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Posted to issues@spark.apache.org by "Max Seiden (JIRA)" <ji...@apache.org> on 2015/04/01 19:44:54 UTC
[jira] [Created] (SPARK-6652) SQLContext and HiveContext do not
handle "tricky" names well
Max Seiden created SPARK-6652:
---------------------------------
Summary: SQLContext and HiveContext do not handle "tricky" names well
Key: SPARK-6652
URL: https://issues.apache.org/jira/browse/SPARK-6652
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.2.1
Reporter: Max Seiden
h3. Summary
There are cases where both the SQLContext and HiveContext fail when handling "tricky names" (containing UTF-8, tabs, newlines, etc) well. For example, the following string:
{noformat}
val tricky = "Tricky-\u4E2D[x.][\",/\\n * ? é\n&$(x)\t(':;#!^-Name"
{noformat}
causes the following exceptions during parsing and resolution (respectively).
h5. SQLContext parse failure
{noformat}
// pseudocode
val data = 0 until 100
val rdd = sc.parallelize(data)
val schema = StructType(StructField(Tricky, IntegerType, false) :: Nil)
val schemaRDD = sqlContext.applySchema(rdd.map(i => Row(i)), schema)
schemaRDD.registerAsTable(Tricky)
sqlContext.sql(s"select `$Tricky` from `$Tricky`")
java.lang.RuntimeException: [1.33] failure: ``UNION'' expected but ErrorToken(``' expected but
found) found
select `Tricky-中[x.][",/\n * ? é
^
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.AbstractSparkSQLParser.apply(SparkSQLParser.scala:33)
at org.apache.spark.sql.SQLContext$$anonfun$1.apply(SQLContext.scala:79)
at org.apache.spark.sql.SQLContext$$anonfun$1.apply(SQLContext.scala:79)
at org.apache.spark.sql.catalyst.SparkSQLParser$$anonfun$org$apache$spark$sql$catalyst$SparkSQLParser$$others$1.apply(SparkSQLParser.scala:174)
at org.apache.spark.sql.catalyst.SparkSQLParser$$anonfun$org$apache$spark$sql$catalyst$SparkSQLParser$$others$1.apply(SparkSQLParser.scala:173)
at scala.util.parsing.combinator.Parsers$Success.map(Parsers.scala:136)
at scala.util.parsing.combinator.Parsers$Success.map(Parsers.scala:135)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:242)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$map$1.apply(Parsers.scala:242)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:222)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1$$anonfun$apply$2.apply(Parsers.scala:254)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1$$anonfun$apply$2.apply(Parsers.scala:254)
at scala.util.parsing.combinator.Parsers$Failure.append(Parsers.scala:202)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1.apply(Parsers.scala:254)
at scala.util.parsing.combinator.Parsers$Parser$$anonfun$append$1.apply(Parsers.scala:254)
at scala.util.parsing.combinator.Parsers$$anon$3.apply(Parsers.scala:222)
at scala.util.parsing.combinator.Parsers$$anon$2$$anonfun$apply$14.apply(Parsers.scala:891)
at scala.util.parsing.combinator.Parsers$$anon$2$$anonfun$apply$14.apply(Parsers.scala:891)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at scala.util.parsing.combinator.Parsers$$anon$2.apply(Parsers.scala:890)
at scala.util.parsing.combinator.PackratParsers$$anon$1.apply(PackratParsers.scala:110)
at org.apache.spark.sql.catalyst.AbstractSparkSQLParser.apply(SparkSQLParser.scala:31)
at org.apache.spark.sql.SQLContext$$anonfun$parseSql$1.apply(SQLContext.scala:83)
at org.apache.spark.sql.SQLContext$$anonfun$parseSql$1.apply(SQLContext.scala:83)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:83)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:303)
{noformat}
h5. HiveContext resolution failure
{noformat}
// pseudocode
val data = 0 until 100
val rdd = sc.parallelize(data)
val schema = StructType(StructField(Tricky, IntegerType, false) :: Nil)
val schemaRDD = sqlContext.applySchema(rdd.map(i => Row(i)), schema)
schemaRDD.registerAsTable(Tricky)
sqlContext.sql(s"select `$Tricky` from `$Tricky`").collect()
// the parse is ok in this case...
15/04/01 10:41:48 WARN HiveConf: DEPRECATED: hive.metastore.ds.retry.* no longer has any effect. Use hive.hmshandler.retry.* instead
15/04/01 10:41:48 INFO ParseDriver: Parsing command: select `Tricky-中[x.][",/\n * ? é
&$(x) (':;#!^-Name` from `Tricky-中[x.][",/\n * ? é
&$(x) (':;#!^-Name`
15/04/01 10:41:48 INFO ParseDriver: Parse Completed
// but resolution fails
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved attributes: 'Tricky-中[x.][",/\n * ? é
&$(x) (':;#!^-Name, tree:
'Project ['Tricky-中[x.][",/\n * ? é
&$(x) (':;#!^-Name]
Subquery tricky-中[x.][",/\n * ? é
&$(x) (':;#!^-name
LogicalRDD [Tricky-中[x.][",/\n * ? é
&$(x) (':;#!^-Name#2], MappedRDD[16] at map at <console>:30
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$1.applyOrElse(Analyzer.scala:80)
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$1.applyOrElse(Analyzer.scala:78)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:144)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:135)
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:78)
at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:76)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:61)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:59)
at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:59)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:51)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.apply(RuleExecutor.scala:51)
at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:411)
at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:411)
at org.apache.spark.sql.SQLContext$QueryExecution.withCachedData$lzycompute(SQLContext.scala:412)
at org.apache.spark.sql.SQLContext$QueryExecution.withCachedData(SQLContext.scala:412)
at org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan$lzycompute(SQLContext.scala:413)
at org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan(SQLContext.scala:413)
at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:418)
at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:416)
at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:422)
at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:422)
at org.apache.spark.sql.SchemaRDD.collect(SchemaRDD.scala:444)
{noformat}
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