Does Spark maintain parquet partitioning on read? : <p>I am having a lot trouble finding the answer to this question. Let's say I write a dataframe to parquet and I use <code>repartition</code> combined with <code>partitionBy</code> to get a nicely partitioned parquet file. See Below:</p>

<pre><code>df.repartition(col("DATE")).write.partitionBy("DATE").parquet("/path/to/parquet/file")
</code></pre>

<p>Now later on I would like to read the parquet file so I do something like this:</p>

<pre><code>val df = spark.read.parquet("/path/to/parquet/file")
</code></pre>

<p>Is the dataframe partitioned by <code>"DATE"</code>? In other words if a parquet file is partitioned does spark maintain that partitioning when reading it into a spark dataframe. Or is it randomly partitioned?</p>

<p>Also the why and why not to this answer would be helpful as well.</p>
