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From "Long, Xindian" <>
Subject RE: How are Dataframes partitioned by default when using spark?
Date Thu, 29 Sep 2016 16:02:00 GMT
Hi, Josh:

Thanks for the reply. I still have some questions/comments

The phoenix-spark integration inherits the underlying splits provided by Phoenix, which is
a function of the HBase regions, salting and other aspects determined by the Phoenix Query

XD: Is there any documentation on what this function actually is ?

Re: #1, as I understand the Spark JDBC connector, it evenly segments the range, although it
will only work on a numeric column, not a compound row key.

Re: #2, again, as I understand Spark JDBC, I don't believe that's an option, or perhaps it
will default to only providing 1 partition, i.e, one very large query.

Re: data-locality, the underlying Phoenix Hadoop Input Format isn't yet node-aware. There
are some data locality advantages gained by co-locating the Spark executors to the RegionServers,
but it could be improved. It's worth filing a JIRA enhancement ticket for that.

XD: A JIRA enhancement will be great.



On Mon, Sep 19, 2016 at 12:48 PM, Long, Xindian <<>>
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?



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