Thanks Michael, hopefully those will get some attention for a not too distant release. Do you think that this is related to, or separate from, a similar issue [1] that a filed a bit earlier, regarding the way that StringIndexer (and perhaps other ML components) handles some of these columns? (I've dug through a bit of the source, but it's not entirely clear to me (I'm not a Scala hacker) how transparently (or non-transparently) column names are passed through to underlying DataFrame methods.)
Joshua [1]: https://issues.apache.org/jira/browse/SPARK-12965 On Mon, Jan 25, 2016 at 4:08 PM, Michael Armbrust <mich...@databricks.com> wrote: > Looks like you found a bug. I've filed them here: > > SPARK-12987 - Drop fails when columns contain dots > SPARK-12988 - Can't drop columns that contain dots > > On Fri, Jan 22, 2016 at 3:18 PM, Joshua TAYLOR <joshuaaa...@gmail.com> > wrote: >> >> (Apologies if this comes through twice; I sent it once before I'd >> confirmed by mailing list subscription.) >> >> >> I've been having lots of trouble with DataFrames whose columns have dots >> in their names today. I know that in many places, backticks can be used to >> quote column names, but the problem I'm running into now is that I can't >> drop a column that has *no* dots in its name when there are *other* columns >> in the table that do. Here's some code that tries four ways of dropping the >> column. One throws a weird exception, one is a semi-expected no-op, and the >> other two work. >> >> public class SparkExample { >> public static void main(String[] args) { >> /* Get the spark and sql contexts. Setting spark.ui.enabled to >> false >> * keeps Spark from using its built in dependency on Jersey. */ >> SparkConf conf = new SparkConf() >> .setMaster("local[*]") >> .setAppName("test") >> .set("spark.ui.enabled", "false"); >> JavaSparkContext sparkContext = new JavaSparkContext(conf); >> SQLContext sqlContext = new SQLContext(sparkContext); >> >> /* Create a schema with two columns, one of which as no dots >> (a_b), >> * and the other which does (a.b). */ >> StructType schema = new StructType(new StructField[] { >> DataTypes.createStructField("a_b", DataTypes.StringType, >> false), >> DataTypes.createStructField("a.c", DataTypes.IntegerType, >> false) >> }); >> >> /* Create an RDD of Rows, and then convert it into a DataFrame. */ >> List<Row> rows = Arrays.asList( >> RowFactory.create("t", 2), >> RowFactory.create("u", 4)); >> JavaRDD<Row> rdd = sparkContext.parallelize(rows); >> DataFrame df = sqlContext.createDataFrame(rdd, schema); >> >> /* Four ways to attempt dropping a_b from the DataFrame. >> * We'll try calling each one of these and looking at >> * the results (or the resulting exception). */ >> Function<DataFrame,DataFrame> x1 = d -> d.drop("a_b"); // >> exception >> Function<DataFrame,DataFrame> x2 = d -> d.drop("`a_b`"); // >> no-op >> Function<DataFrame,DataFrame> x3 = d -> d.drop(d.col("a_b")); // >> works >> Function<DataFrame,DataFrame> x4 = d -> d.drop(d.col("`a_b`")); // >> works >> >> int i=0; >> for (Function<DataFrame,DataFrame> x : Arrays.asList(x1, x2, x3, >> x4)) { >> System.out.println("Case "+i++); >> try { >> x.apply(df).show(); >> } catch (Exception e) { >> e.printStackTrace(System.out); >> } >> } >> } >> } >> >> Here's the output. Case 1 is a no-op, which I think I can understand, >> because DataFrame.drop(String) doesn't do any resolution (it doesn't need >> to), so d.drop("`a_b`") doesn't do anything because there's no column whose >> name is literally "`a_b`". The third and fourth cases work, because >> DataFrame.col() does do resolution, and both "a_b" and "`a_b`" resolve >> correctly. But why does the first case fail? And why with the message that >> it does? Why is it trying to resolve "a.c" at all in this case? >> >> Case 0 >> org.apache.spark.sql.AnalysisException: cannot resolve 'a.c' given input >> columns a_b, a.c; >> 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$$anonfun$transformUp$1.apply(TreeNode.scala:319) >> at >> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319) >> at >> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53) >> at >> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:318) >> at >> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:107) >> at >> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:117) >> at >> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:121) >> at >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >> at >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >> at scala.collection.immutable.List.foreach(List.scala:318) >> at >> scala.collection.TraversableLike$class.map(TraversableLike.scala:244) >> at scala.collection.AbstractTraversable.map(Traversable.scala:105) >> at >> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:121) >> at >> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:125) >> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >> at scala.collection.Iterator$class.foreach(Iterator.scala:727) >> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) >> at >> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) >> at >> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) >> at >> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) >> at >> scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) >> at scala.collection.AbstractIterator.to(Iterator.scala:1157) >> at >> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) >> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) >> at >> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) >> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) >> at >> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:125) >> 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$1.apply(CheckAnalysis.scala:50) >> at >> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:105) >> at >> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50) >> at >> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:44) >> at >> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34) >> at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133) >> at >> org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$withPlan(DataFrame.scala:2165) >> at org.apache.spark.sql.DataFrame.select(DataFrame.scala:751) >> at org.apache.spark.sql.DataFrame.drop(DataFrame.scala:1286) >> at SparkExample.lambda$0(SparkExample.java:45) >> at SparkExample.main(SparkExample.java:54) >> Case 1 >> +---+---+ >> |a_b|a.c| >> +---+---+ >> | t| 2| >> | u| 4| >> +---+---+ >> >> Case 2 >> +---+ >> |a.c| >> +---+ >> | 2| >> | 4| >> +---+ >> >> Case 3 >> +---+ >> |a.c| >> +---+ >> | 2| >> | 4| >> +---+ >> >> >> Thanks in advance, >> Joshua >> >> -- >> Joshua Taylor, http://www.cs.rpi.edu/~tayloj/ > > -- Joshua Taylor, http://www.cs.rpi.edu/~tayloj/ --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
