title: Spark DataFrame Repartition and Parquet Partition
body: <ol>
  <li><p>I am using repartition on columns to store the data in parquet. But
  I see that the no. of parquet partitioned files are not same with the
  no. of Rdd partitions. Is there no correlation between rdd partitions
  and parquet partitions?</p></li>
  <li><p>When I write the data to parquet partition and I use Rdd
  repartition and then I read the data from parquet partition , is
  there any condition when the rdd partition numbers will be same
  during read / write?</p></li>
  <li><p>How is bucketing a dataframe using a column id and repartitioning a
  dataframe via the same column id different?</p></li>
  <li><p>While considering the performance of joins in Spark should we be
  looking at bucketing or repartitioning (or maybe both)</p></li>
  </ol>