develop: 重做 fault-log-analyzer 分析模块（遵守模块开发规范，补齐根因分析 naive/aware 时间归一化与规范交付物）
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README.md
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README.md
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# fault-log-analyzer
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主机监控系统（HMS）故障日志捕获与分析模块。
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## 功能
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1. **故障日志捕获管道**：消费 `logs.raw`，按级别/关键字/正则过滤，结构化解析，
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打标后写入 `logs.fault` 与 Elasticsearch（`hms-fault-log-{yyyy.MM}`）。
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2. **故障日志归类**：特征提取（模板化 + TF-IDF）→ 相似聚类（DBSCAN / 纯 Python 后端）
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→ 映射到 `fault_type`。
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3. **根因分析**：时间/主机关联指标事件 + 指标-故障规则库 + trace 关联，产出
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`root_cause`（含 evidence 与 confidence）。
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4. **REST API**：故障日志查询、根因查询、故障类型/过滤规则管理接口。
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## 目录结构
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```
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src/fault_log_analyzer/
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config.py 配置模型与环境变量加载
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models.py 数据模型（FaultLog / RootCause / FaultType / ...）
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parser.py 日志结构化解析与模板化
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filters.py 故障过滤规则
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fingerprint.py MinHash 指纹与去重
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cluster.py TF-IDF 特征 + DBSCAN 聚类
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classifier.py 簇 -> fault_type 归类
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root_cause.py 根因分析
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storage.py 存储抽象 + 内存实现（测试/离线）
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integrations.py 可选真实后端（Kafka/ES/MySQL/Redis）
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pipeline.py 故障捕获管道
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workers.py 聚类 / 根因分析 worker
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api.py REST API（标准库 http.server 实现）
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__main__.py CLI 入口
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```
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## 快速开始
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```bash
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# 安装（核心零依赖，可选依赖见 requirements.txt）
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pip install -e .
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# 运行单元测试（133 个测试用例）
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python -m unittest discover -s tests -v
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# 启动服务（内存存储，便于本地验证）
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python -m fault_log_analyzer --storage memory --api-host 127.0.0.1 --api-port 8080
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# 健康检查
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curl http://127.0.0.1:8080/healthz
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curl http://127.0.0.1:8080/api/v1/fault-logs
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```
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## 测试
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```bash
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python -m unittest discover -s tests -v
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# Ran 133 tests ... OK
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```
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测试覆盖：配置加载、数据模型、日志解析/模板化、故障过滤规则、MinHash 指纹去重、
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TF-IDF/DBSCAN 聚类、故障归类、根因分析（含 naive/aware 时间归一化）、捕获管道、
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聚类/根因 worker、REST API，以及 Kafka/Elasticsearch/MySQL/Redis 可选后端适配器
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（未安装依赖时的回退行为与索引名）。
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## 环境变量（生产）
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| 变量 | 默认值 | 说明 |
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|---|---|---|
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| `KAFKA_BOOTSTRAP_SERVERS` | `localhost:9092` | Kafka 地址 |
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| `KAFKA_GROUP_ID` | `fault-log-analyzer` | 消费者组 |
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| `KAFKA_LOGS_RAW_TOPIC` | `logs.raw` | 原始日志 topic |
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| `KAFKA_LOGS_FAULT_TOPIC` | `logs.fault` | 故障日志 topic |
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| `ES_HOSTS` | `http://localhost:9200` | ES 地址（逗号分隔） |
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| `MYSQL_HOST` / `MYSQL_PORT` / `MYSQL_USER` / `MYSQL_PASSWORD` / `MYSQL_DB` | localhost | MySQL 连接 |
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| `REDIS_URL` | `redis://localhost:6379/0` | Redis 地址 |
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| `CLUSTER_EPS` | `0.75` | DBSCAN eps（余弦距离） |
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| `CLUSTER_MIN_SAMPLES` | `5` | DBSCAN min_samples |
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| `ROOT_CAUSE_WINDOW_MINUTES` | `5` | 根因分析时间窗口 |
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## 开发说明
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- 模块状态：`modules/fault-log-analyzer.md`
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- 开发说明：`docs/02-develop/fault-log-analyzer-dev-notes.md`
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- 设计依据：`docs/01-design/architecture.md`（5.3）、`database-design.md`、`api-design.md`
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模块 README（133 个测试说明）
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"""根因分析（辅助性）。
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依据 architecture.md 5.3.4：
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1. 时间/主机关联：fault_log.host_id 与 event.host_id 匹配，时间差 ≤ 窗口。
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2. 指标-故障规则库：指标阈值 -> 归因；日志关键字 -> 归因。
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3. trace 关联：trace_id 相同的日志与事件串链。
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4. 产出 root_cause（evidence + confidence）。
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规则优先，统计辅助，置信度评分。
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"""
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from __future__ import annotations
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import math
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import re
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from datetime import datetime, timedelta, timezone
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from typing import Dict, List, Optional, Sequence
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from .models import Event, FaultLog, RootCause
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_METRIC_RULES: List[dict] = [
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{"metric": "disk_used_percent", "operator": "gte", "threshold": 90.0, "cause": "disk_full"},
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{"metric": "mem_used_percent", "operator": "gte", "threshold": 90.0, "cause": "memory_exhausted"},
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{"metric": "cpu_usage", "operator": "gte", "threshold": 95.0, "cause": "cpu_saturation"},
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]
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_LOG_CAUSE_HINTS: List[tuple] = [
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("disk_full", re.compile(r"no space left|disk full|enospc|device full", re.IGNORECASE)),
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("memory_exhausted", re.compile(r"out of memory|oom|oomkilled|memory exhausted", re.IGNORECASE)),
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("cpu_saturation", re.compile(r"cpu throttl|high cpu|load average|saturat", re.IGNORECASE)),
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("network_unreachable", re.compile(r"network unreachable|connection refused|connect timeout", re.IGNORECASE)),
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]
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def _op_ok(operator: str, value: float, threshold: float) -> bool:
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if operator in ("gt", ">"):
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return value > threshold
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if operator in ("gte", ">="):
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return value >= threshold
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if operator in ("lt", "<"):
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return value < threshold
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if operator in ("lte", "<="):
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return value <= threshold
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return False
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def _as_aware(dt: datetime) -> datetime:
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"""将 naive datetime 统一转为 UTC aware，避免跨模块时间比较报错。
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上游模块（如 threshold-event-detector）可能产出不带时区的 datetime，
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而捕获管道内 fault_log.occurred_at 已被规范化为带时区时间；这里做一次
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防御性归一，保证根因分析的时间窗口计算稳健。
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"""
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if dt.tzinfo is None:
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return dt.replace(tzinfo=timezone.utc)
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return dt
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class RootCauseAnalyzer:
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"""根据故障日志与近期事件进行根因分析。"""
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def __init__(self, window_minutes: int = 5):
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self.window_minutes = window_minutes
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def analyze(
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self,
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fault_log: FaultLog,
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events: Sequence[Event],
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related_logs: Optional[Sequence[FaultLog]] = None,
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) -> RootCause:
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related_logs = list(related_logs or [])
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now = _as_aware(fault_log.occurred_at)
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window = timedelta(minutes=self.window_minutes)
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host_events = [
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e
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for e in events
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if e.host_id == fault_log.host_id
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and abs((_as_aware(e.fired_at) - now).total_seconds()) <= window.total_seconds()
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]
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evidence: List[Dict] = []
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cause_scores: Dict[str, float] = {}
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for message in [fault_log.message] + [l.message for l in related_logs]:
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for cause, regex in _LOG_CAUSE_HINTS:
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if regex.search(message or ""):
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cause_scores[cause] = cause_scores.get(cause, 0.0) + 0.6
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evidence.append({"type": "log", "message": message, "cause": cause})
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for event in host_events:
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for rule in _METRIC_RULES:
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if event.metric != rule["metric"]:
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continue
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if _op_ok(event.operator, event.value, rule["threshold"]):
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cause = rule["cause"]
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cause_scores[cause] = cause_scores.get(cause, 0.0) + 0.8
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evidence.append(
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{
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"type": "event",
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"event_id": event.event_id,
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"metric": event.metric,
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"value": event.value,
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"threshold": rule["threshold"],
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"cause": cause,
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}
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)
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if fault_log.trace_id and related_logs:
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trace_logs = [l for l in related_logs if l.trace_id == fault_log.trace_id]
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if trace_logs:
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evidence.append({"type": "trace", "trace_id": fault_log.trace_id, "count": len(trace_logs)})
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for l in trace_logs:
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for cause, regex in _LOG_CAUSE_HINTS:
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if regex.search(l.message or ""):
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cause_scores[cause] = cause_scores.get(cause, 0.0) + 0.3
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if not cause_scores:
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return RootCause(
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fault_log_id=fault_log.fault_log_id,
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cause_type="unknown",
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evidence=evidence,
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confidence=0.0,
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analysis_at=now,
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)
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best_cause = max(cause_scores, key=cause_scores.get)
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raw_score = cause_scores[best_cause]
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confidence = round(1.0 - math.exp(-raw_score), 4)
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return RootCause(
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fault_log_id=fault_log.fault_log_id,
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cause_type=best_cause,
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evidence=evidence,
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confidence=confidence,
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analysis_at=now,
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)
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根因分析（新增 _as_aware naive/aware 时间归一化，详见 git f077188）
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"""根因分析测试。"""
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import _bootstrap # noqa: F401
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import unittest
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from datetime import datetime, timezone
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from fault_log_analyzer.models import Event, FaultLog
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from fault_log_analyzer.root_cause import RootCauseAnalyzer
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BASE = datetime(2025, 1, 1, 10, 18, 0, tzinfo=timezone.utc)
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def _log(msg, host="h-1", trace=None, at=None, fid="fl-1"):
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return FaultLog(
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fault_log_id=fid,
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host_id=host,
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message=msg,
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trace_id=trace,
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occurred_at=at or BASE,
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)
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def _event(host="h-1", metric="disk_used_percent", value=97.2, threshold=90.0, at=None):
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return Event(
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event_id="e-1",
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host_id=host,
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metric=metric,
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value=value,
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threshold=threshold,
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fired_at=at or BASE,
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)
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class TestLogHints(unittest.TestCase):
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def test_disk_hint(self):
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rc = RootCauseAnalyzer().analyze(_log("no space left on device"), [])
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self.assertEqual(rc.cause_type, "disk_full")
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self.assertGreater(rc.confidence, 0.0)
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def test_memory_hint(self):
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rc = RootCauseAnalyzer().analyze(_log("out of memory killed"), [])
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self.assertEqual(rc.cause_type, "memory_exhausted")
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def test_unknown_when_no_signal(self):
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rc = RootCauseAnalyzer().analyze(_log("hello world"), [])
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self.assertEqual(rc.cause_type, "unknown")
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self.assertEqual(rc.confidence, 0.0)
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class TestMetricEvents(unittest.TestCase):
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def test_metric_event_rule(self):
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rc = RootCauseAnalyzer().analyze(_log("some error"), [_event()])
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self.assertEqual(rc.cause_type, "disk_full")
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self.assertGreater(rc.confidence, 0.0)
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def test_event_outside_window_ignored(self):
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later = datetime(2025, 1, 1, 11, 0, 0, tzinfo=timezone.utc)
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rc = RootCauseAnalyzer().analyze(_log("some error"), [_event(at=later)])
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self.assertEqual(rc.cause_type, "unknown")
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def test_different_host_event_ignored(self):
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rc = RootCauseAnalyzer().analyze(_log("some error"), [_event(host="h-2")])
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self.assertEqual(rc.cause_type, "unknown")
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def test_memory_metric_rule(self):
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event = _event(metric="mem_used_percent", value=95.0, threshold=90.0)
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rc = RootCauseAnalyzer().analyze(_log("some error"), [event])
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self.assertEqual(rc.cause_type, "memory_exhausted")
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class TestNaiveDatetimeRobustness(unittest.TestCase):
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"""跨模块时间比较防御性归一：上游可能传入 naive datetime。"""
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def test_naive_fault_log_and_naive_event(self):
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naive = datetime(2025, 1, 1, 10, 18, 0)
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log = _log("no space left on device", at=naive)
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event = _event(value=97.2, at=naive)
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rc = RootCauseAnalyzer().analyze(log, [event])
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self.assertEqual(rc.cause_type, "disk_full")
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def test_naive_event_with_aware_log(self):
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naive_event = _event(value=97.2, at=datetime(2025, 1, 1, 10, 18, 0))
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rc = RootCauseAnalyzer().analyze(_log("some error"), [naive_event])
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self.assertEqual(rc.cause_type, "disk_full")
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class TestTraceAssociation(unittest.TestCase):
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def test_trace_association(self):
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log = _log("request failed", trace="tr-1")
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related = FaultLog(
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fault_log_id="fl-2",
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host_id="h-1",
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message="out of memory",
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trace_id="tr-1",
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occurred_at=BASE,
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)
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rc = RootCauseAnalyzer().analyze(log, [], related_logs=[related])
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self.assertEqual(rc.cause_type, "memory_exhausted")
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def test_evidence_collected(self):
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rc = RootCauseAnalyzer().analyze(_log("no space left on device"), [_event()])
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types = {e["type"] for e in rc.evidence}
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self.assertIn("log", types)
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self.assertIn("event", types)
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if __name__ == "__main__":
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unittest.main()
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根因分析单元测试（新增 naive datetime 鲁棒性 2 用例）
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