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https://issues.apache.org/jira/browse/SPARK-24193?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17102458#comment-17102458
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Xianjin YE commented on SPARK-24193:
------------------------------------
I used `df.rdd.collect` intentionally to trigger the problem as `df.collect` is
converted to `SparkPlan.executeTake` which is getting data correctly.
The problem can also be triggered with a slightly different version:
{code:java}
val spark = SparkSession
.builder
.appName("Spark TopK test")
.master("local-cluster[8, 1, 1024]")
.getOrCreate()
val temp1 = Utils.createTempDir()
val data = spark.range(100000, 0, -1, 10).toDF("id").selectExpr("id + 1 as
id")
spark.conf.set(SQLConf.TOP_K_SORT_FALLBACK_THRESHOLD.key, 100)
data.orderBy("id").limit(200).write.mode("overwrite").parquet(temp1.toString)
val topKInSort = spark.read.parquet(temp1.toString).collect()
spark.conf.set(SQLConf.TOP_K_SORT_FALLBACK_THRESHOLD.key, Int.MaxValue)
data.orderBy("id").limit(200).write.mode("overwrite").parquet(temp1.toString)
val topKInMemory = spark.read.parquet(temp1.toString).collect()
println(topKInMemory.map(_.getLong(0)).mkString("[", ",", "]"))
println(topKInSort.map(_.getLong(0)).mkString("[", ",", "]"))
assert(topKInMemory sameElements topKInSort)
{code}
The real problem is that if I am going to accessing the ordered and limited
data such as joining or writing to external table, the data is incorrect when
falling back into CollectLimitExec.
> Sort by disk when number of limit is big in TakeOrderedAndProjectExec
> ---------------------------------------------------------------------
>
> Key: SPARK-24193
> URL: https://issues.apache.org/jira/browse/SPARK-24193
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Jin Xing
> Assignee: Jin Xing
> Priority: Major
> Fix For: 2.4.0
>
>
> Physical plan of "_select colA from t order by colB limit M_" is
> _TakeOrderedAndProject_;
> Currently _TakeOrderedAndProject_ sorts data in memory, see
> https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala#L158
>
> Shall we add a config -- if the number of limit (M) is too big, we can sort
> by disk ? Thus memory issue can be resolved.
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