Add local storage and retry logic for Azure Metrics Exporter + flush telemetry on exit

contrib/opencensus-ext-azure/opencensus/ext/azure/metrics_exporter/__init__.py

@@ -12,11 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import json
 import logging
 
-import requests
-
 from opencensus.common import utils as common_utils
 from opencensus.ext.azure.common import Options, utils
 from opencensus.ext.azure.common.protocol import (
@@ -25,6 +22,8 @@
     Envelope,
     MetricData,
 )
+from opencensus.ext.azure.common.storage import LocalFileStorage
+from opencensus.ext.azure.common.transport import TransportMixin
 from opencensus.ext.azure.metrics_exporter import standard_metrics
 from opencensus.metrics import transport
 from opencensus.metrics.export.metric_descriptor import MetricDescriptorType
@@ -35,49 +34,65 @@
 logger = logging.getLogger(__name__)
 
 
-class MetricsExporter(object):
+class MetricsExporter(TransportMixin):
     """Metrics exporter for Microsoft Azure Monitor."""
 
-    def __init__(self, options=None):
-        if options is None:
-            options = Options()
-        self.options = options
+    def __init__(self, **options):
+        self.options = Options(**options)
         utils.validate_instrumentation_key(self.options.instrumentation_key)
         if self.options.max_batch_size <= 0:
             raise ValueError('Max batch size must be at least 1.')
+        self.export_interval = self.options.export_interval
         self.max_batch_size = self.options.max_batch_size
+        self.storage = LocalFileStorage(
+            path=self.options.storage_path,
+            max_size=self.options.storage_max_size,
+            maintenance_period=self.options.storage_maintenance_period,
+            retention_period=self.options.storage_retention_period,
+        )
+        super(MetricsExporter, self).__init__()
 
     def export_metrics(self, metrics):
-        if metrics:
-            envelopes = []
-            for metric in metrics:
-                # No support for histogram aggregations
-                type_ = metric.descriptor.type
-                if type_ != MetricDescriptorType.CUMULATIVE_DISTRIBUTION:
-                    md = metric.descriptor
-                    # Each time series will be uniquely identified by its
-                    # label values
-                    for time_series in metric.time_series:
-                        # Using stats, time_series should only have one point
-                        # which contains the aggregated value
-                        data_point = self.create_data_points(
-                            time_series, md)[0]
-                        # The timestamp is when the metric was recorded
-                        time_stamp = time_series.points[0].timestamp
-                        # Get the properties using label keys from metric and
-                        # label values of the time series
-                        properties = self.create_properties(time_series, md)
-                        envelopes.append(self.create_envelope(data_point,
-                                                              time_stamp,
-                                                              properties))
-            # Send data in batches of max_batch_size
-            if envelopes:
-                batched_envelopes = list(common_utils.window(
-                    envelopes, self.max_batch_size))
-                for batch in batched_envelopes:
-                    self._transmit_without_retry(batch)
-
-    def create_data_points(self, time_series, metric_descriptor):
+        envelopes = []
+        for metric in metrics:
+            envelopes.extend(self.metric_to_envelopes(metric))
+        # Send data in batches of max_batch_size
+        batched_envelopes = list(common_utils.window(
+            envelopes, self.max_batch_size))
+        for batch in batched_envelopes:
+            result = self._transmit(batch)
+            if result > 0:
+                self.storage.put(batch, result)
+
+        # If there is still room to transmit envelopes, transmit from storage
+        # if available
+        if len(envelopes) < self.options.max_batch_size:
+            self._transmit_from_storage()
+
+    def metric_to_envelopes(self, metric):
+        envelopes = []
+        # No support for histogram aggregations
+        type_ = metric.descriptor.type
+        if type_ != MetricDescriptorType.CUMULATIVE_DISTRIBUTION:
+            md = metric.descriptor
+            # Each time series will be uniquely identified by its
+            # label values
+            for time_series in metric.time_series:
+                # Using stats, time_series should only have one
+                # point which contains the aggregated value
+                data_point = self._create_data_points(
+                    time_series, md)[0]
+                # The timestamp is when the metric was recorded
+                time_stamp = time_series.points[0].timestamp
+                # Get the properties using label keys from metric
+                # and label values of the time series
+                properties = self._create_properties(time_series, md)
+                envelopes.append(self._create_envelope(data_point,
+                                                       time_stamp,
+                                                       properties))
+        return envelopes
+
+    def _create_data_points(self, time_series, metric_descriptor):
         """Convert a metric's OC time series to list of Azure data points."""
         data_points = []
         for point in time_series.points:
@@ -88,7 +103,7 @@ def create_data_points(self, time_series, metric_descriptor):
             data_points.append(data_point)
         return data_points
 
-    def create_properties(self, time_series, metric_descriptor):
+    def _create_properties(self, time_series, metric_descriptor):
         properties = {}
         # We construct a properties map from the label keys and values. We
         # assume the ordering is already correct
@@ -100,7 +115,7 @@ def create_properties(self, time_series, metric_descriptor):
             properties[metric_descriptor.label_keys[i].key] = value
         return properties
 
-    def create_envelope(self, data_point, time_stamp, properties):
+    def _create_envelope(self, data_point, time_stamp, properties):
         envelope = Envelope(
             iKey=self.options.instrumentation_key,
             tags=dict(utils.azure_monitor_context),
@@ -114,125 +129,13 @@ def create_envelope(self, data_point, time_stamp, properties):
         envelope.data = Data(baseData=data, baseType="MetricData")
         return envelope
 
-    def _transmit_without_retry(self, envelopes):
-        # Contains logic from transport._transmit
-        # TODO: Remove this function from exporter and consolidate with
-        # transport._transmit to cover all exporter use cases. Uses cases
-        # pertain to properly handling failures and implementing a retry
-        # policy for this exporter.
-        # TODO: implement retry policy
-        """
-        Transmit the data envelopes to the ingestion service.
-        Does not perform retry logic. For partial success and
-        non-retryable failure, simply outputs result to logs.
-        This function should never throw exception.
-        """
-        try:
-            response = requests.post(
-                url=self.options.endpoint,
-                data=json.dumps(envelopes),
-                headers={
-                    'Accept': 'application/json',
-                    'Content-Type': 'application/json; charset=utf-8',
-                },
-                timeout=self.options.timeout,
-            )
-        except Exception as ex:
-            # No retry policy, log output
-            logger.warning('Transient client side error %s.', ex)
-            return
-
-        text = 'N/A'
-        data = None
-        # Handle the possible results from the response
-        if response is None:
-            logger.warning('Error: cannot read response.')
-            return
-        try:
-            status_code = response.status_code
-        except Exception as ex:
-            logger.warning('Error while reading response status code %s.', ex)
-            return
-        try:
-            text = response.text
-        except Exception as ex:
-            logger.warning('Error while reading response body %s.', ex)
-            return
-        try:
-            data = json.loads(text)
-        except Exception as ex:
-            logger.warning('Error while loading ' +
-                           'json from response body %s.', ex)
-            return
-        if status_code == 200:
-            logger.info('Transmission succeeded: %s.', text)
-            return
-        # Check for retryable partial content
-        if status_code == 206:
-            if data:
-                try:
-                    retryable_envelopes = []
-                    for error in data['errors']:
-                        if error['statusCode'] in (
-                                429,  # Too Many Requests
-                                500,  # Internal Server Error
-                                503,  # Service Unavailable
-                        ):
-                            retryable_envelopes.append(
-                                envelopes[error['index']])
-                        else:
-                            logger.error(
-                                'Data drop %s: %s %s.',
-                                error['statusCode'],
-                                error['message'],
-                                envelopes[error['index']],
-                            )
-                    # show the envelopes that can be retried manually for
-                    # visibility
-                    if retryable_envelopes:
-                        logger.warning(
-                            'Error while processing data. Data dropped. ' +
-                            'Consider manually retrying for envelopes: %s.',
-                            retryable_envelopes
-                        )
-                    return
-                except Exception:
-                    logger.exception(
-                        'Error while processing %s: %s.',
-                        status_code,
-                        text
-                    )
-                    return
-        # Check for non-retryable result
-        if status_code in (
-                206,  # Partial Content
-                429,  # Too Many Requests
-                500,  # Internal Server Error
-                503,  # Service Unavailable
-        ):
-            # server side error (retryable)
-            logger.warning(
-                'Transient server side error %s: %s. ' +
-                'Consider manually trying.',
-                status_code,
-                text,
-            )
-        else:
-            # server side error (non-retryable)
-            logger.error(
-                'Non-retryable server side error %s: %s.',
-                status_code,
-                text,
-            )
-
 
 def new_metrics_exporter(**options):
-    options_ = Options(**options)
-    exporter = MetricsExporter(options=options_)
+    exporter = MetricsExporter(**options)
     producers = [stats_module.stats]
-    if options_.enable_standard_metrics:
+    if exporter.options.enable_standard_metrics:
         producers.append(standard_metrics.producer)
     transport.get_exporter_thread(producers,
                                   exporter,
-                                  interval=options_.export_interval)
+                                  interval=exporter.options.export_interval)
     return exporter