Any chance you could sketch out the Shark APIs that you use for this?
Matei's response suggests that the preferred API is coming in the next
release (i.e. RDDTable class in 0.8.1). Are you building Shark from the
latest in the repo and using that? Or have you figured out other API
calls that accomplish something similar?
Thanks,
Philip
On 12/8/2013 2:44 AM, Christopher Nguyen wrote:
Philip, fwiw we do go with including Shark as a dependency for our
needs, making a fat jar, and it works very well. It was quite a bit of
pain what with the Hadoop/Hive transitive dependencies, but for us it
was worth it.
I hope that serves as an existence proof that says Mt Everest has been
climbed, likely by more than just ourselves. Going forward this should
be getting easier.
--
Christopher T. Nguyen
Co-founder & CEO, Adatao <http://adatao.com>
linkedin.com/in/ctnguyen <http://linkedin.com/in/ctnguyen>
On Fri, Dec 6, 2013 at 7:06 PM, Philip Ogren <[email protected]
<mailto:[email protected]>> wrote:
I have a simple scenario that I'm struggling to implement. I
would like to take a fairly simple RDD generated from a large log
file, perform some transformations on it, and write the results
out such that I can perform a Hive query either from Hive (via
Hue) or Shark. I'm having troubles with the last step. I am able
to write my data out to HDFS and then execute a Hive create table
statement followed by a load data statement as a separate step. I
really dislike this separate manual step and would like to be able
to have it all accomplished in my Spark application. To this end,
I have investigated two possible approaches as detailed below -
it's probably too much information so I'll ask my more basic
question first:
Does anyone have a basic recipe/approach for loading data in an
RDD to a Hive table from a Spark application?
1) Load it into HBase via PairRDDFunctions.saveAsHadoopDataset.
There is a nice detailed email on how to do this here
<http://mail-archives.apache.org/mod_mbox/incubator-spark-user/201311.mbox/%3ccacyzca3askwd-tujhqi1805bn7sctguaoruhd5xtxcsul1a...@mail.gmail.com%3E>.
I didn't get very far thought because as soon as I added an hbase
dependency (corresponding to the version of hbase we are running)
to my pom.xml file, I had an slf4j dependency conflict that caused
my current application to explode. I tried the latest released
version and the slf4j dependency problem went away but then the
deprecated class TableOutputFormat no longer exists. Even if
loading the data into hbase were trivially easy (and the detailed
email suggests otherwise) I would then need to query HBase from
Hive which seems a little clunky.
2) So, I decided that Shark might be an easier option. All the
examples provided in their documentation seem to assume that you
are using Shark as an interactive application from a shell.
Various threads I've seen seem to indicate that Shark isn't really
intended to be used as dependency in your Spark code (see this
<https://groups.google.com/forum/#%21topic/shark-users/DHhslaOGPLg/discussion>
and that
<https://groups.google.com/forum/#%21topic/shark-users/2_Ww1xlIgvo/discussion>.)
It follows then that one can't add a Shark dependency to a pom.xml
file because Shark isn't released via Maven Central (that I can
tell.... perhaps it's in some other repo?) Of course, there are
ways of creating a local dependency in maven but it starts to feel
very hacky.
I realize that I've given sufficient detail to expose my ignorance
in a myriad of ways. Please feel free to shine light on any of my
misconceptions!
Thanks,
Philip
