feat(origindetection): implement cardinality common field

Signed-off-by: Wassim DHIF <[email protected]>

datadog/__init__.py

@@ -47,6 +47,7 @@ def initialize(
     statsd_constant_tags=None,  # type: Optional[List[str]]
     return_raw_response=False,  # type: bool
     hostname_from_config=True,  # type: bool
+    cardinality=None,  # type: Optional[str]
     **kwargs  # type: Any
 ):
     # type: (...) -> None
@@ -117,6 +118,12 @@ def initialize(
 
     :param hostname_from_config: Set the hostname from the Datadog agent config (agent 5). Will be deprecated
     :type hostname_from_config: boolean
+
+    :param cardinality: Set the global cardinality for all metrics. \
+        Possible values are "none", "low", "orchestrator" and "high".
+        Can also be set via the DATADOG_CARDINALITY or DD_CARDINALITY environment variables.
+    :type cardinality: string
+
     """
     # API configuration
     api._api_key = api_key or api._api_key or os.environ.get("DATADOG_API_KEY", os.environ.get("DD_API_KEY"))
@@ -151,6 +158,9 @@ def initialize(
     statsd.disable_buffering = statsd_disable_buffering
     api._return_raw_response = return_raw_response
 
+    # Set the global cardinality for all metrics
+    statsd.cardinality = cardinality or os.environ.get("DATADOG_CARDINALITY", os.environ.get("DD_CARDINALITY"))
+
     # HTTP client and API options
     for key, value in iteritems(kwargs):
         attribute = "_{}".format(key)

datadog/dogstatsd/aggregator.py

@@ -11,10 +11,11 @@
 )
 from datadog.dogstatsd.metric_types import MetricType
 from datadog.dogstatsd.max_sample_metric_context import MaxSampleMetricContexts
+from datadog.util.format import validate_cardinality
 
 
 class Aggregator(object):
-    def __init__(self, max_samples_per_context=0):
+    def __init__(self, max_samples_per_context=0, cardinality=None):
         self.max_samples_per_context = max_samples_per_context
         self.metrics_map = {
             MetricType.COUNT: {},
@@ -31,6 +32,7 @@ def __init__(self, max_samples_per_context=0):
             MetricType.GAUGE: threading.RLock(),
             MetricType.SET: threading.RLock(),
         }
+        self.cardinality = cardinality
 
     def flush_aggregated_metrics(self):
         metrics = []
@@ -58,53 +60,59 @@ def get_context(self, name, tags):
         tags_str = ",".join(tags) if tags is not None else ""
         return "{}:{}".format(name, tags_str)
 
-    def count(self, name, value, tags, rate, timestamp=0):
+    def count(self, name, value, tags, rate, timestamp=0, cardinality=None):
         return self.add_metric(
-            MetricType.COUNT, CountMetric, name, value, tags, rate, timestamp
+            MetricType.COUNT, CountMetric, name, value, tags, rate, timestamp, cardinality
         )
 
-    def gauge(self, name, value, tags, rate, timestamp=0):
+    def gauge(self, name, value, tags, rate, timestamp=0, cardinality=None):
         return self.add_metric(
-            MetricType.GAUGE, GaugeMetric, name, value, tags, rate, timestamp
+            MetricType.GAUGE, GaugeMetric, name, value, tags, rate, timestamp, cardinality
         )
 
-    def set(self, name, value, tags, rate, timestamp=0):
+    def set(self, name, value, tags, rate, timestamp=0, cardinality=None):
         return self.add_metric(
-            MetricType.SET, SetMetric, name, value, tags, rate, timestamp
+            MetricType.SET, SetMetric, name, value, tags, rate, timestamp, cardinality
         )
 
     def add_metric(
-        self, metric_type, metric_class, name, value, tags, rate, timestamp=0
+        self, metric_type, metric_class, name, value, tags, rate, timestamp=0, cardinality=None
     ):
         context = self.get_context(name, tags)
         with self._locks[metric_type]:
             if context in self.metrics_map[metric_type]:
                 self.metrics_map[metric_type][context].aggregate(value)
             else:
+                if cardinality is None:
+                    cardinality = self.cardinality
+                validate_cardinality(cardinality)
                 self.metrics_map[metric_type][context] = metric_class(
-                    name, value, tags, rate, timestamp
+                    name, value, tags, rate, timestamp, cardinality
                 )
 
-    def histogram(self, name, value, tags, rate):
+    def histogram(self, name, value, tags, rate, cardinality=None):
         return self.add_max_sample_metric(
-            MetricType.HISTOGRAM, name, value, tags, rate
+            MetricType.HISTOGRAM, name, value, tags, rate, cardinality
         )
 
-    def distribution(self, name, value, tags, rate):
+    def distribution(self, name, value, tags, rate, cardinality=None):
         return self.add_max_sample_metric(
-            MetricType.DISTRIBUTION, name, value, tags, rate
+            MetricType.DISTRIBUTION, name, value, tags, rate, cardinality
         )
 
-    def timing(self, name, value, tags, rate):
+    def timing(self, name, value, tags, rate, cardinality=None):
         return self.add_max_sample_metric(
-            MetricType.TIMING, name, value, tags, rate
+            MetricType.TIMING, name, value, tags, rate, cardinality
         )
 
     def add_max_sample_metric(
-        self, metric_type, name, value, tags, rate
+        self, metric_type, name, value, tags, rate, cardinality=None
     ):
         if rate is None:
             rate = 1
         context_key = self.get_context(name, tags)
         metric_context = self.max_sample_metric_map[metric_type]
-        return metric_context.sample(name, value, tags, rate, context_key, self.max_samples_per_context)
+        if cardinality is None:
+            cardinality = self.cardinality
+            validate_cardinality(cardinality)
+        return metric_context.sample(name, value, tags, rate, context_key, self.max_samples_per_context, cardinality)