@ -101,7 +118,8 @@ def _flush_if_need(self, current_time):
if self.config.debug and self.metric_map:
state = ""
for dimension_metrics in self.metric_map:
state += str(dimension_metrics) + "[" + str(self.metric_map[dimension_metrics][0].statistics.sample_count) + "] "
if self.metric_map[dimension_metrics][0].statistics:
state += str(dimension_metrics) + "[" + str(self.metric_map[dimension_metrics][0].statistics.sample_count) + "] "
self._LOGGER.info("[debug] flushing metrics " + state)
self._flush()

Expand Down Expand Up @@ -138,7 +156,8 @@ def _aggregate_metric(self, value_list):
if self.config.debug and self.metric_map:
state = ""
for dimension_metrics in self.metric_map:
state += str(dimension_metrics) + "[" + str(self.metric_map[dimension_metrics][0].statistics.sample_count) + "] "
if self.metric_map[dimension_metrics][0].statistics:
state += str(dimension_metrics) + "[" + str(self.metric_map[dimension_metrics][0].statistics.sample_count) + "] "
self._LOGGER.info("[debug] flushing metrics " + state)
self._flush()
nan_value_count = self._add_metric_to_queue(value_list, adjusted_time, key)
Expand All @@ -150,6 +169,10 @@ def _aggregate_metric(self, value_list):
def _add_metric_to_queue(self, value_list, adjusted_time, key):
nan_value_count = 0
metrics = MetricDataBuilder(self.config, value_list, adjusted_time).build()
if key in self.last_value:
for metric in metrics:
if metric.metric_name in self.last_value[key]:
metric.cumulative(last_update=self.last_value[key][metric.metric_name]['last_update'], last_value=self.last_value[key][metric.metric_name]['last_value'])
nan_value_count = self._add_values_to_metrics(metrics, value_list)
if nan_value_count != len(value_list.values):
self.metric_map[key] = metrics
Expand All @@ -174,7 +197,7 @@ def _add_values_to_metrics(self, dimension_metrics, value_list):
nan_value_count = 0
for value in value_list.values:
if self.is_numerical_value(value):
metric.add_value(value)
metric.add_value(value, value_list.time)
else:
nan_value_count += 1
return nan_value_count
Expand Down Expand Up @@ -207,7 +230,15 @@ def _prepare_batch(self):
metric_batch = []
while self.metric_map:
key, dimension_metrics = self.metric_map.popitem()
if key not in self.last_value:
self.last_value[key] = {}
for metric in dimension_metrics:
self.last_value[key][metric.metric_name] = {
'last_update': metric.last_update,
'last_value': metric.last_value
}
if metric.statistics is None:
continue
if len(metric_batch) < self._MAX_METRICS_PER_PUT_REQUEST:
metric_batch.append(metric)
else:
Expand Down
40 changes: 39 additions & 1 deletion src/cloudwatch/modules/metricdata.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,40 @@ def __init__(self, metric_name='', unit="", dimensions={}, statistic_values=None
self.timestamp = timestamp
else:
self.timestamp = awsutils.get_aws_timestamp()
self.ds_type = None
self.interval = None
self.last_update = None
self.last_value = None

def add_value(self, value):
def cumulative(self, ds_type=None, interval=None, last_update=None, last_value=None):
if ds_type:
self.ds_type = ds_type
if interval:
self.interval = interval
if last_update:
self.last_update = last_update
if last_value:
self.last_value = last_value

def add_value(self, value, time=None):

if self.ds_type in ['derive', 'counter']:
if self.last_update is None or self.last_value is None or \
self.interval and time - self.last_update > 2*self.interval or \
time - self.last_update == 0:
self.last_update, self.last_value = time, value
return

self.last_update, self.last_value, value = \
time, value, (value - self.last_value)/(time - self.last_update)
if self.ds_type == 'counter':
if value < 0:
value += 2**32 - 1
if value < 0:
value += 2**64 - 2**32
else:
self.last_update, self.last_value = time, value

if not self.statistics:
self.statistics = self.Statistics(value)
else:
Expand Down Expand Up @@ -83,6 +115,9 @@ def __init__(self, config_helper, vl, adjusted_time=None):
self.config = config_helper
self.vl = vl
self.adjusted_time = adjusted_time
self.ds_type = None
if vl.meta and 'ds_type' in vl.meta:
self.ds_type = vl.meta['ds_type']

def build(self):
""" Builds metric data object with name and dimensions but without value or statistics """
Expand All @@ -91,6 +126,9 @@ def build(self):
metric_array.append(MetricDataStatistic(metric_name=self._build_metric_name(), dimensions=self._build_asg_dimension(), timestamp=self._build_timestamp()))
if self.config.push_constant:
metric_array.append(MetricDataStatistic(metric_name=self._build_metric_name(), dimensions=self._build_constant_dimension(), timestamp=self._build_timestamp()))
if self.ds_type:
for metric in metric_array:
metric.cumulative(ds_type=self.ds_type, interval=self.vl.interval)
return metric_array

def _build_timestamp(self):
Expand Down