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Posted to issues@spark.apache.org by "Gavrilescu Laurentiu (Jira)" <ji...@apache.org> on 2021/03/05 19:24:00 UTC
[jira] [Updated] (SPARK-34644) UDF returning array followed by
explode returns wrong results
[ https://issues.apache.org/jira/browse/SPARK-34644?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Gavrilescu Laurentiu updated SPARK-34644:
-----------------------------------------
Description:
*Applying an UDF followed by explode looks to be calling the UDF twice.*
Imagine having the following scenario:
1. you have a dataframe with some string columns
2. you have an expensive function that creates a score based on some string input
3. you want to get all the distinct values from all the columns and their score - there is an executor level cache that holds the score values for strings to minimize the execution of the expensive function
consider the following code to reproduce it:
{code:java}
case class RowWithStrings(c1: String, c2: String, c3: String)
case class ValueScore(value: String, score: Double)
object Bug {
val columns: List[String] = List("c1", "c2", "c3")
def score(input: String): Double = {
// insert expensive function here
input.toDouble
}
def main(args: Array[String]) {
lazy val sparkSession: SparkSession = {
val sparkSession = SparkSession.builder.master("local[4]")
.getOrCreate()
sparkSession
}
// some cache over expensive operation
val cache: TrieMap[String, Double] = TrieMap[String, Double]()
// get scores for all columns in the row
val body = (row: Row) => {
val arr = ArrayBuffer[ValueScore]()
columns foreach {
column =>
val value = row.getAs[String](column)
if (!cache.contains(value)) {
val computedScore = score(value)
arr += ValueScore(value, computedScore)
cache(value) = computedScore
}
}
arr
}
val basicUdf = udf(body)
val values = (1 to 5) map {
idx =>
// repeated values
RowWithStrings(idx.toString, idx.toString, idx.toString)
}
import sparkSession.implicits._
val df = values.toDF("c1", "c2", "c3").persist()
val allCols = df.columns.map(col)
df.select(basicUdf(struct(allCols: _*)).as("valuesScore"))
.select(explode(col("valuesScore")))
.distinct()
.show()
}
}
{code}
this shows:
{code:java}
+---+
|col|
+---+
+---+
{code}
When adding persist before explode, the result is correct:
{code:java}
df.select(basicUdf(struct(allCols: _*)).as("valuesScore"))
.persist()
.select(explode(col("valuesScore")))
.distinct()
.show()
{code}
=>
{code:java}
+--------+
| col|
+--------+
|{2, 2.0}|
|{4, 4.0}|
|{3, 3.0}|
|{5, 5.0}|
|{1, 1.0}|
+--------+
{code}
This is not reproducible using 3.0.2 version.
was:
*Applying an UDF followed by explode looks to be calling the UDF twice.*
Imagine having the following scenario:
1. you have a dataframe with some string columns
2. you have an expensive function that creates a score based on some string input
3. you want to get all the distinct values from all the columns and their score - there is an executor level cache that holds the score values for strings to minimize the execution of the expensive function
consider the following code to reproduce it:
{code:java}
case class RowWithStrings(c1: String, c2: String, c3: String)
case class ValueScore(value: String, score: Double)
object Bug {
val columns: List[String] = List("c1", "c2", "c3")
def score(input: String): Double = {
// insert expensive function here
input.toDouble
}
def main(args: Array[String]) {
lazy val sparkSession: SparkSession = {
val sparkSession = SparkSession.builder.master("local[4]")
.getOrCreate()
sparkSession
}
// some cache over expensive operation
val cache: TrieMap[String, Double] = TrieMap[String, Double]()
// get scores for all columns in the row
val body = (row: Row) => {
val arr = ArrayBuffer[ValueScore]()
columns foreach {
column =>
val value = row.getAs[String](column)
if (!cache.contains(value)) {
val computedScore = score(value)
arr += ValueScore(value, computedScore)
cache(value) = computedScore
}
}
arr
}
val basicUdf = udf(body)
val values = (1 to 5) map {
idx =>
// repeated values
RowWithStrings(idx.toString, idx.toString, idx.toString)
}
import sparkSession.implicits._
val df = values.toDF("c1", "c2", "c3").persist()
val allCols = df.columns.map(col)
df.select(basicUdf(struct(allCols: _*)).as("valuesScore"))
.select(explode(col("valuesScore")))
.distinct()
.show()
}
}
{code}
this shows:
{code:java}
+---+
|col|
+---+
+---+
{code}
When adding persist before explode, the result is correct:
{code:java}
df.select(basicUdf(struct(allCols: _*)).as("valuesScore"))
.persist()
.select(explode(col("valuesScore")))
.distinct()
.show()
{code}
=>
{code:java}
+--------+
| col|
+--------+
|{2, 2.0}|
|{4, 4.0}|
|{3, 3.0}|
|{5, 5.0}|
|{1, 1.0}|
+--------+
{code}
> UDF returning array followed by explode returns wrong results
> -------------------------------------------------------------
>
> Key: SPARK-34644
> URL: https://issues.apache.org/jira/browse/SPARK-34644
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.1.1
> Reporter: Gavrilescu Laurentiu
> Priority: Major
>
> *Applying an UDF followed by explode looks to be calling the UDF twice.*
> Imagine having the following scenario:
> 1. you have a dataframe with some string columns
> 2. you have an expensive function that creates a score based on some string input
> 3. you want to get all the distinct values from all the columns and their score - there is an executor level cache that holds the score values for strings to minimize the execution of the expensive function
> consider the following code to reproduce it:
> {code:java}
> case class RowWithStrings(c1: String, c2: String, c3: String)
> case class ValueScore(value: String, score: Double)
> object Bug {
> val columns: List[String] = List("c1", "c2", "c3")
> def score(input: String): Double = {
> // insert expensive function here
> input.toDouble
> }
> def main(args: Array[String]) {
> lazy val sparkSession: SparkSession = {
> val sparkSession = SparkSession.builder.master("local[4]")
> .getOrCreate()
> sparkSession
> }
> // some cache over expensive operation
> val cache: TrieMap[String, Double] = TrieMap[String, Double]()
> // get scores for all columns in the row
> val body = (row: Row) => {
> val arr = ArrayBuffer[ValueScore]()
> columns foreach {
> column =>
> val value = row.getAs[String](column)
> if (!cache.contains(value)) {
> val computedScore = score(value)
> arr += ValueScore(value, computedScore)
> cache(value) = computedScore
> }
> }
> arr
> }
> val basicUdf = udf(body)
> val values = (1 to 5) map {
> idx =>
> // repeated values
> RowWithStrings(idx.toString, idx.toString, idx.toString)
> }
> import sparkSession.implicits._
> val df = values.toDF("c1", "c2", "c3").persist()
> val allCols = df.columns.map(col)
> df.select(basicUdf(struct(allCols: _*)).as("valuesScore"))
> .select(explode(col("valuesScore")))
> .distinct()
> .show()
> }
> }
> {code}
> this shows:
> {code:java}
> +---+
> |col|
> +---+
> +---+
> {code}
> When adding persist before explode, the result is correct:
> {code:java}
> df.select(basicUdf(struct(allCols: _*)).as("valuesScore"))
> .persist()
> .select(explode(col("valuesScore")))
> .distinct()
> .show()
> {code}
> =>
> {code:java}
> +--------+
> | col|
> +--------+
> |{2, 2.0}|
> |{4, 4.0}|
> |{3, 3.0}|
> |{5, 5.0}|
> |{1, 1.0}|
> +--------+
> {code}
> This is not reproducible using 3.0.2 version.
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