do not use instances in service summary

local-app/python-tools/cross-organization/tag-checker/analyze-tag-data.py

@@ -12,7 +12,14 @@
 from datetime import datetime
 from difflib import SequenceMatcher
 
-__version__ = "1.0.8"
+__version__ = "1.0.9"
+
+# Services that do NOT have a resource type in the 6th ARN field
+# They use: arn:aws:service:region:account:resource-name
+SERVICES_WITHOUT_TYPES = [
+    "s3", "sns", "sqs", "codepipeline", "codebuild", 
+    "cloudwatch", "events", "logs", "sns", "states"
+]
 
 def get_args():
     parser = argparse.ArgumentParser(description=f"AWS Tag Data Analyzer v{__version__}")
@@ -27,21 +34,30 @@ def get_args():
 
 def load_service_map(map_path):
     if map_path and os.path.exists(map_path):
-        with open(map_path, 'r') as f: return json.load(f)
+        try:
+            with open(map_path, 'r') as f: return json.load(f)
+        except: return None
     return None
 
 def parse_arn_details(arn):
-    """Extracts service and primary resource type (e.g., ec2 vpc)."""
+    """Extracts service and resource type, handling services with no type segment."""
     try:
         parts = arn.split(':')
-        if len(parts) < 6: return "unknown", "unknown"
+        if len(parts) < 6: return "unknown", ""
+
         service = parts[2]
         resource_part = parts[5]
-        # Break at first / or : to get type (e.g., volume/vol-123 -> volume)
+
+        # If the service is known to skip the type field, just return the service
+        if service in SERVICES_WITHOUT_TYPES:
+            return service, ""
+
+        # Otherwise, find the type (break at first / or :)
+        # Example: 'ec2' 'instance/i-123' -> 'ec2' 'instance'
         res_type = re.split(r'[:/]', resource_part)[0]
         return service, res_type
     except:
-        return "unknown", "unknown"
+        return "unknown", ""
 
 def get_similarity(a, b):
     a_norm = re.sub(r'[:_\-\s]', '', a.lower())
@@ -80,7 +96,6 @@ def analyze():
     tag_values = defaultdict(Counter)
     account_map = defaultdict(set)
     resource_tags = defaultdict(dict) 
-    resource_info = {}
     service_distribution = Counter()
 
     files = []
@@ -90,22 +105,22 @@ def analyze():
         with open(file, mode='r', encoding='utf-8') as f:
             for row in csv.DictReader(f):
                 arn, tag, val = row['arn'], row['tag_name'], row['tag_value']
-                acc, reg = row['account_id'], row['region']
+                acc = row['account_id']
 
                 findings_count[tag] += 1
                 tag_values[tag][val] += 1
                 account_map[tag].add(acc)
                 resource_tags[arn][tag] = val
-                resource_info[arn] = {"acc": acc, "reg": reg}
 
                 svc, r_type = parse_arn_details(arn)
-                service_distribution[f"{svc} {r_type}"] += 1
+                display_name = f"{svc} {r_type}".strip()
+                service_distribution[display_name] += 1
 
     max_tag_len = max([len(t) for t in findings_count.keys()] + [20])
     col1 = max_tag_len + 2
     div = "=" * (col1 + 65)
 
-    # --- SECTIONS 1-5 (Restored) ---
+    # --- SECTIONS 1-5 (Summary, Values, Similarity, Compliance, Legacy) ---
     print(f"\n{div}\nSECTION 1: GLOBAL SUMMARY\n{div}")
     print(f"Input Tags: {stats['total']} | Active: {stats['active']} | Found: {len(findings_count)}")
     print(f"Total Hits: {sum(findings_count.values())} | Accounts: {len(set().union(*account_map.values()))}")
@@ -116,11 +131,11 @@ def analyze():
         print(f"{tag.ljust(col1)} | {vals}")
 
     print(f"\n{div}\nSECTION 3: SUSPECTED DUPLICATES (TYPOS/CASE)\n{div}")
-    tags = sorted(findings_count.keys())
-    for i in range(len(tags)):
-        for j in range(i + 1, len(tags)):
-            if get_similarity(tags[i], tags[j]) >= args.similarity_threshold:
-                print(f"[!] {tags[i]} <-> {tags[j]}")
+    tags_list = sorted(findings_count.keys())
+    for i in range(len(tags_list)):
+        for j in range(i + 1, len(tags_list)):
+            if get_similarity(tags_list[i], tags_list[j]) >= args.similarity_threshold:
+                print(f"[!] {tags_list[i]} <-> {tags_list[j]}")
 
     if required_keys:
         print(f"\n{div}\nSECTION 4: COVERAGE & COMPLIANCE\n{div}")