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Posted to issues@spark.apache.org by "Cheng Hao (JIRA)" <ji...@apache.org> on 2014/11/06 02:04:36 UTC
[jira] [Created] (SPARK-4263) PERCENTILE is not working
Cheng Hao created SPARK-4263:
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Summary: PERCENTILE is not working
Key: SPARK-4263
URL: https://issues.apache.org/jira/browse/SPARK-4263
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: Cheng Hao
Priority: Minor
When query "select percentile(key, array(0, 0.5,1)) from src", it will throws exception like:
{panel}
org.apache.hadoop.hive.ql.exec.NoMatchingMethodException: No matching method for class org.apache.hadoop.hive.ql.udf.UDAFPercentile with (string, array<double>). Possible choices: _FUNC_(bigint, array<double>) _FUNC_(bigint, double)
at org.apache.hadoop.hive.ql.exec.FunctionRegistry.getMethodInternal(FunctionRegistry.java:1213)
at org.apache.hadoop.hive.ql.exec.DefaultUDAFEvaluatorResolver.getEvaluatorClass(DefaultUDAFEvaluatorResolver.java:84)
at org.apache.hadoop.hive.ql.udf.generic.GenericUDAFBridge.getEvaluator(GenericUDAFBridge.java:56)
at org.apache.spark.sql.hive.HiveUdaf.objectInspector$lzycompute(hiveUdfs.scala:234)
at org.apache.spark.sql.hive.HiveUdaf.objectInspector(hiveUdfs.scala:233)
at org.apache.spark.sql.hive.HiveUdaf.dataType(hiveUdfs.scala:241)
at org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.Aggregate$$anonfun$output$6.apply(basicOperators.scala:143)
at org.apache.spark.sql.catalyst.plans.logical.Aggregate$$anonfun$output$6.apply(basicOperators.scala:143)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.sql.catalyst.plans.logical.Aggregate.output(basicOperators.scala:143)
at org.apache.spark.sql.catalyst.plans.logical.Limit.output(basicOperators.scala:147)
at org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$unapply$1.apply(patterns.scala:61)
at org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$unapply$1.apply(patterns.scala:61)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.sql.catalyst.planning.PhysicalOperation$.unapply(patterns.scala:61)
at org.apache.spark.sql.sources.DataSourceStrategy$.apply(DataSourceStrategy.scala:34)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.apply(QueryPlanner.scala:59)
at org.apache.spark.sql.SQLContext$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$QueryExecution.executedPlan(SQLContext.scala:422)
at org.apache.spark.sql.hive.HiveContext$QueryExecution.stringResult(HiveContext.scala:425)
at org.apache.spark.sql.hive.thriftserver.AbstractSparkSQLDriver.run(AbstractSparkSQLDriver.scala:59)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processCmd(SparkSQLCLIDriver.scala:276)
at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:413)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver$.main(SparkSQLCLIDriver.scala:211)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.main(SparkSQLCLIDriver.scala)
{panel}
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