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https://issues.apache.org/jira/browse/SPARK-20174?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15972211#comment-15972211
]
Takeshi Yamamuro edited comment on SPARK-20174 at 4/18/17 6:54 AM:
-------------------------------------------------------------------
To fix this, IMHO it's okay to accept Seq[String] in the first argument of
withColumn for multi aliases;
{code}
scala> val df = Seq((Seq(1, 2, 3))).toDF("a")
scala> df.select(posexplode($"a").as("p" :: "c" :: Nil)).show
+---+---+
| p| c|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
scala> df.withColumn("p" :: "c" :: Nil, posexplode($"a")).show
<console>:26: error: type mismatch;
found : List[String]
required: String
df.withColumn("p" :: "c" :: Nil, posexplode($"a")).show
{code}
was (Author: maropu):
To fix this, it seems to be okay to accept Seq[String] in the first argument of
withColumn for multi aliases;
{code}
scala> val df = Seq((Seq(1, 2, 3))).toDF("a")
scala> df.select(posexplode($"a").as("p" :: "c" :: Nil)).show
+---+---+
| p| c|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
scala> df.withColumn("p" :: "c" :: Nil, posexplode($"a")).show
<console>:26: error: type mismatch;
found : List[String]
required: String
df.withColumn("p" :: "c" :: Nil, posexplode($"a")).show
{code}
> Analyzer gives mysterious AnalysisException when posexplode used in withColumn
> ------------------------------------------------------------------------------
>
> Key: SPARK-20174
> URL: https://issues.apache.org/jira/browse/SPARK-20174
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Jacek Laskowski
> Priority: Minor
>
> Wish I knew how to even describe the issue. It appears that {{posexplode}}
> cannot be used in {{withColumn}}, but the error message does not seem to say
> it.
> [The scaladoc of
> posexplode|http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions$@posexplode(e:org.apache.spark.sql.Column):org.apache.spark.sql.Column]
> is silent about this "limitation", too.
> {code}
> scala> codes.printSchema
> root
> |-- id: integer (nullable = false)
> |-- rate_plan_code: array (nullable = true)
> | |-- element: string (containsNull = true)
> scala> codes.withColumn("code", posexplode($"rate_plan_code")).show
> org.apache.spark.sql.AnalysisException: The number of aliases supplied in the
> AS clause does not match the number of columns output by the UDTF expected 2
> aliases but got code ;
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:90)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate$.makeGeneratorOutput(Analyzer.scala:1744)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator$$anonfun$apply$20$$anonfun$56.apply(Analyzer.scala:1691)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator$$anonfun$apply$20$$anonfun$56.apply(Analyzer.scala:1679)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
> at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator$$anonfun$apply$20.applyOrElse(Analyzer.scala:1679)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator$$anonfun$apply$20.applyOrElse(Analyzer.scala:1664)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:62)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:62)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:61)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator$.apply(Analyzer.scala:1664)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator$.apply(Analyzer.scala:1629)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
> at
> scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:70)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:68)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:51)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2832)
> at org.apache.spark.sql.Dataset.select(Dataset.scala:1137)
> at org.apache.spark.sql.Dataset.withColumn(Dataset.scala:1882)
> ... 48 elided
> scala> codes.select(posexplode($"rate_plan_code")).show
> +---+------+
> |pos| col|
> +---+------+
> | 0| AAA|
> | 1| RACK|
> | 2|SMOBIX|
> | 3|SMOBPX|
> +---+------+
> {code}
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