A key part of what I'm trying to do involves NOT having to bring the data
"through" the driver in order to get the cluster to work on it (which would
involve a network hop from server to laptop and another from laptop to
server). I'd rather have the data stay on the server and the driver stay on
my laptop if possible, but I'm guessing the Spark APIs/topology wasn't
designed that way.
What I was hoping for was some way to be able to say val df =
spark.sql("SELECT * FROM parquet.`*local://*/opt/data/transactions.parquet`")
or similar to convince Spark to not move the data. I'd imagine if I used
HDFS, data locality would kick in anyways to prevent the network shuffles
between the driver and the cluster, but even then I wonder (based on what
you guys are saying) if I'm wrong.
Perhaps I'll just have to modify the workflow to move the JAR to the server
and execute it from there. This isn't ideal but it's better than nothing.
-Ryan
On Wed, Nov 25, 2020 at 9:13 AM Chris Coutinho <chrisbcouti...@gmail.com>
wrote:
> I'm also curious if this is possible, so while I can't offer a solution
> maybe you could try the following.
>
> The driver and executor nodes need to have access to the same
> (distributed) file system, so you could try to mount the file system to
> your laptop, locally, and then try to submit jobs and/or use the
> spark-shell while connected to the same system.
>
> A quick google search led me to find this article where someone shows how
> to mount an HDFS locally. It appears that Cloudera supports some kind of
> FUSE-based library, which may be useful for your use-case.
>
> https://idata.co.il/2018/10/how-to-connect-hdfs-to-local-filesystem/
>
> On Wed, 2020-11-25 at 08:51 -0600, Ryan Victory wrote:
>
> Hello!
>
> I have been tearing my hair out trying to solve this problem. Here is my
> setup:
>
> 1. I have Spark running on a server in standalone mode with data on the
> filesystem of the server itself (/opt/data/).
> 2. I have an instance of a Hive Metastore server running (backed by
> MariaDB) on the same server
> 3. I have a laptop where I am developing my spark jobs (Scala)
>
> I have configured Spark to use the metastore and set the warehouse
> directory to be in /opt/data/warehouse/. What I am trying to accomplish are
> a couple of things:
>
> 1. I am trying to submit Spark jobs (via JARs) using spark-submit, but
> have the driver run on my local machine (my laptop). I want the jobs to use
> the data ON THE SERVER and not try to reference it from my local machine.
> If I do something like this:
>
> val df = spark.sql("SELECT * FROM
> parquet.`/opt/data/transactions.parquet`")
>
> I get an error that the path doesn't exist (because it's trying to find it
> on my laptop). If I run the same thing in a spark-shell on the spark server
> itself, there isn't an issue because the driver has access to the data. If
> I submit the job with submit-mode=cluster then it works too because the
> driver is on the cluster. I don't want this, I want to get the results on
> my laptop.
>
> How can I force Spark to read the data from the cluster's filesystem and
> not the driver's?
>
> 2. I have setup a Hive Metastore and created a table (in the spark shell
> on the spark server itself). The data in the warehouse is in the local
> filesystem. When I create a spark application JAR and try to run it from my
> laptop, I get the same problem as #1, namely that it tries to find the
> warehouse directory on my laptop itself.
>
> Am I crazy? Perhaps this isn't a supported way to use Spark? Any help or
> insights are much appreciated!
>
> -Ryan Victory
>
>
>
