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https://issues.apache.org/jira/browse/SPARK-25963?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-25963:
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Affects Version/s: (was: 3.0.0)
3.1.0
> Optimize generate followed by window
> ------------------------------------
>
> Key: SPARK-25963
> URL: https://issues.apache.org/jira/browse/SPARK-25963
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.1.0
> Reporter: Ohad Raviv
> Priority: Minor
>
> We've noticed that for our use-cases when we have explode followed by a
> window function we can almost always optimize it by adding repartition by the
> windows' partition before the explode.
> for example:
> {code:java}
> import org.apache.spark.sql.functions._
> val N = 1 << 12
> spark.sql("set spark.sql.autoBroadcastJoinThreshold=0")
> val tokens = spark.range(N).selectExpr(
> "floor(id/4) as key", "'asd/asd/asd/asd/asd/asd' as url")
> // .repartition("cust_id")
> .selectExpr("*", "explode(split(url, '/')) as token")
> import org.apache.spark.sql.expressions._
> val w = Window.partitionBy("key", "token")
> val res = tokens.withColumn("cnt", count("token").over(w))
> res.explain(true)
> {code}
> {noformat}
> == Optimized Logical Plan ==
> Window [count(token#11) windowspecdefinition(key#6L, token#11,
> specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$()))
> AS cnt#17L], [key#6L, token#11]
> +- Generate explode([asd,asd,asd,asd,asd,asd]), false, [token#11]
> +- Project [FLOOR((cast(id#4L as double) / 4.0)) AS key#6L,
> asd/asd/asd/asd/asd/asd AS url#7]
> +- Range (0, 4096, step=1, splits=Some(1))
> {noformat}
> currently all the data will be exploded in the first stage, then shuffled and
> then aggregated.
> we can achieve exactly the same computation if we first shuffle the data and
> in the second stage explode and aggregate.
> I have a PR that tries to resolve this. I'm just not sure I thought about all
> the cases.
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