Pranav, proposal looks awesome!
I have a question and feedback,
You said you tested 1,2 and 3. To create SparkIMain per notebook, you
need information of notebook id. Did you get it from InterpreterContext?
Then how did you handle destroying of SparkIMain (when notebook is
deleting)?
As far as i know, interpreter not able to get information of notebook
deletion.
>> 4. Build a queue inside interpreter to allow only one paragraph
execution
>> at a time per notebook.
One downside of this approach is, GUI will display RUNNING instead of
PENDING for jobs inside of queue in interpreter.
Best,
moon
On Sun, Aug 16, 2015 at 12:55 AM IT CTO <goi....@gmail.com
<mailto:goi....@gmail.com>> wrote:
+1 for "to re-factor the Zeppelin architecture so that it can
handle multi-tenancy easily"
On Sun, Aug 16, 2015 at 9:47 AM DuyHai Doan <doanduy...@gmail.com
<mailto:doanduy...@gmail.com>> wrote:
Agree with Joel, we may think to re-factor the Zeppelin
architecture so that it can handle multi-tenancy easily. The
technical solution proposed by Pranav is great but it only
applies to Spark. Right now, each interpreter has to manage
multi-tenancy its own way. Ultimately Zeppelin can propose a
multi-tenancy contract/info (like UserContext, similar to
InterpreterContext) so that each interpreter can choose to use
or not.
On Sun, Aug 16, 2015 at 3:09 AM, Joel Zambrano
<djo...@gmail.com <mailto:djo...@gmail.com>> wrote:
I think while the idea of running multiple notes
simultaneously is great. It is really dancing around the
lack of true multi user support in Zeppelin. While the
proposed solution would work if the applications resources
are those of the whole cluster, if the app is limited (say
they are 8 cores of 16, with some distribution in memory)
then potentially your note can hog all the resources and
the scheduler will have to throttle all other executions
leaving you exactly where you are now.
While I think the solution is a good one, maybe this
question makes us think in adding true multiuser support.
Where we isolate resources (cluster and the notebooks
themselves), have separate login/identity and (I don't
know if it's possible) share the same context.
Thanks,
Joel
> On Aug 15, 2015, at 1:58 PM, Rohit Agarwal
<mindpri...@gmail.com <mailto:mindpri...@gmail.com>> wrote:
>
> If the problem is that multiple users have to wait for
each other while
> using Zeppelin, the solution already exists: they can
create a new
> interpreter by going to the interpreter page and attach
it to their
> notebook - then they don't have to wait for others to
submit their job.
>
> But I agree, having paragraphs from one note wait for
paragraphs from other
> notes is a confusing default. We can get around that in
two ways:
>
> 1. Create a new interpreter for each note and attach
that interpreter to
> that note. This approach would require the least amount of code
changes but
> is resource heavy and doesn't let you share Spark
Context between different
> notes.
> 2. If we want to share the Spark Context between
different notes, we can
> submit jobs from different notes into different
fairscheduler pools (
>
https://spark.apache.org/docs/1.4.0/job-scheduling.html#scheduling-within-an-application).
> This can be done by submitting jobs from different
notes in different
> threads. This will make sure that jobs from one note
are run sequentially
> but jobs from different notes will be able to run in
parallel.
>
> Neither of these options require any change in the Spark
code.
>
> --
> Thanks & Regards
> Rohit Agarwal
> https://www.linkedin.com/in/rohitagarwal003
>
> On Sat, Aug 15, 2015 at 12:01 PM, Pranav Kumar Agarwal
<praag...@gmail.com <mailto:praag...@gmail.com>>
> wrote:
>
>> If someone can share about the idea of sharing single
SparkContext through
>>> multiple SparkILoop safely, it'll be really helpful.
>> Here is a proposal:
>> 1. In Spark code, change SparkIMain.scala to allow
setting the virtual
>> directory. While creating new instances of SparkIMain
per notebook from
>> zeppelin spark interpreter set all the instances of
SparkIMain to the same
>> virtual directory.
>> 2. Start HTTP server on that virtual directory and set
this HTTP server in
>> Spark Context using classserverUri method
>> 3. Scala generated code has a notion of packages. The
default package name
>> is "line$<linenumber>". Package name can be controlled
using System
>> Property scala.repl.name.line. Setting this property to
"notebook id"
>> ensures that code generated by individual instances of
SparkIMain is
>> isolated from other instances of SparkIMain
>> 4. Build a queue inside interpreter to allow only one
paragraph execution
>> at a time per notebook.
>>
>> I have tested 1, 2, and 3 and it seems to provide
isolation across
>> classnames. I'll work towards submitting a formal patch
soon - Is there any
>> Jira already for the same that I can uptake? Also I
need to understand:
>> 1. How does Zeppelin uptake Spark fixes? OR do I need
to first work
>> towards getting Spark changes merged in Apache Spark
github?
>>
>> Any suggestions on comments on the proposal are highly
welcome.
>>
>> Regards,
>> -Pranav.
>>
>>> On 10/08/15 11:36 pm, moon soo Lee wrote:
>>>
>>> Hi piyush,
>>>
>>> Separate instance of SparkILoop SparkIMain for each
notebook while
>>> sharing the SparkContext sounds great.
>>>
>>> Actually, i tried to do it, found problem that
multiple SparkILoop could
>>> generates the same class name, and spark executor
confuses classname since
>>> they're reading classes from single SparkContext.
>>>
>>> If someone can share about the idea of sharing single
SparkContext
>>> through multiple SparkILoop safely, it'll be really
helpful.
>>>
>>> Thanks,
>>> moon
>>>
>>>
>>> On Mon, Aug 10, 2015 at 1:21 AM Piyush Mukati (Data
Platform) <
>>> piyush.muk...@flipkart.com
<mailto:piyush.muk...@flipkart.com>
<mailto:piyush.muk...@flipkart.com
<mailto:piyush.muk...@flipkart.com>>> wrote:
>>>
>>> Hi Moon,
>>> Any suggestion on it, have to wait lot when
multiple people working
>>> with spark.
>>> Can we create separate instance of SparkILoop
SparkIMain and
>>> printstrems for each notebook while sharing
theSparkContext
>>> ZeppelinContext SQLContext and DependencyResolver
and then use parallel
>>> scheduler ?
>>> thanks
>>>
>>> -piyush
>>>
>>> Hi Moon,
>>>
>>> How about tracking dedicated SparkContext for a
notebook in Spark's
>>> remote interpreter - this will allow multiple users
to run their spark
>>> paragraphs in parallel. Also, within a notebook
only one paragraph is
>>> executed at a time.
>>>
>>> Regards,
>>> -Pranav.
>>>
>>>
>>>> On 15/07/15 7:15 pm, moon soo Lee wrote:
>>>> Hi,
>>>>
>>>> Thanks for asking question.
>>>>
>>>> The reason is simply because of it is running code
statements. The
>>>> statements can have order and dependency. Imagine i
have two
>>> paragraphs
>>>>
>>>> %spark
>>>> val a = 1
>>>>
>>>> %spark
>>>> print(a)
>>>>
>>>> If they're not running one by one, that means they
possibly runs in
>>>> random order and the output will be always different.
Either '1' or
>>>> 'val a can not found'.
>>>>
>>>> This is the reason why. But if there are nice idea to
handle this
>>>> problem i agree using parallel scheduler would help a
lot.
>>>>
>>>> Thanks,
>>>> moon
>>>> On 2015년 7월 14일 (화) at 오후 7:59 linxi zeng
>>>> <linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com>
<mailto:linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com>>
>>> <mailto:linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com>
<mailto:linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com>>>>
>>> wrote:
>>>>
>>>> any one who have the same question with me? or
this is not a
>>> question?
>>>>
>>>> 2015-07-14 11:47 GMT+08:00 linxi zeng
<linxizeng0...@gmail.com <mailto:linxizeng0...@gmail.com>
>>> <mailto:linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com>>
>>>> <mailto:linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com> <mailto:
>>> linxizeng0...@gmail.com
<mailto:linxizeng0...@gmail.com>>>>:
>>>>
>>>> hi, Moon:
>>>> I notice that the getScheduler function in the
>>>> SparkInterpreter.java return a FIFOScheduler which
makes the
>>>> spark interpreter run spark job one by one.
It's not a good
>>>> experience when couple of users do some work
on zeppelin at
>>>> the same time, because they have to wait for
each other.
>>>> And at the same time, SparkSqlInterpreter can
chose what
>>>> scheduler to use by
"zeppelin.spark.concurrentSQL".
>>>> My question is, what kind of consideration do
you based on
>>> to
>>>> make such a decision?
>>>
>>>
>>>
>>>
>>>
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