233 lines
7.9 KiB
Python
233 lines
7.9 KiB
Python
"""
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执行日志API
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"""
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy.orm import Session
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from pydantic import BaseModel
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from typing import List, Optional
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from datetime import datetime
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from app.core.database import get_db
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from app.models.execution_log import ExecutionLog
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from app.models.execution import Execution
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from app.models.workflow import Workflow
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from app.api.auth import get_current_user
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from app.models.user import User
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from app.core.exceptions import NotFoundError
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router = APIRouter(prefix="/api/v1/execution-logs", tags=["execution-logs"])
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class ExecutionLogResponse(BaseModel):
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"""执行日志响应模型"""
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id: str
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execution_id: str
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node_id: Optional[str]
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node_type: Optional[str]
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level: str
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message: str
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data: Optional[dict]
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timestamp: datetime
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duration: Optional[int]
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class Config:
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from_attributes = True
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@router.get("/executions/{execution_id}", response_model=List[ExecutionLogResponse])
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async def get_execution_logs(
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execution_id: str,
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level: Optional[str] = Query(None, description="日志级别筛选: INFO/WARN/ERROR/DEBUG"),
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node_id: Optional[str] = Query(None, description="节点ID筛选"),
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skip: int = Query(0, ge=0),
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limit: int = Query(100, ge=1, le=1000),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""获取执行日志列表"""
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# 验证执行记录是否存在且属于当前用户
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execution = db.query(Execution).join(Workflow, Execution.workflow_id == Workflow.id).filter(
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Execution.id == execution_id,
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Workflow.user_id == current_user.id
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).first()
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if not execution:
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raise NotFoundError("执行记录", execution_id)
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# 构建查询
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query = db.query(ExecutionLog).filter(
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ExecutionLog.execution_id == execution_id
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)
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# 日志级别筛选
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if level:
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query = query.filter(ExecutionLog.level == level.upper())
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# 节点ID筛选
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if node_id:
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query = query.filter(ExecutionLog.node_id == node_id)
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# 排序和分页
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logs = query.order_by(ExecutionLog.timestamp.asc()).offset(skip).limit(limit).all()
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return logs
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@router.get("/executions/{execution_id}/summary")
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async def get_execution_log_summary(
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execution_id: str,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""获取执行日志摘要（统计信息）"""
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# 验证执行记录是否存在且属于当前用户
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execution = db.query(Execution).join(Workflow, Execution.workflow_id == Workflow.id).filter(
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Execution.id == execution_id,
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Workflow.user_id == current_user.id
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).first()
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if not execution:
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raise NotFoundError("执行记录", execution_id)
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# 统计各级别日志数量
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from sqlalchemy import func
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level_stats = db.query(
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ExecutionLog.level,
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func.count(ExecutionLog.id).label('count')
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).filter(
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ExecutionLog.execution_id == execution_id
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).group_by(ExecutionLog.level).all()
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# 统计节点执行情况
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node_stats = db.query(
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ExecutionLog.node_id,
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ExecutionLog.node_type,
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func.count(ExecutionLog.id).label('log_count'),
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func.sum(ExecutionLog.duration).label('total_duration')
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).filter(
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ExecutionLog.execution_id == execution_id,
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ExecutionLog.node_id.isnot(None)
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).group_by(ExecutionLog.node_id, ExecutionLog.node_type).all()
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# 获取错误日志
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error_logs = db.query(ExecutionLog).filter(
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ExecutionLog.execution_id == execution_id,
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ExecutionLog.level == 'ERROR'
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).order_by(ExecutionLog.timestamp.desc()).limit(10).all()
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return {
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"level_stats": {level: count for level, count in level_stats},
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"node_stats": [
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{
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"node_id": node_id,
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"node_type": node_type,
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"log_count": log_count,
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"total_duration": total_duration
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}
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for node_id, node_type, log_count, total_duration in node_stats
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],
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"error_logs": [
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{
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"id": log.id,
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"node_id": log.node_id,
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"message": log.message,
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"timestamp": log.timestamp,
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"data": log.data
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}
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for log in error_logs
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],
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"total_logs": db.query(func.count(ExecutionLog.id)).filter(
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ExecutionLog.execution_id == execution_id
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).scalar()
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}
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@router.get("/executions/{execution_id}/performance")
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async def get_execution_performance(
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execution_id: str,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""获取执行性能分析数据"""
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# 验证执行记录是否存在且属于当前用户
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execution = db.query(Execution).join(Workflow, Execution.workflow_id == Workflow.id).filter(
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Execution.id == execution_id,
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Workflow.user_id == current_user.id
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).first()
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if not execution:
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raise NotFoundError("执行记录", execution_id)
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from sqlalchemy import func
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# 获取总执行时间
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total_execution_time = execution.execution_time or 0
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# 统计各节点执行时间（按节点ID分组）
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node_performance = db.query(
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ExecutionLog.node_id,
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ExecutionLog.node_type,
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func.sum(ExecutionLog.duration).label('total_duration'),
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func.avg(ExecutionLog.duration).label('avg_duration'),
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func.min(ExecutionLog.duration).label('min_duration'),
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func.max(ExecutionLog.duration).label('max_duration'),
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func.count(ExecutionLog.id).label('execution_count')
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).filter(
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ExecutionLog.execution_id == execution_id,
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ExecutionLog.node_id.isnot(None),
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ExecutionLog.duration.isnot(None)
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).group_by(ExecutionLog.node_id, ExecutionLog.node_type).all()
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# 按节点类型统计
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type_performance = db.query(
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ExecutionLog.node_type,
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func.sum(ExecutionLog.duration).label('total_duration'),
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func.avg(ExecutionLog.duration).label('avg_duration'),
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func.count(ExecutionLog.id).label('execution_count')
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).filter(
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ExecutionLog.execution_id == execution_id,
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ExecutionLog.node_type.isnot(None),
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ExecutionLog.duration.isnot(None)
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).group_by(ExecutionLog.node_type).all()
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# 获取执行时间线（按时间顺序）
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timeline_logs = db.query(ExecutionLog).filter(
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ExecutionLog.execution_id == execution_id,
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ExecutionLog.duration.isnot(None),
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ExecutionLog.node_id.isnot(None)
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).order_by(ExecutionLog.timestamp.asc()).all()
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return {
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"total_execution_time": total_execution_time,
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"node_performance": [
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{
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"node_id": node_id,
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"node_type": node_type,
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"total_duration": int(total_duration or 0),
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"avg_duration": float(avg_duration or 0),
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"min_duration": int(min_duration or 0),
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"max_duration": int(max_duration or 0),
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"execution_count": int(execution_count or 0)
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}
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for node_id, node_type, total_duration, avg_duration, min_duration, max_duration, execution_count in node_performance
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],
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"type_performance": [
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{
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"node_type": node_type,
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"total_duration": int(total_duration or 0),
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"avg_duration": float(avg_duration or 0),
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"execution_count": int(execution_count or 0)
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}
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for node_type, total_duration, avg_duration, execution_count in type_performance
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],
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"timeline": [
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{
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"timestamp": log.timestamp.isoformat() if log.timestamp else None,
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"node_id": log.node_id,
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"node_type": log.node_type,
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"duration": log.duration,
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"message": log.message
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}
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for log in timeline_logs
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]
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}
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