Joseph K. Bradley created SPARK-19416: -----------------------------------------
Summary: 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: 2.1.0, 2.0.2, 1.6.3, 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.<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} -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org