some more refactor

Author: phuongdnguyen

temporal/scripts/generate_metadata.py

@@ -97,10 +97,10 @@ def append_metric_metadata(metric_name, metric_type='count', unit_name=None):
     for temporal_name, dd_metric in METRIC_MAP.items():
         if isinstance(dd_metric, dict) and dd_metric.get('type') == 'native_dynamic':
             # Native dynamic metrics have its type defined at run time, and usually added manually, see https://github.com/DataDog/integrations-core/pull/18050
-            existing_dd_metric, existed_dd_metric = check_existing_metric(
+            existing_dd_metric = check_existing_metric(
                 dd_metric.get('name'), previous_metadata, added_dd_metrics
             )
-            if existed_dd_metric:
+            if bool(existing_dd_metric):
                 print(
                     f"INFO: dynamic metric `{dd_metric}` is reserved because it's present "
                     "in the current metadata.csv file"
@@ -113,15 +113,14 @@ def append_metric_metadata(metric_name, metric_type='count', unit_name=None):
                 )
             continue
 
-        try:
-            temporal_type = temporal_metric_types[temporal_name]
-        except KeyError:
+        temporal_type = temporal_metric_types.get(temporal_name)
+        if temporal_type is None:
             # If metrics does not exist in this Temporal version, try to search metric in
             # the current metadata file and preserve it if it's already exist
-            existing_dd_metric, existed_dd_metric = check_existing_metric(
+            existing_dd_metric = check_existing_metric(
                 dd_metric, previous_metadata, added_dd_metrics
             )
-            if existed_dd_metric:
+            if bool(existing_dd_metric):
                 metadata.extend(existing_dd_metric)
             else:
                 print(
@@ -189,7 +188,7 @@ def extract_metric_defs(go_code: str) -> dict:
     return results
 
 
-def check_existing_metric(name: str, previous_metadata: dict, added_dd_metrics: set) -> tuple[list, bool]:
+def check_existing_metric(name: str, previous_metadata: dict, added_dd_metrics: set) -> list:
     """
     Check if a metric exists in the previous metadata and add it to the current metadata if found.
 
@@ -201,16 +200,14 @@ def check_existing_metric(name: str, previous_metadata: dict, added_dd_metrics:
         tuple: (metadata list with any existing metrics added, boolean indicating if metric exists)
     """
     pattern = re.compile(rf"^temporal\.server\.{re.escape(name)}(?:\.[a-z]+)*$")
-    exist = False
     result = []
     for dd_metric in previous_metadata:
         if pattern.match(dd_metric) and dd_metric not in added_dd_metrics:
             # A metric were supported in the previous temporal version, but dropped in the current temporal version
             result.append(previous_metadata.get(dd_metric))
             print(f"INFO: {dd_metric} is reserved because it exists in the current metatadata.csv file")
-            exist = True
             added_dd_metrics.add(dd_metric)
-    return result, exist
+    return result
 
 
 def fetch_temporal_metrics(tag: str) -> str: