Issues with Spark SQL, Postgres, and DataFrames - postgresql

Question

I am trying to pull records in postgress via spark sql and and create a RDD with that data.
So far I have coded up querying postgres, loading the table Ifrom postgres into the SQL context, but can not confirm that the data is really there nor the form it is in.
Below is the code I'm using but it seems I can not call first or show on the dataframe as it throws a null pointer exception. The same if I convert it to an RDD and try to see the first Row in the RDD.
DOes anyone have any idea what I am doing wrong? I want to confirm that the rows have the data i expect. I call count on the dataframe and get back a number of row which i assume is the amount of records i pulled, I get a col count which matches what I expect but I can not seem to figure out how to view a row to confirm a record is in the dataframe.
Data looks like this, in postgres:
[Patch_id: string, number: bigint, products: array, type: string, restart_required: string, superseded: array]
[ RECORD 1 ]
patch_id | PATCH-2013-3862
number | 2872339
products |
type | Vendor Fix
restart_required |
superseded |
[ RECORD 2 ]
patch_id | PATCH-2015-2368
number | 3072631
products |
type | Vendor Fix
restart_required |
superseded |
Code written:
val sqlContext = new SQLContext(context)
val df = sqlContext.read.format("jdbc")
.option("url", config.get(CONFIG_VAR_CONNECTION_STRING))
.option("dbtable", "(SELECT * FROM patches) patches")
.option("driver", "org.postgresql.Driver")
.load()
logger.error("Count: " + df.count())
logger.error("Column Count: " + df.columns.length)
logger.error("First: " + df.first()) // Fails here, even fails with .show()
val patchInfoRDD = df.rdd.map(_.mkString(","))
logger.error("First: " + patchInfoRDD.first()) // Fails here
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.getNext(JDBCRDD.scala:422)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.hasNext(JDBCRDD.scala:498)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)