使用配置单元窗口函数的解决方案。我用你的数据来测试它，删除 your_table 用你的table代替。结果如预期。

with your_table as (--remove this CTE, use your table instead
select stack(14,
'242539622', 1,'A',
'242539622', 2,'A',
'242539622', 3,'A',
'242539622', 4,'B',
'242539622', 5,'B',
'242539622', 6,'C',
'242539622', 7,'D',
'242539622', 8,'D',
'242539622', 9,'D',
'242539622',10,'B',
'242539622',11,'B',
'242539622',12,'D',
'242539622',13,'D',
'242539622',14,'F'
) as (id,sequencenumber,appname)
) --remove this CTE, use your table instead

select id,sequencenumber,appname, 
       case when sum(new_grp_flag) over(partition by id, group_name) = 1 then appname --only one group of consequent runs exists (like A)
            else        
            nvl(concat(group_name, '_', 
                       sum(new_grp_flag) over(partition by id, group_name order by sequencenumber) --rolling sum of new_group_flag
                       ),appname) 
        end appname_c       
from
(       

select id,sequencenumber,appname,
       case when appname=prev_appname or appname=next_appname then appname end group_name, --identify group of the same app
       case when appname<>prev_appname or prev_appname is null then 1 end new_grp_flag     --one 1 per each group
from       
(
select id,sequencenumber,appname,
       lag(appname)  over(partition by id order by sequencenumber) prev_appname, --need these columns
       lead(appname) over(partition by id order by sequencenumber) next_appname  --to identify groups of records w same app
from your_table --replace with your table
)s
)s
order by id,sequencenumber
;

结果：

OK
id        sequencenumber     appname    appname_c
242539622       1       A       A
242539622       2       A       A
242539622       3       A       A
242539622       4       B       B_1
242539622       5       B       B_1
242539622       6       C       C
242539622       7       D       D_1
242539622       8       D       D_1
242539622       9       D       D_1
242539622       10      B       B_2
242539622       11      B       B_2
242539622       12      D       D_2
242539622       13      D       D_2
242539622       14      F       F
Time taken: 232.319 seconds, Fetched: 14 row(s)

你需要做两个窗口函数来实现这个结果。
使用pyspark并假设 df 是您的Dataframe：

from pyspark.sql import functions as F, Window

df.withColumn(
    "fg",
    F.lag("appname").over(Window.partitionBy("id").orderBy("sequencenumber)
).withColumn(
    "fg",
    F.when(
        F.col("fg")==F.col("id"),
        0
    ).otherwise(1)
).withColumn(
    "fg",
    F.sum("fg").over(Window.partitionBy("id", "appname"))
).show()