title: AWS Glue takes a long time to finish
body: <p>I just run a very simple job as follows</p>
  
  <pre><code>glueContext = GlueContext(SparkContext.getOrCreate())
  l_table = glueContext.create_dynamic_frame.from_catalog(
               database="gluecatalog",
               table_name="fctable") 
  l_table = l_table.drop_fields(['seq','partition_0','partition_1','partition_2','partition_3']).rename_field('tbl_code','table_code')
  print "Count: ", l_table.count()
  l_table.printSchema()
  l_table.select_fields(['trans_time']).toDF().distinct().show()
  dfc = l_table.relationalize("table_root", "s3://my-bucket/temp/")
  print "Before keys() call "
  dfc.keys()
  print "After keys() call "
  l_table.select_fields('table').printSchema()
  dfc.select('table_root_table').toDF().where("id = 1 or id = 2").orderBy(['id','index']).show()
  dfc.select('table_root').toDF().where("table = 1 or table = 2").show()
  </code></pre>
  
  <p>The data structure is simple too</p>
  
  <pre><code>root
  |-- table: array
  | |-- element: struct
  | | |-- trans_time: string
  | | |-- seq: null
  | | |-- operation: string
  | | |-- order_date: string
  | | |-- order_code: string
  | | |-- tbl_code: string
  | | |-- ship_plant_code: string
  |-- partition_0
  |-- partition_1
  |-- partition_2
  |-- partition_3
  </code></pre>
  
  <p>When I run job test, it took anywhere from 12 to 16 minutes to finish. But the cloud watch log showed that the job took 2 seconds to display all my data. </p>
  
  <p>So my questions are:
   Where does AWS Glue job spend its time beyond the logging could show and is what it doing outside the logging period?</p>