Thanks for awesome clarification / explanation. I have cases where update_time can be same. I am in need of suggestions, where I have very large data like 5 GB, this window based solution which I mentioned is taking very long time.
Thanks again. On Thu, Apr 4, 2019 at 12:11 PM Abdeali Kothari <abdealikoth...@gmail.com> wrote: > So, the above code for min() worked for me fine in general, but there was > one corner case where it failed. > Which was when I have something like: > invoice_id=1, update_time=*2018-01-01 15:00:00.000* > invoice_id=1, update_time=*2018-01-01 15:00:00.000* > invoice_id=1, update_time=2018-02-03 14:00:00.000 > > In this example, the update_time for 2 records is the exact same. So, > doing a filter for the min() will result in 2 records for the invoice_id=1. > This is avoided in your code snippet of row_num - because 2 rows will > never have row_num = 1 > > But note that here - row_num=1 and row_num=2 will be randomly ordered > (because orderBy is on update_time and they have the same value of > update_time). > Hence dropDuplicates can be used there cause it can be either one of those > rows. > > Overall - dropDuplicates seems like it's meant for cases where you > literally have redundant duplicated data. And not for filtering to get > first/last etc. > > > On Thu, Apr 4, 2019 at 11:46 AM Chetan Khatri <chetan.opensou...@gmail.com> > wrote: > >> Hello Abdeali, Thank you for your response. >> >> Can you please explain me this line, And the dropDuplicates at the end >> ensures records with two values for the same 'update_time' don't cause >> issues. >> >> Sorry I didn't get quickly. :) >> >> On Thu, Apr 4, 2019 at 10:41 AM Abdeali Kothari <abdealikoth...@gmail.com> >> wrote: >> >>> I've faced this issue too - and a colleague pointed me to the >>> documentation - >>> https://spark.apache.org/docs/2.4.0/api/python/pyspark.sql.html#pyspark.sql.DataFrame.dropDuplicates >>> dropDuplicates docs does not say that it will guarantee that it will >>> return the "first" record (even if you sort your dataframe) >>> It would give you any record it finds and just ensure that duplicates >>> are not present. >>> >>> The only way I know of how to do this is what you did, but you can avoid >>> the sorting inside the partition with something like (in pyspark): >>> >>> from pyspark.sql import Window, functions as F >>> df = df.withColumn('wanted_time', >>> F.min('update_time').over(Window.partitionBy('invoice_id'))) >>> out_df = df.filter(df['update_time'] == df['wanted_time']) >>> .drop('wanted_time').dropDuplicates('invoice_id', 'update_time') >>> >>> The min() is faster than doing an orderBy() and a row_number(). >>> And the dropDuplicates at the end ensures records with two values for >>> the same 'update_time' don't cause issues. >>> >>> >>> On Thu, Apr 4, 2019 at 10:22 AM Chetan Khatri < >>> chetan.opensou...@gmail.com> wrote: >>> >>>> Hello Dear Spark Users, >>>> >>>> I am using dropDuplicate on a DataFrame generated from large parquet >>>> file from(HDFS) and doing dropDuplicate based on timestamp based column, >>>> every time I run it drops different - different rows based on same >>>> timestamp. >>>> >>>> What I tried and worked >>>> >>>> val wSpec = Window.partitionBy($"invoice_id").orderBy($"update_time". >>>> desc) >>>> >>>> val irqDistinctDF = irqFilteredDF.withColumn("rn", >>>> row_number.over(wSpec)).where($"rn" === 1) >>>> .drop("rn").drop("update_time") >>>> >>>> But this is damn slow... >>>> >>>> Can someone please throw a light. >>>> >>>> Thanks >>>> >>>>
