kudu-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Benjamin Kim <bbuil...@gmail.com>
Subject Re: Spark on Kudu
Date Wed, 15 Jun 2016 01:00:46 GMT
Dan,

Thanks for the information. That would mean both “append” and “overwrite” modes would
be combined or not needed in the future.

Cheers,
Ben

> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com> wrote:
> 
> Right now append uses an update Kudu operation, which requires the row already be present
in the table. Overwrite maps to insert.  Kudu very recently got upsert support baked in, but
it hasn't yet been integrated into the Spark connector.  So pretty soon these sharp edges
will get a lot better, since upsert is the way to go for most spark workloads.
> 
> - Dan
> 
> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I
would assume that now I can use the “overwrite” mode on existing data. Now, I have to
find answers to these questions. What would happen if I “append” to the data in the Kudu
table if the data already exists? What would happen if I “overwrite” existing data when
the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the
best way to simulate the UPSERT behavior in HBase because this is what our use case is.
> 
> Thanks,
> Ben
> 
> 
> 
>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
>> 
>> Hi,
>> 
>> Now, I’m getting this error when trying to write to the table.
>> 
>> import scala.collection.JavaConverters._
>> val key_seq = Seq(“my_id")
>> val key_list = List(“my_id”).asJava
>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list,
100))
>> 
>> df.write
>>     .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>     .mode("overwrite")
>>     .kudu
>> 
>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample
errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key
not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>> 
>> Does the key field need to be first in the DataFrame?
>> 
>> Thanks,
>> Ben
>> 
>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <mailto:dan@cloudera.com>>
wrote:
>>> 
>>> 
>>> 
>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
>>> Dan,
>>> 
>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in
the DataFrame and set the partitioning? Is it like this?
>>> 
>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>> 
>>> java.lang.IllegalArgumentException: Table partitioning must be specified using
setRangePartitionColumns or addHashPartitions
>>> 
>>> Yep.  The `Seq("my_id")` part of that call is specifying the set of primary key
columns, so in this case you have specified the single PK column "my_id".  The `addHashPartitions`
call adds hash partitioning to the table, in this case over the column "my_id" (which is good,
it must be over one or more PK columns, so in this case "my_id" is the one and only valid
combination).  However, the call to `addHashPartition` also takes the number of buckets as
the second param.  You shouldn't get the IllegalArgumentException as long as you are specifying
either `addHashPartitions` or `setRangePartitionColumns`.
>>> 
>>> - Dan
>>>  
>>> 
>>> Thanks,
>>> Ben
>>> 
>>> 
>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <mailto:dan@cloudera.com>>
wrote:
>>>> 
>>>> Looks like we're missing an import statement in that example.  Could you
try:
>>>> 
>>>> import org.kududb.client._
>>>> and try again?
>>>> 
>>>> - Dan
>>>> 
>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
>>>> I encountered an error trying to create a table based on the documentation
from a DataFrame.
>>>> 
>>>> <console>:49: error: not found: type CreateTableOptions
>>>>               kuduContext.createTable(tableName, df.schema, Seq("key"), new
CreateTableOptions().setNumReplicas(1))
>>>> 
>>>> Is there something I’m missing?
>>>> 
>>>> Thanks,
>>>> Ben
>>>> 
>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org
<mailto:jdcryans@apache.org>> wrote:
>>>>> 
>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
<https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>> 
>>>>> J-D
>>>>> 
>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com
<mailto:bbuild11@gmail.com>> wrote:
>>>>> Hi J-D,
>>>>> 
>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
for spark-shell to use. Can you show me where to find it?
>>>>> 
>>>>> Thanks,
>>>>> Ben
>>>>> 
>>>>> 
>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org
<mailto:jdcryans@apache.org>> wrote:
>>>>>> 
>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
<https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>> 
>>>>>> J-D
>>>>>> 
>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com
<mailto:bbuild11@gmail.com>> wrote:
>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>> 
>>>>>> Thanks,
>>>>>> Ben
>>>>>> 
>>>>>> 
>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org
<mailto:jdcryans@apache.org>> wrote:
>>>>>>> 
>>>>>>> It will be in 0.9.0.
>>>>>>> 
>>>>>>> J-D
>>>>>>> 
>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com
<mailto:bbuild11@gmail.com>> wrote:
>>>>>>> Hi Chris,
>>>>>>> 
>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>> 
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>> 
>>>>>>> 
>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com
<mailto:Christopher.George@rms.com>> wrote:
>>>>>>>> 
>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>> It will allow upsert/insert from a dataframe using the datasource
api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>> 
>>>>>>>> Example usages will look something like:
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
<http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>> 
>>>>>>>> -Chris George
>>>>>>>> 
>>>>>>>> 
>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com
<mailto:bbuild11@gmail.com>> wrote:
>>>>>>>> 
>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>> 
>>>>>>>> Also, does anyone have any sample code on how to update/insert
data in Kudu using DataFrames?
>>>>>>>> 
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>> 
>>>>>>>> 
>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com
<mailto:Christopher.George@rms.com>> wrote:
>>>>>>>>> 
>>>>>>>>> SparkSQL cannot support these type of statements but
we may be able to implement similar functionality through the api.
>>>>>>>>> -Chris
>>>>>>>>> 
>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com
<mailto:bbuild11@gmail.com>> wrote:
>>>>>>>>> 
>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for
an “upsert” if it were to be implemented.
>>>>>>>>> 
>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>  WHEN MATCHED THEN
>>>>>>>>>  UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>  WHEN NOT MATCHED THEN
>>>>>>>>>  INSERT (column1 [, column2 ...]) VALUES (value1 [, value2
…])
>>>>>>>>> 
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>> 
>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com
<mailto:Christopher.George@rms.com>> wrote:
>>>>>>>>>> 
>>>>>>>>>> I have a wip kuduRDD that I made a few months ago.
I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/
<http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>> It does pushdown predicates which the existing input
formatter based rdd does not.
>>>>>>>>>> 
>>>>>>>>>> Within the next two weeks I’m planning to implement
a datasource for spark that will have pushdown predicates and insertion/update functionality
(need to look more at cassandra and the hbase datasource for best way to do this) I agree
that server side upsert would be helpful.
>>>>>>>>>> Having a datasource would give us useful data frames
and also make spark sql usable for kudu.
>>>>>>>>>> 
>>>>>>>>>> My reasoning for having a spark datasource and not
using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when
compared to spark 2. We interact with datasources which do not integrate with impala. 3. We
have custom sql query planners for extended sql functionality.
>>>>>>>>>> 
>>>>>>>>>> -Chris George
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org
<mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>> 
>>>>>>>>>> You guys make a convincing point, although on the
upsert side we'll need more support from the servers. Right now all you can do is an INSERT
then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client
side that would manage it, but it wouldn't be atomic.
>>>>>>>>>> 
>>>>>>>>>> J-D
>>>>>>>>>> 
>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com
<mailto:mark@clearstorydata.com>> wrote:
>>>>>>>>>> It's pretty simple, actually.  I need to support
versioned datasets in a Spark SQL environment.  Instead of a hack on top of a Parquet data
store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based
read operations to support not only appending data, but also updating existing data, and even
some schema migration.  The most typical use case is a dataset that is updated periodically
(e.g., weekly or monthly) in which the the preliminary data in the previous window (week or
month) is updated with values that are expected to remain unchanged from then on, and a new
set of preliminary values for the current window need to be added/appended.
>>>>>>>>>> 
>>>>>>>>>> Using Kudu's Java API and developing additional functionality
on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark
SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives
before making that decision. 
>>>>>>>>>> 
>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans
<jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>> Mark,
>>>>>>>>>> 
>>>>>>>>>> Thanks for taking some time to reply in this thread,
glad it caught the attention of other folks!
>>>>>>>>>> 
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com
<mailto:mark@clearstorydata.com>> wrote:
>>>>>>>>>> Do they care being able to insert into Kudu with
SparkSQL
>>>>>>>>>> 
>>>>>>>>>> I care about insert into Kudu with Spark SQL.  I'm
currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying
to evaluate what to expect from Kudu.  Whether Kudu does a good job supporting inserts with
Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>> 
>>>>>>>>>> I'd like to know more about why SparkSQL inserts
in necessary for you. Is it just that you currently do it that way into some database or parquet
so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into
Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>> 
>>>>>>>>>> Additionally, what do you expect to gain from using
Kudu VS your current solution? If it's not completely clear, I'd love to help you think through
it.
>>>>>>>>>>  
>>>>>>>>>> 
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans
<jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>> 
>>>>>>>>>> What are your DS folks looking for in terms of functionality
related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care
being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything
more specific to Spark that I'm missing?
>>>>>>>>>> 
>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early
Fall. At Cloudera all our resources are committed to making things happen in time, and a more
fully featured Spark integration isn't in our plans during that period. I'm really hoping
someone in the community will help with Spark, the same way we got a big contribution for
the Flume sink. 
>>>>>>>>>> 
>>>>>>>>>> J-D
>>>>>>>>>> 
>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com
<mailto:bbuild11@gmail.com>> wrote:
>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7
versions. But, since it’s not “production-ready”, upper management doesn’t want to
fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler
and easier to use in every aspect compared to HBase. Impala was great for the report writers
and analysts to experiment with for the short time it was up. But, once again, the only blocker
was the lack of Spark support for our Data Developers/Scientists. So, production-level data
population won’t happen until then.
>>>>>>>>>> 
>>>>>>>>>> I hope this helps you get an idea where I am coming
from…
>>>>>>>>>> 
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans
<jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>>> 
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim
<bbuild11@gmail.com <mailto:bbuild11@gmail.com>> wrote:
>>>>>>>>>>> J-D,
>>>>>>>>>>> 
>>>>>>>>>>> The main thing I hear that Cassandra is being
used as an updatable hot data store to ensure that duplicates are taken care of and idempotency
is maintained. Whether data was directly retrieved from Cassandra for analytics, reports,
or searches, it was not clear as to what was its main use. Some also just used it for a staging
area to populate downstream tables in parquet format. The last thing I heard was that CQL
was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>> 
>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant
for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should
beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not
the former.
>>>>>>>>>>>  
>>>>>>>>>>> 
>>>>>>>>>>> As for our company, we have been looking for
an updatable data store for a long time that can be quickly queried directly either using
Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance
degradation and many configuration headaches. For now, we are using HBase to take on this
role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way
to fill this gap easily, especially being the closest thing to other relational databases
out there in familiarity for the many SQL analytics people in our company. The other alternative
would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course.
If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract
from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using
by analysts or the reporting software we use..
>>>>>>>>>>> 
>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're
not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>  
>>>>>>>>>>> 
>>>>>>>>>>> I hope this helps.
>>>>>>>>>>> 
>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>  
>>>>>>>>>>> 
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben 
>>>>>>>>>>> 
>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans
<jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>>>> 
>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside
Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues
who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>> 
>>>>>>>>>>>> There's definitely an overlap in the use
cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is
just an interim solution for the use case you describe.
>>>>>>>>>>>> 
>>>>>>>>>>>> Nothing significant happened in Kudu over
the past month, it's a storage engine so things move slowly *smile*. I'd love to see more
contributions on the Spark front. I know there's code out there that could be integrated in
kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>> 
>>>>>>>>>>>> Do you have relevant experiences you can
share? I'd love to learn more about the use cases for which you envision using Kudu as a C*
replacement.
>>>>>>>>>>>> 
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> 
>>>>>>>>>>>> J-D
>>>>>>>>>>>> 
>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin
Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>> wrote:
>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>> 
>>>>>>>>>>>> My colleagues recently came back from Strata
in San Jose. They told me that everything was about Spark and there is a big buzz about the
SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an
interim solution as a low-latency, easily queried data store. I was wondering if anything
significant happened in regards to Kudu, especially on the Spark front. Plus, can you come
up with your own proposed stack acronym to promote?
>>>>>>>>>>>> 
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel
Cryans <jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>> 
>>>>>>>>>>>>> AFAIK no one in the dev community committed
to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but
that's about it.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>> 
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>> 
>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin
Kim <bkim@amobee.com <mailto:bkim@amobee.com>> wrote:
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Quick question… Is there an ETA for
KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for
our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>> 
>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining
digital marketing.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200  |  Santa
Monica, CA 90405  |  www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>> 
>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel
Cryans <jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> The DStream stuff isn't there at
all. I'm not sure if it's needed either.
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> The kuduRDD is just leveraging the
MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> The SparkSQL stuff is there but it
doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> The goal was to provide something
for others to contribute to. We have some basic unit tests that others can easily extend.
None of us on the team are Spark experts, but we'd be really happy to assist one improve the
kudu-spark code.
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM,
Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>> wrote:
>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> It looks like it fulfills most of
the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring
up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs
to be done? Optimizations?
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> I believe that it’s a good place
to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM,
Jean-Daniel Cryans <jdcryans@apache.org <mailto:jdcryans@apache.org>> wrote:
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> AFAIK no one is working on it,
but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> It's a really simple wrapper,
and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08
PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>> wrote:
>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214>
targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that
Spark will be able to work with Kudu both programmatically and as a client via Spark SQL?
Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>> 
>>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>> 
>>>>>> 
>>>>> 
>>>>> 
>>>> 
>>>> 
>>> 
>>> 
>> 
> 
> 


Mime
View raw message