You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@spark.apache.org by Koert Kuipers <ko...@tresata.com> on 2016/02/15 16:12:29 UTC
Dataset in spark 2.0.0-SNAPSHOT missing columns
i noticed some things stopped working on datasets in spark 2.0.0-SNAPSHOT,
and with a confusing error message (cannot resolved some column with input
columns []).
for example in 1.6.0-SNAPSHOT:
scala> val ds = sc.parallelize(1 to 10).toDS
ds: org.apache.spark.sql.Dataset[Int] = [value: int]
scala> ds.map(x => Option(x))
res0: org.apache.spark.sql.Dataset[Option[Int]] = [value: int]
and same commands in 2.0.0-SNAPSHOT:
scala> val ds = sc.parallelize(1 to 10).toDS
ds: org.apache.spark.sql.Dataset[Int] = [value: int]
scala> ds.map(x => Option(x))
org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input
columns: [];
at
org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:60)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:283)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:162)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org
$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:172)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:176)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org
$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:176)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:181)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
at scala.collection.Iterator$class.foreach(Iterator.scala:742)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:308)
at scala.collection.AbstractIterator.to(Iterator.scala:1194)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:300)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1194)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:287)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1194)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:181)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)
at
org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:122)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
at scala.collection.immutable.List.foreach(List.scala:381)
at
org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:121)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:46)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:322)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:81)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:92)
at org.apache.spark.sql.Dataset.mapPartitions(Dataset.scala:339)
at org.apache.spark.sql.Dataset.map(Dataset.scala:323)
... 43 elided
i observed similar issues with user defined types
(org.apache.spark.sql.types.UserDefinedType) in Dataset. trying to insert a
UserDefinedType in Dataset[Row] fails with input columns [].
Re: Dataset in spark 2.0.0-SNAPSHOT missing columns
Posted by Koert Kuipers <ko...@tresata.com>.
yeah i was surprised with the Option. it works in 1.6.0-SNAPSHOT, and its a
pretty neat way to indicate nullability i guess.
i will file jira. i saw similar behavior with other types than Option. this
was just the easiest to show.
On Mon, Feb 15, 2016 at 3:52 PM, Reynold Xin <rx...@databricks.com> wrote:
> Looks like a bug. I'm also not sure whether we support Option yet. (If
> not, we should definitely support that in 2.0.)
>
> Can you file a JIRA ticket?
>
>
> On Mon, Feb 15, 2016 at 7:12 AM, Koert Kuipers <ko...@tresata.com> wrote:
>
>> i noticed some things stopped working on datasets in spark
>> 2.0.0-SNAPSHOT, and with a confusing error message (cannot resolved some
>> column with input columns []).
>>
>> for example in 1.6.0-SNAPSHOT:
>> scala> val ds = sc.parallelize(1 to 10).toDS
>> ds: org.apache.spark.sql.Dataset[Int] = [value: int]
>>
>> scala> ds.map(x => Option(x))
>> res0: org.apache.spark.sql.Dataset[Option[Int]] = [value: int]
>>
>> and same commands in 2.0.0-SNAPSHOT:
>> scala> val ds = sc.parallelize(1 to 10).toDS
>> ds: org.apache.spark.sql.Dataset[Int] = [value: int]
>>
>> scala> ds.map(x => Option(x))
>> org.apache.spark.sql.AnalysisException: cannot resolve 'value' given
>> input columns: [];
>> at
>> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:60)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
>> at
>> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:283)
>> at
>> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:162)
>> at org.apache.spark.sql.catalyst.plans.QueryPlan.org
>> $apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:172)
>> at
>> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:176)
>> at
>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
>> at
>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
>> at scala.collection.immutable.List.foreach(List.scala:381)
>> at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
>> at scala.collection.immutable.List.map(List.scala:285)
>> at org.apache.spark.sql.catalyst.plans.QueryPlan.org
>> $apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:176)
>> at
>> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:181)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
>> at scala.collection.Iterator$class.foreach(Iterator.scala:742)
>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
>> at
>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
>> at
>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
>> at
>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
>> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:308)
>> at scala.collection.AbstractIterator.to(Iterator.scala:1194)
>> at
>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:300)
>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1194)
>> at
>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:287)
>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1194)
>> at
>> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:181)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:122)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
>> at scala.collection.immutable.List.foreach(List.scala:381)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:121)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)
>> at
>> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:46)
>> at
>> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:322)
>> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:81)
>> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:92)
>> at org.apache.spark.sql.Dataset.mapPartitions(Dataset.scala:339)
>> at org.apache.spark.sql.Dataset.map(Dataset.scala:323)
>> ... 43 elided
>>
>> i observed similar issues with user defined types
>> (org.apache.spark.sql.types.UserDefinedType) in Dataset. trying to insert a
>> UserDefinedType in Dataset[Row] fails with input columns [].
>>
>
>
Re: Dataset in spark 2.0.0-SNAPSHOT missing columns
Posted by Reynold Xin <rx...@databricks.com>.
Looks like a bug. I'm also not sure whether we support Option yet. (If not,
we should definitely support that in 2.0.)
Can you file a JIRA ticket?
On Mon, Feb 15, 2016 at 7:12 AM, Koert Kuipers <ko...@tresata.com> wrote:
> i noticed some things stopped working on datasets in spark 2.0.0-SNAPSHOT,
> and with a confusing error message (cannot resolved some column with input
> columns []).
>
> for example in 1.6.0-SNAPSHOT:
> scala> val ds = sc.parallelize(1 to 10).toDS
> ds: org.apache.spark.sql.Dataset[Int] = [value: int]
>
> scala> ds.map(x => Option(x))
> res0: org.apache.spark.sql.Dataset[Option[Int]] = [value: int]
>
> and same commands in 2.0.0-SNAPSHOT:
> scala> val ds = sc.parallelize(1 to 10).toDS
> ds: org.apache.spark.sql.Dataset[Int] = [value: int]
>
> scala> ds.map(x => Option(x))
> org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input
> columns: [];
> at
> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:60)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:283)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:162)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org
> $apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:172)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:176)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
> at scala.collection.immutable.List.map(List.scala:285)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org
> $apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:176)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:181)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
> at scala.collection.Iterator$class.foreach(Iterator.scala:742)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:308)
> at scala.collection.AbstractIterator.to(Iterator.scala:1194)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:300)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1194)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:287)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1194)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:181)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:122)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:121)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:46)
> at
> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:322)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:81)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:92)
> at org.apache.spark.sql.Dataset.mapPartitions(Dataset.scala:339)
> at org.apache.spark.sql.Dataset.map(Dataset.scala:323)
> ... 43 elided
>
> i observed similar issues with user defined types
> (org.apache.spark.sql.types.UserDefinedType) in Dataset. trying to insert a
> UserDefinedType in Dataset[Row] fails with input columns [].
>