DataOps-platform/app/api/production_line/routes.py

import json
import logging
import requests
from flask import request, jsonify, current_app
from app.api.production_line import bp
from app.models.result import success, failed
from app.api.graph.routes import MyEncoder, connect_graph
from app.core.production_line import production_draw_graph
# from app.core.production_line.production_line import execute_production_line  # 注释掉原来的导入


logger = logging.getLogger(__name__)

# 生产线列表
@bp.route('/production/line/list', methods=['POST'])
def production_line_list():
    """
    获取生产线列表，支持分页和名称过滤
    
    Args (通过JSON请求体):
        current (int): 当前页码，默认为1
        size (int): 每页大小，默认为10
        name (str, optional): 名称过滤条件
        
    Returns:
        JSON: 包含生产线列表和分页信息的响应
    """
    try:
        receiver = request.get_json()
        page = int(receiver.get('current', 1))
        page_size = int(receiver.get('size', 10))
        name_filter = receiver.get('name', None)

        # 计算跳过的记录的数量
        skip_count = (page - 1) * page_size

        if name_filter:
            where_clause = f"n.name CONTAINS'{name_filter}'"
        else:
            where_clause = "TRUE"
        cql = f"""
        MATCH (n)
        WHERE (n:data_model OR n:data_resource OR n:data_metric) AND {where_clause}
        WITH id(n) AS id, n.name AS name, labels(n)[0] AS type,n
        RETURN  {{
            id: id,
            name: name,
            en_name: n.en_name,
            type: type
        }} AS result,n.time as time
        ORDER BY time desc
        SKIP {skip_count}
        LIMIT {page_size}
        """
        
        # 修复：使用正确的session方式执行查询
        driver = connect_graph()
        if not driver:
            return json.dumps(failed("无法连接到数据库"), ensure_ascii=False, cls=MyEncoder)
            
        with driver.session() as session:
            result = session.run(cql)
            data = result.data()
            records = []
            for item in data:
                records.append(item['result'])

            # 获取总量
            total_query = f"MATCH (n) WHERE (n:data_model OR n:data_resource OR n:data_metric) AND {where_clause}" \
                        f" RETURN COUNT(n) AS total"
            total_result = session.run(total_query).single()["total"]
        
        response_data = {'records': records, 'total': total_result, 'size': page_size, 'current': page}
        res = success(response_data, "success")
        return json.dumps(res, ensure_ascii=False, cls=MyEncoder)

    except Exception as e:
        res = failed({}, {"error": f"{e}"})
        return json.dumps(res, ensure_ascii=False, cls=MyEncoder)


# 根据生产线列表,传入id,绘制图谱
@bp.route('/production/line/graph', methods=['POST'])
def production_line_graph():
    """
    根据生产线ID绘制关系图谱
    
    Args (通过JSON请求体):
        id (int): 节点ID
        
    Returns:
        JSON: 包含图谱数据的响应
    """
    try:
        # 获取请求参数
        receiver = request.get_json()
        if not receiver or 'id' not in receiver:
            return json.dumps(failed("缺少必要参数: id"), ensure_ascii=False, cls=MyEncoder)
            
        id = receiver['id']
        # 修改: 专门处理ID为0的情况，将ID类型视为整数
        if id is None:
            return json.dumps(failed("节点ID不能为空"), ensure_ascii=False, cls=MyEncoder)
            
        # 确保ID是整数类型
        try:
            id = int(id)
        except (ValueError, TypeError):
            return json.dumps(failed("节点ID必须是整数"), ensure_ascii=False, cls=MyEncoder)
            
        # 修复：使用正确的session方式执行查询
        driver = connect_graph()
        if not driver:
            return json.dumps(failed("无法连接到数据库"), ensure_ascii=False, cls=MyEncoder)
            
        with driver.session() as session:
            # 检查节点是否存在
            check_query = """
            MATCH (n) WHERE id(n) = $nodeId 
            RETURN labels(n)[0] as type, n.name as name
            """
            result = session.run(check_query, nodeId=id)
            record = result.single()
            
            if not record:
                return json.dumps(failed(f"节点不存在: ID={id}"), ensure_ascii=False, cls=MyEncoder)
                
            type = record["type"]
            
        # 生成图谱
        data = production_draw_graph(id, type)
        
        # 检查返回结果是否包含错误
        if "error" in data:
            error_msg = data.pop("error")
            return json.dumps(failed(error_msg, data), ensure_ascii=False, cls=MyEncoder)
        
        return json.dumps(success(data, "success"), ensure_ascii=False, cls=MyEncoder)

    except Exception as e:
        logger.error(f"生成图谱失败: {str(e)}")
        return json.dumps(failed(str(e)), ensure_ascii=False, cls=MyEncoder)


"""
Manual execution API endpoint
Author: paul
Date: 2024-03-20
"""

@bp.route('/production/line/execute', methods=['POST'])
def production_line_execute():
    """
    手动执行数据资源加载 - 通过调用 Airflow REST API
    
    Args (通过JSON请求体):
        id (int): 数据资源ID
        target_table (str): 目标表名
        dependency_level (str): 依赖级别，默认为 "resource"
        
    Returns:
        JSON: 执行结果
    """
    try:
        # 获取请求参数
        receiver = request.get_json()
        if not receiver:
            return jsonify(failed("请求体不能为空"))
            
        #resource_id = receiver.get('id')
        target_table = receiver.get('target_table')
        dependency_level = receiver.get('dependency_level', 'resource')
            
        if not target_table:
            return jsonify(failed("目标表名不能为空"))
        
        # Airflow API 配置 - 从配置文件获取
        AIRFLOW_API_ENDPOINT = '/api/v1/dags/dataops_productline_manual_trigger_dag/dagRuns'
        airflow_base_url = current_app.config['AIRFLOW_BASE_URL']
        airflow_url = f"{airflow_base_url}{AIRFLOW_API_ENDPOINT}"
        
        # 构建请求数据
        payload = {
            "conf": {
                "target_table": target_table,
                "dependency_level": dependency_level
            }
        }
        
        # 设置认证信息（Basic Auth） - 从配置文件获取
        auth = (current_app.config['AIRFLOW_AUTH_USER'], current_app.config['AIRFLOW_AUTH_PASSWORD'])
        
        # 设置请求头
        headers = {
            'Content-Type': 'application/json'
        }
        
        # 记录请求信息到日志
        logger.info(f"准备调用 Airflow API: {airflow_url}")
        logger.info(f"请求参数: {json.dumps(payload, ensure_ascii=False, indent=2)}")
        
        # 调用 Airflow API
        response = requests.post(
            airflow_url,
            json=payload,
            auth=auth,
            headers=headers,
            timeout=30
        )
        
        # 检查响应状态
        if response.status_code == 200:
            airflow_result = response.json()
            
            # 打印Airflow API返回的结果到日志
            logger.info(f"Airflow API 返回结果: {json.dumps(airflow_result, ensure_ascii=False, indent=2)}")
            
            # 获取状态
            state = airflow_result.get("state")
            
            # 根据状态设置消息
            if state == "queued":
                message = "触发成功，正在执行"
                logger.info(f"DAG触发成功，状态: {state}, 运行ID: {airflow_result.get('dag_run_id')}")
                # 返回成功响应，包含完整的 Airflow 返回结果
                return jsonify(success(airflow_result, message))
            else:
                message = "无法触发执行，请联系管理员"
                logger.warning(f"DAG触发状态异常，状态: {state}, 运行ID: {airflow_result.get('dag_run_id')}")
                # 即使状态不是 queued，也返回完整的 Airflow 结果，但使用 failed 状态
                return jsonify(failed(message, airflow_result))
            
        else:
            # 处理错误响应
            error_msg = f"Airflow API 调用失败，状态码: {response.status_code}"
            try:
                error_detail = response.json()
                error_msg += f"，错误详情: {error_detail}"
            except:
                error_msg += f"，响应内容: {response.text}"
                
            logger.error(error_msg)
            return jsonify(failed(error_msg))
            
    except requests.exceptions.Timeout:
        error_msg = "调用 Airflow API 超时"
        logger.error(error_msg)
        return jsonify(failed(error_msg))
        
    except requests.exceptions.ConnectionError:
        error_msg = "无法连接到 Airflow 服务器"
        logger.error(error_msg)
        return jsonify(failed(error_msg))
        
    except requests.exceptions.RequestException as e:
        error_msg = f"请求 Airflow API 时发生错误: {str(e)}"
        logger.error(error_msg)
        return jsonify(failed(error_msg))
        
    except Exception as e:
        error_msg = f"执行数据资源加载失败: {str(e)}"
        logger.error(error_msg)
        return jsonify(failed(error_msg))