I believe this normally comes when Spark is unable to perform union due to "difference" in schema of the operands. Can you check if the schema of both the datasets are semantically same ?
On Tue, Oct 18, 2016 at 9:06 AM, Efe Selcuk <efema...@gmail.com> wrote: > Bump! > > On Thu, Oct 13, 2016 at 8:25 PM Efe Selcuk <efema...@gmail.com> wrote: > >> I have a use case where I want to build a dataset based off of >> conditionally available data. I thought I'd do something like this: >> >> case class SomeData( ... ) // parameters are basic encodable types like >> strings and BigDecimals >> >> var data = spark.emptyDataset[SomeData] >> >> // loop, determining what data to ingest and process into datasets >> data = data.union(someCode.thatReturnsADataset) >> // end loop >> >> However I get a runtime exception: >> >> Exception in thread "main" org.apache.spark.sql.AnalysisException: >> unresolved operator 'Union; >> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class. >> failAnalysis(CheckAnalysis.scala:40) >> at org.apache.spark.sql.catalyst.analysis.Analyzer. >> failAnalysis(Analyzer.scala:58) >> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$ >> anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:361) >> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$ >> anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67) >> at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp( >> TreeNode.scala:126) >> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class. >> checkAnalysis(CheckAnalysis.scala:67) >> at org.apache.spark.sql.catalyst.analysis.Analyzer. >> checkAnalysis(Analyzer.scala:58) >> at org.apache.spark.sql.execution.QueryExecution. >> assertAnalyzed(QueryExecution.scala:49) >> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:161) >> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:167) >> at org.apache.spark.sql.Dataset$.apply(Dataset.scala:59) >> at org.apache.spark.sql.Dataset.withTypedPlan(Dataset.scala:2594) >> at org.apache.spark.sql.Dataset.union(Dataset.scala:1459) >> >> Granted, I'm new at Spark so this might be an anti-pattern, so I'm open >> to suggestions. However it doesn't seem like I'm doing anything incorrect >> here, the types are correct. Searching for this error online returns >> results seemingly about working in dataframes and having mismatching >> schemas or a different order of fields, and it seems like bugfixes have >> gone into place for those cases. >> >> Thanks in advance. >> Efe >> >> -- Thanks & Regards, Agraj Mangal
