我正在使用PySpark on Databricks将数据写入SQL数据库的模式OCC和dbo，如下所示：

l_schemas=["OCC"]# list for storing your schemas
l_tables=[table] # list for storing respective tables in that particular indexed schema
for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    my_dfone.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

和

l_schemas=["dbo"]# list for storing your schemas
l_tables=[table] # list for storing respective tables in that particular indexed schema
for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    my_dftwo.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

有人建议我使用下面的代码（这要干净得多）

l_schemas=["OCC","dbo","one"]# list for storing your schemas
l_tables=[table] # list for storing respective tables in that particular indexed schema
for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    df.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

上面的方法很好，但是我必须将df赋给一个变量，因为我需要同时执行my_dfone和my_dftwo
我的代码尝试如下：

l_schemas=["OCC", "dbo"]# list for storing your schemas
l_tables=[table] # list for storing respective tables in that particular indexed schema

for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    my_dfone.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()\
    .my_dftwo.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

但它失败了。
因此，有人能告诉我如何将代码写入SQL数据库，而不必实际复制代码吗？基本上，这样会更干净
P.S.表是变量
我还尝试了以下方法：

l_schemas=["HR", "FPP"]# list for storing your schemas
l_tables= str(table) # list for storing respective tables in that particular indexed schema

for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    df_fpp.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()
    
    df_hr.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

但是失败了
我现在尝试以下方法，但还是没有成功：

l_schemas=["FPP","HR"]# list for storing schemas
l_tables=[table, table] # list for storing respective tables in the particular indexed schema

for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    df_fpp.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

for i in range(0,len(l_schemas)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    df_hr.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

更新问题，很遗憾，提供的以下代码不起作用

table = Ancestor['ancestorPath'].split("/")[7]
l_tables=[table]
l_schemas = ['dbo']
for i in range(0,len(l_tables)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    my_dfone.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()
    
    my_dftwo.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

我尝试修改代码如下：

table = Ancestor['ancestorPath'].split("/")[7]
l_tables=[table]
l_schemas = ['FPP','HR']
for i in range(0,len(l_tables)):
    s=l_schemas[i]+"."+l_tables[i] # concatenation and making as schema.table_name
    df_fpp.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()
    
    df_hr.write.mode("overwrite") \
    .format("jdbc") \
    .option("url", jdbcUrl) \
    .option("dbtable", s)\
    .save()

但是，它一直只显示df_hr.write.mode（“overwrite”）的结果