perf_test/carddef2sql.py

import ast
import json
import re
import sys
import traceback

import pandas as pd

# 聚合函数
AGGREGATION_MAP = {
    'SUM': 'SUM',
    'AVG': 'AVG',
    'CNT': 'COUNT',
    'MAX': 'MAX',
    'MIN': 'MIN',
    'CNT_DISTINCT': 'COUNT(DISTINCT {})'
}

AGGREGATION_SUFFIX_MAP = {
    'SUM': '求和',
    'AVG': '均值',
    'CNT': '计数',
    'MAX': '最大值',
    'MIN': '最小值',
    'CNT_DISTINCT': '去重计数'
}

# 筛选操作符
FILTER_OPERATOR_MAP = {
    'BT': {"val_nums": 2, 'template': "{field} BETWEEN {value_1} AND {value_2}"},
    'CLOSE_BT_OPEN': {"val_nums": 2, 'template': "{field} >= {value_1} AND {field} < {value_2}"},
    'EQ': {"val_nums": 1, 'template': "{field} = {value}"},
    'GE': {"val_nums": 1, 'template': "{field} >= {value}"},
    'GT': {"val_nums": 1, 'template': "{field} > {value}"},
    'IN': {"val_nums": 9, 'template': "{field} IN ({values})"},
    'IS_NULL': {"val_nums": 0, 'template': "{field} IS NULL"},
    'LE': {"val_nums": 1, 'template': "{field} <= {value}"},
    'LT': {"val_nums": 1, 'template': "{field} < {value}"},
    'NE': {"val_nums": 1, 'template': "{field} != {value}"},
    'NI': {"val_nums": 9, 'template': "{field} NOT IN ({values})"},
    'NOT_NULL': {"val_nums": 0, 'template': "{field} IS NOT NULL"},
    'OPEN_BT_CLOSE': {"val_nums": 2, 'template': "{field} > {value_1} AND {field} <= {value_2}"},
    'OPEN_BT_OPEN': {"val_nums": 2, 'template': "{field} > {value_1} AND {field} < {value_2}"},
    'CONTAINS': {"val_nums": 1, 'template': "{field} LIKE '%{value}%'"},
    'NOT_CONTAINS': {"val_nums": 1, 'template': "{field} NOT LIKE '%{value}%'"},
    'STARTS_WITH': {"val_nums": 1, 'template': "{field} LIKE '{value}%'"},
    'NOT_STARTS_WITH': {"val_nums": 1, 'template': "{field} NOT LIKE '{value}%'"},
    'ENDS_WITH': {"val_nums": 1, 'template': "{field} LIKE '%{value}'"},
    'NOT_ENDS_WITH': {"val_nums": 1, 'template': "{field} NOT LIKE '%{value}'"},
    'CUSTOM': 'CUSTOM',
    'SPARK_EXPR': 'SPARK_EXPR'
}

# 引用
IDENTIFIER_QUOTE = '`'
QUOTE_FLAG = True

# 副词
ADV_FILTER_EXP_MAP = {
    'TODAY': "{field} = '{{today}}'",
    'YESTERDAY': "{field} = date_sub('{{today}}', 1)",
    'DAY_BEFORE_YESTERDAY': "{field} = date_sub('{{today}}', 2)",
    'LAST_14_DAY': "{field} between date_sub('{{today}}', 13) and '{{today}}'",
    'LAST_1_YEAR': "{field} between date_sub('{{today}}', 364) and '{{today}}'",
    'LAST_30_DAY': "{field} between date_sub('{{today}}', 29) and '{{today}}'",
    'LAST_7_DAY': "{field} between date_sub('{{today}}', 6) and '{{today}}'",
    'LAST_90_DAY': "{field} between date_sub('{{today}}', 89) and '{{today}}'",
    'LAST_MONTH': "{field} between datesub(add_months('{{today}}', -1), day('{{today}}') - 1) and date_sub('{{today}}', day('{{today}}'))",
    'LAST_WEEK': "{field} between date_sub('{{today}}', case when dayofweek('{{today}}') = 1 then 13 else dayofweek('{{today}}')+5 end) and date_sub('{{today}}', case when dayofweek('{{today}}') = 1 then 7 else dayofweek('{{today}}')-1 end)",
    'MONTH_BEFORE_LAST_MONTH': "{field} between date_sub(add_months('{{today}}', -2), day(add_months('{{today}}', -2))-1) and date_sub(add_months('{{today}}', -1), day(add_months('{{today}}', -1)))",
    'MONTH_TO_DAY': "{field} between date_sub('{{today}}', day('{{today}}')-1) and '{{today}}'",
    'MONTH_TO_YESTERDAY': "{field} between date_sub('{{today}}', day('{{today}}')-1) and date_sub('{{today}}', 1)",
    'QUARTER_TO_DAY': """{field} between concat(year('{{today}}'), case when quarter('{{today}}') = 1 then '-01-01'
                                                                     when quarter('{{today}}') = 2 then '-04-01'
                                                                     when quarter('{{today}}') = 3 then '-07-01'
                                                                     when quarter('{{today}}') = 4 then '-10-01' end) and '{{today}}'""",
    'QUARTER_TO_YESTERDAY': """{field} between concat(year('{{today}}'), case when quarter('{{today}}') = 1 then '-01-01'
                                                                                when quarter('{{today}}') = 2 then '-04-01'
                                                                                when quarter('{{today}}') = 3 then '-07-01'
                                                                                when quarter('{{today}}') = 4 then '-10-01' end) and date_sub('{{today}}', 1)""",
    'WEEK_TO_DAY': "{field} between date_sub('{{today}}', (dayofweek('{{today}}')+5)%7) and '{{today}}'",
    'WEEK_TO_YESTERDAY': "{field} between date_sub('{{today}}', (dayofweek('{{today}}')+5)%7) and date_sub('{{today}}', 1)",
    'YEAR_TO_DAY': "{field} between concat(year('{{today}}'), '-01-01') and '{{today}}'",
    'YEAR_TO_LAST_MONTH': "{field} between concat(year('{{today}}'), '-01-01') and date_sub('{{today}}', day('{{today}}'))",
    'YEAR_TO_LAST_QUARTER': """{field} between concat(year('{{today}}'), '-01-01') and date_sub(concat(year('{{today}}'), case when quarter('{{today}}') = 1 then '-01-01'
                                                                                when quarter('{{today}}') = 2 then '-04-01'
                                                                                when quarter('{{today}}') = 3 then '-07-01'
                                                                                when quarter('{{today}}') = 4 then '-10-01' end), 1)""",
    'YEAR_TO_YESTERDAY': "{field} between concat(year('{{today}}'), '-01-01') and date_sub('{{today}}', 1)",
}

# 自带日期转换
PARTIAL_DATE_EXPRESSION = {
    'day': "`{old}` as `{new}`",
    'month': "concat(year(`{old}`), '-', lpad(month(`{old}`), 2, '0') as `{new}`",
    'year': "year(`{old}`) as `{new}`",
    'quarter': "concat(year(`{old}`), '-S', quarter(`{old}`)) as `{new}`",
    'week': "weekofyear(`{old}`) as `{new}`",
    'dayofweek': "dayofweek(`{old}`)-1 as `{new}`",
    'hour': "hour(`{old}`)-8 as `{new}`",
    'minute': "minute(`{old}`)-8 as `{new}`",
}

# 获取新增字段
def get_added_fields_info(added_fields_df):
    if added_fields_df.empty:
        return {}
    added_fields_info = {}
    for _, row in added_fields_df.iterrows():
        try:
            content = json.loads(row['calc_field_logic'])
        except:
            print(f'ERROR: 新增字段解析错误: {row["calc_field_logic"]}')
            continue
        field_name = content['name']
        field_id = content['fdId']
        added_fields_info[field_id] = {"field_name": field_name, 'calculation': content}
        added_fields_info[field_name] = {"field_id": field_id, 'calculation': content}
    return added_fields_info

def get_fid_name_map(field_def_df):
    field_id_name = {}
    for i, row in field_def_df.iterrows():
        field_id_name[row['field_id']] = row['field_name']
    return field_id_name

# 字段映射关系
def get_fields_rename_map(field_info):
    ret = {}
    try:
        tmp_map = json.loads(field_info)
    except:
        return ret
    dimensions, metrics = tmp_map.get("dimensions"), tmp_map.get("metrics")
    if dimensions and dimensions != 'null':
        for one_map in dimensions:
            ret[one_map["name"]] = one_map["alias"]
    if metrics and metrics != 'null':
        for one_map in metrics:
            ret[one_map["name"]] = one_map["alias"]
    return ret

# 递归解析嵌套的计算字段
def resolve_calculation_formula(formula, calculation_fields, visited=None):
    if not formula:
        return formula
    if visited is None:
        visited = set()

    def replace_calculation_field(match):
        field_key = match.group(1).strip()
        field_def = calculation_fields.get(field_key)
        if not field_def:
            return match.group(0)
        field_id = field_def.get("field_id") or field_key
        if field_id in visited:
            raise ValueError(f"计算字段存在循环引用: {field_key}")
        nested_formula = field_def["calculation"].get("formula", "")
        if "consolidation" in nested_formula:
            return match.group(0)
        resolved = resolve_calculation_formula(nested_formula, calculation_fields, visited | {field_id})
        return f"({resolved})"

    return re.sub(r"\[([^\[\]]+)\]", replace_calculation_field, formula)

def build_with_part(new_date_fields, new_dimension_fields, dataset_fid_name_map, added_fields_info, dataset_id):
    sql_part = 'WITH tmp as (\nSELECT *,\n'
    with_expressions = []
    for fid, new_name in new_date_fields:
        old_fid, partial_date = fid.split('_')
        if old_fid in dataset_fid_name_map:
            old_name = dataset_fid_name_map[old_fid]
        elif old_fid in added_fields_info:
            old_name = added_fields_info[old_fid]["calculation"]["formula"].replace("[", "").replace("]", '')
        else:
            raise ValueError(f"字段 {fid} {new_name} 不存在")
        tmp_part = PARTIAL_DATE_EXPRESSION.get(partial_date, None)
        if tmp_part:
            tmp_part = tmp_part.format(old=old_name, new=new_name)
            with_expressions.append(tmp_part)
        else:
            raise ValueError(f"日期转换方式 {partial_date} 不存在")
    for fid, new_name in new_dimension_fields:
        field_def = added_fields_info[fid]
        new_name = field_def["field_name"]
        formula = field_def["calculation"]["formula"]
        if "consolidation" in formula:
            consolidation_dict = json.loads(formula)["consolidation"]
            tmp_part = get_consolidation_field(consolidation_dict)
            tmp_part += f" AS `{new_name}`"
        else:
            # 递归解析计算字段是否有嵌套情况
            formula = resolve_calculation_formula(formula, added_fields_info, {fid})
            tmp_part = quote_identifier(formula, formula=True) + f" AS `{new_name}`"
        with_expressions.append(tmp_part)
    sql_part += ',\n'.join(with_expressions)
    sql_part += f"\nFROM `{dataset_id}\n`"
    return sql_part

# 处理计算字段
def process_measure_fields(measure_fields, measure_aggs, calculation_fields, card_id, card_name):
    ## 数值字段数量 小于 聚合函数数量，不合法
    if len(measure_fields) < len(measure_aggs):
        print(f"警告: 卡片 {card_id} {card_name}: 数值字段数量小于聚合函数数量，不合法")
        print(f"警告: 卡片 {card_id} {card_name}: 不添加任何数值字段.")
        return [], [], [], False
    ## 数值字段 大于 聚合函数数量，存在聚合类型的计算字段，尝试填充
    elif len(measure_fields) > len(measure_aggs):
        ## 计算数值字段数量
        num_cals = 0
        for field in measure_fields:
            if field in calculation_fields: # and calculation_fields[field]["calculation"]["isAggregated"] is True:
                num_cals += 1
        ## 如果不存在任何计算字段，补全剩余的NUL聚合函数
        if num_cals == 0:
            measure_aggs.extend(['NULL'] * (len(measure_fields) - len(measure_aggs)))
            return [quote_identifier(field) for field in measure_fields], measure_aggs, [False] * len(measure_fields), True
        ## 如果存在计算字段，且相加后的 聚合函数数量 仍小于 数值字段数量，不合法
        if num_cals + len(measure_aggs) != len(measure_fields):
            print(f"警告: 卡片 {card_id} {card_name}: 数值字段数量大于聚合函数数量，不合法")
            print(f"警告: 卡片 {card_id} {card_name}: 不添加任何数值字段.")
            return [], [], [], False
    ## 通过验证，填充聚合函数
    new_measure_fields, new_measure_aggs, measure_is_aggregated, agg_flag = [], [], [], False
    for i, field in enumerate(measure_fields):
        ## 非计算字段
        if field not in calculation_fields:
            new_measure_fields.append(quote_identifier(field))
            new_measure_aggs.append(measure_aggs.pop(0))
            measure_is_aggregated.append(False)
        ## 计算字段
        else:
            formula = calculation_fields[field]["calculation"]["formula"]
            formula = resolve_calculation_formula(formula, calculation_fields, {calculation_fields[field]["field_id"]})
            new_measure_fields.append(quote_identifier(formula, formula=True))
            if calculation_fields[field]["calculation"]["isAggregated"] is True:
                new_measure_aggs.append("NUL")
                measure_is_aggregated.append(True)
                agg_flag = True
            else:
                new_measure_aggs.append(measure_aggs.pop(0))
                measure_is_aggregated.append(False)
    return new_measure_fields, new_measure_aggs, measure_is_aggregated, agg_flag

# sql部分去重
def dedupe_sql_parts(parts):
    deduped = []
    seen = set()
    for part in parts:
        if not part or part in seen:
            continue
        seen.add(part)
        deduped.append(part)
    return deduped

def quote_identifier(identifier, formula=False):
    if not QUOTE_FLAG:
        return identifier
    if not identifier:
        return ''
    # 简单处理，如果包含非字母数字下划线或可能是关键字，则加反引号
    # 更复杂的关键字检查可以添加
    if formula:
        params = re.findall(r"\[DYNAMIC_PARAMS\.\w+\]", identifier)
        for p in params:
            subs = p[1:-1]
            subs = "{{{"+subs+"}}}"
            identifier = identifier.replace(p, subs, 1)
        # 仅替换配置里用于包裹字段名的 [字段]，保留 Hive 下标访问里的 [2] 等表达式
        def replace_bracket_identifier(match):
            content = match.group(1)
            if re.fullmatch(r"\d+", content.strip()):
                return match.group(0)
            return f"{IDENTIFIER_QUOTE}{content}{IDENTIFIER_QUOTE}"

        identifier = re.sub(r"\[([^\[\]]+)\]", replace_bracket_identifier, identifier)
    else:
        identifier = identifier.replace('\n', ' ')
        if not re.match(r'[a-zA-Z_][a-zA-Z0-9_]*$', identifier):
            return f'{IDENTIFIER_QUOTE}{identifier}{IDENTIFIER_QUOTE}'
    return identifier

def parse_multi_value_field(field_value):
    # 解析包含多个值的字段
    if not field_value or field_value == "":
        return []
    try:
        res = ast.literal_eval(field_value)
    except Exception:
        print(field_value)
        print(traceback.format_exc())
    return ast.literal_eval(field_value)

# 处理过滤条件的操作符
def get_format_args(field, fd_type, op_dict, values):
    # 按照数据类型及操作符，判断是否需要加引号
    if fd_type in ('DECIMAL', 'DOUBLE', 'INT', 'FLOAT', 'LONG', 'SHORT'):
        values = [x for x in values if x]
    elif fd_type in ('DATE', 'STRING', 'SUB_DATE', 'TIMESTAMP'):
        if op_dict.get('quote', True):
            values = [f"'{x}'" for x in values]
    elif fd_type == 'BOOL':
        values = [value.upper() for value in values]
    else:
        pass
    # 按照操作符所需参数个数构造format参数
    format_dict = {}
    value_nums = op_dict['val_nums']
    if value_nums == 9:
        format_dict.update(**{"values": ", ".join(values)})
    elif value_nums == 2:
        format_dict.update(**{"value_1": values[0], "value_2": values[1]})
    elif value_nums == 1:
        format_dict.update(**{"value": values[0]})
    else:
        pass
    format_dict["field"] = field
    return format_dict

# 处理过滤条件中的consolidation
def get_consolidation_field(consolidation_dict):
    field_name = quote_identifier(consolidation_dict["sourceName"])
    group_type = consolidation_dict["groupType"]
    fd_type = consolidation_dict["sourceFdType"]
    group_rules = consolidation_dict.get('groups')
    fixed_step = consolidation_dict.get('fixedStepSetting')
    when_part = []
    else_part = None
    if group_type == 'ITEM':
        for group in group_rules:
            group_name = group["groupName"]
            if group.get('isOtherGroup', False):
                else_part = f"ELSE '{group_name}'"
            else:
                selected_values = group.get('selectedValues', [])
                when_value = str(selected_values)
                when_value = when_value[1:-1] # 去除中括号
                if when_value is None:
                    when_part.append(f"WHEN {field_name} IS NULL THEN '{group_name}'")
                else:
                    when_part.append(f"WHEN {field_name} IN ({when_value}) THEN '{group_name}'")
    elif group_type == "CONDITION":
        for group in group_rules:
            group_name = group["groupName"]
            if group.get('isOtherGroup', False):
                else_part = f"ELSE '{group_name}'"
            else:
                rules = group["rules"]
                cond_list = []
                combine_type = " " + rules["combineType"] + " " # 加空格以便join
                for cond in rules["conditions"]:
                    filter_type = cond["filterType"]
                    filter_value = cond["filterValue"]
                    op_dict = FILTER_OPERATOR_MAP[filter_type]
                    format_args = get_format_args(field_name, fd_type, op_dict, [filter_value])
                    cond_str = op_dict["template"].format(**format_args)
                    cond_list.append(cond_str)
                when_str = "WHEN " + combine_type.join(cond_list) + f" THEN '{group_name}'"
                when_part.append(when_str)
    elif group_type == "CUSTOM_STEP":
        for group in group_rules:
            group_name = group["groupName"]
            if group.get('isOtherGroup', False):
                else_part = f"ELSE '{group_name}'"
            else:
                setting = group['customStepSetting']
                operator = setting['operator']
                start = setting['startValue']
                end = setting['endValue']
                condition = ''
                if operator == 'BT':
                    condition = f"{field_name} BETWEEN {start} AND {end}"
                elif operator == 'OPEN_BT_CLOSE':
                    condition = f"{field_name} > {start} AND {field_name} <= {end}"
                elif operator == 'OPEN_BT_OPEN':
                    condition = f"{field_name} > {start} AND {field_name} < {end}"
                elif operator == 'CLOSE_BT_OPEN':
                    condition = f"{field_name} >= {start} AND {field_name} < {end}"
                else:
                    raise ValueError(f"未知的操作符: {operator}")
                when_part.append(f"WHEN {condition} THEN '{group_name}'")
    elif group_type == "FIXED_STEP":
        start = fixed_step['startValue']
        end = fixed_step['endValue']
        step = fixed_step['stepSize']
        # 生成每个区间的case when部分
        lower = start
        while lower < end:
            upper = lower + step
            case_part = f"WHEN {field_name} >= {lower} AND {field_name} < {upper} THEN '{lower}-{upper}'"
            when_part.append(case_part)
            lower = upper
        # 处理最后一个区间
        case_part = f"WHEN {field_name} >= {lower} AND {field_name} <= {end} THEN '{lower}-{end}'"
        when_part.append(case_part)
        else_part = "ELSE NULL"
    else:
        raise ValueError(f"未知的groupType: {group_type}")
    field = "CASE "
    field += "\n".join(when_part)
    if else_part:
        field += f"\n{else_part}"
    field += "\nEND"
    return field

def parse_filter_string(filter_relation_str):
    conditions = {}
    if not filter_relation_str or filter_relation_str == "[]":
        return conditions

    raw_conditions = json.loads(filter_relation_str)
    for cond_dict in raw_conditions:
        fdId = cond_dict.get("fdId")
        field = cond_dict.get("name")
        fd_type = cond_dict.get("fdType")
        op_name = cond_dict.get("filterType")
        op_dict = FILTER_OPERATOR_MAP.get(op_name)
        values = cond_dict.get("filterValue") # list
        is_aggregated = cond_dict.get("isAggregated", False)

        # 检查条件合法
        if any([fdId is None, field is None, fd_type is None, op_name is None, values is None]):
            print(f"fdId: {fdId} field: {field} fd_type: {fd_type} op_name: {op_name} values: {values}")
            print(f"警告: 无法解析筛选条件，缺少必须字段，跳过此条件。")
            continue
        if op_dict is None:
            print(f"警告: 无法解析筛选条件，未定义的筛选类型: {op_name}，跳过此条件。")
            continue

        # 特殊操作符
        if op_dict == 'CUSTOM':
            if "advFilter" not in cond_dict:
                print(f"警告: CUSTOM筛选类型不存在advFilter, 跳过此条件。")
                continue
            if 'formula' in cond_dict:
                field = quote_identifier(cond_dict['formula'], formula=True)
            else:
                field = quote_identifier(cond_dict['name'])
            expression = ADV_FILTER_EXP_MAP.get(cond_dict["advFilter"])
            if not expression:
                print(f"警告: CUSTOM筛选类型出现未定义的advFilter: {cond_dict['advFilter']}, 跳过此条件。")
                continue
            expression = expression.format(field=field)
            conditions[fdId] = {"exp": expression, "agg": is_aggregated}
            continue
        elif op_dict == 'SPARK_EXPR':
            if 'formula' in cond_dict:
                formula = quote_identifier(cond_dict['formula'], formula=True)
                conditions[fdId] = {"exp": formula, "agg": is_aggregated}
            else:
                if isinstance(cond_dict['filterValue'], list) and len(cond_dict['filterValue']) == 1:
                    field = quote_identifier(cond_dict['name'])
                    value = cond_dict['filterValue'][0]
                    conditions[fdId] = {"exp": f"{field} = {value}", "agg": is_aggregated}
                else:
                    print(f"警告: 无法解析筛选条件，SPARK_EXPR中未定义。跳过此条件。")
            continue

        # 处理条件
        value_nums = op_dict["val_nums"]
        if value_nums != 0 and len(values) != value_nums:
            print(f"警告: 无法解析筛选条件，值数量与操作符不匹配。跳过此条件。")
            continue

        field = quote_identifier(field)
        # consolidation 情况，将consolidation公式替换条件左边的field
        if "consolidation" in cond_dict:
            consolidation = cond_dict["consolidation"]
            consolidation_field = get_consolidation_field(consolidation)
            if not consolidation_field:
                print(f"警告: 无法解析consolidation字段。跳过此条件。")
                continue
            else:
                field = consolidation_field
        else:
            # 公式，非 consolidation情况
            if "formula" in cond_dict:
                field = quote_identifier(cond_dict["formula"], formula=True)
            
        if op_name in ("NI", "IN") and len(values) == 0:
            print(f"警告: 无法解析筛选条件，IN或NI中参数个数为0。跳过此条件。")
            continue
            
        # 特殊情况
        if op_name in ('NI', 'IN') and None in values:
            conditions[fdId] = {"exp": f"{field} IS NOT NULL", "agg": is_aggregated}
            values = [x for x in values if x is not None]
            if len(values) == 0:
                continue
        
        # 填充模板所需要的参数
        format_args = get_format_args(field, fd_type, op_dict, values)
        condition_str = op_dict["template"].format(**format_args)
        conditions[fdId] = {"exp": condition_str, "agg": is_aggregated}
    return conditions

def build_sql_query(card_data, added_fields_info, dataset_fid_name_map):
    card_id = card_data["card_id"]
    card_name = card_data["card_name"]
    dataset_id = card_data.get("ds_id")
    if not dataset_id:
        print(f"错误: {card_id} {card_name} 数据集ID为空.")
        return "", "", "", ""
    
    added_fields_info = get_added_fields_info(added_fields_info)
    dataset_fid_name_map = get_fid_name_map(dataset_fid_name_map)

    dimension_fids = parse_multi_value_field(card_data.get("field_id", []))
    dimension_fields = parse_multi_value_field(card_data.get("field_name", []))
    dimension_fid_name_map = dict(zip(dimension_fids, dimension_fields))
    dimension_name_fid_map = dict(zip(dimension_fields, dimension_fids))

    measure_fids = parse_multi_value_field(card_data.get("num_value_field_id", []))
    measure_fields = parse_multi_value_field(card_data.get("num_value_field_name", []))
    measure_aggs = parse_multi_value_field(card_data.get("num_value_field_merge_way", []))
    filter_relation_str = card_data.get("filters_field_value_name_rela")

    sort_fids = parse_multi_value_field(card_data.get("sort_field_id", []))
    sort_fields = parse_multi_value_field(card_data.get("sort_field_name", []))
    sort_method = parse_multi_value_field(card_data.get("sort_way", []))

    all_field_ids = dimension_fids + \
                    parse_multi_value_field(card_data.get("filters_field_id", [])) + \
                    sort_fids + \
                    measure_fids
    all_field_names = dimension_fields + \
                    parse_multi_value_field(card_data.get("filters_field_name", [])) + \
                    sort_fields + \
                    measure_fields
    all_field_id_name_map = dict(zip(all_field_ids, all_field_names))

    # 处理字段重命名关系
    fields_rename_map = get_fields_rename_map(card_data.get("field_info", ""))

    # 处理field_id与重命名关系，用于筛选Order by子句中的字段
    # 需要处理的只有日期转换类型，将转换前的原始字段名加入map
    # 只需要更新有重命名的字段即可
    selected_fid_alias_map = dict(zip(dimension_fids+measure_fids, dimension_fields+measure_fields))

    # 构建WITH
    with_part = ""
    new_date_fields = []
    # 日期转换
    for fid, name in all_field_id_name_map.items():
        fid_splits = fid.split('_')
        if len(fid_splits) == 2:
            new_date_fields.append((fid, name))
            old_fid = fid_splits[0]
            selected_fid_alias_map[old_fid] = name
    # 新增维度字段
    new_dimension_fields = []
    for fid, name in dimension_fid_name_map.items():
        if fid in added_fields_info:
            new_dimension_fields.append((fid, name))
    # 如果有新增日期字段、新增维度字段，构建WITH
    if new_date_fields or new_dimension_fields:
        with_part = build_with_part(new_date_fields, new_dimension_fields, dataset_fid_name_map, added_fields_info, dataset_id)
    
    # 构建SELECT
    select_parts = []
    has_aggregation = False
    non_aggregated_select_parts = []
    
    # 添加维度字段
    for field in dimension_fields:
        fid = dimension_name_fid_map[field]
        alias = fields_rename_map.get(field)
        if alias and alias != "null":
            select_parts.append(f"{quote_identifier(field)} AS {quote_identifier(alias)}")
            selected_fid_alias_map[fid] = alias
        else:
            select_parts.append(f"{quote_identifier(field)}")
            selected_fid_alias_map[fid] = field
    
    # 加工计算字段
    new_measure_fields, measure_aggs, measure_is_aggregated, agg_flag = process_measure_fields(measure_fields, measure_aggs, added_fields_info, card_id, card_name)
    if agg_flag:
        has_aggregation = True
    for i, field in enumerate(new_measure_fields):
        fid = measure_fids[i]
        alias = fields_rename_map.get(field.strip('`'))
        # measure_agg是NUL，不需要聚合（等同于维度字段）或公式本身已经有聚合函数
        agg_func_template = AGGREGATION_MAP.get(measure_aggs[i])
        if not agg_func_template:
            if not alias or alias == "null":
                alias = measure_fields[i]
            select_parts.append(f"{field} AS {quote_identifier(alias)}")
            # 属于计算字段，但没有聚合函数，等同于维度字段，需要加入groupbyby。
            if not measure_is_aggregated[i] and field and re.search(r"\b(sum|avg|count|max|min|stddev|variance|collect_list|collect_set|percentile|percentile_approx)|\s*\(", field, flags=re.IGNORECASE) is None:
                non_aggregated_select_parts.append(field)
            selected_fid_alias_map[fid] = alias
        else:
            has_aggregation = True
            # 特殊处理 count distinct
            if '{}' in agg_func_template:
                agg_expression = agg_func_template.format(field)
            else:
                agg_expression = f"{agg_func_template}({field})"
            # 添加别名
            if not alias or alias == "null":
                suffix = AGGREGATION_SUFFIX_MAP.get(measure_aggs[i])
                alias = f"{measure_fields[i]}_{suffix}"
            select_parts.append(f"{agg_expression} AS {quote_identifier(alias)}")
            selected_fid_alias_map[fid] = alias
    
    if not select_parts:
        print(f"错误: {card_id} {card_name} 没有select字段。")
        return '', '', '', ''
    else:
        select_clause = "SELECT " + ",\n    ".join(select_parts)
    
    # 构建FROM
    if with_part:
        from_clause = "FROM tmp"
    else:
        from_clause = f"FROM {quote_identifier(str(dataset_id))}"
    
    # 构建WHERE
    filter_conditions = {}
    try:
        filter_conditions = parse_filter_string(filter_relation_str)
    except Exception as e:
        print(f"错误: 卡片 {card_id} {card_name} 解析筛选条件出错：{e}。WHERE字句缺失。")
        print("详细错误信息:")
        print(traceback.format_exc())

    # 构建GROUPBY
    group_by_clause = ""
    if has_aggregation:
        group_by_parts = [quote_identifier(field) for field in dimension_fields]
        group_by_parts.extend(non_aggregated_select_parts)
        group_by_parts = dedupe_sql_parts(group_by_parts)
        if group_by_parts:
            group_by_clause = "GROUP BY " + ", ".join(group_by_parts)
    
    # 构建ORDERBY
    order_by_clause = ""
    if sort_fields and sort_method and len(sort_fields) == len(sort_method):
        order_by_parts = []
        for i, field in enumerate(sort_fields):
            fid = sort_fids[i]
            if fid not in selected_fid_alias_map:
                continue
            alias = selected_fid_alias_map[fid]
            order_by_parts.append(f"{quote_identifier(alias)} {sort_method[i]}")
        if order_by_parts:
            order_by_clause = "ORDER BY " + ", ".join(order_by_parts)
    
    # 组装SQL
    sql_parts = [with_part, select_clause, from_clause]
    # 返回 select, where, groupby, orderby
    return ("\n".join(sql_parts)).strip(), json.dumps(filter_conditions, ensure_ascii=False), group_by_clause, order_by_clause

def generate(start=None, end=None, test_card_id=None):
    res_list = []
    df = pd.read_csv("data/card.csv").fillna("").reset_index()
    add_field_info = pd.read_csv("data/calc.csv").fillna('').set_index("card_id")
    all_field_info = pd.read_csv("data/field.csv").fillna('').set_index("ds_id")
    for i, row in df.iterrows():
        if start and i < start:
            continue
        if end and i > end:
            break
        card_id = row["card_id"]
        if test_card_id and card_id != test_card_id:
            continue
        if row["card_type_cd"] != '图表' or row["ds_id"] == "":
            continue

        try:
            added_fields_info = add_field_info.loc[[card_id]]
        except KeyError:
            added_fields_info = pd.DataFrame()
        try:
            dataset_fid_name_map = all_field_info.loc[[row["ds_id"]]]
        except KeyError:
            print(f"错误: 没有数据及字段信息: {card_id}")
            continue

        select, where, groupby, orderby = '', '', '', ''
        try:
            select, where, groupby, orderby = build_sql_query(row, added_fields_info, dataset_fid_name_map)
        except Exception as e:
            print(f"错误: 卡片 {card_id} 发生未知错误: {e}")
            print(i, traceback.format_exc())
        if not select:
            print(f"{card_id} 生成失败")
            continue
        res_list.append([str(card_id), str(row["card_name"]), select, where, groupby, orderby])
    res_df = pd.DataFrame(res_list, columns=["card_id", "card_name", "select", 'where', 'groupby', 'orderby'])
    return res_df

if __name__ == "__main__":
    df = generate()
    df.to_parquet("output/sql.parquet")
    df.to_excel("output/sql.xlsx")