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From Benjamin Kim <bbuil...@gmail.com>
Subject Re: Spark on Kudu
Date Thu, 06 Oct 2016 23:38:41 GMT
Anyone know if the Spark package will ever allow for creating tables in Spark SQL?

Such as:
       CREATE EXTERNAL TABLE <table-name>
       USING org.apache.kudu.spark.kudu
       OPTIONS (Map("kudu.master" -> “<kudu-master>", "kudu.table" -> “table-name”));

In this way, plain SQL can be used to do DDL, DML statements whether in Spark SQL code or
using JDBC to interface with Spark SQL Thriftserver.

By the way, we are trying to create a DMP in Kudu with the a farm of RESTful Endpoints to
do cookie sync, ad serving, segmentation data exchange. And, the Spark compute cluster and
the Kudu cluster will reside on the same racks in the same datacenter.

Thanks,
Ben

> On Sep 20, 2016, at 3:02 PM, Jordan Birdsell <jordantbirdsell@gmail.com> wrote:
> 
> http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark <http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark>
> 
> On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
> I see that the API has changed a bit so my old code doesn’t work anymore. Can someone
direct me to some code samples?
> 
> Thanks,
> Ben
> 
> 
>> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <todd@cloudera.com <mailto:todd@cloudera.com>>
wrote:
>> 
>> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bbuild11@gmail.com <mailto:bbuild11@gmail.com>>
wrote:
>> Now that Kudu 1.0.0 is officially out and ready for production use, where do we find
the spark connector jar for this release?
>> 
>> 
>> It's available in the official ASF maven repository:  https://repository.apache.org/#nexus-search;quick~kudu-spark
<https://repository.apache.org/#nexus-search;quick~kudu-spark>
>> 
>> <dependency>
>>   <groupId>org.apache.kudu</groupId>
>>   <artifactId>kudu-spark_2.10</artifactId>
>>   <version>1.0.0</version>
>> </dependency>
>> 
>> 
>> -Todd
>>  
>> 
>> 
>>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <dan@cloudera.com <mailto: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
>>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>>> 
>>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>> 
>>>>>>>> 
>>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>> 
>>>>> 
>>>>> 
>>>> 
>>>> 
>>> 
>>> 
>> 
>> 
>> 
>> 
>> -- 
>> Todd Lipcon
>> Software Engineer, Cloudera
> 


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