Using spark to distribute jobs to standalone servers

[Larry White <lj...@gmail.com>]

Hi,

I have a bit of an unusual use-case and would *greatly* *appreciate* some
feedback as to whether it is a good fit for spark.

I have a network of compute/data servers configured as a tree as shown below

   - controller
   - server 1
      - server 2
      - server 3
      - etc.

There are ~20 servers, but the number is increasing to ~100.

Each server contains a different dataset, all in the same format. Each is
hosted by a different organization, and the data on every individual server
is unique to that organization.

Data *cannot* be replicated across servers using RDDs or any other means,
for privacy/ownership reasons.

Data *cannot* be retrieved to the controller, except in aggregate form, as
the result of a query, for example.

Because of this, there are currently no operations that treats the data as
if it were a single data set: We could run a classifier on each site
individually, but cannot for legal reasons, pull all the data into a single
*physical* dataframe to run the classifier on all of it together.

The servers are located across a wide geographic region (1,000s of miles)

We would like to send jobs from the controller to be executed in parallel
on all the servers, and retrieve the results to the controller. The jobs
would consist of SQL-Heavy Java code for 'production' queries, and python
or R code for ad-hoc queries and predictive modeling.

Spark seems to have the capability to meet many of the individual
requirements, but is it a reasonable platform overall for building this
application?

Thank you very much for your assistance.

Larry

Re: Using spark to distribute jobs to standalone servers

[Igor Berman <ig...@gmail.com>]

imho, you'll need to implement custom rdd with your locality settings(i.e.
custom implementation of discovering where each partition is located) +
setting for spark.locality.wait

On 24 August 2016 at 03:48, Mohit Jaggi <mo...@gmail.com> wrote:

> It is a bit hacky but possible. A lot depends on what kind of queries etc
> you want to run. You could write a data source that reads your data and
> keeps it partitioned the way you want, then use mapPartitions() to execute
> your code…
>
>
> Mohit Jaggi
> Founder,
> Data Orchard LLC
> www.dataorchardllc.com
>
>
>
>
> On Aug 22, 2016, at 7:59 AM, Larry White <lj...@gmail.com> wrote:
>
> Hi,
>
> I have a bit of an unusual use-case and would *greatly* *appreciate* some
> feedback as to whether it is a good fit for spark.
>
> I have a network of compute/data servers configured as a tree as shown
> below
>
>    - controller
>    - server 1
>       - server 2
>       - server 3
>       - etc.
>
> There are ~20 servers, but the number is increasing to ~100.
>
> Each server contains a different dataset, all in the same format. Each is
> hosted by a different organization, and the data on every individual server
> is unique to that organization.
>
> Data *cannot* be replicated across servers using RDDs or any other means,
> for privacy/ownership reasons.
>
> Data *cannot* be retrieved to the controller, except in aggregate form,
> as the result of a query, for example.
>
> Because of this, there are currently no operations that treats the data as
> if it were a single data set: We could run a classifier on each site
> individually, but cannot for legal reasons, pull all the data into a single
> *physical* dataframe to run the classifier on all of it together.
>
> The servers are located across a wide geographic region (1,000s of miles)
>
> We would like to send jobs from the controller to be executed in parallel
> on all the servers, and retrieve the results to the controller. The jobs
> would consist of SQL-Heavy Java code for 'production' queries, and python
> or R code for ad-hoc queries and predictive modeling.
>
> Spark seems to have the capability to meet many of the individual
> requirements, but is it a reasonable platform overall for building this
> application?
>
> Thank you very much for your assistance.
>
> Larry
>
>
>
>

Re: Using spark to distribute jobs to standalone servers

[Mohit Jaggi <mo...@gmail.com>]

It is a bit hacky but possible. A lot depends on what kind of queries etc you want to run. You could write a data source that reads your data and keeps it partitioned the way you want, then use mapPartitions() to execute your code…


Mohit Jaggi
Founder,
Data Orchard LLC
www.dataorchardllc.com




> On Aug 22, 2016, at 7:59 AM, Larry White <lj...@gmail.com> wrote:
> 
> Hi,
> 
> I have a bit of an unusual use-case and would greatly appreciate some feedback as to whether it is a good fit for spark.
> 
> I have a network of compute/data servers configured as a tree as shown below
> controller
> server 1
> server 2
> server 3
> etc.
> There are ~20 servers, but the number is increasing to ~100. 
> 
> Each server contains a different dataset, all in the same format. Each is hosted by a different organization, and the data on every individual server is unique to that organization.
> 
> Data cannot be replicated across servers using RDDs or any other means, for privacy/ownership reasons.
> 
> Data cannot be retrieved to the controller, except in aggregate form, as the result of a query, for example. 
> 
> Because of this, there are currently no operations that treats the data as if it were a single data set: We could run a classifier on each site individually, but cannot for legal reasons, pull all the data into a single physical dataframe to run the classifier on all of it together. 
> 
> The servers are located across a wide geographic region (1,000s of miles)
> 
> We would like to send jobs from the controller to be executed in parallel on all the servers, and retrieve the results to the controller. The jobs would consist of SQL-Heavy Java code for 'production' queries, and python or R code for ad-hoc queries and predictive modeling. 
> 
> Spark seems to have the capability to meet many of the individual requirements, but is it a reasonable platform overall for building this application?
> 
> Thank you very much for your assistance. 
> 
> Larry 
>