274 lines
9.9 KiB
Python
274 lines
9.9 KiB
Python
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"""
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Trace 文件加载节点
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用于从 Unreal Insights 导出和加载 Trace 数据
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"""
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import polars as pl
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from server.app.core.user_manager import CLOUD_ROOT
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import server.app.utils.io as io
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import subprocess
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from pathlib import Path
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from typing import Any, Dict, Optional
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from server.app.core.node_base import (
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TraceNode,
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input_port,
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output_port,
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param,
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context_var,
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NodeType,
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CachePolicy
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)
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from server.app.core.node_registry import register_node
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import yaml
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def load_system_config():
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"""加载系统配置"""
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config_path = Path(__file__).parent.parent.parent / "system_config.yaml"
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if config_path.exists():
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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return {}
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@register_node
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class TraceLoader_Metadata(TraceNode):
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"""
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Trace 元数据加载器
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功能：
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1. 调用 UnrealInsights.exe 打开 .utrace 文件
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2. 执行 ExportMetadata 命令导出 CSV
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3. 读取 CSV 文件并加载到内存
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4. 返回 DataFrame 格式的数据表
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使用场景：
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- 分析 Trace 文件的元数据信息
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- 作为数据处理工作流的起点
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"""
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CATEGORY = "Loader"
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DISPLAY_NAME = "Trace 元数据加载器"
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DESCRIPTION = "从 .utrace 文件导出并加载元数据"
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ICON = "📥"
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NODE_TYPE = NodeType.INPUT # 输入节点（无输入端口）
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CACHE_POLICY = CachePolicy.DISK # 使用磁盘缓存（元数据较大）
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@output_port("metadata", "DataTable", description="导出的元数据表")
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@param("utrace_file", "String", default="", description="Utrace 文件路径", widget="file", required=True)
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@param("tokens", "String", description="Token事件列表，以 | 进行分割")
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def process(self, inputs: Dict[str, Any], context: Optional[Dict] = None) -> Dict[str, Any]:
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# 获取参数
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tokens = self.get_param("tokens", "")
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utrace_file = self.get_param("utrace_file", "")
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output_csv = self.get_param("output_csv", "temp/metadata.csv")
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print("TraceLoader_Metadata: process called with utrace_file =", utrace_file, ", tokens =", tokens)
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if not utrace_file:
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raise ValueError("必须指定 utrace_file 参数")
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# 加载系统配置
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config = load_system_config()
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insights_path = config.get("unreal", {}).get("insights_path", "")
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# 构建完整路径
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# utrace 文件路径（用户目录下）
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if not insights_path or not Path(insights_path).exists():
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raise FileNotFoundError(
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f"UnrealInsights.exe 未找到：{insights_path}\n"
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f"请在 system_config.yaml 中配置正确的 insights_path"
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)
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utrace_full_path = CLOUD_ROOT / utrace_file
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if not utrace_full_path.exists():
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raise FileNotFoundError(f"Utrace 文件不存在：{utrace_full_path}")
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# 构建输出 CSV 完整路径
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csv_full_path = CLOUD_ROOT / output_csv
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# 确保输出目录存在
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csv_full_path.parent.mkdir(parents=True, exist_ok=True)
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if csv_full_path.exists():
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df = pl.read_csv(csv_full_path)
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return {
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"metadata": df,
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"file_path": str(csv_full_path)
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}
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io.safe_delete(csv_full_path)
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# 构建命令
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# UnrealInsights.exe -OpenTraceFile="path/to/file.utrace" -ExecOnAnalysisCompleteCmd="TimingInsights.ExportMetadata path/to/output.csv"
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cmd = [
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str(insights_path),
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f'-OpenTraceFile="{utrace_full_path}"',
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f'-ExecOnAnalysisCompleteCmd="TimingInsights.ExportMetadata {csv_full_path} -tokens={tokens}"'
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]
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print("TraceLoader_Metadata: 2.Execute::", " ".join(cmd))
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# 执行命令
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result = subprocess.run(
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' '.join(cmd),
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shell=True,
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capture_output=True,
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text=True,
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encoding='utf-8',
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errors='replace'
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)
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# 检查执行结果
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if result.returncode != 0:
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error_msg = f"UnrealInsights 执行失败 (退出码: {result.returncode})\n"
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if result.stderr:
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error_msg += f"错误输出:\n{result.stderr}"
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raise RuntimeError(error_msg)
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if not csv_full_path.exists():
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raise FileNotFoundError(
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f"CSV 文件未生成：{csv_full_path}\n"
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f"命令输出: {result.stdout}"
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)
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# 读取 CSV 文件
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df = pl.read_csv(csv_full_path)
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# 准备输出
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outputs = {
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"metadata": df,
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"file_path": str(csv_full_path)
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}
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#io.safe_delete(csv_full_path)
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return outputs
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@register_node
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class TraceLoader_Events(TraceNode):
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"""
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Trace 事件加载器
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功能：
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导出 Trace 文件中的事件数据（Timing Events）
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"""
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CATEGORY = "Loader"
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DISPLAY_NAME = "Trace 事件加载器"
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DESCRIPTION = "从 .utrace 文件导出并加载事件数据"
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ICON = "⏱️"
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NODE_TYPE = NodeType.INPUT
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CACHE_POLICY = CachePolicy.DISK
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@output_port("events", "DataTable", description="导出的事件数据表")
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@output_port("file_path", "String", description="导出的 CSV 文件路径")
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@param("utrace_file", "String", default="", description="Utrace 文件路径", widget="file", required=True)
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@param("output_csv", "String", default="temp/events.csv", description="输出 CSV 路径", required=True)
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@param("timeout", "Number", default=120, description="命令执行超时时间（秒）", min=10, max=600, step=10)
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@param("auto_cleanup", "Boolean", default=False, description="执行后自动删除 CSV 文件")
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@context_var("row_count", "Integer", description="事件数量")
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@context_var("column_count", "Integer", description="列数")
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@context_var("execution_time", "Number", description="执行时间（秒）")
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def process(self, inputs: Dict[str, Any], context: Optional[Dict] = None) -> Dict[str, Any]:
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import time
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start_time = time.time()
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# 获取参数
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utrace_file = self.get_param("utrace_file", "")
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output_csv = self.get_param("output_csv", "temp/events.csv")
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timeout = self.get_param("timeout", 120)
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auto_cleanup = self.get_param("auto_cleanup", False)
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if not utrace_file:
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raise ValueError("必须指定 utrace_file 参数")
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# 加载系统配置
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config = load_system_config()
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insights_path = config.get("unreal", {}).get("insights_path", "")
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cloud_root = Path(config.get("storage", {}).get("cloud_root", "./cloud"))
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user_id = (context or {}).get("user_id", "guest")
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# 构建路径
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if not insights_path or not Path(insights_path).exists():
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raise FileNotFoundError(f"UnrealInsights.exe 未找到：{insights_path}")
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if Path(utrace_file).is_absolute():
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utrace_full_path = Path(utrace_file)
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else:
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utrace_full_path = cloud_root / "users" / user_id / utrace_file
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if not utrace_full_path.exists():
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raise FileNotFoundError(f"Utrace 文件不存在：{utrace_full_path}")
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if Path(output_csv).is_absolute():
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csv_full_path = Path(output_csv)
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else:
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csv_full_path = cloud_root / output_csv
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csv_full_path.parent.mkdir(parents=True, exist_ok=True)
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# 构建命令（导出事件数据）
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cmd = [
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str(insights_path),
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f'-OpenTraceFile="{utrace_full_path}"',
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f'-ExecOnAnalysisCompleteCmd="TimingInsights.ExportTimingEvents {csv_full_path}"'
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]
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try:
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result = subprocess.run(
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' '.join(cmd),
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shell=True,
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capture_output=True,
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text=True,
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timeout=timeout,
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encoding='utf-8',
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errors='replace'
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)
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if result.returncode != 0:
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error_msg = f"UnrealInsights 执行失败 (退出码: {result.returncode})\n"
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if result.stderr:
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error_msg += f"错误输出:\n{result.stderr}"
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raise RuntimeError(error_msg)
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# 等待文件生成
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wait_time = 0
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while not csv_full_path.exists() and wait_time < 5:
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time.sleep(0.5)
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wait_time += 0.5
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if not csv_full_path.exists():
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raise FileNotFoundError(f"CSV 文件未生成：{csv_full_path}")
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# 读取 CSV（使用 polars，保持后端一致）
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df = pl.read_csv(csv_full_path)
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execution_time = time.time() - start_time
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outputs = {
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"events": df,
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"file_path": str(csv_full_path)
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}
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result_context = {
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"row_count": len(df),
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"column_count": len(df.columns),
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"execution_time": round(execution_time, 2)
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}
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if auto_cleanup:
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try:
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csv_full_path.unlink()
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result_context["cleaned_up"] = True
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except Exception as e:
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result_context["cleanup_error"] = str(e)
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out = outputs
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# 可选地将 context 信息并入 outputs.meta 或单独管理；这里仅返回 outputs
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return out
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except subprocess.TimeoutExpired:
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raise TimeoutError(f"命令执行超时（超过 {timeout} 秒）")
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except Exception as e:
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if csv_full_path.exists():
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try:
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csv_full_path.unlink()
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except:
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pass
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raise
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