Hey everyone!
@Felix: thanks for putting this together. i sent some of you a quick calendar
event - mostly for me, so i don’t forget! :)
Coincidentally, this is the focus of June 6th's Advanced Spark and TensorFlow
Meetup
Hi Andrew,
Please just go ahead and make the pull request. It's easier to review and
give feedback, thanks!
Bryan
On Thu, May 31, 2018 at 9:44 AM, Long, Andrew wrote:
> Hello Friends,
>
>
>
> I’m a new committer and I’ve submitted my first patch and I had some
> questions about documentation
Hossein might be slow to respond (OOO), but I just commented on the JIRA.
I'd recommend we follow the same process as the SparkR package.
+1 on this from me (and I'll be happy to help shepherd it, though Felix and
Shivaram are the experts in this area). CRAN presents challenges, but this
is a
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Hello,
We use partitioned + bucketed datasets for use-cases where we can afford to
take a perf hit at write time, so that reads are optimised. But I feel Spark
could more optimally exploit the data layout in query planning. Here I
describe why this is a problem, and how it could be improved.
Hello Friends,
I’m a new committer and I’ve submitted my first patch and I had some questions
about documentation standards. In my patch(jira below) I’ve added a config
parameter to adjust the number of records show when a user calls .show() on a
dataframe. I was hoping someone could double
Hossein -- Can you clarify what the resolution on the repository /
release issue discussed on SPIP ?
Shivaram
On Thu, May 31, 2018 at 9:06 AM, Felix Cheung wrote:
> +1
> With my concerns in the SPIP discussion.
>
>
> From: Hossein
> Sent: Wednesday, May 30,
+1
With my concerns in the SPIP discussion.
From: Hossein
Sent: Wednesday, May 30, 2018 2:03:03 PM
To: dev@spark.apache.org
Subject: [VOTE] SPIP ML Pipelines in R
Hi,
I started discussion
Please see https://issues.apache.org/jira/browse/SPARK-24258
On Wed, May 30, 2018 at 10:40 PM Dongjin Lee wrote:
> How is this issue going? Is there any Jira ticket about this?
>
> Thanks,
> Dongjin
>
> On Sat, Mar 24, 2018 at 1:39 PM, Himanshu Mohan <
> himanshu.mo...@aexp.com.invalid> wrote:
>
Yay! That's right!!! Thanks Reynold. Such a short answer with so much
information. Thanks.
Pozdrawiam,
Jacek Laskowski
https://about.me/JacekLaskowski
Mastering Spark SQL https://bit.ly/mastering-spark-sql
Spark Structured Streaming https://bit.ly/spark-structured-streaming
Mastering Kafka
Ok! Thank you very much!
- Thodoris
On Thu, 2018-05-31 at 11:30 +0200, Szuromi Tamás wrote:
> I see it in the Serenity docs. Anyway, I guess you are able to use
> the newest version of Spark with the Mesos 0.27 without any issues so
> don't have to dispense newer Spark features and fixes.
>
I see it in the Serenity docs. Anyway, I guess you are able to use the
newest version of Spark with the Mesos 0.27 without any issues so don't
have to dispense newer Spark features and fixes.
Thodoris Zois ezt írta (időpont: 2018. máj. 31., Cs,
11:22):
> Hello,
>
> The reason is that I want to
Hello,
The reason is that I want to make some tests and use the
oversubscription feature of Mesos along with Spark. Intel and
Mesosphere have built a project, called Serenity that actually measures
the usage slack on each Mesos agent and returns resources to the
cluster.
Unfortunately, Serenity
Hey,
I'm sure we used Spark 1.6 on Mesos 0.27 as well but at that time we used
with fine-grained scheduling and not dynamic allocation. Also, newer Spark
versions should work with older mesos versions like 0.27.
Why do you have Mesos 0.27 btw?
cheers,
Tamas
Thodoris Zois ezt írta (időpont:
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