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
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