I am trying to test the water mark concept in structured streaming using the
below program
import java.sql.Timestamp
import org.apache.spark.sql.functions.{col, expr}
import org.apache.spark.sql.streaming.Trigger
val lines_stream = spark.readStream.
format("kafka").
opt
I built an Aggregator that computes PCA on grouped datasets. I wanted to
use the PCA functions provided by MLlib, but they only work on a full
dataset, and I needed to do it on a grouped dataset (like a
RelationalGroupedDataset).
So I built a little Aggregator that can do that, here’s an example o
I'll +1 on removing those legacy mllib code. Many users are confused about the
APIs, and some of them have weird behaviors (for example, in gradient descent,
the intercept is regularized which supports not to).
DB Tsai | Siri Open Source Technologies [not a contribution] | Apple, Inc
> O
On Wed, Oct 17, 2018 at 10:25 AM Yin Huai wrote:
> Shane, Thank you for initiating this work! Can we do an audit of jenkins
> users and trim down the list?
>
> re pruning external (spark-specific) users w/shell and jenkins login
access: we can absolutely do this.
limiting logins for EECS studen
Shane, Thank you for initiating this work! Can we do an audit of jenkins
users and trim down the list?
Also, for packaging jobs, those branch snapshot jobs are active (for
example,
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20Packaging/job/spark-master-maven-snapshots/
for publishing snapsh
:) thanks I am wondering how did I miss that :) :)
On Wed, 17 Oct 2018 at 21:58, Sean Owen wrote:
> "/" is integer division, so "x / y * y" is not x, but more like the
> biggest multiple of y that's <= x.
> On Wed, Oct 17, 2018 at 11:25 AM Sandeep Katta
> wrote:
> >
> > Hi Guys,
> >
> > I am tr
"/" is integer division, so "x / y * y" is not x, but more like the
biggest multiple of y that's <= x.
On Wed, Oct 17, 2018 at 11:25 AM Sandeep Katta
wrote:
>
> Hi Guys,
>
> I am trying to understand structured streaming code flow by doing so I came
> across below code flow
>
> def nextBatchTime(
Rounding.
On Wed, Oct 17, 2018 at 6:25 PM Sandeep Katta <
sandeep0102.opensou...@gmail.com> wrote:
> Hi Guys,
>
> I am trying to understand structured streaming code flow by doing so I
> came across below code flow
>
> def nextBatchTime(now: Long): Long = {
> if (intervalMs == 0) now else now /
Hi Guys,
I am trying to understand structured streaming code flow by doing so I came
across below code flow
def nextBatchTime(now: Long): Long = {
if (intervalMs == 0) now else now / intervalMs * intervalMs + intervalMs
}
else part could also have been written as
now + intervalMs
is there a
My understanding was that the legacy mllib api was frozen, with all new dev
going to ML, but it was not going to be removed. Although removing it would
get rid of a lot of `OldXxx` shims.
On Wed, Oct 17, 2018 at 12:55 AM Marco Gaido wrote:
> Hi all,
>
> I think a very big topic on this would be:
See the discussion at https://github.com/apache/spark/pull/21588
2018년 10월 17일 (수) 오전 5:06, t4 님이 작성:
> has anyone got spark jars working with hadoop3.1 that they can share? i am
> looking to be able to use the latest hadoop-aws fixes from v3.1
>
>
>
> --
> Sent from: http://apache-spark-develop
Hi all,
I think a very big topic on this would be: what do we want to do with the
old mllib API? For long I have been told that it was going to be removed on
3.0. Is this still the plan?
Thanks,
Marco
Il giorno mer 17 ott 2018 alle ore 03:11 Marcelo Vanzin
ha scritto:
> Might be good to take a
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