Hi Sir,
Please unsubscribe me
--
Regards,
Ram Krishna KT
Hi Mich,
Thank you for your reply.
Let me explain more clearly.
File with 100 records needs to joined with a Big lookup File created in ORC
format (500 million records). The Spark process i wrote is returing back
the matching records and is working fine. My concern is that it loads the
entire fi
Hi,
To start when you store the data in ORC file can you verify that the data
is there?
For example register it as tempTable
processDF.register("tmp")
sql("select count(1) from tmp).show
Also what do you mean by index file in ORC?
HTH
Dr Mich Talebzadeh
LinkedIn *
https://www.linkedi
I am trying to join a Dataframe(say 100 records) with an ORC file with 500
million records through Spark(can increase to 4-5 billion, 25 bytes each
record).
I used Spark hiveContext API.
*ORC File Creation Code*
//fsdtRdd is JavaRDD, fsdtSchema is StructType schema
DataFrame fsdtDf = hiveContext
Thank you all sirs
Appreciated Mich your clarification.
On Sunday, 19 June 2016, 19:31, Mich Talebzadeh
wrote:
Thanks Jonathan for your points
I am aware of the fact yarn-client and yarn-cluster are both depreciated (still
work in 1.6.1), hence the new nomenclature.
Bear in mind this
Please help
From: amit assudani
Date: Thursday, June 16, 2016 at 6:11 PM
To: "user@spark.apache.org"
Subject: Update Batch DF with Streaming
Hi All,
Can I update batch data frames loaded in memory with Streaming data,
For eg,
I have employee DF is registered as temporary table, it has
Thanks Jonathan for your points
I am aware of the fact yarn-client and yarn-cluster are both depreciated
(still work in 1.6.1), hence the new nomenclature.
Bear in mind this is what I stated in my notes:
"YARN Cluster Mode, the Spark driver runs inside an application master
process which is mana
Mich, what Jacek is saying is not that you implied that YARN relies on two
masters. He's just clarifying that yarn-client and yarn-cluster modes are
really both using the same (type of) master (simply "yarn"). In fact, if
you specify "--master yarn-client" or "--master yarn-cluster", spark-submit
w
Mind sharing code? I think only shuffle failures lead to stage failures and
re-tries.
Jacek
On 19 Jun 2016 4:35 p.m., "Ted Yu" wrote:
> You can utilize a counter in external storage (NoSQL e.g.)
> When the counter reaches 2, stop throwing exception so that the task
> passes.
>
> FYI
>
> On Sun,
Have you looked at http://spark.apache.org/docs/latest/ec2-scripts.html ?
There is description on setting AWS_SECRET_ACCESS_KEY.
On Sun, Jun 19, 2016 at 4:46 AM, Mohamed Taher AlRefaie
wrote:
> Hello all:
>
> I have an application that requires accessing DynamoDB tables. Each worker
> establish
You can utilize a counter in external storage (NoSQL e.g.)
When the counter reaches 2, stop throwing exception so that the task passes.
FYI
On Sun, Jun 19, 2016 at 3:22 AM, Jacek Laskowski wrote:
> Hi,
>
> Thanks Burak for the idea, but it *only* fails the tasks that
> eventually fail the entir
I think good practice is not to hold on to SparkContext in mapFunction.
On Sun, Jun 19, 2016 at 7:10 AM, Takeshi Yamamuro
wrote:
> How about using `transient` annotations?
>
> // maropu
>
> On Sun, Jun 19, 2016 at 10:51 PM, Daniel Haviv <
> daniel.ha...@veracity-group.com> wrote:
>
>> Hi,
>> Jus
How about using `transient` annotations?
// maropu
On Sun, Jun 19, 2016 at 10:51 PM, Daniel Haviv <
daniel.ha...@veracity-group.com> wrote:
> Hi,
> Just updating on my findings for future reference.
> The problem was that after refactoring my code I ended up with a scala
> object which held Spar
Hi,
Just updating on my findings for future reference.
The problem was that after refactoring my code I ended up with a scala
object which held SparkContext as a member, eg:
object A {
sc: SparkContext = new SparkContext
def mapFunction {}
}
and when I called rdd.map(A.mapFunction) it
Hello all:
I have an application that requires accessing DynamoDB tables. Each worker
establishes a connection with the database on its own.
I have added both `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` to both
master's and workers `spark-env.sh` file. I have also run the file using
`sh` to m
Hi,
Thanks for that input, I tried doing that but apparently thats not working
as well. I thought i am having problems with my spark installation so I ran
simple word count and that works, so I am not really sure what the problem
is now.
Is my translation of the scala code correct? I don't unders
Good points but I am an experimentalist
In Local mode I have this
In local mode with:
--master local
This will start with one thread or equivalent to –master local[1]. You can
also start by more than one thread by specifying the number of threads *k*
in –master local[k]. You can also start us
On Sun, Jun 19, 2016 at 12:30 PM, Mich Talebzadeh
wrote:
> Spark Local - Spark runs on the local host. This is the simplest set up and
> best suited for learners who want to understand different concepts of Spark
> and those performing unit testing.
There are also the less-common master URLs:
*
Spark works on different modes, either local (Spark or anything else does
not manager) resources and standalone (Spark itself manages resources)
plus others (see below)
These are from my notes, excluding mesos that I have not used
- Spark Local - Spark runs on the local host. This is the sim
Hi,
Thanks Burak for the idea, but it *only* fails the tasks that
eventually fail the entire job not a particular stage (just once or
twice) before the entire job is failed. The idea is to see the
attempts in web UI as there's a special handling for cases where a
stage failed once or twice before
There are many technical differences inside though, how to use is the
almost same with each other.
yea, in a standalone mode, spark runs in a cluster way: see
http://spark.apache.org/docs/1.6.1/cluster-overview.html
// maropu
On Sun, Jun 19, 2016 at 6:14 PM, Ashok Kumar wrote:
> thank you
>
> W
thank you
What are the main differences between a local mode and standalone mode. I
understand local mode does not support cluster. Is that the only difference?
On Sunday, 19 June 2016, 9:52, Takeshi Yamamuro
wrote:
Hi,
In a local mode, spark runs in a single JVM that has a master an
Hi,
In a local mode, spark runs in a single JVM that has a master and one
executor with `k` threads.
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/local/LocalSchedulerBackend.scala#L94
// maropu
On Sun, Jun 19, 2016 at 5:39 PM, Ashok Kumar
wrote:
>
hi,
who can get score for each row of classification algortithmes , and how i
can plot features importance of variables like sickit learn ?
thanks.
Hi,
I have been told Spark in Local mode is simplest for testing. Spark document
covers little on local mode except the cores used in --master local[k].
Where are the the driver program, executor and resources. Do I need to start
worker threads and how many app I can use safely without exceeding
Hi, Joseph,
This is a known issue but not a bug.
This issue does not occur when you use interactive SparkR session, while it
does occur when you execute an R file.
The reason behind this is that in case you execute an R file, the R backend
launches before the R interpreter, so there is no oppo
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