What are you interested in accomplishing?
The spark.ml package has provided a machine learning API based on
DataFrames for quite some time. If you are interested in mixing query
processing and machine learning, this is certainly the best place to start.
See here: https://spark.apache.org/docs/l
I wanted to call any interested eyes to this discussion:
https://github.com/apache/spark/pull/22179
>There are a few PRs to fix Scala 2.12 issues. I think they will keep coming
up and we don't need to block Spark 2.4 on this.
I think it can be better to wait a bit for Scala 2.12 support in 2.4 than to
suffer many months until Spark 2.5 with 2.12 support will be released. Scala
2.12 is not only a
Let's see how they go. At some point we do need to cut the release. That
argument can be made on every feature, and different people place different
value / importance on different features, so we could just end up never
making a release.
On Thu, Aug 30, 2018 at 1:56 PM antonkulaga wrote:
> >T
I know it's famous last words, but we really might be down to the last fix:
https://github.com/apache/spark/pull/22264 More a question of making tests
happy at this point I think than fundamental problems. My goal is to make
sure we can release a usable, but beta-quality, 2.12 release of Spark in
2
+1 that would be great Sean, also you put a lot of effort in there, would
make sense to wait a bit.
Stavros
On Fri, Aug 31, 2018 at 12:00 AM, Sean Owen wrote:
> I know it's famous last words, but we really might be down to the last
> fix: https://github.com/apache/spark/pull/22264 More a questi
+1 on beta support for scala 2.12
On Thu, Aug 30, 2018 at 2:33 PM, Stavros Kontopoulos <
stavros.kontopou...@lightbend.com> wrote:
> +1 that would be great Sean, also you put a lot of effort in there, would
> make sense to wait a bit.
>
> Stavros
>
> On Fri, Aug 31, 2018 at 12:00 AM, Sean Owen w
I think beta-support will be good. I am ok to trade stability for being
able to use 2.12-only libraries in my code and if there is something
mission-critical nobody will block from using stable 2.11
Sincerely,
Anton Kulaga
Bioinformatician at Computational Biology of Aging Group
296 Splaiul Indep
Hi,
I am trying to run below code to read file as a dataframe onto a Stream (for
Spark Streaming) developed via Eclipse IDE, defining schemas appropriately, by
running thin jar on server and am getting error below. Tried out suggestions
from researching on internet based on "spark.read.option.sc
Yea, this is exactly what I have been worried of the recent changes
(discussed in https://issues.apache.org/jira/browse/SPARK-24924)
See https://github.com/apache/spark/pull/17916. This should be fine in
upper Spark versions.
FYI, +Wechen and Dongjoon
I want to add Thomas Graves and Gengliang Wang
Can’t you remove the dependency to the databricks CSV data source? Spark has
them now integrated since some versions so it is not needed.
> On 31. Aug 2018, at 05:52, Srabasti Banerjee
> wrote:
>
> Hi,
>
> I am trying to run below code to read file as a dataframe onto a Stream (for
> Spark S
Great we are already discussing/working to fix the issue.Happy to help if I can
:-)
Any workarounds that we can use for now?
Please note I am not invoking any additional packages while running spark
submit on the thin jar.
Thanks,Srabasti Banerjee
On Thursday, 30 August, 2018, 9:02:11 PM
Hi Jorn,
Do you have suggestions as to how to do that?
The conflicting packages are being picked up by default from pom.xml. I am not
invoking any additional packages while running spark submit on the thin jar.
ThanksSrabasti Banerjee
On Thursday, 30 August, 2018, 9:45:36 PM GMT-7, Jörn Fran
I spent some time last week looking at the current data source v2 apis, and
I thought we should be a bit more buttoned up in terms of the abstractions
and the guarantees Spark provides. In particular, I feel we need the
following levels of "abstractions", to fit the use cases in Spark, from
batch,
+1
From: Mridul Muralidharan
Sent: Wednesday, August 29, 2018 1:27:27 PM
To: dev@spark.apache.org
Subject: Re: SPIP: Executor Plugin (SPARK-24918)
+1
I left a couple of comments in NiharS's PR, but this is very useful to
have in spark !
Regards,
Mridul
On Fri, Au
+1 on what Li said.
And +1 on getting more coverage in unit tests - however often times we omit
python unit tests deliberately if the python “wrapper” is trivial. This is what
I’ve learned over the years from the previous pyspark maintainers. Admittedly
gaps are there.
___
I actually had a similar use case a while ago, but not entirely the same.
In my use case, Spark is already up, but I want to make sure all existing
(and new) executors run some specific code. Can we update the API to
support that? I think that's doable if we split the design into two: one is
the ab
+1
On Fri, Aug 31, 2018 at 8:11 AM, Reynold Xin wrote:
> I actually had a similar use case a while ago, but not entirely the same.
> In my use case, Spark is already up, but I want to make sure all existing
> (and new) executors run some specific code. Can we update the API to
> support that? I
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