Python数据源描述

class TableX(Struct):
    
    # Text,Date,Number,SmartList,Dimension类型：字段描述description(非必填，图表组件需要，建议赋值)
    channelType: Text = Text(description='channelType_description')
    time: Date = Date(description='time_description')
    cost: Number = Number(description='cost_description')
    # SmarList类型的必填字段：element_name，path
    # 业务必填字段：value_field(前端组件需要)，含义为该字段对应的值列表，例：value_field=['已读'，'未读']
    read: SmartList = SmartList(element_name='read_sml', path='/yjx',value_field=value_field,
                                                   description=description)

    # Dimension类型的必填字段：element_name，path
    # 业务必填字段：value_key(前端组件需要)，含义为：该字段对应的维度表达式，例：value_key="staff{IDescendant(#root,0)}"
    staff: Dimension = Dimension(element_name='staff_dim', path='/yjx',value_key=value_key,
                                                   description=description)

@as_datasource(struct=TableX)

def main(p1, p2):

    new_cube = DataTableMySQL(element_name='Order_detail',path='/map_dimension/zhouliufab/Demo_data/Demo_data/yewu/Data')
    df = new_cube.select(columns=['orderdate','cost','number','sales'])
    print("数据库查询出来的信息为:", df)
    # warnings!!!：df 必须是DataFrame格式
    return df

class TableX(Struct):
    orderdate: Date = Date(description='订单日期')
    cost: Number = Number(description='成本')
    number: Number = Number(description='数量')
    sales: Number = Number(description='销售额')
@as_datasource(struct=TableX)

def main(p1, p2):

    new_cube = DataTableMySQL(element_name='Order_detail',path='/map_dimension/zhouliufab/Demo_data/Demo_data/yewu/Data')
    df = new_cube.select(columns=['orderdate','cost','number','sales'])
    print("数据库查询出来的信息为:", df)
    # warnings!!!：df 必须是DataFrame格式
    return df

salary_model = BusinessModel(element_name='New_Cube1', path='/A_Python')
dict_fields = {}

for col in salary_model.meta.logicTable.columns:
    print("=====================")
    print(col.logicInfo)
    if not col.logicInfo:
        continue
    value_type = col.logicInfo.get('valueType')
    if value_type == 8:  # Dimension
        # field_name = col.logicInfo.get('elementDetail').get('elementName')
        field_name = col.baseInfo.name
        element_name = col.logicInfo.get('dimensionName')
        path = col.logicInfo.get('elementDetail').get('path')
        description = col.baseInfo.description
        dict_fields[field_name]: Dimension = Dimension(element_name=element_name, path=path, description=description)
    elif value_type == 3:  # SmartList
        # field_name = col.logicInfo.get('elementDetail').get('elementName')
        field_name = col.baseInfo.name
        element_name = col.logicInfo.get('valueKey')
        path = col.logicInfo.get('elementDetail').get('path')
        description = col.baseInfo.description
        dict_fields[field_name]: SmartList = SmartList(element_name=element_name, path=path, description=description)
    elif value_type == 15:  # Number
        field_name = col.baseInfo.name
        description = col.baseInfo.description
        dict_fields[field_name]: Number = Number(field_name, description=description)
    elif value_type == 1:  # Text
        field_name = col.baseInfo.name
        description = col.baseInfo.description
        dict_fields[field_name]: Text = Text(field_name, description=description)
    elif value_type == 11:  # Date
        field_name = col.baseInfo.name
        description = col.baseInfo.description
        dict_fields[field_name]: Date = Date(field_name, description=description)
Table = StructMeta('TableX', (), dict_fields)
@as_datasource(struct=Table)
def main(p1, p2):
    new_cube = DataTableMySQL(element_name='table_68e3',path='/A_Python')

    df = new_cube.select()
    print("数据库查询出来的信息为:", df)

    # warnings!!!：df 必须是DataFrame格式
    return df