作为一个资深的py算法工程师，你需要对hivesql，clickhouse，mysql，sqlserver进行数据血缘分析解析得到输出表和字段级别映射，输出一套完整的python代码

以下是一个基本的 Python 代码示例，用于对 Hivesql、ClickHouse、MySQL 和 SQL Server 数据库进行数据血缘分析解析，并输出表和字段级别映射： ``` import sqlparse import re import pyodbc import mysql.connector from clickhouse_driver import Client # 数据库连接配置 hiveserver2_host = 'localhost' hiveserver2_port = 10000 clickhouse_host = 'localhost' clickhouse_port = 9000 mysql_host = 'localhost' mysql_port = 3306 mysql_user = 'root' mysql_password = 'password' mysql_database = 'test' sqlserver_host = 'localhost' sqlserver_port = 1433 sqlserver_user = 'sa' sqlserver_password = 'password' sqlserver_database = 'test' # 数据库连接函数 def connect_to_hiveserver2(): conn = pyodbc.connect('DSN=hive;HOST={0};PORT={1}'.format(hiveserver2_host, hiveserver2_port), autocommit=True) return conn.cursor() def connect_to_clickhouse(): client = Client(host=clickhouse_host, port=clickhouse_port) return client def connect_to_mysql(): conn = mysql.connector.connect(host=mysql_host, port=mysql_port, user=mysql_user, password=mysql_password, database=mysql_database) return conn.cursor() def connect_to_sqlserver(): conn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER={0},{1};DATABASE={2};UID={3};PWD={4}'.format(sqlserver_host, sqlserver_port, sqlserver_database, sqlserver_user, sqlserver_password)) return conn.cursor() # 解析 SQL 语句函数 def parse_sql(sql): parsed = sqlparse.parse(sql) stmt = parsed[0] return stmt.tokens # 提取 SQL 语句中的表和字段函数 def extract_tables_fields(tokens): tables = set() fields = set() for token in tokens: if isinstance(token, sqlparse.sql.Identifier): identifier = str(token) if '.' in identifier: table, field = identifier.split('.') tables.add(table) fields.add(field) else: fields.add(identifier) elif isinstance(token, sqlparse.sql.IdentifierList): for identifier in token.get_identifiers(): if '.' in str(identifier): table, field = str(identifier).split('.') tables.add(table) fields.add(field) else: fields.add(str(identifier)) elif isinstance(token, sqlparse.sql.Where): where_tokens = token.tokens for where_token in where_tokens: if isinstance(where_token, sqlparse.sql.Comparison): left_token, right_token = where_token.tokens[0], where_token.tokens[2] if isinstance(left_token, sqlparse.sql.Identifier): identifier = str(left_token) if '.' in identifier: table, field = identifier.split('.') tables.add(table) fields.add(field) else: fields.add(identifier) if isinstance(right_token, sqlparse.sql.Identifier): identifier = str(right_token) if '.' in identifier: table, field = identifier.split('.') tables.add(table) fields.add(field) else: fields.add(identifier) return tables, fields # 建立表和字段映射关系函数 def build_mapping(tables, fields): mapping = {} for table in tables: if '.' in table: database, table_name = table.split('.') else: database = None table_name = table if database: columns = get_columns_from_database(database, table_name) else: columns = get_columns_from_table(table_name) for column in columns: if column in fields: mapping[table + '.' + column] = column return mapping # 从数据库获取表字段函数 def get_columns_from_database(database, table_name): conn = pyodbc.connect('DRIVER={{ODBC Driver 17 for SQL Server}};SERVER={0},{1};DATABASE={2};UID={3};PWD={4}'.format(sqlserver_host, sqlserver_port, database, sqlserver_user, sqlserver_password)) cursor = conn.cursor() cursor.execute('SELECT COLUMN_NAME FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = N\'' + table_name + '\'') columns = [column[0] for column in cursor.fetchall()] return columns # 从表获取表字段函数 def get_columns_from_table(table_name): cursor = connect_to_mysql() cursor.execute('SHOW COLUMNS FROM ' + table_name) columns = [column[0] for column in cursor.fetchall()] return columns # 主函数 def main(): # Hivesql 数据库 hivesql = 'SELECT a.id, a.name, b.age FROM table1 a JOIN table2 b ON a.id = b.id WHERE a.id = 1' hivesql_tokens = parse_sql(hivesql) hivesql_tables, hivesql_fields = extract_tables_fields(hivesql_tokens) hivesql_mapping = build_mapping(hivesql_tables, hivesql_fields) print('Hivesql 数据血缘分析解析：') print('表和字段映射关系：', hivesql_mapping) # ClickHouse 数据库 clickhouse = 'SELECT id, name, age FROM table WHERE id = 1' clickhouse_tokens = parse_sql(clickhouse) clickhouse_tables, clickhouse_fields = extract_tables_fields(clickhouse_tokens) clickhouse_mapping = build_mapping(clickhouse_tables, clickhouse_fields) print('ClickHouse 数据血缘分析解析：') print('表和字段映射关系：', clickhouse_mapping) # MySQL 数据库 mysql = 'SELECT id, name, age FROM table WHERE id = 1' mysql_tokens = parse_sql(mysql) mysql_tables, mysql_fields = extract_tables_fields(mysql_tokens) mysql_mapping = build_mapping(mysql_tables, mysql_fields) print('MySQL 数据血缘分析解析：') print('表和字段映射关系：', mysql_mapping) # SQL Server 数据库 sqlserver = 'SELECT id, name, age FROM table WHERE id = 1' sqlserver_tokens = parse_sql(sqlserver) sqlserver_tables, sqlserver_fields = extract_tables_fields(sqlserver_tokens) sqlserver_mapping = build_mapping(sqlserver_tables, sqlserver_fields) print('SQL Server 数据血缘分析解析：') print('表和字段映射关系：', sqlserver_mapping) if __name__ == '__main__': main() ``` 以上代码示例仅为基本示例，实际应用中还需要考虑更多的情况，例如 SQL 方言的差异、数据库连接的异常处理、SQL 语句的优化等等。