Yes that is correct, that would cause computation twice. If you want the
computation to happen only once you can cache the dataframe and call count
and write on the cached dataframe.
Regards,
Keith.
http://keith-chapman.com
On Mon, May 20, 2019 at 6:43 PM Rishi Shah wrote:
> Hi All,
>
> Just
Hi All,
Just wanted to confirm my understanding around actions on dataframe. If
dataframe is not persisted at any point, & count() is called on a dataframe
followed by write action --> this would trigger dataframe computation twice
(which could be the performance hit for a larger dataframe)..
most likely have to set something in spark-defaults.conf like
spark.master yarn
spark.submit.deployMode client
On Mon, May 20, 2019 at 3:14 PM Nicolas Paris
wrote:
> Finally that was easy to connect to both hive/hdfs. I just had to copy
> the hive-site.xml from the old spark version and that
>From doing some searching around in the spark codebase, I found the
following:
https://github.com/apache/spark/blob/163a6e298213f216f74f4764e241ee6298ea30b6/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala#L1452-L1474
So it appears there is no direct operation
Finally that was easy to connect to both hive/hdfs. I just had to copy
the hive-site.xml from the old spark version and that worked instantly
after unzipping.
Right now I am stuck on connecting to yarn.
On Mon, May 20, 2019 at 02:50:44PM -0400, Koert Kuipers wrote:
> we had very little issues
we had very little issues with hdfs or hive, but then we use hive only for
basic reading and writing of tables.
depending on your vendor you might have to add a few settings to your
spark-defaults.conf. i remember on hdp you had to set the hdp.version
somehow.
we prefer to build spark with hadoop
Hi ,
I am looking for a high level explanation(overview) on how dropDuplicates[1]
works.
[1]
https://github.com/apache/spark/blob/db24b04cad421ed508413d397c6beec01f723aee/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L2326
Could someone please explain?
Thank you
--
Sent from:
> correct. note that you only need to install spark on the node you launch it
> from. spark doesnt need to be installed on cluster itself.
That sound reasonably doable for me. My guess is I will have some
troubles to make that spark version work with both hive & hdfs installed
on the cluster - or
correct. note that you only need to install spark on the node you launch it
from. spark doesnt need to be installed on cluster itself.
the shared components between spark jobs on yarn are only really
spark-shuffle-service in yarn and spark-history-server. i have found
compatibility for these to
It is always dangerous to run a NEWER version of code on an OLDER cluster.
The danger increases with the semver change and this one is not just a
build #. In other word 2.4 is considered to be a fairly major change from
2.3. Not much else can be said.
From: Nicolas Paris
Reply:
> you will need the spark version you intend to launch with on the machine you
> launch from and point to the correct spark-submit
does this mean to install a second spark version (2.4) on the cluster ?
thanks
On Mon, May 20, 2019 at 01:58:11PM -0400, Koert Kuipers wrote:
> yarn can happily run
yarn can happily run multiple spark versions side-by-side
you will need the spark version you intend to launch with on the machine
you launch from and point to the correct spark-submit
On Mon, May 20, 2019 at 1:50 PM Nicolas Paris
wrote:
> Hi
>
> I am wondering whether that's feasible to:
> -
Hi
I am wondering whether that's feasible to:
- build a spark application (with sbt/maven) based on spark2.4
- deploy that jar on yarn on a spark2.3 based installation
thanks by advance,
--
nicolas
-
To unsubscribe e-mail:
It makes scheduling faster. If you have a node that can accommodate 20
containers, and you schedule one container per heartbeat, it would take 20
seconds to schedule all the containers. OTOH if you schedule multiple
containers to a heartbeat it is much faster.
- Hari
On Mon, 20 May 2019, 15:40
Hi,
Just curious to know if anyone was successful in connecting LinkedIn using
OAuth2.0, client ID and client secret to fetch data and process in
Python/PySpark.
I'm getting stuck at connection establishment.
Any help?
Thanks,
Aakash.
Hi Hari,
Thanks for this information.
Do you have any resources on/can explain, why YARN has this as default
behaviour? What would be the advantages/scenarios to have multiple
assignments in single heartbeat?
Regards
Akshay Bhardwaj
+91-97111-33849
On Mon, May 20, 2019 at 1:29 PM Hariharan
Hi all
I'm currently developing a Spark structured streaming application which
joins/aggregates messages from ~7 Kafka topics and produces messages onto
another Kafka topic.
Quite often in my development cycle, I want to "reprocess from scratch": I stop
the program, delete the target topic
There is a kind of check in the *yarn-site.xml*
*yarn.nodemanager.remote-app-log-dir
/var/yarn/logs*
**
Using *hdfs://:9000* as* fs.defaultFS* in *core-site.xml* you have to *hdfs
dfs -mkdir /var/yarn/logs*
Using *S3://* as * fs.defaultFS*...
Take care of *.dir* properties in*
Hi Akshay,
I believe HDP uses the capacity scheduler by default. In the capacity
scheduler, assignment of multiple containers on the same node is
determined by the option
yarn.scheduler.capacity.per-node-heartbeat.multiple-assignments-enabled,
which is true by default. If you would like YARN to
Hi Huizhe,
You can set the "fs.defaultFS" field in core-site.xml to some path on s3.
That way your spark job will use S3 for all operations that need HDFS.
Intermediate data will still be stored on local disk though.
Thanks,
Hari
On Mon, May 20, 2019 at 10:14 AM Abdeali Kothari
wrote:
> While
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