Random Sampling base on 1 column after Groupby

Question

I have a Spark Table, which contains 400+ millions records/rows. I used spark.table to convert it into a DF. The DF looks like this below

  id          pub_date   version         unique_id     c_id    p_id    type      source
lni001        20220301      1           64WP-UI-POLI    002     P02    org      internet
lni001        20220301      1           64WP-UI-POLI    002     P02    org      internet
lni001        20220301      1           64WP-UI-POLI    002     P02    org      internet
lni001        20220301      2           64WP-UI-CFGT    012     K21   location  internet
lni001        20220301      2           64WP-UI-CFGT    012     K21   location  internet
lni001        20220301      3           64WP-UI-CFGT    012     K21   location  internet
lni001        20220301      3           64WP-UI-POLI    002     P02    org      internet
lni002        20220301      85          64WP-UI-POLI    002     P02    org      internet
lni002        20220301      85          64WP-UI-POLI    002     P02    org      internet
lni002        20220301      5           64WP-UI-CFGT    012     K21   location  internet
lni002        20220301      1           64WP-UI-CFGT    012     K21   location  internet
 ::
 ::

I am trying to randomly select rows base on the id column. I want to be able to randomly select group of id rows data after doing a groupBy or partitionBy on the id column.

If I want want 2 random samples, then I should get back with all the rows associate to the id column. For example, under id column, "lni001" has 7 records and "lni002" has 4 records. I will need all the records under "lni001" and "lni002"

I am trying to use groupBy and patritionBy but still couldnt figure out how to do it. That will be great if you all have give me some ideas or suggestions. Thanks!