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From Jean Georges Perrin <>
Subject Re: SPARK Issue in Standalone cluster
Date Fri, 04 Aug 2017 12:10:09 GMT
I use CIFS and it works reasonably well and easily cross platform, well documented...

> On Aug 4, 2017, at 6:50 AM, Steve Loughran <> wrote:
>> On 3 Aug 2017, at 19:59, Marco Mistroni <> wrote:
>> Hello
>> my 2 cents here, hope it helps
>> If you want to just to play around with Spark, i'd leave Hadoop out, it's an unnecessary
dependency that you dont need for just running a python script
>> Instead do the following:
>> - got to the root of our master / slave node. create a directory /root/pyscripts

>> - place your csv file there as well as the python script
>> - run the script to replicate the whole directory  across the cluster (i believe
it's called
>> - then run your spark-submit , it will be something lke
>>    ./spark-submit /root/pyscripts/  file:///root/pyscripts/tree_addhealth.csv
10 --master spark://
>> - in your python script, as part of your processing, write the parquet file in directory
> That's going to hit the commit problem discussed: only the spark driver executes the
final commit process; the output from the other servers doesn't get picked up and promoted.
You need a shared stpre (NFS is the easy one)
>> If you have an AWS account and you are versatile with that - you need to setup bucket
permissions etc - , you can just
>> - store your file in one of your S3 bucket
>> - create an EMR cluster
>> - connect to master or slave
>> - run your  scritp that reads from the s3 bucket and write to the same s3 bucket
> Aah, and now we are into the problem of implementing a safe commit protocol for an inconsistent
> My current stance there is out-the-box S3 isn't safe to use as the direct output of work,
Azure is. It mostly works for a small experiment, but I wouldn't use it in production.
> Simplest: work on one machine, if you go to 2-3 for exploratory work: NFS
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