Presto is so far good at joining different sources/databases. I tried a simple join query in Spark SQL, it fails as the followings errors
val a = cql("select test.a from test JOIN test1 on test.a = test1.a") a: org.apache.spark.sql.SchemaRDD = SchemaRDD[0] at RDD at SchemaRDD.scala:104 == Query Plan == Project [a#7] Filter (a#7 = a#21) CartesianProduct org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:0 failed 4 times, most recent failure: Exception failure in TID 3 on host 127.0.0.1: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: No function to evaluate expression. type: AttributeReference, tree: a#7 org.apache.spark.sql.catalyst.expressions.AttributeReference.eval(namedExpressions.scala:158) org.apache.spark.sql.catalyst.expressions.EqualTo.eval(predicates.scala:146) org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$1.apply(basicOperators.scala:54) org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$1.apply(basicOperators.scala:54) scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:390) scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) scala.collection.Iterator$class.foreach(Iterator.scala:727) scala.collection.AbstractIterator.foreach(Iterator.scala:1157) scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) scala.collection.AbstractIterator.to(Iterator.scala:1157) scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) scala.collection.AbstractIterator.toArray(Iterator.scala:1157) org.apache.spark.rdd.RDD$$anonfun$16.apply(RDD.scala:731) org.apache.spark.rdd.RDD$$anonfun$16.apply(RDD.scala:731) org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1083) org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1083) org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111) org.apache.spark.scheduler.Task.run(Task.scala:51) org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:183) java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) java.lang.Thread.run(Thread.java:745) It looks like Spark SQL has long way to go to be compatible with SQL -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/Spark-SQL-Query-and-join-different-data-sources-tp7914p7945.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org