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}
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