Hi Jay, Thanks for your response. Are you saying to append the new data and then remove the duplicates to the whole data set afterwards overwriting the existing data set with new data set with appended values? I will give that a try.
Cheers, Ben On Fri, Jun 1, 2018 at 11:49 PM Jay <jayadeep.jayara...@gmail.com> wrote: > Benjamin, > > The append will append the "new" data to the existing data with removing > the duplicates. You would need to overwrite the file everytime if you need > unique values. > > Thanks, > Jayadeep > > On Fri, Jun 1, 2018 at 9:31 PM Benjamin Kim <bbuil...@gmail.com> wrote: > >> I have a situation where I trying to add only new rows to an existing >> data set that lives in S3 as gzipped parquet files, looping and appending >> for each hour of the day. First, I create a DF from the existing data, then >> I use a query to create another DF with the data that is new. Here is the >> code snippet. >> >> df = spark.read.parquet(existing_data_path) >> df.createOrReplaceTempView(‘existing_data’) >> new_df = spark.read.parquet(new_data_path) >> new_df.createOrReplaceTempView(’new_data’) >> append_df = spark.sql( >> """ >> WITH ids AS ( >> SELECT DISTINCT >> source, >> source_id, >> target, >> target_id >> FROM new_data i >> LEFT ANTI JOIN existing_data im >> ON i.source = im.source >> AND i.source_id = im.source_id >> AND i.target = im.target >> AND i.target = im.target_id >> """ >> ) >> append_df.coalesce(1).write.parquet(existing_data_path, mode='append', >> compression='gzip’) >> >> >> I thought this would append new rows and keep the data unique, but I am >> see many duplicates. Can someone help me with this and tell me what I am >> doing wrong? >> >> Thanks, >> Ben >> >
