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From Jörn Franke <jornfra...@gmail.com>
Subject Re: How to avoid long-running jobs blocking short-running jobs
Date Sat, 03 Nov 2018 09:16:56 GMT
Hi,

What does your Spark deployment architecture looks like? Standalone? Yarn? Mesos? Kubernetes?
Those have resource managers (not middlewares) that allow to implement scenarios as you want
to achieve.

 In any case you can try the FairScheduler of any of those solutions.

Best regards

> Am 03.11.2018 um 10:04 schrieb conner <mitiskysean@gmail.com>:
> 
> Hi,
> 
> I use spark cluster to run ETL jobs and analysis computation about the data
> after elt stage.
> The elt jobs can keep running for several hours, but analysis computation is
> a short-running job which can finish in a few seconds.
> The dilemma I entrapped is that my application runs in a single JVM and
> can't be a cluster application, so just one spark context in my application
> currently. But when the elt jobs are running,
> the jobs will occupy all resource including worker executors too long to
> block all my analysis computation jobs. 
> 
> My solution is to find a good way to divide the spark cluster resource into
> two. One part for analysis computation jobs, another for
> elt jobs. if the part for elt jobs is free, I can allocate analysis
> computation jobs to it.
> So I want to find a middleware that can support two spark context and it
> must be embedded in my application. I do some research on the third party
> project spark job server. It can divide spark resource by launching another
> JVM to run spark context with a specific resource.
> these operations are invisible to the upper layer, so it's a good solution
> for me. But this project is running in a single JVM  and just support REST
> API, I can't endure the data transfer by TCP again
> which too slow to me. I want to get a result from spark cluster by TCP and
> give this result to view layer to show.
> Can anybody give me some good suggestion? I shall be so grateful.
> 
> 
> 
> 
> 
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