Hi folks,
I interrupt your regularly scheduled user / dev list to bring you some pretty
cool news for the project, which is that we've been able to use Spark to break
MapReduce's 100 TB and 1 PB sort records, sorting data 3x faster on 10x fewer
nodes. There's a detailed writeup at
http://datab
Awesome news Matei !
Congratulations to the databricks team and all the community members...
On Fri, Oct 10, 2014 at 7:54 AM, Matei Zaharia
wrote:
> Hi folks,
>
> I interrupt your regularly scheduled user / dev list to bring you some
> pretty cool news for the project, which is that we've been
Brilliant stuff ! Congrats all :-)
This is indeed really heartening news !
Regards,
Mridul
On Fri, Oct 10, 2014 at 8:24 PM, Matei Zaharia wrote:
> Hi folks,
>
> I interrupt your regularly scheduled user / dev list to bring you some pretty
> cool news for the project, which is that we've been a
This is a bad deal, great job.
On Fri, Oct 10, 2014 at 11:19 AM, Mridul Muralidharan
wrote:
> Brilliant stuff ! Congrats all :-)
> This is indeed really heartening news !
>
> Regards,
> Mridul
>
>
> On Fri, Oct 10, 2014 at 8:24 PM, Matei Zaharia
> wrote:
> > Hi folks,
> >
> > I interrupt your r
Wow.. Cool.. Congratulations.. :)
On Fri, Oct 10, 2014 at 8:51 PM, Ted Malaska
wrote:
> This is a bad deal, great job.
>
> On Fri, Oct 10, 2014 at 11:19 AM, Mridul Muralidharan
> wrote:
>
> > Brilliant stuff ! Congrats all :-)
> > This is indeed really heartening news !
> >
> > Regards,
> > Mri
Great! Congratulations!
--
Nan Zhu
On Friday, October 10, 2014 at 11:19 AM, Mridul Muralidharan wrote:
> Brilliant stuff ! Congrats all :-)
> This is indeed really heartening news !
>
> Regards,
> Mridul
>
>
> On Fri, Oct 10, 2014 at 8:24 PM, Matei Zaharia (mailto:matei.zaha...@gmail.com)
Wonderful !!
On 11 Oct, 2014, at 12:00 am, Nan Zhu wrote:
> Great! Congratulations!
>
> --
> Nan Zhu
> On Friday, October 10, 2014 at 11:19 AM, Mridul Muralidharan wrote:
>
>> Brilliant stuff ! Congrats all :-)
>> This is indeed really heartening news !
>>
>> Regards,
>> Mridul
>>
>>
>> On
Great stuff. Wonderful to see such progress in so short a time.
How about some links to code and instructions so that these benchmarks can
be reproduced?
Regards,
- Steve
From: Debasish Das
Date: Friday, October 10, 2014 at 8:17
To: Matei Zaharia
Cc: user , dev
Subject: Re: Breaking the
Running dev/run-tests as-is should work and will test everything. That's
what the contributing guide recommends, if I remember correctly.
At some point we should make it easier to test individual components
locally using the dev script, but calling sbt on the various tests suites
as Michael pointe
I think this would require fairly significant refactoring of the PR board code.
I’d love it if the PR board code was more easily configurable to support
different JIRA / GitHub repositories, etc, but I don’t have the time to work on
this myself.
- Josh
On October 9, 2014 at 6:20:12 PM, Nichol
reminder: this IS happening, first thing monday morning PDT. :)
On Wed, Oct 8, 2014 at 3:01 PM, shane knapp wrote:
> greetings!
>
> i've got some updates regarding our new jenkins infrastructure, as well as
> the initial date and plan for rolling things out:
>
> *** current testing/build break
Congrats to Reynold et al leading this effort!
- Henry
On Fri, Oct 10, 2014 at 7:54 AM, Matei Zaharia wrote:
> Hi folks,
>
> I interrupt your regularly scheduled user / dev list to bring you some pretty
> cool news for the project, which is that we've been able to use Spark to
> break MapReduc
It works fine, thanks for the help Michael.
Liancheng also told me a trick, using a subquery with LIMIT n. It works in
latest 1.2.0
BTW, looks like the broadcast optimization won't be recognized if I do a
left join instead of a inner join. Is that true? How can I make it work for
left joins?
Che
Hi Matei - I read your post with great interest. Could you possibly comment
in more depth on some of the issues you guys saw when scaling up spark and
how you resolved them? I am interested specifically in spark-related
problems. I'm working on scaling up spark to very large datasets and have
been
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