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  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"  > 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 > > 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" > > 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 > > 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 > > 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 > > 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 > > 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 

Re: Spark on Kudu

[Chris George]

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" 
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 
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" 
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 
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 
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 
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 
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?

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 
mailto:bbuil...@gmail.com>> wrote:
Yes, we took Kudu for a test run using 0.6