removed pg and latency counters

ceph_osd_analyzer.py

@@ -67,9 +67,7 @@ def get_osd_metadata(osd_id):
     """Get metadata for specific OSD"""
     return run_command(f"ceph osd metadata osd.{osd_id} -f json", parse_json=True)
 
-def get_osd_perf():
+# Performance metrics removed for simplicity
-    """Get OSD performance statistics"""
-    return run_command("ceph osd perf -f json", parse_json=True)
 
 def get_osd_host_mapping(osd_tree):
     """Build mapping of OSD ID to hostname"""
@@ -344,38 +342,7 @@ def calculate_resilience_score(osd_data, host_name, all_hosts_data, osd_tree):
     
     return score, factors
 
-def calculate_performance_score(osd_perf_data, pg_count, avg_pg_count):
+# Performance metrics removed for simplicity
-    """Calculate score based on performance metrics"""
-    score = 0.0
-    factors = []
-    
-    if not osd_perf_data:
-        return 0, ["No performance data available"]
-    
-    commit_latency = osd_perf_data.get('commit_latency_ms', 0)
-    apply_latency = osd_perf_data.get('apply_latency_ms', 0)
-    
-    # High latency indicates slow drive
-    if commit_latency > 50:
-        score += 15
-        factors.append(f"High commit latency ({commit_latency}ms)")
-    elif commit_latency > 30:
-        score += 8
-        factors.append(f"Elevated commit latency ({commit_latency}ms)")
-    
-    if apply_latency > 50:
-        score += 15
-        factors.append(f"High apply latency ({apply_latency}ms)")
-    
-    # PG imbalance
-    if pg_count > avg_pg_count * 1.3:
-        score += 10
-        factors.append(f"High PG count ({pg_count} vs avg {avg_pg_count:.0f})")
-    elif pg_count < avg_pg_count * 0.7:
-        score -= 5
-        factors.append(f"Low PG count ({pg_count}) - already underutilized")
-    
-    return score, factors
 
 def analyze_cluster():
     """Main analysis function"""
@@ -385,7 +352,6 @@ def analyze_cluster():
     print("Gathering cluster data...")
     osd_tree = get_osd_tree()
     osd_df = get_osd_df()
-    osd_perf = get_osd_perf()
     
     if not osd_tree or not osd_df:
         print(f"{Colors.RED}Failed to gather cluster data{Colors.END}")
@@ -396,11 +362,6 @@ def analyze_cluster():
     
     # Parse OSD data
     osd_df_map = {node['id']: node for node in osd_df['nodes']}
-    osd_perf_map = {p['id']: p for p in osd_perf.get('osd_perf_infos', [])} if osd_perf else {}
-    
-    # Calculate average PG count
-    pg_counts = [node['pgs'] for node in osd_df['nodes'] if node.get('pgs', 0) > 0]
-    avg_pg_count = sum(pg_counts) / len(pg_counts) if pg_counts else 0
     
     # Build host data map
     host_osds_map = defaultdict(list)
@@ -438,7 +399,6 @@ def analyze_cluster():
         
         # Get OSD data
         osd_df_data = osd_df_map.get(osd_id, {})
-        osd_perf_data = osd_perf_map.get(osd_id, {})
         
         # SMART health analysis
         health_data = get_device_health(osd_id, host_name)
@@ -457,17 +417,11 @@ def analyze_cluster():
             node, host_name, host_osds_map, osd_tree
         )
         
-        # Performance score
+        # Calculate total score (weighted: 60% health, 30% capacity, 10% resilience)
-        performance_score, performance_factors = calculate_performance_score(
-            osd_perf_data, osd_df_data.get('pgs', 0), avg_pg_count
-        )
-        
-        # Calculate total score (weighted)
         total_score = (
-            (100 - health_score) * 0.40 +  # Health is most important
+            (100 - health_score) * 0.60 +  # Health is most important
             capacity_score * 0.30 +          # Capacity optimization
-            resilience_score * 0.20 +        # Cluster resilience
+            resilience_score * 0.10          # Cluster resilience
-            performance_score * 0.10         # Performance issues
         )
         
         candidates.append({
@@ -478,14 +432,12 @@ def analyze_cluster():
             'weight': osd_df_data.get('crush_weight', 0),
             'size': osd_df_data.get('kb', 0) / 1024 / 1024 / 1024,  # TB
             'utilization': osd_df_data.get('utilization', 0),
-            'pgs': osd_df_data.get('pgs', 0),
             'total_score': total_score,
             'health_score': health_score,
             'health_issues': health_issues,
             'health_metrics': health_metrics,
             'capacity_factors': capacity_factors,
             'resilience_factors': resilience_factors,
-            'performance_factors': performance_factors,
         })
     
     print(" " * 80, end='\r')
@@ -512,7 +464,7 @@ def analyze_cluster():
         print(f"{Colors.BOLD}#{rank} - {candidate['osd_name']} ({candidate['device_class'].upper()}){Colors.END}")
         print(f"  Host: {candidate['host']}")
         print(f"  Size: {candidate['size']:.2f} TB (weight: {candidate['weight']:.2f})")
-        print(f"  Utilization: {candidate['utilization']:.1f}% | PGs: {candidate['pgs']}")
+        print(f"  Utilization: {candidate['utilization']:.1f}%")
         print(f"  {score_color}Replacement Score: {candidate['total_score']:.1f}/100{Colors.END}")
         print(f"  {health_color}Health Score: {candidate['health_score']:.1f}/100{Colors.END}")
         
@@ -527,15 +479,10 @@ def analyze_cluster():
                 print(f"    • {factor}")
         
         if candidate['resilience_factors']:
-            print(f"  Resilience Impact:")
+            print(f"  Host Distribution:")
             for factor in candidate['resilience_factors'][:2]:
                 print(f"    • {factor}")
         
-        if candidate['performance_factors']:
-            print(f"  Performance Metrics:")
-            for factor in candidate['performance_factors'][:2]:
-                print(f"    • {factor}")
-        
         print()
     
     # Summary by class