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https://issues.apache.org/jira/browse/SPARK-12823?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16171668#comment-16171668
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Simeon H.K. Fitch edited comment on SPARK-12823 at 9/19/17 1:30 PM:
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Here is a combined, runnable example, including an attempt at using SQL:
{code:java}
package examples
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
object UDFSadness extends App {
implicit val spark = SparkSession.builder()
.master("local").appName(getClass.getName).getOrCreate()
import spark.implicits._
case class KV(key: Long, value: String)
case class MyRow(kv: KV)
val ds: Dataset[MyRow] = spark.createDataset(List(MyRow(KV(1L, "a")),
MyRow(KV(5L, "b"))))
val firstColumn = ds(ds.columns.head)
// Works, but is not what we want (can't always use `map` over `select`)
ds.map(_.kv.value).show
// +-----+
// |value|
// +-----+
// | a|
// | b|
// +-----+
// This is what we want to be able to implement
val udf1 = udf((row: MyRow) ⇒ row.kv.value)
try {
ds.select(udf1(firstColumn)).show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// Exception in thread "main" org.apache.spark.sql.AnalysisException:
// cannot resolve 'UDF(kv)' due to data type mismatch: argument 1 requires
// struct<kv:struct<key:bigint,value:string>> type, however,
// '`kv`' is of struct<key:bigint,value:string> type.;;
}
// So lets try something of the form reported in the error
val udf2 = udf((kv: KV) ⇒ kv.value)
try {
ds.select(udf2(firstColumn)).show
}
catch {
case t: Throwable ⇒ //t.printStackTrace()
// java.lang.ClassCastException:
org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
// cannot be cast to examples.UDFSadness$KV
}
// What if it's a problem with the use of untyped columns?
// Try the above again with typed columns.
try {
ds.select(udf1(firstColumn.as[MyRow])).show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(kv)' due to
data type
// mismatch: argument 1 requires struct<kv:struct<key:bigint,value:string>>
type,
// however, '`kv`' is of struct<key:bigint,value:string> type.;;
}
try {
ds.select(udf2(firstColumn.as[KV])).show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// java.lang.ClassCastException:
org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
// cannot be cast to examples.UDFSadness$KV
}
// Let's see if we can use SQL as a back door.
spark.sqlContext.udf.register("udf1", (row: MyRow) ⇒ row.kv.value)
try {
ds.createOrReplaceTempView("myKVs")
spark.sql(s"select udf1($firstColumn) from myKVs").show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(kv)' due to
data
// type mismatch: argument 1 requires
struct<kv:struct<key:bigint,value:string>> type,
// however, 'mykvs.`kv`' is of struct<key:bigint,value:string> type.; line
1 pos 7;
}
// This is the unfortunate workaround. Note that `Row` is
// `org.apache.spark.sql.Row`
val udf3 = udf((row: Row) ⇒ row.getString(1))
ds.select(udf3(firstColumn)).show
// +-------+
// |UDF(kv)|
// +-------+
// | a|
// | b|
// +-------+
}
{code}
was (Author: metasim):
Here is a combined, runnable example:
{code:java}
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
object UDFSadness extends App {
implicit val spark = SparkSession.builder()
.master("local").appName(getClass.getName).getOrCreate()
import spark.implicits._
case class KV(key: Long, value: String)
case class MyRow(kv: KV)
val ds: Dataset[MyRow] = spark.createDataset(List(MyRow(KV(1L, "a")),
MyRow(KV(5L, "b"))))
val firstColumn = ds(ds.columns.head)
// Works, but is not what we want (can't always use `map` over `select`)
ds.map(_.kv.value).show
// +-----+
// |value|
// +-----+
// | a|
// | b|
// +-----+
// This is what we want to be able to implement
val udf1 = udf((row: MyRow) ⇒ row.kv.value)
try {
ds.select(udf1(firstColumn)).show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// Exception in thread "main" org.apache.spark.sql.AnalysisException:
// cannot resolve 'UDF(kv)' due to data type mismatch: argument 1 requires
// struct<kv:struct<key:bigint,value:string>> type, however,
// '`kv`' is of struct<key:bigint,value:string> type.;;
}
// So lets try something of the form reported in the error
val udf2 = udf((kv: KV) ⇒ kv.value)
try {
ds.select(udf2(firstColumn)).show
}
catch {
case t: Throwable ⇒ //t.printStackTrace()
// java.lang.ClassCastException:
org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
// cannot be cast to examples.UDFSadness$KV
}
// What if it's a problem with the use of untyped columns?
// Try the above again with typed columns.
try {
ds.select(udf1(firstColumn.as[MyRow])).show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(kv)' due to
data type
// mismatch: argument 1 requires struct<kv:struct<key:bigint,value:string>>
type,
// however, '`kv`' is of struct<key:bigint,value:string> type.;;
}
try {
ds.select(udf2(firstColumn.as[KV])).show
}
catch {
case t: Throwable ⇒ t.printStackTrace()
// java.lang.ClassCastException:
org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
// cannot be cast to examples.UDFSadness$KV
}
// This is the unfortunate workaround. Note that `Row` is
// `org.apache.spark.sql.Row`
val udf3 = udf((row: Row) ⇒ row.getString(1))
ds.select(udf3(firstColumn)).show
// +-------+
// |UDF(kv)|
// +-------+
// | a|
// | b|
// +-------+
}
{code}
> Cannot create UDF with StructType input
> ---------------------------------------
>
> Key: SPARK-12823
> URL: https://issues.apache.org/jira/browse/SPARK-12823
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.2
> Reporter: Frank Rosner
>
> h5. Problem
> It is not possible to apply a UDF to a column that has a struct data type.
> Two previous requests to the mailing list remained unanswered.
> h5. How-To-Reproduce
> {code}
> val sql = new org.apache.spark.sql.SQLContext(sc)
> import sql.implicits._
> case class KV(key: Long, value: String)
> case class Row(kv: KV)
> val df = sc.parallelize(List(Row(KV(1L, "a")), Row(KV(5L, "b")))).toDF
> val udf1 = org.apache.spark.sql.functions.udf((kv: KV) => kv.value)
> df.select(udf1(df("kv"))).show
> // java.lang.ClassCastException:
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast
> to $line78.$read$$iwC$$iwC$KV
> val udf2 = org.apache.spark.sql.functions.udf((kv: (Long, String)) => kv._2)
> df.select(udf2(df("kv"))).show
> // org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(kv)' due to
> data type mismatch: argument 1 requires struct<_1:bigint,_2:string> type,
> however, 'kv' is of struct<key:bigint,value:string> type.;
> {code}
> h5. Mailing List Entries
> -
> https://mail-archives.apache.org/mod_mbox/spark-user/201511.mbox/%3CCACUahd8M=ipCbFCYDyein_=vqyoantn-tpxe6sq395nh10g...@mail.gmail.com%3E
> - https://www.mail-archive.com/user@spark.apache.org/msg43092.html
> h5. Possible Workaround
> If you create a {{UserDefinedFunction}} manually, not using the {{udf}}
> helper functions, it works. See https://github.com/FRosner/struct-udf, which
> exposes the {{UserDefinedFunction}} constructor (public from package
> private). However, then you have to work with a {{Row}}, because it does not
> automatically convert the row to a case class / tuple.
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