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