The number of concurrent Streaming job is controlled by
"spark.streaming.concurrentJobs". It's 1 by default. However, you need to
keep in mind that setting it to a bigger number will allow jobs of several
batches running at the same time. It's hard to predicate the behavior and
sometimes will surprise you.
On Tue, Jan 26, 2016 at 9:57 AM, Sebastian Piu <sebastian....@gmail.com>
wrote:
> Hi,
>
> I'm trying to get *FAIR *scheduling to work in a spark streaming app
> (1.6.0).
>
> I've found a previous mailing list where it is indicated to do:
>
> dstream.foreachRDD { rdd =>
> rdd.sparkContext.setLocalProperty("spark.scheduler.pool", "pool1") // set
> the pool rdd.count() // or whatever job }
>
> This seems to work, in the sense that If I have 5 foreachRDD in my code,
> each one is sent to a different queue, but they still get executed one
> after the other rather than at the same time.
> Am I missing something?
>
> The scheduler config and scheduler mode are being picked alright as I can
> see them on the Spark UI
>
> //Context config
>
> *spark.scheduler.mode=FAIR*
>
> Here is my scheduler config:
>
>
> *<?xml version="1.0"?> <allocations> <pool name="A">
> <schedulingMode>FAIR</schedulingMode> <weight>2</weight>
> <minShare>1</minShare> </pool> <pool name="B">
> <schedulingMode>FAIR</schedulingMode> <weight>1</weight>
> <minShare>0</minShare> </pool> <pool name=C">
> <schedulingMode>FAIR</schedulingMode> <weight>1</weight>
> <minShare>0</minShare> </pool> <pool name=D">
> <schedulingMode>FAIR</schedulingMode> <weight>1</weight>
> <minShare>0</minShare> </pool> <pool name="E">
> <schedulingMode>FAIR</schedulingMode> <weight>2</weight>
> <minShare>1</minShare> </pool>*
>
>
> Any idea on what could be wrong?
>
