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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>
--
Regards,
Rishi Shah
