kudu-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Benjamin Kim <bbuil...@gmail.com>
Subject Re: Spark on Kudu
Date Sat, 28 May 2016 23:42:47 GMT
JD,

That’s awesome! I can’t wait to start working with it.

Thanks,
Ben


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


Mime
View raw message