Re: Spark on Kudu

[Benjamin Kim]

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 <d...@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 <d...@cloudera.com 
>> <mailto:d...@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 <bbuil...@gmail.com 
>> <mailto:bbuil...@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 <d...@cloudera.com 
>>> <mailto:d...@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 <bbuil...@gmail.com 
>>> <mailto:bbuil...@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 

Re: Spark on Kudu

[Benjamin Kim]

Dan,

The roadmap is very informative. I am looking forward to the official 1.0 
release! It would be so much easier for us to use in every aspect compared to 
HBase.

Cheers,
Ben


> On Jun 17, 2016, at 11:08 AM, Dan Burkert <d...@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 <d...@cloudera.com 
>> <mailto:d...@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 <bbuil...@gmail.com 
>> <mailto:bbuil...@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 <d...@cloudera.com 
>>> <mailto:d...@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 <bbuil...@gmail.com 
>>> <mailto:bbuil...@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 <bbuil...@gmai

Re: Spark on Kudu

[Dan Burkert]

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 <d...@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 <bbuil...@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 <d...@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 <bbuil...@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 <bbuil...@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 wri

Re: Spark on Kudu

[Benjamin Kim]

I am assuming that more Spark support along with semantic changes below will be 
incorporated into Kudu 0.9.1.

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.

import java.util.UUID
val generateUUID = udf(() => UUID.randomUUID().toString)

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?

Thanks,
Ben

> On Jun 14, 2016, at 6:08 PM, Dan Burkert <d...@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 <bbuil...@gmail.com 
> <mailto:bbuil...@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 <d...@cloudera.com 
>> <mailto:d...@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 <bbuil...@gmail.com 
>> <mailto:bbuil...@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 <bbuil...@gmail.com 
>>> <mailto:bbuil...@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 <d...@cloudera.com 
>>>> <mailto:d...@cloudera.com>> wrote:
>>>> 
>>>> 
>>>> 
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuil...@gmail.com 
>>>> <mailto:bbuil...@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 sp

Re: Spark on Kudu

[Benjamin Kim]

hPartitions` 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 <d...@cloudera.com 
>>>>> <mailto:d...@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 <bbuil...@gmail.com 
>>>>> <mailto:bbuil...@gmail.com>> wrote:
>>>>> I encountered an error trying to create a table based on the 
>>>>> documentation from a DataFrame.
>>>>> 
>>>>> :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 <jdcry...@apache.org 
>>>>>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>>>>>> <mailto:bbuil...@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 <jdcry...@apache.org 
>>>>>>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>>>>>>> <mailto:bbuil...@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 <jdcry...@apache.org 
>>>>>>>> <mailto:jdcry...@apache.org>> wrote:
>>>>>>>> 
>>>>>>>> It will be in 0.9.0.
>>>>>>>> 
>>>>>>>> J-D
>>>>>>>> 
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuil...@gmail.com 
>>>>>>>> <mailto:bbuil...@gmail.com>> wrote:
>>>>>>>> Hi Chris,
>>>>>>>> 
>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>> 
>>>>>>>> Thanks,
>>>>>>>> B

Re: Spark on Kudu

[Dan Burkert]

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 <bbuil...@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 <d...@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 <bbuil...@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 <bbuil...@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 <d...@cloudera.com> wrote:
>>
>>
>>
>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuil...@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 <d...@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 <bbuil...@gmail.com>
>

Re: Spark on Kudu

[Dan Burkert]

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 <bbuil...@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 <bbuil...@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 <d...@cloudera.com> wrote:
>
>
>
> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuil...@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 <d...@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 <bbuil...@gmail.com> wrote:
>>
>>> I encountered an error trying to create a table based on the
>>> documentation from a DataFrame.
>>>
>>> :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 <jdcry...@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/
>>>
>>> J-D
>>>
>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuil...@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 us

Re: Spark on Kudu

[Benjamin Kim]

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 <bbuil...@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 <d...@cloudera.com 
>> <mailto:d...@cloudera.com>> wrote:
>> 
>> 
>> 
>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuil...@gmail.com 
>> <mailto:bbuil...@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 <d...@cloudera.com 
>>> <mailto:d...@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 <bbuil...@gmail.com 
>>> <mailto:bbuil...@gmail.com>> wrote:
>>> I encountered an error trying to create a table based on the documentation 
>>> from a DataFrame.
>>> 
>>> :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 <jdcry...@apache.org 
>>>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>>>> <mailto:bbuil...@gmail.com>> wrote:
>>>> Hi J-D,
>>>> 
>>>> I installed Kudu 0.9.0 using CM, but I can’t find t

Re: Spark on Kudu

[Benjamin Kim]

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 <d...@cloudera.com> wrote:
> 
> 
> 
> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuil...@gmail.com 
> <mailto:bbuil...@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 <d...@cloudera.com 
>> <mailto:d...@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 <bbuil...@gmail.com 
>> <mailto:bbuil...@gmail.com>> wrote:
>> I encountered an error trying to create a table based on the documentation 
>> from a DataFrame.
>> 
>> :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 <jdcry...@apache.org 
>>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>>> <mailto:bbuil...@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 <jdcry...@apache.org 
>>>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>>>> <mailto:bbuil...@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 <jdcry...@apache.org 
>

Re: Spark on Kudu

[Dan Burkert]

On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuil...@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 <d...@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 <bbuil...@gmail.com> wrote:
>
>> I encountered an error trying to create a table based on the
>> documentation from a DataFrame.
>>
>> :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 <jdcry...@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/
>>
>> J-D
>>
>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuil...@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 <jdcry...@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
>>>
>>> J-D
>>>
>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuil...@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 <jdcry...@apache.org>
>>>> wrote:
>>>>
>>>> It will be in 0.9.0.
>>>>
>>>> J-D
>>>>
>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuil...@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.geo...@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/
>>>>>
>>>>> Example usages will look something like:
>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>
>>>>> -Chris George
>>>>>
>>>>>
>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuil...@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?
>>>>>
&g

Re: Spark on Kudu

[Benjamin Kim]

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

Thanks,
Ben


> On Jun 14, 2016, at 4:07 PM, Dan Burkert <d...@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 <bbuil...@gmail.com 
> <mailto:bbuil...@gmail.com>> wrote:
> I encountered an error trying to create a table based on the documentation 
> from a DataFrame.
> 
> :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 <jdcry...@apache.org 
>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>> <mailto:bbuil...@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 <jdcry...@apache.org 
>>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>>> <mailto:bbuil...@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 <jdcry...@apache.org 
>>>> <mailto:jdcry...@apache.org>> wrote:
>>>> 
>>>> It will be in 0.9.0.
>>>> 
>>>> J-D
>>>> 
>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuil...@gmail.com 
>>>> <mailto:bbuil...@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.geo...@rms.com 
>>>>> <mailto:christopher.geo...@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" <bbuil...@gmail.com 
>>>>> <mailto:bbuil...@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 <chris

Re: Spark on Kudu

[Dan Burkert]

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 <bbuil...@gmail.com> wrote:

> I encountered an error trying to create a table based on the documentation
> from a DataFrame.
>
> :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 <jdcry...@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/
>
> J-D
>
> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuil...@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 <jdcry...@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
>>
>> J-D
>>
>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuil...@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 <jdcry...@apache.org>
>>> wrote:
>>>
>>> It will be in 0.9.0.
>>>
>>> J-D
>>>
>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuil...@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.geo...@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/
>>>>
>>>> Example usages will look something like:
>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuil...@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.geo...@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" <bbuil...@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.geo...@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/
>>>> 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
>>>&g

Re: Spark on Kudu

[Jean-Daniel Cryans]

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

J-D

On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuil...@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 <jdcry...@apache.org>
> wrote:
>
> It will be in 0.9.0.
>
> J-D
>
> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuil...@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.geo...@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/
>>
>> Example usages will look something like:
>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>
>> -Chris George
>>
>>
>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuil...@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.geo...@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" <bbuil...@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.geo...@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/
>> 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" <jdcry...@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 <m...@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

Re: Spark on Kudu

[Jean-Daniel Cryans]

It will be in 0.9.0.

J-D

On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuil...@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.geo...@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/
>
> Example usages will look something like:
> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>
> -Chris George
>
>
> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuil...@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.geo...@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" <bbuil...@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.geo...@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/
> 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" <jdcry...@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 <m...@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 <jdcry...@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 <m...@clearstorydata.com>
>>> wrote:
>>>
>>>> Do they care being able to insert into Kudu wit

Re: Spark on Kudu

[Benjamin Kim]

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.geo...@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" <bbuil...@gmail.com 
> <mailto:bbuil...@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.geo...@rms.com 
>> <mailto:christopher.geo...@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" <bbuil...@gmail.com 
>> <mailto:bbuil...@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.geo...@rms.com 
>>> <mailto:christopher.geo...@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" <jdcry...@apache.org 
>>> <mailto:jdcry...@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 <m...@clearstorydata.com 
>>> <mailto:m...@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 an

Re: Spark on Kudu

[Chris George]

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/

Example usages will look something like:
http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc

-Chris George


On 5/18/16, 9:45 AM, "Benjamin Kim" 
<bbuil...@gmail.com<mailto:bbuil...@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.geo...@rms.com<mailto:christopher.geo...@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" 
<bbuil...@gmail.com<mailto:bbuil...@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.geo...@rms.com<mailto:christopher.geo...@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/
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" 
<jdcry...@apache.org<mailto:jdcry...@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 
<m...@clearstorydata.com<mailto:m...@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 
<jdcry...@apache.org<mailto:jdcry...@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 
<m...@clearstorydata.com<mailto:m...@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 

Re: Spark on Kudu

[Benjamin Kim]

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.geo...@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" <bbuil...@gmail.com 
> <mailto:bbuil...@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.geo...@rms.com 
>> <mailto:christopher.geo...@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" <jdcry...@apache.org 
>> <mailto:jdcry...@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 <m...@clearstorydata.com 
>> <mailto:m...@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 <jdcry...@apache.org 
>> <mailto:jdcry...@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 <m...@clearstorydata.com 
>> <mailto:m...@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 necessa

Re: Spark on Kudu

[Benjamin Kim]

Chris,

That would be great! And a first! I think everyone would take notice if KImpala 
had this.

Cheers,
Ben


> On Apr 13, 2016, at 8:22 AM, Chris George <christopher.geo...@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" <bbuil...@gmail.com 
> <mailto:bbuil...@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.geo...@rms.com 
>> <mailto:christopher.geo...@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" <jdcry...@apache.org 
>> <mailto:jdcry...@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 <m...@clearstorydata.com 
>> <mailto:m...@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 <jdcry...@apache.org 
>> <mailto:jdcry...@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 <m...@clearstorydata.com 
>> <mailto:m...@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 

Re: Spark on Kudu

[Benjamin Kim]

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.geo...@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" <jdcry...@apache.org 
> <mailto:jdcry...@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 <m...@clearstorydata.com 
> <mailto:m...@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 <jdcry...@apache.org 
> <mailto:jdcry...@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 <m...@clearstorydata.com 
> <mailto:m...@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 <jdcry...@apache.org 
> <mailto:jdcry...@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?
&

Re: Spark on Kudu

[Jean-Daniel Cryans]

Ben,

Thanks for the additional information. You know, I was expecting that
querying would be the most important part and writing into Kudu was
secondary since it can easily be done with the Java API, but you guys are
proving me wrong.

I'm starting to think we should host a Spark + Kudu hackathon here in the
Bay Area. Bringing experts together from both sides might unlock some
potential. We did that with Drill and it was successful:
https://issues.apache.org/jira/browse/DRILL-4241

J-D

On Sun, Apr 10, 2016 at 1:03 PM, Benjamin Kim <bbuil...@gmail.com> wrote:

> J-D,
>
> Priority is data population of tables using DataFrames. That’s all I heard
> the most. It is the same with HBase. But, I bet once this is taken care of,
> the fast querying part would follow because the data is now in Kudu. If
> SparkSQL integration is there, that would simplify things even more. That
> wouldn’t be bad to have.
>
> Cheers,
> Ben
>
>
> On Apr 10, 2016, at 12:23 PM, Jean-Daniel Cryans <jdcry...@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 <bbuil...@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 <jdcry...@apache.org>
>> wrote:
>>
>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuil...@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 

Re: Spark on Kudu

[Jean-Daniel Cryans]

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 <m...@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 <jdcry...@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 <bbuil...@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 <jdcry...@apache.org>
>>> wrote:
>>>
>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuil...@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 com

Re: Spark on Kudu

[Benjamin Kim]

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 <jdcry...@apache.org> wrote:
> 
> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuil...@gmail.com 
> <mailto:bbuil...@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 <jdcry...@apache.org 
>> <mailto:jdcry...@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 <bbuil...@gmail.com 
>> <mailto:bbuil...@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, Je

Re: Spark on Kudu

[Benjamin Kim]

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
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 
<jdcry...@apache.org<mailto:jdcry...@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 
<bbuil...@gmail.com<mailto:bbuil...@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 
<jdcry...@apache.org<mailto:jdcry...@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

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 
<bbuil...@gmail.com<mailto:bbuil...@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

Re: Spark on Kudu

[Jean-Daniel Cryans]

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 <bbuil...@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 <jdcry...@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
>
> 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 <bbuil...@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
>>
>>
>
>

Re: Spark on Kudu

[Benjamin Kim]

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 <jdcry...@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 <bbuil...@gmail.com 
> <mailto:bbuil...@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
> 
> 

Re: Spark on Kudu

[Jean-Daniel Cryans]

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

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  wrote:

> I see this 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
>
>

Spark on Kudu

[Benjamin Kim]

I see this 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