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From Dark Crusader <relinquisheddra...@gmail.com>
Subject Re: Spark streaming receivers
Date Mon, 10 Aug 2020 03:44:38 GMT
Hi Russell,
This is super helpful. Thank you so much.

Can you elaborate on the differences between structured streaming vs
dstreams? How would the number of receivers required etc change?

On Sat, 8 Aug, 2020, 10:28 pm Russell Spitzer, <russell.spitzer@gmail.com>
wrote:

> Note, none of this applies to Direct streaming approaches, only receiver
> based Dstreams.
>
> You can think of a receiver as a long running task that never finishes.
> Each receiver is submitted to an executor slot somewhere, it then runs
> indefinitely and internally has a method which passes records over to a
> block management system. There is a timing that you set which decides when
> each block is "done" and records after that time has passed go into the
> next block (See parameter
> <https://spark.apache.org/docs/latest/configuration.html#spark-streaming>
> spark.streaming.blockInterval)  Once a block is done it can be processed
> in the next Spark batch.. The gap between a block starting and a block
> being finished is why you can lose data in Receiver streaming without
> WriteAheadLoging. Usually your block interval is divisible into your batch
> interval so you'll get X blocks per batch. Each block becomes one partition
> of the job being done in a Streaming batch. Multiple receivers can be
> unified into a single dstream, which just means the blocks produced by all
> of those receivers are handled in the same Streaming batch.
>
> So if you have 5 different receivers, you need at minimum 6 executor
> cores. 1 core for each receiver, and 1 core to actually do your processing
> work. In a real world case you probably want significantly more  cores on
> the processing side than just 1. Without repartitioning you will never have
> more that
>
> A quick example
>
> I run 5 receivers with block interval of 100ms and spark batch interval of
> 1 second. I use union to group them all together, I will most likely end up
> with one Spark Job for each batch every second running with 50 partitions
> (1000ms / 100(ms / partition / receiver) * 5 receivers). If I have a total
> of 10 cores in the system. 5 of them are running receivers, The remaining 5
> must process the 50 partitions of data generated by the last second of work.
>
> And again, just to reiterate, if you are doing a direct streaming approach
> or structured streaming, none of this applies.
>
> On Sat, Aug 8, 2020 at 10:03 AM Dark Crusader <
> relinquisheddragon@gmail.com> wrote:
>
>> Hi,
>>
>> I'm having some trouble figuring out how receivers tie into spark
>> driver-executor structure.
>> Do all executors have a receiver that is blocked as soon as it
>> receives some stream data?
>> Or can multiple streams of data be taken as input into a single executor?
>>
>> I have stream data coming in at every second coming from 5 different
>> sources. I want to aggregate data from each of them. Does this mean I need
>> 5 executors or does it have to do with threads on the executor?
>>
>> I might be mixing in a few concepts here. Any help would be appreciated.
>> Thank you.
>>
>

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