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https://issues.apache.org/jira/browse/SPARK-44768?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17753035#comment-17753035
]
Franck Tago commented on SPARK-44768:
-------------------------------------
!image-2023-08-10-20-32-55-684.png!
> Improve WSCG handling of row buffer by accounting for executor memory .
> Exploding nested arrays can easily lead to out of memory errors.
> -------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-44768
> URL: https://issues.apache.org/jira/browse/SPARK-44768
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 3.3.2, 3.4.0, 3.4.1
> Reporter: Franck Tago
> Priority: Major
> Attachments: image-2023-08-10-20-32-55-684.png,
> spark-jira_wscg_code.txt
>
>
> consider a scenario where you flatten a nested array
> // e.g you can use the following steps to create the dataframe
> //create a partClass case class
> case class partClass (PARTNAME: String , PartNumber: String , PartPrice :
> Double )
> //create a nested array array class
> case class array_array_class (
> col_int: Int,
> arr_arr_string : Seq[Seq[String]],
> arr_arr_bigint : Seq[Seq[Long]],
> col_string : String,
> parts_s : Seq[Seq[partClass]]
>
> )
> //create a dataframe
> var df_array_array = sc.parallelize(
> Seq(
> (1,Seq(Seq("a","b" ,"c" ,"d") ,Seq("aa","bb" ,"cc","dd")) ,
> Seq(Seq(1000,20000), Seq(30000,-10000)),"ItemPart1",
> Seq(Seq(partClass("PNAME1","P1",20.75),partClass("PNAME1_1","P1_1",30.75)))
> ) ,
>
> (2,Seq(Seq("ab","bc" ,"cd" ,"de") ,Seq("aab","bbc"
> ,"ccd","dde"),Seq("aaaaaabbbbb")) , Seq(Seq(-1000,-20000,-1,-2),
> Seq(0,30000,-10000)),"ItemPart2",
>
> Seq(Seq(partClass("PNAME2","P2",170.75),partClass("PNAME2_1","P2_1",33.75),partClass("PNAME2_2","P2_2",73.75)))
> )
>
> )
> ).toDF("c1" ,"c2" ,"c3" ,"c4" ,"c5")
> //explode a nested array
> var result = df_array_array.select( col("c1"),
> explode(col("c2"))).select('c1 , explode('col))
> result.explain
>
> The physical for this operator is seen below.
> -------------------------------------
> Physical plan
> == Physical Plan ==
> *(1) Generate explode(col#27), [c1#17|#17], false, [col#30|#30]
> +- *(1) Filter ((size(col#27, true) > 0) AND isnotnull(col#27))
> +- *(1) Generate explode(c2#18), [c1#17|#17], false, [col#27|#27]
> +- *(1) Project [_1#6 AS c1#17, _2#7 AS c2#18|#6 AS c1#17, _2#7 AS
> c2#18]
> +- *(1) Filter ((size(_2#7, true) > 0) AND isnotnull(_2#7))
> +- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0,
> scala.Tuple5, true]))._1 AS _1#6, mapobjects(lambdavariable(MapObject,
> ObjectType(class java.lang.Object), true, -1),
> mapobjects(lambdavariable(MapObject, ObjectType(class java.lang.Object),
> true, -2), staticinvoke(class org.apache.spark.unsafe.types.UTF8String,
> StringType, fromString, validateexternaltype(lambdavariable(MapObject,
> ObjectType(class java.lang.Object), true, -2), StringType, ObjectType(class
> java.lang.String)), true, false, true),
> validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -1), ArrayType(StringType,true),
> ObjectType(interface scala.collection.Seq)), None),
> knownnotnull(assertnotnull(input[0, scala.Tuple5, true]))._2, None) AS _2#7,
> mapobjects(lambdavariable(MapObject, ObjectType(class java.lang.Object),
> true, -3), mapobjects(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -4),
> assertnotnull(validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -4), IntegerType, IntegerType)),
> validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -3), ArrayType(IntegerType,false),
> ObjectType(interface scala.collection.Seq)), None),
> knownnotnull(assertnotnull(input[0, scala.Tuple5, true]))._3, None) AS _3#8,
> staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType,
> fromString, knownnotnull(assertnotnull(input[0, scala.Tuple5, true]))._4,
> true, false, true) AS _4#9, mapobjects(lambdavariable(MapObject,
> ObjectType(class java.lang.Object), true, -5),
> mapobjects(lambdavariable(MapObject, ObjectType(class java.lang.Object),
> true, -6), if (isnull(validateexternaltype(lambdavariable(MapObject,
> ObjectType(class java.lang.Object), true, -6),
> StructField(PARTNAME,StringType,true),
> StructField(PartNumber,StringType,true),
> StructField(PartPrice,DoubleType,false), ObjectType(class
> $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$partClass)))) null else
> named_struct(PARTNAME, staticinvoke(class
> org.apache.spark.unsafe.types.UTF8String, StringType, fromString,
> knownnotnull(validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -6), StructField(PARTNAME,StringType,true),
> StructField(PartNumber,StringType,true),
> StructField(PartPrice,DoubleType,false), ObjectType(class
> $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$partClass))).PARTNAME, true,
> false, true), PartNumber, staticinvoke(class
> org.apache.spark.unsafe.types.UTF8String, StringType, fromString,
> knownnotnull(validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -6), StructField(PARTNAME,StringType,true),
> StructField(PartNumber,StringType,true),
> StructField(PartPrice,DoubleType,false), ObjectType(class
> $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$partClass))).PartNumber, true,
> false, true), PartPrice,
> knownnotnull(validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -6), StructField(PARTNAME,StringType,true),
> StructField(PartNumber,StringType,true),
> StructField(PartPrice,DoubleType,false), ObjectType(class
> $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$partClass))).PartPrice),
> validateexternaltype(lambdavariable(MapObject, ObjectType(class
> java.lang.Object), true, -5),
> ArrayType(StructType(StructField(PARTNAME,StringType,true),StructField(PartNumber,StringType,true),StructField(PartPrice,DoubleType,false)),true),
> ObjectType(interface scala.collection.Seq)), None),
> knownnotnull(assertnotnull(input[0, scala.Tuple5, true]))._5, None) AS _5#10]
> +- Scan[obj#5|#5]
>
>
> Because the explode function can create multiple rows from a single row , we
> should account for the memory available when adding rows to the buffer .
>
> This is even more important when we are exploding nested arrays .
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