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From Benjamin Kim <bbuil...@gmail.com>
Subject Re: Spark on Kudu
Date Tue, 21 Jun 2016 01:07:20 GMT
Dan,

Out of curiosity, I was looking through the spark-csv code in Github and tried to see what
makes it work for the “CREATE TABLE” statement, while it doesn’t for spark-kudu. There
are differences in the way both are done, CsvRelation vs. KuduRelation. I’m still learning
how this works though and what implications these differences are. In your opinion, is this
the right place to start?

Thanks,
Ben


> On Jun 17, 2016, at 11:08 AM, Dan Burkert <dan@cloudera.com> wrote:
> 
> Hi Ben,
> 
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think
we support that at this point.  I haven't looked deeply into it, but we may hit issues specifying
Kudu-specific options (partitioning, column encoding, etc.).  Probably issues that can be
worked through eventually, though.  If you are interested in contributing to Kudu, this is
an area that could obviously use improvement!  Most or all of our Spark features have been
completely community driven to date.
>  
> I am assuming that more Spark support along with semantic changes below will be incorporated
into Kudu 0.9.1.
> 
> As a rule we do not release new features in patch releases, but the good news is that
we are releasing regularly, and our next scheduled release is for the August timeframe (see
JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E>
email about what we are aiming to include).  Also, Cloudera does publish snapshot versions
of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>,
so the jars are available if you don't mind using snapshots.
>  
> Anyone know of a better way to make unique primary keys other than using UUID to make
every row unique if there is no unique column (or combination thereof) to use.
> 
> Not that I know of.  In general it's pretty rare to have a dataset without a natural
primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>  
> This is what I am using. I know auto incrementing is coming down the line (don’t know
when), but is there a way to simulate this in Kudu using Spark out of curiosity?
> 
> To my knowledge there is no plan to have auto increment in Kudu.  Distributed, consistent,
auto incrementing counters is a difficult problem, and I don't think there are any known solutions
that would be fast enough for Kudu (happy to be proven wrong, though!).
> 
> - Dan
>  
> 
> Thanks,
> Ben
> 
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <mailto:dan@cloudera.com>>
wrote:
>> 
>> I'm not sure exactly what the semantics will be, but at least one of them will be
upsert.  These modes come from spark, and they were really designed for file-backed storage
and not table storage.  We may want to do append = upsert, and overwrite = truncate + insert.
 I think that may match the normal spark semantics more closely.
>> 
>> - Dan
>> 
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
>> 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 <mailto: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
>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>> 
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>>>>>> 
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>>>>> 
>>>>> 
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>>> 
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> 
> 


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