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in Big Data Hadoop & Spark by (11.4k points)

I have been of late trying out apache spark. My question is more specific to trigger spark jobs. Here I had posted question on understanding spark jobs. After getting dirty on jobs I moved on to my requirement.

I have a REST end point where I expose API to trigger Jobs, I have used Spring4.0 for Rest Implementation. Now going ahead I thought of implementing Jobs as Service in Spring where I would submit Job programmatically, meaning when the endpoint is triggered, with given parameters I would trigger the job. I have now few design options.

  • Similar to the below written job, I need to maintain several Jobs called by a Abstract Class may be JobScheduler .

 /*Can this Code be abstracted from the application and written as
  as a seperate job. Because my understanding is that the
 Application code itself has to have the addJars embedded
 which internally  sparkContext takes care.*/

 SparkConf sparkConf = new SparkConf().setAppName("MyApp").setJars(
 new String[] { "/path/to/jar/submit/cluster" })

        sparkConf.set("spark.scheduler.mode", "FAIR");
        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        sc.setLocalProperty("spark.scheduler.pool", "test");

    // Application with Algorithm , transformations

  • extending above point have multiple versions of jobs handled by service.
  • Or else use an Spark Job Server to do this.

Firstly, I would like to know what is the best solution in this case, execution wise and also scaling wise.

1 Answer

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by (32.3k points)

You can use the Spark JobServer

There are a lot of things to consider when one is making a service, and the Spark JobServer has most of them covered already. If you find things that aren't good enough, it should be easy to make a request and add code to their system rather than reinventing it from scratch

Also, after doing a lot of research I found that Spark has a hidden REST API to submit a job, check status and kill.

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