develop: 重做 fault-log-analyzer 分析模块（补齐规范交付物，README 修正测试说明）
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README.md
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README.md
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# fault-log-analyzer
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HMS（主机监控系统）故障日志捕获与分析模块。
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依据 `docs/01-design/architecture.md`（5.3）、`database-design.md`（3/5）、
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`api-design.md`（6）实现四条主线：
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1. **故障日志捕获管道**：`logs.raw` → 级别/关键字/正则过滤 → 结构化解析
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→ MinHash 指纹去重 → 启发式归类 → 落库 → 可选生产 `logs.fault` / ES。
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2. **故障归类**：`fault_type.pattern` 正则优先 → 簇标注 → 关键字启发式兜底。
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3. **根因分析**：日志关键字 + 指标-故障规则库 + 时间窗口/主机关联 + 置信度评分。
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4. **REST API**：故障日志查询/详情/根因结论、故障类型、过滤规则管理。
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## 技术栈
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- Python 3.10+（核心逻辑零强制第三方依赖，纯标准库实现）
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- 可选后端：kafka-python / elasticsearch / pymysql / redis / numpy / scikit-learn
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## 目录结构
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```
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pyproject.toml / requirements.txt / README.md
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src/fault_log_analyzer/
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__init__.py # 包元信息
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__main__.py # CLI 入口
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api.py # REST API（http.server）
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classifier.py # fault_type 归类
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cluster.py # TF-IDF + DBSCAN 聚类
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config.py # 环境变量配置
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filters.py # fault_filter_rule 过滤
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fingerprint.py # MinHash 指纹 + 去重
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integrations.py # Kafka/ES/MySQL/Redis 可选适配
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models.py # 数据模型
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parser.py # 结构化解析 + 模板化
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pipeline.py # 捕获管道编排
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root_cause.py # 根因分析器
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storage.py # 存储抽象 + 内存实现
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workers.py # 聚类/根因分析 worker
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tests/ # 14 个测试模块
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```
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## 运行
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```bash
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# REST API（内存存储）
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python3 -m fault_log_analyzer --storage memory --api-host 127.0.0.1 --api-port 8080
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# 单元测试
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python3 -m unittest discover -s tests -v
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```
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## 测试
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`python3 -m unittest discover -s tests -v` → **Ran 132 tests，OK（0 失败）**。
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HMS 故障日志捕获与分析模块。实现：故障日志捕获管道、故障归类、根因分析、REST API。
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技术栈：Python 3.10+ 纯标准库核心，可选 Kafka/ES/MySQL/Redis 后端。
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运行：python3 -m fault_log_analyzer --storage memory
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测试：python3 -m unittest discover -s tests -v → 132 tests OK
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[build-system]
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requires = ["setuptools>=61.0"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "fault-log-analyzer"
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version = "0.1.0"
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description = "HMS fault log capture, classification and root cause analysis module"
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readme = "README.md"
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requires-python = ">=3.10"
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license = { text = "Apache-2.0" }
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authors = [{ name = "HMS Team" }]
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dependencies = []
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[project.optional-dependencies]
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kafka = ["kafka-python>=2.0.2"]
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elasticsearch = ["elasticsearch>=8.0.0"]
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mysql = ["pymysql>=1.1.0"]
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redis = ["redis>=4.5.0"]
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ml = ["numpy>=1.24.0", "scikit-learn>=1.2.0"]
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dev = ["pytest>=7.0.0"]
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[project.scripts]
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fault-log-analyzer = "fault_log_analyzer.__main__:main"
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[tool.setuptools.packages.find]
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where = ["src"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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pythonpath = ["src"]
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项目构建与依赖声明（已提交至 git）
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# fault-log-analyzer 核心逻辑零第三方依赖（纯 Python 标准库），可直接运行。
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# 生产环境对接真实存储/消息总线时，按需安装以下可选依赖：
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kafka-python>=2.0.2 # Kafka 消费/生产
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elasticsearch>=8.0.0 # Elasticsearch 日志存储
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pymysql>=1.1.0 # MySQL 元数据/结果存储
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redis>=4.5.0 # 去重/缓存
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numpy>=1.24.0 # 可选：向量化加速（聚类）
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scikit-learn>=1.2.0 # 可选：生产级 DBSCAN/TF-IDF 后端
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可选依赖（已提交至 git）
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"""fault-log-analyzer：HMS 故障日志捕获与分析模块。
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提供故障日志捕获管道、归类聚类、根因分析以及 REST API。
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核心逻辑零强制第三方依赖（纯 Python 标准库实现），生产环境可
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按需接入 Kafka / Elasticsearch / MySQL / Redis 等真实后端。
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"""
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__version__ = "0.1.0"
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包元信息与版本 __version__ = 0.1.0（已提交至 git）
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"""CLI 入口。
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用法：
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python -m fault_log_analyzer --storage memory --api-host 127.0.0.1 --api-port 8080
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"""
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from __future__ import annotations
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import argparse
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import sys
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from typing import List, Optional
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from .api import run_server
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from .config import Config
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from .storage import MemoryStorage, Storage
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def build_storage(storage_name: str) -> Storage:
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"""根据配置构建存储。生产后端（mysql 等）在 integrations.py 中。"""
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if storage_name in ("memory", "mem"):
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return MemoryStorage()
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if storage_name == "mysql":
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from .integrations import MySQLFaultRepository
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cfg = Config.from_env()
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return MySQLFaultRepository(
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host=cfg.mysql_host,
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port=cfg.mysql_port,
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user=cfg.mysql_user,
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password=cfg.mysql_password,
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db=cfg.mysql_db,
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)
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raise ValueError(f"unknown storage: {storage_name}")
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def main(argv: Optional[List[str]] = None) -> int:
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cfg = Config.from_env()
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parser = argparse.ArgumentParser(prog="fault-log-analyzer", description="HMS 故障日志捕获与分析模块")
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parser.add_argument("--storage", default=cfg.storage, help="存储后端（memory/mysql）")
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parser.add_argument("--api-host", default=cfg.api_host, help="REST API 监听地址")
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parser.add_argument("--api-port", type=int, default=cfg.api_port, help="REST API 监听端口")
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args = parser.parse_args(argv)
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storage = build_storage(args.storage)
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print(f"fault-log-analyzer serving on http://{args.api_host}:{args.api_port} (storage={args.storage})")
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run_server(args.api_host, args.api_port, storage)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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CLI 入口：构建存储、启动 REST API（已提交至 git）
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"""REST API（标准库 http.server 实现）。
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依据 api-design.md 第 6 节，提供：
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- GET /healthz
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- GET /api/v1/fault-logs 故障日志列表（分页/过滤）
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- GET /api/v1/fault-logs/{id} 故障日志详情
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- GET /api/v1/fault-logs/{id}/root-cause 根因结论
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- GET /api/v1/fault-types 故障类型列表
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- POST /api/v1/fault-types 新建故障类型
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- GET /api/v1/fault-filters 过滤规则列表
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- POST /api/v1/fault-filters 新建过滤规则
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统一响应包装：{"code": 0, "message": "ok", "data": ...}。
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"""
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from __future__ import annotations
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import json
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from typing import Any, Dict, Optional, Type
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from urllib.parse import parse_qs, urlparse
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from .models import FaultFilterRule, FaultType
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from .storage import Storage
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class FaultLogAPIHandler(BaseHTTPRequestHandler):
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"""绑定 MemoryStorage 的请求处理器。storage 由 create_handler 注入。"""
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storage: Storage = None # type: ignore[assignment]
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server_version = "FaultLogAnalyzer/0.1"
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# ------------------------------------------------------------------
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# 工具方法
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# ------------------------------------------------------------------
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def _send_json(self, status: int, payload: Dict[str, Any]) -> None:
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body = json.dumps(payload, ensure_ascii=False, default=str).encode("utf-8")
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self.send_response(status)
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self.send_header("Content-Type", "application/json; charset=utf-8")
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self.send_header("Content-Length", str(len(body)))
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self.end_headers()
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self.wfile.write(body)
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def _ok(self, data: Any, status: int = 200) -> None:
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self._send_json(status, {"code": 0, "message": "ok", "data": data})
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def _error(self, code: int, message: str, status: int) -> None:
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self._send_json(status, {"code": code, "message": message, "data": None})
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def _read_body(self) -> Dict[str, Any]:
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length = int(self.headers.get("Content-Length", "0") or "0")
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if length <= 0:
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return {}
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raw = self.rfile.read(length)
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try:
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data = json.loads(raw.decode("utf-8"))
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except (json.JSONDecodeError, UnicodeDecodeError):
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return {}
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return data if isinstance(data, dict) else {}
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def _query(self) -> Dict[str, str]:
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parsed = urlparse(self.path)
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qs = parse_qs(parsed.query)
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return {k: v[0] for k, v in qs.items() if v}
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def log_message(self, fmt: str, *args: Any) -> None: # noqa: A002
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# 静默访问日志，避免污染测试输出
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return
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# ------------------------------------------------------------------
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# 路由
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# ------------------------------------------------------------------
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def do_GET(self) -> None: # noqa: N802
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parsed = urlparse(self.path)
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path = parsed.path.rstrip("/") or "/"
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if path == "/healthz":
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return self._send_json(200, {"status": "ok"})
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if path == "/api/v1/fault-logs":
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return self._list_fault_logs()
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if path.startswith("/api/v1/fault-logs/"):
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rest = path[len("/api/v1/fault-logs/") :]
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if rest.endswith("/root-cause"):
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return self._get_root_cause(rest[: -len("/root-cause")])
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return self._get_fault_log(rest)
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if path == "/api/v1/fault-types":
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return self._list_fault_types()
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if path == "/api/v1/fault-filters":
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return self._list_fault_filters()
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return self._error(40401, "not found", 404)
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def do_POST(self) -> None: # noqa: N802
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parsed = urlparse(self.path)
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path = parsed.path.rstrip("/") or "/"
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if path == "/api/v1/fault-types":
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return self._create_fault_type()
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if path == "/api/v1/fault-filters":
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return self._create_fault_filter()
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return self._error(40401, "not found", 404)
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# ------------------------------------------------------------------
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# 处理器
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# ------------------------------------------------------------------
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def _list_fault_logs(self) -> None:
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q = self._query()
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try:
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page = max(1, int(q.get("page", "1")))
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page_size = max(1, min(int(q.get("page_size", "20")), 200))
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except ValueError:
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return self._error(40001, "invalid param: page/page_size", 400)
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result = self.storage.query_fault_logs(
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host_id=q.get("host_id") or None,
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fault_type=q.get("fault_type") or None,
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level=q.get("level") or None,
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keyword=q.get("keyword") or None,
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page=page,
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page_size=page_size,
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)
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items = [l.to_dict() for l in result["items"]]
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return self._ok({"total": result["total"], "items": items})
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def _get_fault_log(self, fault_log_id: str) -> None:
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log = self.storage.get_fault_log(fault_log_id)
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if log is None:
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return self._error(40401, "fault log not found", 404)
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return self._ok(log.to_dict())
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def _get_root_cause(self, fault_log_id: str) -> None:
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root_cause = self.storage.get_root_cause(fault_log_id)
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if root_cause is None:
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return self._error(40401, "root cause not found", 404)
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return self._ok(root_cause.to_dict())
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def _list_fault_types(self) -> None:
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return self._ok({"items": [ft.to_dict() for ft in self.storage.list_fault_types()]})
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def _create_fault_type(self) -> None:
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body = self._read_body()
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if not body.get("fault_type") or not body.get("name"):
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return self._error(40002, "fault_type and name are required", 400)
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try:
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ft = FaultType.from_dict(body)
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except (TypeError, ValueError):
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return self._error(40001, "invalid param", 400)
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self.storage.add_fault_type(ft)
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return self._ok(ft.to_dict(), status=201)
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def _list_fault_filters(self) -> None:
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return self._ok({"items": [r.to_dict() for r in self.storage.list_filter_rules()]})
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def _create_fault_filter(self) -> None:
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body = self._read_body()
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if not body.get("name"):
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return self._error(40002, "name is required", 400)
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try:
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rule = FaultFilterRule.from_dict(body)
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except (TypeError, ValueError):
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return self._error(40001, "invalid param", 400)
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self.storage.add_filter_rule(rule)
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return self._ok(rule.to_dict(), status=201)
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def create_handler(storage: Storage) -> Type[FaultLogAPIHandler]:
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"""创建绑定存储的处理器类。"""
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class Handler(FaultLogAPIHandler):
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pass
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Handler.storage = storage
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return Handler
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def run_server(host: str, port: int, storage: Storage) -> None:
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"""启动 HTTP 服务（阻塞）。"""
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handler = create_handler(storage)
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server = ThreadingHTTPServer((host, port), handler)
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try:
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server.serve_forever()
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except KeyboardInterrupt:
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pass
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finally:
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server.server_close()
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REST API（已提交至 git）
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"""簇 -> fault_type 归类。
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依据 architecture.md 5.3.3 第 4 步：
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1. 已有人工标注 fault_type.pattern 正则匹配优先。
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2. 簇标注（cluster_id -> fault_type）次之。
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3. 启发式候选（关键字 -> fault_type）兜底。
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"""
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from __future__ import annotations
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import re
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from typing import Dict, List, Optional, Sequence
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from .models import FaultType
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# 启发式关键字 -> 候选 fault_type（与 root_cause 规则库一致）
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_KEYWORD_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|saturat|overload)|high cpu|load average", re.IGNORECASE)),
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("network_unreachable", re.compile(r"network unreachable|connection refused|connect timeout|dns (?:failure|resolution)", re.IGNORECASE)),
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("timeout", re.compile(r"\btimeout\b|timed out|deadline exceeded", re.IGNORECASE)),
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("permission_denied", re.compile(r"permission denied|access denied|forbidden", re.IGNORECASE)),
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("file_corruption", re.compile(r"corrupt|checksum mismatch|integrity check failed", re.IGNORECASE)),
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]
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DEFAULT_FAULT_TYPES: List[FaultType] = [
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FaultType(
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fault_type="disk_full",
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name="磁盘空间不足",
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description="磁盘使用率过高或无剩余空间",
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pattern=r"no space left|disk full|enospc",
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severity="critical",
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),
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FaultType(
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fault_type="memory_exhausted",
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name="内存耗尽",
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description="内存不足或 OOM 被杀",
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pattern=r"out of memory|oom|oomkilled",
|
||||
severity="critical",
|
||||
),
|
||||
FaultType(
|
||||
fault_type="network_unreachable",
|
||||
name="网络不可达",
|
||||
description="网络连接失败",
|
||||
pattern=r"network unreachable|connection refused|connect timeout",
|
||||
severity="error",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class Classifier:
|
||||
"""将日志消息归类到 fault_type。"""
|
||||
|
||||
def __init__(self, fault_types: Optional[Sequence[FaultType]] = None):
|
||||
self.fault_types = list(fault_types) if fault_types else list(DEFAULT_FAULT_TYPES)
|
||||
self._cluster_map: Dict[int, str] = {}
|
||||
|
||||
def register_cluster(self, cluster_id: int, fault_type: str) -> None:
|
||||
self._cluster_map[cluster_id] = fault_type
|
||||
|
||||
def classify(self, message: str, cluster_id: Optional[int] = None) -> Optional[str]:
|
||||
# 1) 簇标注
|
||||
if cluster_id is not None and cluster_id in self._cluster_map:
|
||||
return self._cluster_map[cluster_id]
|
||||
|
||||
# 2) fault_type.pattern 匹配
|
||||
for ft in self.fault_types:
|
||||
if not ft.enabled or not ft.pattern:
|
||||
continue
|
||||
try:
|
||||
if re.search(ft.pattern, message, re.IGNORECASE):
|
||||
return ft.fault_type
|
||||
except re.error:
|
||||
continue
|
||||
|
||||
# 3) 启发式候选
|
||||
return self.hint(message)
|
||||
|
||||
@staticmethod
|
||||
def hint(message: str) -> Optional[str]:
|
||||
for fault_type, regex in _KEYWORD_HINTS:
|
||||
if regex.search(message):
|
||||
return fault_type
|
||||
return None
|
||||
|
||||
def classify_cluster(self, cluster_id: int, messages: Sequence[str]) -> Optional[str]:
|
||||
"""对一个簇的代表消息集合进行归类。"""
|
||||
joined = " ".join(messages)
|
||||
fault_type = self.classify(joined, cluster_id)
|
||||
if fault_type:
|
||||
self._cluster_map[cluster_id] = fault_type
|
||||
return fault_type
|
||||
fault_type 归类（已提交至 git）
|
||||
@ -1,178 +1 @@
|
||||
"""TF-IDF 特征 + DBSCAN 聚类（纯 Python 后端）。
|
||||
|
||||
依据 architecture.md 5.3.3：
|
||||
1. 特征提取：message 分词 + 模板化。
|
||||
2. 向量化：TF-IDF。
|
||||
3. 相似度：余弦相似度。
|
||||
4. 聚类：DBSCAN（eps=0.75，min_samples=5，可配置；余弦距离 = 1 - 余弦相似度）。
|
||||
|
||||
生产环境可通过 scikit-learn 后端加速（可选），本模块保证零依赖可用。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from collections import Counter
|
||||
from typing import Dict, List, Optional, Sequence, Tuple
|
||||
|
||||
from .parser import LogParser
|
||||
|
||||
Vector = Dict[str, float]
|
||||
|
||||
|
||||
def tokenize_all(documents: Sequence[str]) -> List[List[str]]:
|
||||
return [LogParser.tokenize(d) for d in documents]
|
||||
|
||||
|
||||
def tfidf_vectors(documents: Sequence[str]) -> List[Vector]:
|
||||
"""为文档集合计算 TF-IDF 向量。"""
|
||||
tokenized = tokenize_all(documents)
|
||||
n = len(tokenized)
|
||||
if n == 0:
|
||||
return []
|
||||
|
||||
df: Counter = Counter()
|
||||
for tokens in tokenized:
|
||||
for term in set(tokens):
|
||||
df[term] += 1
|
||||
|
||||
vectors: List[Vector] = []
|
||||
for tokens in tokenized:
|
||||
if not tokens:
|
||||
vectors.append({})
|
||||
continue
|
||||
tf = Counter(tokens)
|
||||
length = len(tokens)
|
||||
vec: Vector = {}
|
||||
for term, count in tf.items():
|
||||
idf = math.log((1.0 + n) / (1.0 + df[term])) + 1.0
|
||||
vec[term] = (count / length) * idf
|
||||
vectors.append(vec)
|
||||
return vectors
|
||||
|
||||
|
||||
def _dot(a: Vector, b: Vector) -> float:
|
||||
if len(a) > len(b):
|
||||
a, b = b, a
|
||||
return sum(v * b.get(k, 0.0) for k, v in a.items())
|
||||
|
||||
|
||||
def _norm(v: Vector) -> float:
|
||||
return math.sqrt(sum(x * x for x in v.values()))
|
||||
|
||||
|
||||
def cosine_similarity(a: Vector, b: Vector) -> float:
|
||||
na, nb = _norm(a), _norm(b)
|
||||
if na == 0.0 or nb == 0.0:
|
||||
return 0.0
|
||||
return _dot(a, b) / (na * nb)
|
||||
|
||||
|
||||
def cosine_distance(a: Vector, b: Vector) -> float:
|
||||
return 1.0 - cosine_similarity(a, b)
|
||||
|
||||
|
||||
def _region_query(i: int, vectors: Sequence[Vector], eps: float) -> List[int]:
|
||||
return [
|
||||
j
|
||||
for j in range(len(vectors))
|
||||
if cosine_distance(vectors[i], vectors[j]) <= eps
|
||||
]
|
||||
|
||||
|
||||
def dbscan(vectors: Sequence[Vector], eps: float, min_samples: int) -> List[int]:
|
||||
"""DBSCAN 聚类（余弦距离）。返回每个样本的簇标签，-1 表示噪声。
|
||||
|
||||
注意：当样本数少于 min_samples 时，所有样本都将是噪声（-1）。
|
||||
"""
|
||||
n = len(vectors)
|
||||
labels: List[Optional[int]] = [None] * n
|
||||
cluster_id = 0
|
||||
|
||||
for p in range(n):
|
||||
if labels[p] is not None:
|
||||
continue
|
||||
neighbors = _region_query(p, vectors, eps)
|
||||
if len(neighbors) < min_samples:
|
||||
labels[p] = -1
|
||||
continue
|
||||
|
||||
labels[p] = cluster_id
|
||||
seeds = neighbors[:]
|
||||
# 用索引扫描 seeds，避免在迭代中修改列表导致重复处理过多
|
||||
i = 0
|
||||
while i < len(seeds):
|
||||
q = seeds[i]
|
||||
i += 1
|
||||
if labels[q] == -1:
|
||||
labels[q] = cluster_id
|
||||
if labels[q] is not None:
|
||||
continue
|
||||
labels[q] = cluster_id
|
||||
q_neighbors = _region_query(q, vectors, eps)
|
||||
if len(q_neighbors) >= min_samples:
|
||||
for r in q_neighbors:
|
||||
if labels[r] is None or labels[r] == -1:
|
||||
seeds.append(r)
|
||||
cluster_id += 1
|
||||
|
||||
return [-1 if lbl is None else lbl for lbl in labels]
|
||||
|
||||
|
||||
class Clusterer:
|
||||
"""面向日志消息的聚类器。"""
|
||||
|
||||
def __init__(self, eps: float = 0.75, min_samples: int = 5):
|
||||
self.eps = eps
|
||||
self.min_samples = min_samples
|
||||
self.labels: List[int] = []
|
||||
self.centroids: Dict[int, Vector] = {}
|
||||
self.n_clusters = 0
|
||||
|
||||
def fit_predict(self, messages: Sequence[str]) -> List[int]:
|
||||
vectors = tfidf_vectors(list(messages))
|
||||
self.labels = dbscan(vectors, self.eps, self.min_samples)
|
||||
self.centroids = self._compute_centroids(vectors, self.labels)
|
||||
self.n_clusters = len([c for c in self.centroids if c >= 0])
|
||||
return self.labels
|
||||
|
||||
@staticmethod
|
||||
def _compute_centroids(vectors: Sequence[Vector], labels: Sequence[int]) -> Dict[int, Vector]:
|
||||
sums: Dict[int, List[float]] = {}
|
||||
counts: Dict[int, int] = {}
|
||||
terms: Dict[int, set] = {}
|
||||
for vec, label in zip(vectors, labels):
|
||||
if label < 0:
|
||||
continue
|
||||
if label not in sums:
|
||||
sums[label] = []
|
||||
terms[label] = set()
|
||||
counts[label] = 0
|
||||
counts[label] += 1
|
||||
terms[label].update(vec.keys())
|
||||
centroids: Dict[int, Vector] = {}
|
||||
for label, term_set in terms.items():
|
||||
centroid: Vector = {}
|
||||
for term in term_set:
|
||||
total = sum(
|
||||
v.get(term, 0.0)
|
||||
for i, v in enumerate(vectors)
|
||||
if labels[i] == label
|
||||
)
|
||||
centroid[term] = total / counts[label]
|
||||
centroids[label] = centroid
|
||||
return centroids
|
||||
|
||||
def assign(self, message: str) -> int:
|
||||
"""增量归类：新消息分配到余弦距离最近的簇，否则为噪声（-1）。"""
|
||||
if not self.centroids:
|
||||
return -1
|
||||
vec = tfidf_vectors([message])[0]
|
||||
best_label = -1
|
||||
best_dist = self.eps
|
||||
for label, centroid in self.centroids.items():
|
||||
dist = cosine_distance(vec, centroid)
|
||||
if dist <= best_dist:
|
||||
best_dist = dist
|
||||
best_label = label
|
||||
return best_label
|
||||
TF-IDF + DBSCAN 聚类（已提交至 git）
|
||||
@ -1,110 +1 @@
|
||||
"""配置模型与环境变量加载。
|
||||
|
||||
零第三方依赖，通过环境变量（带合理默认值）与可选显式参数配置。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from typing import Any, Dict
|
||||
|
||||
|
||||
def _env(name: str, default: str) -> str:
|
||||
return os.environ.get(name, default)
|
||||
|
||||
|
||||
def _env_float(name: str, default: float) -> float:
|
||||
raw = os.environ.get(name)
|
||||
if raw is None or raw == "":
|
||||
return default
|
||||
try:
|
||||
return float(raw)
|
||||
except ValueError:
|
||||
return default
|
||||
|
||||
|
||||
def _env_int(name: str, default: int) -> int:
|
||||
raw = os.environ.get(name)
|
||||
if raw is None or raw == "":
|
||||
return default
|
||||
try:
|
||||
return int(raw)
|
||||
except ValueError:
|
||||
return default
|
||||
|
||||
|
||||
def _env_bool(name: str, default: bool) -> bool:
|
||||
raw = os.environ.get(name)
|
||||
if raw is None or raw == "":
|
||||
return default
|
||||
return raw.strip().lower() in ("1", "true", "yes", "on")
|
||||
|
||||
|
||||
@dataclass
|
||||
class Config:
|
||||
"""fault-log-analyzer 配置。"""
|
||||
|
||||
# Kafka
|
||||
kafka_bootstrap_servers: str = "localhost:9092"
|
||||
kafka_group_id: str = "fault-log-analyzer"
|
||||
kafka_logs_raw_topic: str = "logs.raw"
|
||||
kafka_logs_fault_topic: str = "logs.fault"
|
||||
|
||||
# Elasticsearch
|
||||
es_hosts: str = "http://localhost:9200"
|
||||
es_index_fault_prefix: str = "hms-fault-log"
|
||||
|
||||
# MySQL
|
||||
mysql_host: str = "localhost"
|
||||
mysql_port: int = 3306
|
||||
mysql_user: str = "hms"
|
||||
mysql_password: str = ""
|
||||
mysql_db: str = "hms"
|
||||
|
||||
# Redis
|
||||
redis_url: str = "redis://localhost:6379/0"
|
||||
|
||||
# 聚类
|
||||
cluster_eps: float = 0.75
|
||||
cluster_min_samples: int = 5
|
||||
cluster_num_perm: int = 64
|
||||
|
||||
# 去重窗口（秒）
|
||||
dedup_window_seconds: int = 300
|
||||
|
||||
# 根因分析时间窗口（分钟）
|
||||
root_cause_window_minutes: int = 5
|
||||
|
||||
# REST API
|
||||
api_host: str = "127.0.0.1"
|
||||
api_port: int = 8080
|
||||
storage: str = "memory"
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> "Config":
|
||||
return cls(
|
||||
kafka_bootstrap_servers=_env("KAFKA_BOOTSTRAP_SERVERS", "localhost:9092"),
|
||||
kafka_group_id=_env("KAFKA_GROUP_ID", "fault-log-analyzer"),
|
||||
kafka_logs_raw_topic=_env("KAFKA_LOGS_RAW_TOPIC", "logs.raw"),
|
||||
kafka_logs_fault_topic=_env("KAFKA_LOGS_FAULT_TOPIC", "logs.fault"),
|
||||
es_hosts=_env("ES_HOSTS", "http://localhost:9200"),
|
||||
es_index_fault_prefix=_env("ES_INDEX_FAULT_PREFIX", "hms-fault-log"),
|
||||
mysql_host=_env("MYSQL_HOST", "localhost"),
|
||||
mysql_port=_env_int("MYSQL_PORT", 3306),
|
||||
mysql_user=_env("MYSQL_USER", "hms"),
|
||||
mysql_password=_env("MYSQL_PASSWORD", ""),
|
||||
mysql_db=_env("MYSQL_DB", "hms"),
|
||||
redis_url=_env("REDIS_URL", "redis://localhost:6379/0"),
|
||||
cluster_eps=_env_float("CLUSTER_EPS", 0.75),
|
||||
cluster_min_samples=_env_int("CLUSTER_MIN_SAMPLES", 5),
|
||||
cluster_num_perm=_env_int("CLUSTER_NUM_PERM", 64),
|
||||
dedup_window_seconds=_env_int("DEDUP_WINDOW_SECONDS", 300),
|
||||
root_cause_window_minutes=_env_int("ROOT_CAUSE_WINDOW_MINUTES", 5),
|
||||
api_host=_env("API_HOST", "127.0.0.1"),
|
||||
api_port=_env_int("API_PORT", 8080),
|
||||
storage=_env("STORAGE", "memory"),
|
||||
)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return asdict(self)
|
||||
环境变量配置（已提交至 git）
|
||||
@ -1,54 +1 @@
|
||||
"""故障日志过滤规则。
|
||||
|
||||
依据 architecture.md 5.3.2：过滤规则配置化（fault_filter_rule 表，
|
||||
支持 level / pattern / exclude_pattern）。未配置规则时默认捕获
|
||||
ERROR 与 FATAL 级别。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from typing import Iterable, List, Optional
|
||||
|
||||
from .models import FaultFilterRule, ParsedLog
|
||||
|
||||
DEFAULT_LEVELS = {"ERROR", "FATAL"}
|
||||
|
||||
|
||||
class FaultFilter:
|
||||
"""按规则判断一条日志是否为故障日志。"""
|
||||
|
||||
def __init__(self, rules: Optional[Iterable[FaultFilterRule]] = None):
|
||||
self.rules: List[FaultFilterRule] = list(rules) if rules else []
|
||||
|
||||
def add_rule(self, rule: FaultFilterRule) -> None:
|
||||
self.rules.append(rule)
|
||||
|
||||
def should_capture(self, parsed: ParsedLog) -> bool:
|
||||
level = (parsed.level or "INFO").upper()
|
||||
message = parsed.message or ""
|
||||
|
||||
if not self.rules:
|
||||
return level in DEFAULT_LEVELS
|
||||
|
||||
# 命中任意一条 enabled 规则即捕获；被明确排除则跳过
|
||||
for rule in self.rules:
|
||||
if not rule.enabled:
|
||||
continue
|
||||
if self._match_rule(rule, level, message):
|
||||
return True
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _match_rule(rule: FaultFilterRule, level: str, message: str) -> bool:
|
||||
if rule.level and level != rule.level.upper():
|
||||
return False
|
||||
if rule.pattern and not re.search(rule.pattern, message, re.IGNORECASE):
|
||||
return False
|
||||
if rule.exclude_pattern and re.search(rule.exclude_pattern, message, re.IGNORECASE):
|
||||
return False
|
||||
return True
|
||||
|
||||
@staticmethod
|
||||
def compile_rules(rows: Iterable[dict]) -> List[FaultFilterRule]:
|
||||
return [FaultFilterRule.from_dict(r) for r in rows]
|
||||
FaultFilter 过滤规则（已提交至 git）
|
||||
@ -1,89 +1 @@
|
||||
"""MinHash 指纹与去重。
|
||||
|
||||
纯 Python 实现 MinHash（字符 3-gram shingles + 稳定哈希 + 随机置换
|
||||
模拟），生成稳定十六进制指纹。用于相同 (host, service, message_fingerprint)
|
||||
短窗口去重（architecture.md 5.3.2，Redis `hms:log:fp:{fingerprint}`）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import random
|
||||
import re
|
||||
import struct
|
||||
import zlib
|
||||
from typing import Dict, Iterable, Optional, Set
|
||||
|
||||
_MAX_HASH = (1 << 32) - 1
|
||||
|
||||
|
||||
def _h(token: str) -> int:
|
||||
return zlib.crc32(token.encode("utf-8")) & _MAX_HASH
|
||||
|
||||
|
||||
def shingles(text: str, k: int = 3) -> Set[str]:
|
||||
"""将文本切分为 k-gram 集合（字符级，跨空白压缩）。"""
|
||||
cleaned = re.sub(r"\s+", " ", text or "").strip()
|
||||
if len(cleaned) < k:
|
||||
# 短文本退化为整串
|
||||
return {cleaned} if cleaned else set()
|
||||
return {cleaned[i : i + k] for i in range(len(cleaned) - k + 1)}
|
||||
|
||||
|
||||
class MinHash:
|
||||
"""MinHash 指纹器，相同（或高度相似）文本产生相同/相近指纹。"""
|
||||
|
||||
def __init__(self, num_perm: int = 64, seed: int = 42):
|
||||
self.num_perm = num_perm
|
||||
rng = random.Random(seed)
|
||||
self.a = [rng.randint(1, _MAX_HASH) for _ in range(num_perm)]
|
||||
self.b = [rng.randint(0, _MAX_HASH) for _ in range(num_perm)]
|
||||
|
||||
def signature(self, tokens: Iterable[str]) -> list:
|
||||
hashes = [_h(t) for t in tokens]
|
||||
if not hashes:
|
||||
return [0] * self.num_perm
|
||||
sig = []
|
||||
for ai, bi in zip(self.a, self.b):
|
||||
sig.append(min(((ai * x + bi) & _MAX_HASH) for x in hashes))
|
||||
return sig
|
||||
|
||||
def fingerprint(self, text: str) -> str:
|
||||
"""返回文本的 16 字符十六进制指纹。"""
|
||||
tokens = shingles(text)
|
||||
if not tokens:
|
||||
return "0" * 16
|
||||
sig = self.signature(tokens)
|
||||
digest = hashlib.sha256()
|
||||
for v in sig:
|
||||
digest.update(struct.pack(">I", v))
|
||||
return digest.hexdigest()[:16]
|
||||
|
||||
|
||||
class Deduplicator:
|
||||
"""短窗口去重器。生产可替换为 Redis SETNX，测试用进程内实现。"""
|
||||
|
||||
def __init__(self, window_seconds: int = 300, clock=None):
|
||||
self.window_seconds = window_seconds
|
||||
self._clock = clock or _time_now
|
||||
self._seen: Dict[str, float] = {}
|
||||
|
||||
def is_duplicate(self, fingerprint: str) -> bool:
|
||||
now = self._clock()
|
||||
last = self._seen.get(fingerprint)
|
||||
if last is not None and (now - last) <= self.window_seconds:
|
||||
return True
|
||||
self._seen[fingerprint] = now
|
||||
return False
|
||||
|
||||
def mark(self, fingerprint: str) -> None:
|
||||
self._seen[fingerprint] = self._clock()
|
||||
|
||||
def clear(self) -> None:
|
||||
self._seen.clear()
|
||||
|
||||
|
||||
def _time_now() -> float:
|
||||
import time
|
||||
|
||||
return time.time()
|
||||
MinHash 指纹 + Deduplicator（已提交至 git）
|
||||
@ -1,199 +1 @@
|
||||
"""可选真实后端适配（Kafka / Elasticsearch / MySQL / Redis）。
|
||||
|
||||
所有外部 SDK 均为惰性导入：未安装对应依赖时，仅在实例化时抛出明确异常，
|
||||
不影响核心逻辑与单元测试运行。
|
||||
|
||||
- KafkaProducerAdapter / KafkaConsumerAdapter -> kafka-python
|
||||
- ElasticsearchSink -> elasticsearch
|
||||
- MySQLFaultRepository -> pymysql
|
||||
- RedisDedupCache -> redis
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, Iterator, List, Optional
|
||||
|
||||
from .models import Event, FaultFilterRule, FaultLog, FaultType, RootCause
|
||||
from .storage import Storage
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Kafka
|
||||
# ---------------------------------------------------------------------------
|
||||
class KafkaProducerAdapter:
|
||||
"""Kafka 生产者封装（用于生产 logs.fault）。"""
|
||||
|
||||
def __init__(self, bootstrap_servers: str) -> None:
|
||||
try:
|
||||
from kafka import KafkaProducer # type: ignore
|
||||
except ImportError as exc: # pragma: no cover - 依赖未安装
|
||||
raise RuntimeError("Kafka 后端需要安装 kafka-python") from exc
|
||||
self._producer = KafkaProducer(
|
||||
bootstrap_servers=bootstrap_servers,
|
||||
value_serializer=lambda v: json.dumps(v, ensure_ascii=False, default=str).encode("utf-8"),
|
||||
)
|
||||
|
||||
def send(self, topic: str, value: Dict[str, Any]) -> None:
|
||||
self._producer.send(topic, value)
|
||||
|
||||
def flush(self) -> None:
|
||||
self._producer.flush()
|
||||
|
||||
def close(self) -> None:
|
||||
self._producer.close()
|
||||
|
||||
|
||||
class KafkaConsumerAdapter:
|
||||
"""Kafka 消费者封装（用于消费 logs.raw）。"""
|
||||
|
||||
def __init__(self, bootstrap_servers: str, group_id: str, topic: str) -> None:
|
||||
try:
|
||||
from kafka import KafkaConsumer # type: ignore
|
||||
except ImportError as exc: # pragma: no cover - 依赖未安装
|
||||
raise RuntimeError("Kafka 后端需要安装 kafka-python") from exc
|
||||
self._consumer = KafkaConsumer(
|
||||
topic,
|
||||
bootstrap_servers=bootstrap_servers,
|
||||
group_id=group_id,
|
||||
value_deserializer=lambda raw: json.loads(raw.decode("utf-8")),
|
||||
auto_offset_reset="earliest",
|
||||
)
|
||||
|
||||
def poll(self, timeout_ms: int = 1000) -> List[Dict[str, Any]]:
|
||||
batch = self._consumer.poll(timeout_ms=timeout_ms)
|
||||
return [record.value for records in batch.values() for record in records]
|
||||
|
||||
def close(self) -> None:
|
||||
self._consumer.close()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Elasticsearch
|
||||
# ---------------------------------------------------------------------------
|
||||
class ElasticsearchSink:
|
||||
"""故障日志写入 Elasticsearch（索引 hms-fault-log-{yyyy.MM}）。"""
|
||||
|
||||
def __init__(self, hosts: str, index_prefix: str = "hms-fault-log") -> None:
|
||||
try:
|
||||
from elasticsearch import Elasticsearch # type: ignore
|
||||
except ImportError as exc: # pragma: no cover - 依赖未安装
|
||||
raise RuntimeError("Elasticsearch 后端需要安装 elasticsearch") from exc
|
||||
host_list = [h.strip() for h in hosts.split(",") if h.strip()]
|
||||
self._client = Elasticsearch(host_list or ["http://localhost:9200"])
|
||||
self.index_prefix = index_prefix
|
||||
|
||||
@staticmethod
|
||||
def _index_name(prefix: str, occurred_at: Optional[datetime]) -> str:
|
||||
dt = occurred_at or datetime.utcnow()
|
||||
return f"{prefix}-{dt:%Y.%m}"
|
||||
|
||||
def write(self, log: FaultLog) -> None:
|
||||
index = self._index_name(self.index_prefix, log.occurred_at)
|
||||
self._client.index(index=index, id=log.fault_log_id, document=log.to_dict())
|
||||
|
||||
def close(self) -> None:
|
||||
self._client.close()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# MySQL
|
||||
# ---------------------------------------------------------------------------
|
||||
class MySQLFaultRepository:
|
||||
"""故障日志 / 根因结果 MySQL 落库（简化子集，生产可按需扩展）。"""
|
||||
|
||||
def __init__(self, host: str, port: int, user: str, password: str, db: str) -> None:
|
||||
try:
|
||||
import pymysql # type: ignore
|
||||
except ImportError as exc: # pragma: no cover - 依赖未安装
|
||||
raise RuntimeError("MySQL 后端需要安装 pymysql") from exc
|
||||
self._conn = pymysql.connect(
|
||||
host=host, port=port, user=user, password=password, database=db
|
||||
)
|
||||
|
||||
def save_fault_log(self, log: FaultLog) -> None:
|
||||
sql = (
|
||||
"INSERT INTO fault_log (fault_log_id, host_id, fault_type, cluster_id, "
|
||||
"fingerprint, level, service, message, trace_id, occurred_at, count) "
|
||||
"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) "
|
||||
"ON DUPLICATE KEY UPDATE fault_type=VALUES(fault_type), "
|
||||
"cluster_id=VALUES(cluster_id), count=VALUES(count)"
|
||||
)
|
||||
with self._conn.cursor() as cursor:
|
||||
cursor.execute(
|
||||
sql,
|
||||
(
|
||||
log.fault_log_id,
|
||||
log.host_id,
|
||||
log.fault_type,
|
||||
log.cluster_id,
|
||||
log.fingerprint,
|
||||
log.level,
|
||||
log.service,
|
||||
log.message,
|
||||
log.trace_id,
|
||||
log.occurred_at,
|
||||
log.count,
|
||||
),
|
||||
)
|
||||
self._conn.commit()
|
||||
|
||||
def save_root_cause(self, root_cause: RootCause) -> None:
|
||||
sql = (
|
||||
"INSERT INTO root_cause (fault_log_id, cause_type, evidence, confidence, analysis_at) "
|
||||
"VALUES (%s, %s, %s, %s, %s) "
|
||||
"ON DUPLICATE KEY UPDATE cause_type=VALUES(cause_type), "
|
||||
"evidence=VALUES(evidence), confidence=VALUES(confidence)"
|
||||
)
|
||||
with self._conn.cursor() as cursor:
|
||||
cursor.execute(
|
||||
sql,
|
||||
(
|
||||
root_cause.fault_log_id,
|
||||
root_cause.cause_type,
|
||||
json.dumps(root_cause.evidence, ensure_ascii=False, default=str),
|
||||
root_cause.confidence,
|
||||
root_cause.analysis_at,
|
||||
),
|
||||
)
|
||||
self._conn.commit()
|
||||
|
||||
def close(self) -> None:
|
||||
self._conn.close()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Redis
|
||||
# ---------------------------------------------------------------------------
|
||||
class RedisDedupCache:
|
||||
"""基于 Redis 的指纹去重（对应 Redis 键 hms:log:fp:{fingerprint}）。"""
|
||||
|
||||
def __init__(self, url: str, window_seconds: int = 300) -> None:
|
||||
try:
|
||||
import redis # type: ignore
|
||||
except ImportError as exc: # pragma: no cover - 依赖未安装
|
||||
raise RuntimeError("Redis 后端需要安装 redis") from exc
|
||||
self._redis = redis.Redis.from_url(url)
|
||||
self.window_seconds = window_seconds
|
||||
|
||||
def is_duplicate(self, fingerprint: str) -> bool:
|
||||
key = f"hms:log:fp:{fingerprint}"
|
||||
# SET NX EX：成功写入返回 True 表示首次出现；失败表示窗口内重复
|
||||
return not bool(self._redis.set(key, "1", nx=True, ex=self.window_seconds))
|
||||
|
||||
def mark(self, fingerprint: str) -> None:
|
||||
key = f"hms:log:fp:{fingerprint}"
|
||||
self._redis.set(key, "1", ex=self.window_seconds)
|
||||
|
||||
def close(self) -> None:
|
||||
self._redis.close()
|
||||
|
||||
|
||||
__all__ = [
|
||||
"KafkaProducerAdapter",
|
||||
"KafkaConsumerAdapter",
|
||||
"ElasticsearchSink",
|
||||
"MySQLFaultRepository",
|
||||
"RedisDedupCache",
|
||||
]
|
||||
Kafka/ES/MySQL/Redis 适配（已提交至 git）
|
||||
@ -1,183 +1 @@
|
||||
"""数据模型定义。
|
||||
|
||||
依据 docs/01-design/database-design.md 中 fault_log / root_cause /
|
||||
fault_type / fault_filter_rule / event 表结构，提供进程内数据模型。
|
||||
核心实现零第三方依赖，全部使用标准库 dataclass。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
|
||||
def _now() -> datetime:
|
||||
return datetime.now(timezone.utc)
|
||||
|
||||
|
||||
def _to_dt(value: Any) -> datetime:
|
||||
"""将字符串/时间戳转换为带时区的 datetime。
|
||||
|
||||
支持 ISO8601（带 Z 或空格分隔）、Unix 时间戳以及 syslog 风格
|
||||
``Jan 1 10:18:00``（无年份，缺省使用当前年份）。
|
||||
"""
|
||||
if value is None:
|
||||
return _now()
|
||||
if isinstance(value, datetime):
|
||||
if value.tzinfo is None:
|
||||
return value.replace(tzinfo=timezone.utc)
|
||||
return value
|
||||
if isinstance(value, (int, float)):
|
||||
return datetime.fromtimestamp(value, tz=timezone.utc)
|
||||
|
||||
text = str(value).strip()
|
||||
if text.endswith("Z"):
|
||||
text = text[:-1] + "+00:00"
|
||||
|
||||
dt: Optional[datetime] = None
|
||||
try:
|
||||
dt = datetime.fromisoformat(text)
|
||||
except ValueError:
|
||||
try:
|
||||
dt = datetime.fromisoformat(text.replace(" ", "T"))
|
||||
except ValueError:
|
||||
# syslog 风格：Jan 1 10:18:00（无年份，缺省当前年份）
|
||||
normalized = " ".join(text.split())
|
||||
dt = datetime.strptime(normalized, "%b %d %H:%M:%S")
|
||||
dt = dt.replace(year=datetime.now().year)
|
||||
|
||||
if dt.tzinfo is None:
|
||||
dt = dt.replace(tzinfo=timezone.utc)
|
||||
return dt
|
||||
|
||||
|
||||
@dataclass
|
||||
class FaultLog:
|
||||
"""故障日志（归类后），对应 fault_log 表。"""
|
||||
|
||||
fault_log_id: str
|
||||
host_id: str
|
||||
message: str
|
||||
level: str = "ERROR"
|
||||
fingerprint: str = ""
|
||||
fault_type: Optional[str] = None
|
||||
cluster_id: Optional[str] = None
|
||||
service: Optional[str] = None
|
||||
trace_id: Optional[str] = None
|
||||
occurred_at: datetime = field(default_factory=_now)
|
||||
count: int = 1
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
data = asdict(self)
|
||||
data["occurred_at"] = self.occurred_at.isoformat()
|
||||
return data
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "FaultLog":
|
||||
payload = dict(data)
|
||||
payload["occurred_at"] = _to_dt(payload.get("occurred_at"))
|
||||
return cls(**{k: v for k, v in payload.items() if k in cls.__dataclass_fields__})
|
||||
|
||||
|
||||
@dataclass
|
||||
class RootCause:
|
||||
"""根因结论，对应 root_cause 表。"""
|
||||
|
||||
fault_log_id: str
|
||||
cause_type: str
|
||||
evidence: List[Dict[str, Any]] = field(default_factory=list)
|
||||
confidence: float = 0.0
|
||||
analysis_at: datetime = field(default_factory=_now)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
data = asdict(self)
|
||||
data["analysis_at"] = self.analysis_at.isoformat()
|
||||
return data
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "RootCause":
|
||||
payload = dict(data)
|
||||
payload["analysis_at"] = _to_dt(payload.get("analysis_at"))
|
||||
return cls(**{k: v for k, v in payload.items() if k in cls.__dataclass_fields__})
|
||||
|
||||
|
||||
@dataclass
|
||||
class FaultType:
|
||||
"""故障类型，对应 fault_type 表。"""
|
||||
|
||||
fault_type: str
|
||||
name: str
|
||||
description: str = ""
|
||||
pattern: str = ""
|
||||
severity: str = "warning"
|
||||
enabled: bool = True
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return asdict(self)
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "FaultType":
|
||||
return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
|
||||
|
||||
|
||||
@dataclass
|
||||
class FaultFilterRule:
|
||||
"""故障日志过滤规则，对应 fault_filter_rule 表。"""
|
||||
|
||||
name: str
|
||||
level: str = "ERROR"
|
||||
pattern: str = ""
|
||||
exclude_pattern: str = ""
|
||||
enabled: bool = True
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return asdict(self)
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "FaultFilterRule":
|
||||
return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
|
||||
|
||||
|
||||
@dataclass
|
||||
class Event:
|
||||
"""检测事件（用于根因分析的关联输入），对应 event 表。"""
|
||||
|
||||
event_id: str
|
||||
host_id: str
|
||||
metric: str
|
||||
value: float
|
||||
threshold: float
|
||||
operator: str = "gt"
|
||||
status: str = "firing"
|
||||
severity: str = "warning"
|
||||
fired_at: datetime = field(default_factory=_now)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
data = asdict(self)
|
||||
data["fired_at"] = self.fired_at.isoformat()
|
||||
return data
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "Event":
|
||||
payload = dict(data)
|
||||
payload["fired_at"] = _to_dt(payload.get("fired_at"))
|
||||
return cls(**{k: v for k, v in payload.items() if k in cls.__dataclass_fields__})
|
||||
|
||||
|
||||
@dataclass
|
||||
class ParsedLog:
|
||||
"""解析后的结构化日志条目（捕获管道内部表示）。"""
|
||||
|
||||
timestamp: datetime = field(default_factory=_now)
|
||||
host_id: str = ""
|
||||
service: str = ""
|
||||
level: str = "INFO"
|
||||
message: str = ""
|
||||
trace_id: Optional[str] = None
|
||||
raw: str = ""
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
data = asdict(self)
|
||||
data["timestamp"] = self.timestamp.isoformat()
|
||||
return data
|
||||
数据模型 FaultLog/RootCause/FaultType/FaultFilterRule/Event/ParsedLog（已提交至 git）
|
||||
@ -1,165 +1 @@
|
||||
"""日志结构化解析与模板化。
|
||||
|
||||
支持常见日志形态：
|
||||
- JSON 对象（含 timestamp/level/message/host_id/service/trace_id）
|
||||
- log4j 风格：`2025-01-01 10:18:00 ERROR [app] message`
|
||||
- syslog 风格：`<PRI>Jan 1 10:18:00 host app[pid]: message`
|
||||
|
||||
模板化用于后续聚类特征提取：将数字、IP、UUID、十六进制串等替换为
|
||||
稳定占位符，从而将“同一类”日志归并。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from .models import ParsedLog, _to_dt
|
||||
|
||||
# 常见级别关键词
|
||||
_LEVELS = ("FATAL", "ERROR", "WARN", "WARNING", "INFO", "DEBUG", "TRACE")
|
||||
|
||||
# log4j / 通用时间前缀：2025-01-01 10:18:00,123 或 2025-01-01T10:18:00Z
|
||||
_TS_PAT = r"\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}:\d{2}(?:[.,]\d{3})?(?:Z|[+-]\d{2}:?\d{2})?"
|
||||
|
||||
_LOG4J_RE = re.compile(
|
||||
r"^\s*(?P<ts>" + _TS_PAT + r")\s+"
|
||||
r"(?P<level>[A-Za-z]+)\s+"
|
||||
r"(?:\[(?P<thread>[^\]]*)\]\s*)?"
|
||||
r"(?:-\s*)?"
|
||||
r"(?P<message>.*)$"
|
||||
)
|
||||
|
||||
_SYSLOG_RE = re.compile(
|
||||
r"^<(?P<pri>\d{1,3})>\d?\s*(?P<ts>\w{3}\s+\d{1,2}\s+\d{2}:\d{2}:\d{2})\s+"
|
||||
r"(?P<host>\S+)\s+(?P<app>\S+?)(?:\[(?P<pid>\d+)\])?:\s*(?P<message>.*)$"
|
||||
)
|
||||
|
||||
|
||||
class LogParser:
|
||||
"""将原始日志（字符串或映射）解析为结构化 ParsedLog。"""
|
||||
|
||||
def parse(self, raw: Any) -> ParsedLog:
|
||||
if isinstance(raw, ParsedLog):
|
||||
return raw
|
||||
if isinstance(raw, dict):
|
||||
return self._parse_dict(raw)
|
||||
if isinstance(raw, (bytes, bytearray)):
|
||||
raw = bytes(raw).decode("utf-8", errors="replace")
|
||||
return self._parse_line(str(raw))
|
||||
|
||||
def _parse_dict(self, data: Dict[str, Any]) -> ParsedLog:
|
||||
message = str(data.get("message", data.get("msg", "")))
|
||||
level = str(data.get("level", data.get("severity", "INFO"))).upper()
|
||||
host_id = str(data.get("host_id", data.get("host", "")))
|
||||
service = str(data.get("service", data.get("app", "")))
|
||||
trace_id = data.get("trace_id", data.get("traceId")) or None
|
||||
timestamp = data.get("timestamp", data.get("time"))
|
||||
ts = _to_dt(timestamp) if timestamp is not None else ParsedLog.__dataclass_fields__["timestamp"].default_factory() # type: ignore[attr-defined]
|
||||
return ParsedLog(
|
||||
timestamp=ts,
|
||||
host_id=host_id,
|
||||
service=service,
|
||||
level=level,
|
||||
message=message,
|
||||
trace_id=str(trace_id) if trace_id is not None else None,
|
||||
raw=json.dumps(data, ensure_ascii=False, default=str),
|
||||
)
|
||||
|
||||
def _parse_line(self, line: str) -> ParsedLog:
|
||||
stripped = line.strip()
|
||||
# 1) JSON 整行
|
||||
if stripped.startswith("{") and stripped.endswith("}"):
|
||||
try:
|
||||
return self._parse_dict(json.loads(stripped))
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
pass
|
||||
|
||||
# 2) log4j 风格
|
||||
m = _LOG4J_RE.match(stripped)
|
||||
if m:
|
||||
level = m.group("level").upper()
|
||||
thread = (m.group("thread") or "").strip()
|
||||
message = (m.group("message") or "").strip()
|
||||
return ParsedLog(
|
||||
timestamp=_to_dt(m.group("ts")),
|
||||
host_id=self._extract_host(message) or thread,
|
||||
service=thread,
|
||||
level=level,
|
||||
message=message,
|
||||
trace_id=self._extract_trace(message),
|
||||
raw=line,
|
||||
)
|
||||
|
||||
# 3) syslog 风格
|
||||
m = _SYSLOG_RE.match(stripped)
|
||||
if m:
|
||||
host = m.group("host")
|
||||
app = m.group("app")
|
||||
message = m.group("message") or ""
|
||||
return ParsedLog(
|
||||
timestamp=_to_dt(m.group("ts")),
|
||||
host_id=self._extract_host(message) or host,
|
||||
service=app,
|
||||
level=self._guess_level(message),
|
||||
message=message.strip(),
|
||||
trace_id=self._extract_trace(message),
|
||||
raw=line,
|
||||
)
|
||||
|
||||
# 4) 兜底：整行作为 message，尝试在行内识别级别
|
||||
return ParsedLog(
|
||||
level=self._guess_level(line),
|
||||
message=stripped,
|
||||
host_id=self._extract_host(line),
|
||||
trace_id=self._extract_trace(line),
|
||||
raw=line,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _extract_host(text: str) -> str:
|
||||
m = re.search(r"\bhost[=:]\s*([A-Za-z0-9_.\-]+)", text, re.IGNORECASE)
|
||||
if m:
|
||||
return m.group(1)
|
||||
m = re.search(r"\bhost_id[=:]\s*([A-Za-z0-9_.\-]+)", text, re.IGNORECASE)
|
||||
return m.group(1) if m else ""
|
||||
|
||||
@staticmethod
|
||||
def _extract_trace(text: str) -> Optional[str]:
|
||||
m = re.search(
|
||||
r"\b(?:trace_id|traceId|trace)[=:]\s*([A-Za-z0-9\-]{6,64})", text
|
||||
)
|
||||
return m.group(1) if m else None
|
||||
|
||||
@staticmethod
|
||||
def _guess_level(text: str) -> str:
|
||||
upper = text.upper()
|
||||
for level in _LEVELS:
|
||||
# 级别通常出现在行首或紧跟时间戳后
|
||||
if re.search(rf"\b{level}\b", upper):
|
||||
return level
|
||||
return "INFO"
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 模板化（供聚类特征提取）
|
||||
# ------------------------------------------------------------------
|
||||
@staticmethod
|
||||
def template(message: str) -> str:
|
||||
"""将 message 中的变量替换为稳定占位符，用于聚类归并。"""
|
||||
text = str(message)
|
||||
text = re.sub(r"\b\d{1,3}(?:\.\d{1,3}){3}\b", "<IP>", text)
|
||||
text = re.sub(r"\b[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-"
|
||||
r"[0-9a-fA-F]{4}-[0-9a-fA-F]{12}\b", "<UUID>", text)
|
||||
text = re.sub(r"\b0x[0-9a-fA-F]+\b", "<HEX>", text)
|
||||
text = re.sub(r"\b\d{10,}\b", "<LONG>", text)
|
||||
text = re.sub(r"\b\d+(?:\.\d+)?\b", "<NUM>", text)
|
||||
text = re.sub(r"\s+", " ", text)
|
||||
return text.strip()
|
||||
|
||||
@staticmethod
|
||||
def tokenize(message: str) -> list:
|
||||
"""分词：模板化后按非字母数字切分并过滤过短词。"""
|
||||
templated = LogParser.template(message)
|
||||
tokens = re.findall(r"[A-Za-z_][A-Za-z0-9_\-]*", templated)
|
||||
return [t for t in tokens if len(t) > 1]
|
||||
结构化解析 + 模板化 + 分词（已提交至 git）
|
||||
@ -1,97 +1 @@
|
||||
"""故障日志捕获管道。
|
||||
|
||||
依据 architecture.md 5.3.2：
|
||||
```
|
||||
logs.raw → 级别过滤(ERROR/FATAL) → 关键字/正则过滤 → 结构化解析
|
||||
→ 去重打标 → Kafka(logs.fault) → Elasticsearch(hms-fault-log-*)
|
||||
```
|
||||
|
||||
本模块把「过滤 → 解析 → 指纹去重 → 启发式归类 → 落库 → 可选生产」串成
|
||||
一个可独立测试的管道。聚类与根因分析由 workers.py 异步完成。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
from typing import Any, Callable, List, Optional
|
||||
|
||||
from .classifier import Classifier
|
||||
from .filters import FaultFilter
|
||||
from .fingerprint import Deduplicator, MinHash
|
||||
from .models import FaultLog
|
||||
from .parser import LogParser
|
||||
from .storage import Storage
|
||||
|
||||
|
||||
class CapturePipeline:
|
||||
"""故障日志捕获管道。
|
||||
|
||||
参数：
|
||||
storage: 故障日志存储。
|
||||
capture_filter: 过滤规则，默认 FaultFilter（ERROR/FATAL）。
|
||||
parser: 日志解析器，默认 LogParser。
|
||||
classifier: 启发式归类器，默认 Classifier。
|
||||
dedup: 去重器（实现 is_duplicate 接口），默认进程内 Deduplicator。
|
||||
minhash: 指纹器，默认 MinHash。
|
||||
producer: 可选回调，捕获成功后调用（生产到 logs.fault / ES）。
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
storage: Storage,
|
||||
capture_filter: Optional[FaultFilter] = None,
|
||||
parser: Optional[LogParser] = None,
|
||||
classifier: Optional[Classifier] = None,
|
||||
dedup: Optional[Deduplicator] = None,
|
||||
minhash: Optional[MinHash] = None,
|
||||
producer: Optional[Callable[[FaultLog], None]] = None,
|
||||
) -> None:
|
||||
self.storage = storage
|
||||
self.capture_filter = capture_filter or FaultFilter()
|
||||
self.parser = parser or LogParser()
|
||||
self.classifier = classifier or Classifier()
|
||||
self.dedup = dedup or Deduplicator()
|
||||
self.minhash = minhash or MinHash()
|
||||
self.producer = producer
|
||||
self.captured = 0
|
||||
self.duplicates = 0
|
||||
|
||||
def process(self, raw: Any) -> Optional[FaultLog]:
|
||||
"""处理一条原始日志；不是故障日志或重复时返回 None。"""
|
||||
parsed = self.parser.parse(raw)
|
||||
|
||||
if not self.capture_filter.should_capture(parsed):
|
||||
return None
|
||||
|
||||
fingerprint = self.minhash.fingerprint(parsed.message or "")
|
||||
if self.dedup.is_duplicate(fingerprint):
|
||||
self.duplicates += 1
|
||||
return None
|
||||
|
||||
fault_type = self.classifier.classify(parsed.message or "")
|
||||
log = FaultLog(
|
||||
fault_log_id=uuid.uuid4().hex,
|
||||
host_id=parsed.host_id or "",
|
||||
message=parsed.message or "",
|
||||
level=parsed.level or "ERROR",
|
||||
fingerprint=fingerprint,
|
||||
fault_type=fault_type,
|
||||
service=parsed.service or None,
|
||||
trace_id=parsed.trace_id,
|
||||
occurred_at=parsed.timestamp,
|
||||
count=1,
|
||||
)
|
||||
self.storage.save_fault_log(log)
|
||||
if self.producer is not None:
|
||||
self.producer(log)
|
||||
self.captured += 1
|
||||
return log
|
||||
|
||||
def process_batch(self, raws: List[Any]) -> List[FaultLog]:
|
||||
"""批量处理原始日志，返回捕获到的 FaultLog 列表。"""
|
||||
results: List[FaultLog] = []
|
||||
for raw in raws:
|
||||
log = self.process(raw)
|
||||
if log is not None:
|
||||
results.append(log)
|
||||
return results
|
||||
捕获管道编排（已提交至 git）
|
||||
@ -1,150 +1 @@
|
||||
"""根因分析（RootCauseAnalyzer）。
|
||||
|
||||
依据 architecture.md 5.3.4 与 database-design.md root_cause 表：
|
||||
|
||||
- 输入：故障日志（FaultLog）+ 同主机近时间窗口的指标事件（Event）。
|
||||
- 策略：规则优先、统计辅助、置信度评分。
|
||||
1. 日志关键字启发式（复用 Classifier.hint）给出候选故障类型。
|
||||
2. 指标-故障规则库：如 disk_used_percent >= 90 -> disk_full。
|
||||
3. 时间窗口 + 主机 + 事件指标关联，匹配则提升置信度。
|
||||
- 输出：RootCause{fault_log_id, cause_type, evidence[], confidence}。
|
||||
|
||||
时间比较统一经过 ``_as_aware`` 归一化，兼容 naive / aware datetime，
|
||||
避免直接比较时抛出 TypeError（见 tests/test_root_cause.py 鲁棒性用例）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import Dict, List, Optional, Sequence, Tuple
|
||||
|
||||
from .classifier import Classifier
|
||||
from .models import Event, FaultLog, RootCause
|
||||
|
||||
# 指标-故障规则库：(metric, operator, threshold, cause_type)
|
||||
# operator 支持：gt / gte / lt / lte / eq
|
||||
_METRIC_RULES: List[Tuple[str, str, float, str]] = [
|
||||
("disk_used_percent", "gte", 90.0, "disk_full"),
|
||||
("mem_used_percent", "gte", 90.0, "memory_exhausted"),
|
||||
("cpu_usage", "gte", 95.0, "cpu_saturation"),
|
||||
("load_1m", "gte", 20.0, "cpu_saturation"),
|
||||
("net_pkt_drop", "gte", 100.0, "network_unreachable"),
|
||||
]
|
||||
|
||||
# OOM 日志关键字（补充 Classifier.hint 已覆盖之外的显式规则，保持可读性）
|
||||
_OOM_RE = re.compile(r"oomkilled|out of memory|oom", re.IGNORECASE)
|
||||
|
||||
|
||||
def _as_aware(dt: Optional[datetime]) -> Optional[datetime]:
|
||||
"""将 naive datetime 归一化为 UTC aware datetime。
|
||||
|
||||
aware datetime 原样返回；naive datetime 视为 UTC。
|
||||
"""
|
||||
if dt is None:
|
||||
return None
|
||||
if dt.tzinfo is None:
|
||||
return dt.replace(tzinfo=timezone.utc)
|
||||
return dt
|
||||
|
||||
|
||||
def _compare(value: float, operator: str, threshold: float) -> bool:
|
||||
if operator == "gt":
|
||||
return value > threshold
|
||||
if operator == "gte":
|
||||
return value >= threshold
|
||||
if operator == "lt":
|
||||
return value < threshold
|
||||
if operator == "lte":
|
||||
return value <= threshold
|
||||
if operator == "eq":
|
||||
return value == threshold
|
||||
return False
|
||||
|
||||
|
||||
def _hint_cause(message: str) -> Optional[str]:
|
||||
"""日志消息 -> 候选故障类型（复用分类器启发式规则）。"""
|
||||
return Classifier.hint(message or "")
|
||||
|
||||
|
||||
def _match_metric_rule(event: Event) -> Optional[str]:
|
||||
"""指标事件命中规则库时返回对应 cause_type，否则 None。"""
|
||||
for metric, operator, threshold, cause_type in _METRIC_RULES:
|
||||
if event.metric == metric and _compare(event.value, operator, threshold):
|
||||
return cause_type
|
||||
return None
|
||||
|
||||
|
||||
class RootCauseAnalyzer:
|
||||
"""规则优先 + 统计辅助 + 置信度评分的根因分析器。"""
|
||||
|
||||
def __init__(self, window_minutes: int = 5):
|
||||
self.window_minutes = window_minutes
|
||||
|
||||
def analyze(self, log: FaultLog, events: Sequence[Event]) -> RootCause:
|
||||
"""分析单条故障日志，返回根因结论。
|
||||
|
||||
置信度规则（确定性、可测试）：
|
||||
- 日志关键字命中 +0.6
|
||||
- 指标规则命中 +0.25
|
||||
- 二者同时命中且结论一致 +0.15（互相印证）
|
||||
"""
|
||||
hint_cause = _hint_cause(log.message)
|
||||
event_cause, event_evidence = self._correlate_events(log, events)
|
||||
|
||||
cause_type = event_cause or hint_cause or "unknown"
|
||||
|
||||
confidence = 0.0
|
||||
if hint_cause:
|
||||
confidence += 0.6
|
||||
if event_cause:
|
||||
confidence += 0.25
|
||||
if hint_cause and event_cause and hint_cause == event_cause:
|
||||
confidence += 0.15
|
||||
|
||||
evidence: List[Dict] = [{"type": "log", "message": log.message}]
|
||||
evidence.extend(event_evidence)
|
||||
|
||||
return RootCause(
|
||||
fault_log_id=log.fault_log_id,
|
||||
cause_type=cause_type,
|
||||
evidence=evidence,
|
||||
confidence=round(min(1.0, confidence), 2),
|
||||
analysis_at=datetime.now(timezone.utc),
|
||||
)
|
||||
|
||||
def _correlate_events(
|
||||
self, log: FaultLog, events: Sequence[Event]
|
||||
) -> Tuple[Optional[str], List[Dict]]:
|
||||
"""按时间窗口 + 主机过滤事件，并匹配指标规则库。"""
|
||||
window = timedelta(minutes=self.window_minutes)
|
||||
log_time = _as_aware(log.occurred_at)
|
||||
start = log_time - window if log_time else None
|
||||
end = log_time + window if log_time else None
|
||||
|
||||
event_cause: Optional[str] = None
|
||||
evidence: List[Dict] = []
|
||||
|
||||
for event in events:
|
||||
fired = _as_aware(event.fired_at)
|
||||
if fired is None:
|
||||
continue
|
||||
if start is not None and end is not None:
|
||||
if not (start <= fired <= end):
|
||||
continue
|
||||
if log.host_id and event.host_id and event.host_id != log.host_id:
|
||||
continue
|
||||
|
||||
evidence.append(
|
||||
{
|
||||
"type": "event",
|
||||
"event_id": event.event_id,
|
||||
"metric": event.metric,
|
||||
"value": event.value,
|
||||
}
|
||||
)
|
||||
matched = _match_metric_rule(event)
|
||||
if matched and event_cause is None:
|
||||
event_cause = matched
|
||||
|
||||
return event_cause, evidence
|
||||
根因分析器（已提交至 git）
|
||||
@ -1,180 +1 @@
|
||||
"""存储抽象与内存实现。
|
||||
|
||||
依据 docs/01-design/database-design.md 中 fault_log / root_cause /
|
||||
fault_type / fault_filter_rule / event 表结构，提供统一的进程内存储接口。
|
||||
生产环境通过 integrations.py 对接 MySQL / Elasticsearch / Redis；
|
||||
本模块保证零第三方依赖，测试与离线运行使用 MemoryStorage。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import threading
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from .models import Event, FaultFilterRule, FaultLog, FaultType, RootCause
|
||||
|
||||
|
||||
class Storage:
|
||||
"""存储接口（抽象基类）。
|
||||
|
||||
设计上把五类实体（故障日志 / 根因 / 故障类型 / 过滤规则 / 事件）收敛到
|
||||
一个 Storage 门面，便于 pipeline / workers / api 复用同一份存储。
|
||||
"""
|
||||
|
||||
# ---- 故障日志 ------------------------------------------------------
|
||||
def save_fault_log(self, log: FaultLog) -> None:
|
||||
raise NotImplementedError
|
||||
|
||||
def get_fault_log(self, fault_log_id: str) -> Optional[FaultLog]:
|
||||
raise NotImplementedError
|
||||
|
||||
def list_fault_logs(self) -> List[FaultLog]:
|
||||
raise NotImplementedError
|
||||
|
||||
def query_fault_logs(
|
||||
self,
|
||||
host_id: Optional[str] = None,
|
||||
fault_type: Optional[str] = None,
|
||||
level: Optional[str] = None,
|
||||
keyword: Optional[str] = None,
|
||||
page: int = 1,
|
||||
page_size: int = 20,
|
||||
) -> Dict:
|
||||
raise NotImplementedError
|
||||
|
||||
# ---- 根因 ----------------------------------------------------------
|
||||
def save_root_cause(self, root_cause: RootCause) -> None:
|
||||
raise NotImplementedError
|
||||
|
||||
def get_root_cause(self, fault_log_id: str) -> Optional[RootCause]:
|
||||
raise NotImplementedError
|
||||
|
||||
# ---- 故障类型 ------------------------------------------------------
|
||||
def list_fault_types(self) -> List[FaultType]:
|
||||
raise NotImplementedError
|
||||
|
||||
def get_fault_type(self, fault_type: str) -> Optional[FaultType]:
|
||||
raise NotImplementedError
|
||||
|
||||
def add_fault_type(self, fault_type: FaultType) -> FaultType:
|
||||
raise NotImplementedError
|
||||
|
||||
# ---- 过滤规则 ------------------------------------------------------
|
||||
def list_filter_rules(self) -> List[FaultFilterRule]:
|
||||
raise NotImplementedError
|
||||
|
||||
def add_filter_rule(self, rule: FaultFilterRule) -> FaultFilterRule:
|
||||
raise NotImplementedError
|
||||
|
||||
# ---- 事件（根因分析关联输入） --------------------------------------
|
||||
def save_event(self, event: Event) -> None:
|
||||
raise NotImplementedError
|
||||
|
||||
def list_events_by_host(
|
||||
self, host_id: str, start: datetime, end: datetime
|
||||
) -> List[Event]:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class MemoryStorage(Storage):
|
||||
"""进程内内存实现，用于单元测试与离线运行。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._lock = threading.RLock()
|
||||
self._fault_logs: Dict[str, FaultLog] = {}
|
||||
self._root_causes: Dict[str, RootCause] = {}
|
||||
self._fault_types: Dict[str, FaultType] = {}
|
||||
self._filter_rules: Dict[str, FaultFilterRule] = {}
|
||||
self._events: List[Event] = []
|
||||
|
||||
# ---- 故障日志 ------------------------------------------------------
|
||||
def save_fault_log(self, log: FaultLog) -> None:
|
||||
with self._lock:
|
||||
self._fault_logs[log.fault_log_id] = log
|
||||
|
||||
def get_fault_log(self, fault_log_id: str) -> Optional[FaultLog]:
|
||||
with self._lock:
|
||||
return self._fault_logs.get(fault_log_id)
|
||||
|
||||
def list_fault_logs(self) -> List[FaultLog]:
|
||||
with self._lock:
|
||||
return sorted(
|
||||
self._fault_logs.values(),
|
||||
key=lambda l: (l.occurred_at, l.fault_log_id),
|
||||
)
|
||||
|
||||
def query_fault_logs(
|
||||
self,
|
||||
host_id: Optional[str] = None,
|
||||
fault_type: Optional[str] = None,
|
||||
level: Optional[str] = None,
|
||||
keyword: Optional[str] = None,
|
||||
page: int = 1,
|
||||
page_size: int = 20,
|
||||
) -> Dict:
|
||||
with self._lock:
|
||||
items = list(self._fault_logs.values())
|
||||
if host_id:
|
||||
items = [l for l in items if l.host_id == host_id]
|
||||
if fault_type:
|
||||
items = [l for l in items if l.fault_type == fault_type]
|
||||
if level:
|
||||
items = [l for l in items if l.level.upper() == level.upper()]
|
||||
if keyword:
|
||||
needle = keyword.lower()
|
||||
items = [l for l in items if needle in (l.message or "").lower()]
|
||||
items.sort(key=lambda l: (l.occurred_at, l.fault_log_id), reverse=True)
|
||||
total = len(items)
|
||||
page = max(1, page)
|
||||
page_size = max(1, min(page_size, 200))
|
||||
start = (page - 1) * page_size
|
||||
return {"total": total, "items": items[start : start + page_size]}
|
||||
|
||||
# ---- 根因 ----------------------------------------------------------
|
||||
def save_root_cause(self, root_cause: RootCause) -> None:
|
||||
with self._lock:
|
||||
self._root_causes[root_cause.fault_log_id] = root_cause
|
||||
|
||||
def get_root_cause(self, fault_log_id: str) -> Optional[RootCause]:
|
||||
with self._lock:
|
||||
return self._root_causes.get(fault_log_id)
|
||||
|
||||
# ---- 故障类型 ------------------------------------------------------
|
||||
def list_fault_types(self) -> List[FaultType]:
|
||||
with self._lock:
|
||||
return list(self._fault_types.values())
|
||||
|
||||
def get_fault_type(self, fault_type: str) -> Optional[FaultType]:
|
||||
with self._lock:
|
||||
return self._fault_types.get(fault_type)
|
||||
|
||||
def add_fault_type(self, fault_type: FaultType) -> FaultType:
|
||||
with self._lock:
|
||||
self._fault_types[fault_type.fault_type] = fault_type
|
||||
return fault_type
|
||||
|
||||
# ---- 过滤规则 ------------------------------------------------------
|
||||
def list_filter_rules(self) -> List[FaultFilterRule]:
|
||||
with self._lock:
|
||||
return list(self._filter_rules.values())
|
||||
|
||||
def add_filter_rule(self, rule: FaultFilterRule) -> FaultFilterRule:
|
||||
with self._lock:
|
||||
self._filter_rules[rule.name] = rule
|
||||
return rule
|
||||
|
||||
# ---- 事件 ----------------------------------------------------------
|
||||
def save_event(self, event: Event) -> None:
|
||||
with self._lock:
|
||||
self._events.append(event)
|
||||
|
||||
def list_events_by_host(
|
||||
self, host_id: str, start: datetime, end: datetime
|
||||
) -> List[Event]:
|
||||
with self._lock:
|
||||
return [
|
||||
e
|
||||
for e in self._events
|
||||
if e.host_id == host_id and start <= e.fired_at <= end
|
||||
]
|
||||
存储抽象 + 内存实现（已提交至 git）
|
||||
@ -1,108 +1 @@
|
||||
"""聚类与根因分析 worker。
|
||||
|
||||
依据 architecture.md 5.3.3 / 5.3.4：
|
||||
- ClusterWorker：批量对未归类故障日志做 TF-IDF + DBSCAN 聚类，映射到 fault_type。
|
||||
- RootCauseWorker：批量关联同主机近期指标事件，产出 root_cause。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import timedelta
|
||||
from typing import Dict, List, Optional, Sequence
|
||||
|
||||
from .classifier import Classifier
|
||||
from .cluster import Clusterer
|
||||
from .models import FaultLog, RootCause
|
||||
from .root_cause import RootCauseAnalyzer
|
||||
from .storage import Storage
|
||||
|
||||
|
||||
def _cluster_label(cluster_id: int) -> str:
|
||||
return f"c-{cluster_id}"
|
||||
|
||||
|
||||
class ClusterWorker:
|
||||
"""批量聚类并归类故障日志。"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
storage: Storage,
|
||||
clusterer: Optional[Clusterer] = None,
|
||||
classifier: Optional[Classifier] = None,
|
||||
) -> None:
|
||||
self.storage = storage
|
||||
self.clusterer = clusterer or Clusterer()
|
||||
self.classifier = classifier or Classifier()
|
||||
|
||||
def run(self, logs: Optional[Sequence[FaultLog]] = None) -> Dict:
|
||||
"""对故障日志聚类并持久化归类结果。
|
||||
|
||||
返回：{"clustered": n, "clusters": m, "noise": k}
|
||||
"""
|
||||
items: List[FaultLog] = list(logs) if logs is not None else self.storage.list_fault_logs()
|
||||
if not items:
|
||||
return {"clustered": 0, "clusters": 0, "noise": 0}
|
||||
|
||||
messages = [l.message or "" for l in items]
|
||||
labels = self.clusterer.fit_predict(messages)
|
||||
|
||||
# 按簇收集代表消息，用于簇级归类
|
||||
cluster_messages: Dict[int, List[str]] = {}
|
||||
for label, message in zip(labels, messages):
|
||||
if label >= 0:
|
||||
cluster_messages.setdefault(label, []).append(message)
|
||||
|
||||
fault_type_by_cluster: Dict[int, Optional[str]] = {}
|
||||
for label, msgs in cluster_messages.items():
|
||||
fault_type_by_cluster[label] = self.classifier.classify_cluster(label, msgs)
|
||||
|
||||
clustered = 0
|
||||
for log, label in zip(items, labels):
|
||||
if label >= 0:
|
||||
log.cluster_id = _cluster_label(label)
|
||||
log.fault_type = fault_type_by_cluster[label] or log.fault_type
|
||||
clustered += 1
|
||||
self.storage.save_fault_log(log)
|
||||
|
||||
noise = sum(1 for lbl in labels if lbl < 0)
|
||||
return {
|
||||
"clustered": clustered,
|
||||
"clusters": self.clusterer.n_clusters,
|
||||
"noise": noise,
|
||||
}
|
||||
|
||||
|
||||
class RootCauseWorker:
|
||||
"""批量执行根因分析并持久化结论。"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
storage: Storage,
|
||||
analyzer: Optional[RootCauseAnalyzer] = None,
|
||||
window_minutes: int = 5,
|
||||
) -> None:
|
||||
self.storage = storage
|
||||
self.window_minutes = window_minutes
|
||||
self.analyzer = analyzer or RootCauseAnalyzer(window_minutes=window_minutes)
|
||||
|
||||
def run(self, logs: Optional[Sequence[FaultLog]] = None) -> List[RootCause]:
|
||||
"""对尚未分析过的故障日志执行根因分析，返回新产出的结论列表。"""
|
||||
items: List[FaultLog] = list(logs) if logs is not None else self.storage.list_fault_logs()
|
||||
results: List[RootCause] = []
|
||||
window = timedelta(minutes=self.window_minutes)
|
||||
|
||||
for log in items:
|
||||
if self.storage.get_root_cause(log.fault_log_id) is not None:
|
||||
continue
|
||||
start = log.occurred_at - window
|
||||
end = log.occurred_at + window
|
||||
events = (
|
||||
self.storage.list_events_by_host(log.host_id, start, end)
|
||||
if log.host_id
|
||||
else []
|
||||
)
|
||||
root_cause = self.analyzer.analyze(log, events)
|
||||
self.storage.save_root_cause(root_cause)
|
||||
results.append(root_cause)
|
||||
|
||||
return results
|
||||
聚类/根因分析 worker（已提交至 git）
|
||||
Loading…
x
Reference in New Issue
Block a user