177 lines
8.0 KiB
Python
177 lines
8.0 KiB
Python
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from typing import Any, Dict
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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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)
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from server.app.core.node_registry import register_node
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# ============= 聚合节点 =============
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@register_node
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class DataFrameConcatNode(TraceNode):
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"""聚合节点 - 将多个 polars DataFrame 按指定列聚合为一张表"""
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CATEGORY = "Transform"
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DISPLAY_NAME = "表聚合"
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DESCRIPTION = "将多个表合并为一张表，可指定聚合方式和列"
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ICON = "🧩"
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@input_port("tables", "Array<DataTable>", description="待聚合的表数组", required=True)
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@output_port("result", "DataTable", description="聚合后的表")
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@param("how", "String", default="vertical", description="聚合方式，可选 vertical/horizontal/groupby")
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@param("on", "String", default="", description="聚合列（横向聚合/join 或 groupby 时用逗号分隔）")
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@param("agg_mode", "String", default="{}", description="""聚合模式，json字符串，如{"Duration":"mean","Score":"sum"}，未指定的列默认first。若需在输出中加入分组计数列，请在 agg_mode 中添加 "my_count_col":"count"（键为希望输出的列名，值为 "count"）。""")
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def process(self, inputs, context=None):
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import polars as pl
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import json
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tables = inputs.get("tables", [])
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if not tables:
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raise ValueError("输入 tables 不能为空")
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non_pl_types = [type(df).__name__ for df in tables if not isinstance(df, pl.DataFrame)]
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if non_pl_types:
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raise ValueError(f"输入必须为 polars.DataFrame 的数组，发现类型: {non_pl_types}. 后端仅支持 polars。")
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how = self.get_param("how", "vertical")
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on = self.get_param("on", "")
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agg_mode = self.get_param("agg_mode", "{}")
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if how == "vertical":
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# 纵向拼接
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result = pl.concat(tables, how="vertical")
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elif how == "horizontal":
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# 横向聚合，按 on 指定的列做 join
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on_cols = [col.strip() for col in on.split(",") if col.strip()]
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if not on_cols:
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raise ValueError("横向聚合必须指定聚合列 on")
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result = tables[0]
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for df in tables[1:]:
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result = result.join(df, on=on_cols, how="inner")
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elif how == "groupby":
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# 分组聚合，on 作为分组列
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result = pl.concat(tables, how="diagonal")
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group_cols = [col.strip() for col in on.split(",") if col.strip()]
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if not group_cols:
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raise ValueError("分组聚合必须指定 on")
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# 解析聚合表达式
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try:
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agg_dict = json.loads(agg_mode) if isinstance(agg_mode, str) else (agg_mode or {})
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except Exception as e:
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raise ValueError(f"聚合模式(agg_mode)必须为合法json字符串: {e} {agg_mode}")
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agg_exprs = []
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# 1) 先处理 agg_mode 中显式指定的聚合（支持为不存在的键指定 count，生成自定义计数列）
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if isinstance(agg_dict, dict):
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for out_col, method in agg_dict.items():
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# 忽略分组列在 agg_dict 中的指定
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if out_col in group_cols:
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continue
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if isinstance(method, str) and method == "count":
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# 如果方法为 count，不要求 out_col 存在于原始列，可以作为自定义计数列名
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agg_exprs.append(pl.count().alias(out_col))
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continue
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# 如果 out_col 对应原始列，则按方法聚合
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if out_col in result.columns:
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m = (method or "first")
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if m == "mean":
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agg_exprs.append(pl.col(out_col).mean().alias(out_col))
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elif m == "sum":
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agg_exprs.append(pl.col(out_col).sum().alias(out_col))
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elif m == "min":
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agg_exprs.append(pl.col(out_col).min().alias(out_col))
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elif m == "max":
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agg_exprs.append(pl.col(out_col).max().alias(out_col))
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elif m == "first":
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agg_exprs.append(pl.col(out_col).first().alias(out_col))
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elif m == "last":
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agg_exprs.append(pl.col(out_col).last().alias(out_col))
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elif m == "count":
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agg_exprs.append(pl.col(out_col).count().alias(out_col))
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else:
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raise ValueError(f"不支持的聚合方式: {m} (列: {out_col})")
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else:
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# 非 count 的自定义列名不支持（没有对应源列）
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raise ValueError(f"未知聚合列: {out_col}，仅支持对存在列的聚合或使用 'count' 生成计数列")
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# 2) 对未在 agg_mode 中显式指定的原始列使用默认聚合 first
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for col in result.columns:
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if col in group_cols:
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continue
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if isinstance(agg_dict, dict) and col in agg_dict:
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continue
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agg_exprs.append(pl.col(col).first().alias(col))
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result = result.group_by(group_cols).agg(agg_exprs)
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else:
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raise ValueError("不支持的聚合方式: %s" % how)
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return {"result": result}
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# ============= 列操作节点 =============
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@register_node
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class ColumnOpsNode(TraceNode):
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"""列操作节点 - 支持新增/删除/修改列"""
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CATEGORY = "Transform"
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DISPLAY_NAME = "列操作"
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DESCRIPTION = "支持新增、删除、修改列，表达式灵活"
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ICON = "🛠️"
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@input_port("table", "DataTable", description="待变换的表", required=True)
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@output_port("result", "DataTable", description="变换后的表")
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@param("ops", "String", default="{}", description="""操作表，json字符串，支持新增/修改/删除列。如 {"add": {"Duration": "(EndTime-StartTime)*1000"}, "drop": ["EndTime", "StartTime", "Depth", "ThreadId"]} """)
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def process(self, inputs, context=None):
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import polars as pl
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import json
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table = inputs.get("table", None)
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if table is None or not isinstance(table, pl.DataFrame):
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raise ValueError("输入必须为 polars.DataFrame")
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ops = self.get_param("ops", "{}")
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try:
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ops_dict = json.loads(ops) if isinstance(ops, str) else (ops or {})
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except Exception as e:
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raise ValueError(f"操作表(ops)必须为合法json字符串: {e} {ops}")
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df = table
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# 新增/修改列
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add_ops = ops_dict.get("add", {})
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new_columns = []
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for col, expr in add_ops.items():
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# expr 支持简单表达式，如 '(EndTime-StartTime)*1000'
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# 用 eval 安全地解析表达式
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try:
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# 构造 polars 表达式
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expr_code = expr.replace(" ", "")
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# 支持加减乘除和括号
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# 只允许列名和运算符
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import re
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tokens = re.split(r'([\+\-\*/\(\)])', expr_code)
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pl_expr = ""
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for t in tokens:
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if t in {"+", "-", "*", "/", "(", ")"}:
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pl_expr += t
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elif t:
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pl_expr += f"pl.col('{t}')" if t.isidentifier() else t
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# 通过 eval 构造 polars 表达式
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new_columns.append(eval(pl_expr).alias(col))
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except Exception as e:
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raise ValueError(f"新增/修改列 {col} 表达式错误: {expr}, {e}")
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if new_columns:
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df = df.with_columns(new_columns)
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# 删除列
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drop_ops = ops_dict.get("drop", [])
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keep_cols = [col for col in df.columns if col not in drop_ops]
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df = df.select(keep_cols)
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return {"result": df}
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