I would just map to pair using the id. Then do a reduceByKey where you compare
the scores and keep the highest. Then do .values and that should do it.
Sent from my iPhone
> On Jan 11, 2020, at 11:14 AM, Rishi Shah <rishishah.s...@gmail.com> wrote:
>
>
> Thanks everyone for your contribution on this topic, I wanted to check-in to
> see if anyone has discovered a different or have an opinion on better
> approach to deduplicating data using pyspark. Would really appreciate any
> further insight on this.
>
> Thanks,
> -Rishi
>
>> On Wed, Jun 12, 2019 at 4:21 PM Yeikel <em...@yeikel.com> wrote:
>> Nicholas , thank you for your explanation.
>>
>> I am also interested in the example that Rishi is asking for. I am sure
>> mapPartitions may work , but as Vladimir suggests it may not be the best
>> option in terms of performance.
>>
>> @Vladimir Prus , are you aware of any example about writing a "custom
>> physical exec operator"?
>>
>> If anyone needs a further explanation for the follow up question Rishi
>> posted , please see the example below :
>>
>>
>> import org.apache.spark.sql.types._
>> import org.apache.spark.sql.Row
>>
>>
>> val someData = Seq(
>> Row(1, 10),
>> Row(1, 20),
>> Row(1, 11)
>> )
>>
>> val schema = List(
>> StructField("id", IntegerType, true),
>> StructField("score", IntegerType, true)
>> )
>>
>> val df = spark.createDataFrame(
>> spark.sparkContext.parallelize(someData),
>> StructType(schema)
>> )
>>
>> // Goal : Drop duplicates using the "id" as the primary key and keep the
>> highest "score".
>>
>> df.sort($"score".desc).dropDuplicates("id").show
>>
>> == Physical Plan ==
>> *(2) HashAggregate(keys=[id#191], functions=[first(score#192, false)])
>> +- Exchange hashpartitioning(id#191, 200)
>> +- *(1) HashAggregate(keys=[id#191], functions=[partial_first(score#192,
>> false)])
>> +- *(1) Sort [score#192 DESC NULLS LAST], true, 0
>> +- Exchange rangepartitioning(score#192 DESC NULLS LAST, 200)
>> +- Scan ExistingRDD[id#191,score#192]
>>
>> This seems to work , but I don't know what are the implications if we use
>> this approach with a bigger dataset or what are the alternatives. From the
>> explain output I can see the two Exchanges , so it may not be the best
>> approach?
>>
>>
>>
>>
>>
>>
>>
>> --
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>>
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>>
>
>
> --
> Regards,
>
> Rishi Shah
