How are Dataframes/Datasets/RDD partitioned by default when using spark? assuming the Dataframe/Datasets/RDD is the result of a query like that:
select col1, col2, col3 from table3 where col3 > xxx I noticed that for HBase, a partitioner partitions the rowkeys based on region splits, can Phoenix do this as well? I also read that if I use spark with the Phoenix jdbc interface "it's only able to parallelize queries by partioning on a numeric column. It also requires a known lower bound, upper bound and partition count in order to create split queries." Question 1, If I specify an option like this, is the partitioning based on segmenting the range evenly, i.e. each partition gets a rowkey in ranges like: upperlimit-lowerlmit)/partitionCount ? Question 2, if I do not specify any range, or the row key is not a numeric column, how is the result partitioned using jdbc? If I use the spark-phoenix plug in, it is mentioned that it is able to leverage the underlying splits provided by Phoenix? Are there any example scenarios of that? e.g. can it partition the resulted Dataframe based on regions in the underling HBase table, so that spark can take advantage the locality of the data? Thanks Xindian
