My thinking is that if you run everything in one partition - say 12 GB - then you don't experience the partitioning problem - one partition will have all duplicates.
If that's not the case, there are other options, but would probably require a design change. On Thu, Apr 4, 2019 at 8:46 AM Jason Nerothin <jasonnerot...@gmail.com> wrote: > How much memory do you have per partition? > > On Thu, Apr 4, 2019 at 7:49 AM Chetan Khatri <chetan.opensou...@gmail.com> > wrote: > >> I will get the information and will share with you. >> >> On Thu, Apr 4, 2019 at 5:03 PM Abdeali Kothari <abdealikoth...@gmail.com> >> wrote: >> >>> How long does it take to do the window solution ? (Also mention how many >>> executors was your spark application using on average during that time) >>> I am not aware of anything that is faster. When I ran is on my data >>> ~8-9GB I think it took less than 5 mins (don't remember exact time) >>> >>> On Thu, Apr 4, 2019 at 1:09 PM Chetan Khatri < >>> chetan.opensou...@gmail.com> wrote: >>> >>>> 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 >>>>>>>> >>>>>>>> > > -- > Thanks, > Jason > -- Thanks, Jason
