[jira] [Commented] (SPARK-19416) Dataset.schema is inconsistent with Dataset in handling columns with periods

2018-02-27 Thread Joseph K. Bradley (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19416?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16379034#comment-16379034
 ] 

Joseph K. Bradley commented on SPARK-19416:
---

[~rxin] Shall we close this as Won't Do, or shall we mark it as a thing to 
break in 3.0?

> Dataset.schema is inconsistent with Dataset in handling columns with periods
> 
>
> Key: SPARK-19416
> URL: https://issues.apache.org/jira/browse/SPARK-19416
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0
>Reporter: Joseph K. Bradley
>Priority: Minor
>
> When you have a DataFrame with a column with a period in its name, the API is 
> inconsistent about how to quote the column name.
> Here's a reproduction:
> {code}
> import org.apache.spark.sql.functions.col
> val rows = Seq(
>   ("foo", 1),
>   ("bar", 2)
> )
> val df = spark.createDataFrame(rows).toDF("a.b", "id")
> {code}
> These methods are all consistent:
> {code}
> df.select("a.b") // fails
> df.select("`a.b`") // succeeds
> df.select(col("a.b")) // fails
> df.select(col("`a.b`")) // succeeds
> df("a.b") // fails
> df("`a.b`") // succeeds
> {code}
> But {{schema}} is inconsistent:
> {code}
> df.schema("a.b") // succeeds
> df.schema("`a.b`") // fails
> {code}
> "fails" produces error messages like:
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input 
> columns: [a.b, id];;
> 'Project ['a.b]
> +- Project [_1#1511 AS a.b#1516, _2#1512 AS id#1517]
>+- LocalRelation [_1#1511, _2#1512]
>   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:77)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:296)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:296)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
>   at 
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)
>   at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>   at 
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2822)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1121)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1139)
>   at 
> line9667c6d14e79417280e5882aa52e0de727.$read$$iw$$iw$$iw$$

[jira] [Commented] (SPARK-19416) Dataset.schema is inconsistent with Dataset in handling columns with periods

2017-03-14 Thread Reynold Xin (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19416?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15924646#comment-15924646
 ] 

Reynold Xin commented on SPARK-19416:
-

We probably can't change any of them now, unless we introduce a config flag for 
the more consistent behavior.


> Dataset.schema is inconsistent with Dataset in handling columns with periods
> 
>
> Key: SPARK-19416
> URL: https://issues.apache.org/jira/browse/SPARK-19416
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0
>Reporter: Joseph K. Bradley
>Priority: Minor
>
> When you have a DataFrame with a column with a period in its name, the API is 
> inconsistent about how to quote the column name.
> Here's a reproduction:
> {code}
> import org.apache.spark.sql.functions.col
> val rows = Seq(
>   ("foo", 1),
>   ("bar", 2)
> )
> val df = spark.createDataFrame(rows).toDF("a.b", "id")
> {code}
> These methods are all consistent:
> {code}
> df.select("a.b") // fails
> df.select("`a.b`") // succeeds
> df.select(col("a.b")) // fails
> df.select(col("`a.b`")) // succeeds
> df("a.b") // fails
> df("`a.b`") // succeeds
> {code}
> But {{schema}} is inconsistent:
> {code}
> df.schema("a.b") // succeeds
> df.schema("`a.b`") // fails
> {code}
> "fails" produces error messages like:
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input 
> columns: [a.b, id];;
> 'Project ['a.b]
> +- Project [_1#1511 AS a.b#1516, _2#1512 AS id#1517]
>+- LocalRelation [_1#1511, _2#1512]
>   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:77)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:296)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:296)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
>   at 
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)
>   at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>   at 
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2822)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1121)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1139)
>   at 
> line9667c6d14e79417280e5882aa52e0de727.$read$$iw

[jira] [Commented] (SPARK-19416) Dataset.schema is inconsistent with Dataset in handling columns with periods

2017-03-13 Thread Joseph K. Bradley (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19416?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15923286#comment-15923286
 ] 

Joseph K. Bradley commented on SPARK-19416:
---

Hm, I'd call my synopsis above a "complaint" but not a "solution."  I'll defer 
to Spark SQL component experts for a decision.  CC [~davies] or 
[~r...@databricks.com] for a start.

> Dataset.schema is inconsistent with Dataset in handling columns with periods
> 
>
> Key: SPARK-19416
> URL: https://issues.apache.org/jira/browse/SPARK-19416
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0
>Reporter: Joseph K. Bradley
>Priority: Minor
>
> When you have a DataFrame with a column with a period in its name, the API is 
> inconsistent about how to quote the column name.
> Here's a reproduction:
> {code}
> import org.apache.spark.sql.functions.col
> val rows = Seq(
>   ("foo", 1),
>   ("bar", 2)
> )
> val df = spark.createDataFrame(rows).toDF("a.b", "id")
> {code}
> These methods are all consistent:
> {code}
> df.select("a.b") // fails
> df.select("`a.b`") // succeeds
> df.select(col("a.b")) // fails
> df.select(col("`a.b`")) // succeeds
> df("a.b") // fails
> df("`a.b`") // succeeds
> {code}
> But {{schema}} is inconsistent:
> {code}
> df.schema("a.b") // succeeds
> df.schema("`a.b`") // fails
> {code}
> "fails" produces error messages like:
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input 
> columns: [a.b, id];;
> 'Project ['a.b]
> +- Project [_1#1511 AS a.b#1516, _2#1512 AS id#1517]
>+- LocalRelation [_1#1511, _2#1512]
>   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:77)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:296)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:296)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
>   at 
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)
>   at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>   at 
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2822)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1121)
>   at org.apache.spark.sql.Dataset.select(D

[jira] [Commented] (SPARK-19416) Dataset.schema is inconsistent with Dataset in handling columns with periods

2017-02-09 Thread Thomas Sebastian (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19416?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15859401#comment-15859401
 ] 

Thomas Sebastian commented on SPARK-19416:
--

[~josephkb]
If I understand correctly the consistent behaviour should be as follows:
The statement df.schema("`a.b`")  should succeed and
df.schema("a.b")  should fail.
Please confirm.

+ [~jayadevan.m]

> Dataset.schema is inconsistent with Dataset in handling columns with periods
> 
>
> Key: SPARK-19416
> URL: https://issues.apache.org/jira/browse/SPARK-19416
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0
>Reporter: Joseph K. Bradley
>Priority: Minor
>
> When you have a DataFrame with a column with a period in its name, the API is 
> inconsistent about how to quote the column name.
> Here's a reproduction:
> {code}
> import org.apache.spark.sql.functions.col
> val rows = Seq(
>   ("foo", 1),
>   ("bar", 2)
> )
> val df = spark.createDataFrame(rows).toDF("a.b", "id")
> {code}
> These methods are all consistent:
> {code}
> df.select("a.b") // fails
> df.select("`a.b`") // succeeds
> df.select(col("a.b")) // fails
> df.select(col("`a.b`")) // succeeds
> df("a.b") // fails
> df("`a.b`") // succeeds
> {code}
> But {{schema}} is inconsistent:
> {code}
> df.schema("a.b") // succeeds
> df.schema("`a.b`") // fails
> {code}
> "fails" produces error messages like:
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input 
> columns: [a.b, id];;
> 'Project ['a.b]
> +- Project [_1#1511 AS a.b#1516, _2#1512 AS id#1517]
>+- LocalRelation [_1#1511, _2#1512]
>   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:77)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:296)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:296)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
>   at 
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)
>   at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>   at 
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2822)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1121)
>   at org.apache.spark.

[jira] [Commented] (SPARK-19416) Dataset.schema is inconsistent with Dataset in handling columns with periods

2017-02-04 Thread koert kuipers (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19416?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15852876#comment-15852876
 ] 

koert kuipers commented on SPARK-19416:
---

would it be simpler to ban columns with a period in the name?

> Dataset.schema is inconsistent with Dataset in handling columns with periods
> 
>
> Key: SPARK-19416
> URL: https://issues.apache.org/jira/browse/SPARK-19416
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0
>Reporter: Joseph K. Bradley
>Priority: Minor
>
> When you have a DataFrame with a column with a period in its name, the API is 
> inconsistent about how to quote the column name.
> Here's a reproduction:
> {code}
> import org.apache.spark.sql.functions.col
> val rows = Seq(
>   ("foo", 1),
>   ("bar", 2)
> )
> val df = spark.createDataFrame(rows).toDF("a.b", "id")
> {code}
> These methods are all consistent:
> {code}
> df.select("a.b") // fails
> df.select("`a.b`") // succeeds
> df.select(col("a.b")) // fails
> df.select(col("`a.b`")) // succeeds
> df("a.b") // fails
> df("`a.b`") // succeeds
> {code}
> But {{schema}} is inconsistent:
> {code}
> df.schema("a.b") // succeeds
> df.schema("`a.b`") // fails
> {code}
> "fails" produces error messages like:
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input 
> columns: [a.b, id];;
> 'Project ['a.b]
> +- Project [_1#1511 AS a.b#1516, _2#1512 AS id#1517]
>+- LocalRelation [_1#1511, _2#1512]
>   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:77)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:296)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>   at 
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:296)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
>   at 
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
>   at 
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
>   at 
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)
>   at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>   at 
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2822)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1121)
>   at org.apache.spark.sql.Dataset.select(Dataset.scala:1139)
>   at 
> line9667c6d14e79417280e5882aa52e0de727.$read$$iw$$iw$$iw$$iw.(:34)
>   at 
> line9667c6d14