spark jdbc df limit... what is it doing?
<p>I'm trying to learn how to get a feel of what is going on inside Spark, and here's my current confusion.  I'm trying to read first 200 rows from an Oracle table into Spark:</p>

<pre><code>val jdbcDF = spark.read.format("jdbc").options(
  Map("url" -&gt; "jdbc:oracle:thin:...",
  "dbtable" -&gt; "schema.table",
  "fetchSize" -&gt; "5000",
  "partitionColumn" -&gt; "my_row_id",
  "numPartitions" -&gt; "16",
  "lowerBound" -&gt; "0",
  "upperBound" -&gt; "9999999"
  )).load()

jdbcDF.limit(200).count()
</code></pre>

<p>This, I would expect, to be fairly quick.  Similar action on a table with 500K rows completes in a reasonable time.  In this particular case, the table is much bigger (hundreds of millions of rows), but limit(200) would, I'd think, make it fast?  How do I go about figuring out where it spending its time?</p>