DataOps-platform/app/models/data_product.py

"""
数据产品模型
用于记录数据工厂加工完成后的数据产品信息
"""

from __future__ import annotations

from typing import Any

from app import db
from app.core.common.timezone_utils import now_china_naive


class DataProduct(db.Model):
    """数据产品模型，记录数据工厂加工后的数据产品信息"""

    __tablename__ = "data_products"
    __table_args__ = {"schema": "public"}

    id = db.Column(db.Integer, primary_key=True)

    # 数据产品基本信息
    product_name = db.Column(db.String(200), nullable=False)
    product_name_en = db.Column(db.String(200), nullable=False)
    description = db.Column(db.Text, nullable=True)

    # 关联信息
    source_dataflow_id = db.Column(db.Integer, nullable=True)
    source_dataflow_name = db.Column(db.String(200), nullable=True)

    # 目标表信息
    target_table = db.Column(db.String(200), nullable=False)
    target_schema = db.Column(db.String(100), nullable=False, default="public")

    # 数据统计信息
    record_count = db.Column(db.BigInteger, nullable=False, default=0)
    column_count = db.Column(db.Integer, nullable=False, default=0)

    # 更新提示相关
    last_updated_at = db.Column(db.DateTime, nullable=True)
    last_viewed_at = db.Column(db.DateTime, nullable=True)

    # 状态信息
    status = db.Column(db.String(50), nullable=False, default="active")

    # 审计字段
    created_at = db.Column(db.DateTime, nullable=False, default=now_china_naive)
    created_by = db.Column(db.String(100), nullable=False, default="system")
    updated_at = db.Column(db.DateTime, nullable=False, default=now_china_naive)

    def to_dict(self) -> dict[str, Any]:
        """
        将模型转换为字典

        Returns:
            包含所有字段的字典
        """
        return {
            "id": self.id,
            "product_name": self.product_name,
            "product_name_en": self.product_name_en,
            "description": self.description,
            "source_dataflow_id": self.source_dataflow_id,
            "source_dataflow_name": self.source_dataflow_name,
            "target_table": self.target_table,
            "target_schema": self.target_schema,
            "record_count": self.record_count,
            "column_count": self.column_count,
            "last_updated_at": (
                self.last_updated_at.isoformat() if self.last_updated_at else None
            ),
            "last_viewed_at": (
                self.last_viewed_at.isoformat() if self.last_viewed_at else None
            ),
            "status": self.status,
            "created_at": self.created_at.isoformat() if self.created_at else None,
            "created_by": self.created_by,
            "updated_at": self.updated_at.isoformat() if self.updated_at else None,
            "has_new_data": self._has_new_data(),
        }

    def _has_new_data(self) -> bool:
        """
        判断是否有新数据（用于更新提示）

        Returns:
            如果 last_updated_at > last_viewed_at 则返回 True
        """
        if self.last_updated_at is None:
            return False
        if self.last_viewed_at is None:
            return True
        return self.last_updated_at > self.last_viewed_at

    def mark_as_viewed(self) -> None:
        """标记为已查看，更新 last_viewed_at 时间"""
        self.last_viewed_at = now_china_naive()
        self.updated_at = now_china_naive()

    def update_data_stats(
        self,
        record_count: int,
        column_count: int | None = None,
    ) -> None:
        """
        更新数据统计信息

        Args:
            record_count: 记录数
            column_count: 列数（可选）
        """
        self.record_count = record_count
        if column_count is not None:
            self.column_count = column_count
        self.last_updated_at = now_china_naive()
        self.updated_at = now_china_naive()

    def __repr__(self) -> str:
        return f"<DataProduct {self.product_name} ({self.target_table})>"


class DataOrder(db.Model):
    """
    数据订单模型
    用于记录用户提交的数据需求订单，通过 LLM 提取实体并在图谱中检测连通性
    """

    __tablename__ = "data_orders"
    __table_args__ = {"schema": "public"}

    id = db.Column(db.Integer, primary_key=True)

    # 订单基本信息
    order_no = db.Column(db.String(50), unique=True, nullable=False)  # 订单编号
    title = db.Column(db.String(200), nullable=False)  # 订单标题
    description = db.Column(db.Text, nullable=False)  # 需求描述

    # LLM 提取结果
    extracted_domains = db.Column(db.JSON, nullable=True)  # 提取的业务领域列表
    extracted_fields = db.Column(db.JSON, nullable=True)  # 提取的数据字段列表
    extraction_purpose = db.Column(db.Text, nullable=True)  # 提取的数据用途

    # 图谱分析结果
    graph_analysis = db.Column(db.JSON, nullable=True)  # 连通性分析结果
    can_connect = db.Column(db.Boolean, nullable=True)  # 是否可连通
    connection_path = db.Column(db.JSON, nullable=True)  # 连通路径

    # 状态管理
    # pending-待处理, analyzing-分析中, processing-加工中,
    # completed-完成, rejected-驳回, need_supplement-待补充,
    # manual_review-待人工处理, updated-已更新
    status = db.Column(db.String(50), nullable=False, default="pending")

    reject_reason = db.Column(db.Text, nullable=True)  # 驳回原因

    # 关联数据
    result_product_id = db.Column(db.Integer, nullable=True)  # 生成的数据产品ID
    result_dataflow_id = db.Column(db.Integer, nullable=True)  # 生成的数据流ID

    # 审计字段
    created_by = db.Column(db.String(100), nullable=False, default="user")
    created_at = db.Column(db.DateTime, nullable=False, default=now_china_naive)
    updated_at = db.Column(db.DateTime, nullable=False, default=now_china_naive)
    processed_by = db.Column(db.String(100), nullable=True)  # 处理人
    processed_at = db.Column(db.DateTime, nullable=True)  # 处理时间

    # 状态常量
    STATUS_PENDING = "pending"
    STATUS_ANALYZING = "analyzing"
    STATUS_PENDING_APPROVAL = "pending_approval"  # 待审批
    STATUS_PROCESSING = "processing"
    STATUS_ONBOARD = "onboard"  # 数据产品就绪
    STATUS_COMPLETED = "completed"
    STATUS_REJECTED = "rejected"
    STATUS_NEED_SUPPLEMENT = "need_supplement"
    STATUS_MANUAL_REVIEW = "manual_review"
    STATUS_UPDATED = "updated"

    # 状态标签映射
    STATUS_LABELS = {
        "pending": "待处理",
        "analyzing": "分析中",
        "pending_approval": "待审批",
        "processing": "加工中",
        "onboard": "数据产品就绪",
        "completed": "已完成",
        "rejected": "已驳回",
        "need_supplement": "待补充",
        "manual_review": "待人工处理",
        "updated": "已更新",
    }

    def to_dict(self) -> dict[str, Any]:
        """
        将模型转换为字典

        Returns:
            包含所有字段的字典
        """
        return {
            "id": self.id,
            "order_no": self.order_no,
            "title": self.title,
            "description": self.description,
            "extracted_domains": self.extracted_domains,
            "extracted_fields": self.extracted_fields,
            "extraction_purpose": self.extraction_purpose,
            "graph_analysis": self.graph_analysis,
            "can_connect": self.can_connect,
            "connection_path": self.connection_path,
            "status": self.status,
            "status_label": self.STATUS_LABELS.get(self.status, "未知"),
            "reject_reason": self.reject_reason,
            "result_product_id": self.result_product_id,
            "result_dataflow_id": self.result_dataflow_id,
            "created_by": self.created_by,
            "created_at": self.created_at.isoformat() if self.created_at else None,
            "updated_at": self.updated_at.isoformat() if self.updated_at else None,
            "processed_by": self.processed_by,
            "processed_at": (
                self.processed_at.isoformat() if self.processed_at else None
            ),
        }

    def update_status(self, new_status: str, processed_by: str | None = None) -> None:
        """
        更新订单状态

        Args:
            new_status: 新状态
            processed_by: 处理人
        """
        self.status = new_status
        self.updated_at = now_china_naive()
        if processed_by:
            self.processed_by = processed_by
            self.processed_at = now_china_naive()

    def set_extraction_result(
        self,
        domains: list[str] | None,
        fields: list[str] | None,
        purpose: str | None = None,
    ) -> None:
        """
        设置 LLM 提取结果

        Args:
            domains: 提取的业务领域列表
            fields: 提取的数据字段列表
            purpose: 数据用途
        """
        self.extracted_domains = domains
        self.extracted_fields = fields
        self.extraction_purpose = purpose
        self.updated_at = now_china_naive()

    def set_graph_analysis(
        self,
        analysis: dict[str, Any] | None,
        can_connect: bool,
        connection_path: dict[str, Any] | None = None,
    ) -> None:
        """
        设置图谱分析结果

        Args:
            analysis: 分析结果详情
            can_connect: 是否可连通
            connection_path: 连通路径
        """
        self.graph_analysis = analysis
        self.can_connect = can_connect
        self.connection_path = connection_path
        self.updated_at = now_china_naive()

    def set_result(
        self,
        product_id: int | None = None,
        dataflow_id: int | None = None,
    ) -> None:
        """
        设置订单结果关联

        Args:
            product_id: 生成的数据产品ID
            dataflow_id: 生成的数据流ID
        """
        if product_id is not None:
            self.result_product_id = product_id
        if dataflow_id is not None:
            self.result_dataflow_id = dataflow_id
        self.updated_at = now_china_naive()

    def reject(self, reason: str, processed_by: str | None = None) -> None:
        """
        驳回订单

        Args:
            reason: 驳回原因
            processed_by: 处理人
        """
        self.status = self.STATUS_REJECTED
        self.reject_reason = reason
        self.updated_at = now_china_naive()
        if processed_by:
            self.processed_by = processed_by
            self.processed_at = now_china_naive()

    def __repr__(self) -> str:
        return f"<DataOrder {self.order_no} ({self.status})>"