1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
| class PerformanceDiagnosticTool:
"""性能诊断工具"""
async def diagnose_performance_issue(
self,
agent_id: AgentId,
performance_metrics: Dict[str, float]
) -> PerformanceDiagnosisReport:
"""
诊断性能问题
Args:
agent_id: 代理标识
performance_metrics: 性能指标数据
Returns:
PerformanceDiagnosisReport: 性能诊断报告
"""
diagnosis = PerformanceDiagnosisReport(agent_id=agent_id)
# 1. 响应时间分析
response_time = performance_metrics.get('avg_response_time', 0)
if response_time > 2.0: # 2秒阈值
await self._analyze_slow_response(agent_id, response_time, diagnosis)
# 2. 内存使用分析
memory_usage = performance_metrics.get('memory_usage_mb', 0)
if memory_usage > 512: # 512MB阈值
await self._analyze_high_memory_usage(agent_id, memory_usage, diagnosis)
# 3. CPU使用分析
cpu_usage = performance_metrics.get('cpu_usage_percent', 0)
if cpu_usage > 80: # 80%阈值
await self._analyze_high_cpu_usage(agent_id, cpu_usage, diagnosis)
# 4. 错误率分析
error_rate = performance_metrics.get('error_rate', 0)
if error_rate > 0.05: # 5%阈值
await self._analyze_high_error_rate(agent_id, error_rate, diagnosis)
# 5. 并发性分析
concurrent_requests = performance_metrics.get('concurrent_requests', 0)
max_concurrency = performance_metrics.get('max_concurrency', 100)
if concurrent_requests / max_concurrency > 0.9: # 90%阈值
await self._analyze_concurrency_bottleneck(agent_id, concurrent_requests, diagnosis)
return diagnosis
async def _analyze_slow_response(
self,
agent_id: AgentId,
response_time: float,
diagnosis: PerformanceDiagnosisReport
) -> None:
"""分析慢响应问题"""
# 1. 检查是否有阻塞操作
blocking_operations = await self._detect_blocking_operations(agent_id)
if blocking_operations:
diagnosis.add_issue(PerformanceIssue(
category="阻塞操作",
description=f"检测到阻塞操作: {', '.join(blocking_operations)}",
impact="高",
recommendations=[
"将同步操作改为异步操作",
"使用asyncio.create_task()避免阻塞",
"考虑使用线程池处理CPU密集型任务"
]
))
# 2. 检查外部API调用
external_calls = await self._analyze_external_api_calls(agent_id)
if external_calls:
slow_apis = [call for call in external_calls if call['avg_time'] > 1.0]
if slow_apis:
diagnosis.add_issue(PerformanceIssue(
category="外部API慢调用",
description=f"慢速外部API: {[api['name'] for api in slow_apis]}",
impact="中",
recommendations=[
"增加API调用超时设置",
"实现API响应缓存",
"考虑异步调用和结果轮询",
"联系API提供方优化性能"
]
))
# 3. 检查数据库查询
db_queries = await self._analyze_database_queries(agent_id)
if db_queries:
slow_queries = [q for q in db_queries if q['duration'] > 0.5]
if slow_queries:
diagnosis.add_issue(PerformanceIssue(
category="数据库慢查询",
description=f"发现 {len(slow_queries)} 个慢查询",
impact="高",
recommendations=[
"优化SQL查询语句",
"添加必要的数据库索引",
"使用查询结果缓存",
"考虑数据库分片"
]
))
async def _analyze_high_memory_usage(
self,
agent_id: AgentId,
memory_usage: float,
diagnosis: PerformanceDiagnosisReport
) -> None:
"""分析高内存使用问题"""
# 1. 检查内存泄漏
memory_trend = await self._get_memory_usage_trend(agent_id)
if memory_trend and memory_trend['slope'] > 0.1: # 内存持续增长
diagnosis.add_issue(PerformanceIssue(
category="疑似内存泄漏",
description=f"内存使用持续增长，当前: {memory_usage}MB",
impact="严重",
recommendations=[
"检查是否有未关闭的资源(文件、连接等)",
"检查缓存是否有过期机制",
"使用内存分析工具如memory_profiler",
"实现定期的内存清理任务"
]
))
# 2. 检查大对象缓存
large_objects = await self._identify_large_cached_objects(agent_id)
if large_objects:
total_cache_size = sum(obj['size'] for obj in large_objects)
diagnosis.add_issue(PerformanceIssue(
category="大对象缓存",
description=f"缓存中有大对象，总计: {total_cache_size}MB",
impact="中",
recommendations=[
"为大对象实现LRU淘汰机制",
"考虑将大对象存储到外部存储",
"实现分片存储策略",
"定期清理过期缓存"
]
))
|