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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:00:33 UTC
[jira] [Updated] (SPARK-19416) Dataset.schema is inconsistent with
Dataset in handling columns with periods
[ https://issues.apache.org/jira/browse/SPARK-19416?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-19416:
---------------------------------
Labels: bulk-closed (was: )
> 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
> Labels: bulk-closed
>
> 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.<init>(<console>:34)
> at line9667c6d14e79417280e5882aa52e0de727.$read$$iw$$iw$$iw.<init>(<console>:41)
> at line9667c6d14e79417280e5882aa52e0de727.$read$$iw$$iw.<init>(<console>:43)
> at line9667c6d14e79417280e5882aa52e0de727.$read$$iw.<init>(<console>:45)
> at line9667c6d14e79417280e5882aa52e0de727.$eval$.$print$lzycompute(<console>:7)
> at line9667c6d14e79417280e5882aa52e0de727.$eval$.$print(<console>:6)
> {code}
> "succeeds" produces:
> {code}
> org.apache.spark.sql.DataFrame = [a.b: string]
> {code}
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