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From Keith Simmons <ke...@pulse.io>
Subject Re: Spark Memory Bounds
Date Wed, 28 May 2014 02:22:31 GMT
A dash of both.  I want to know enough that I can "reason about", rather
than "strictly control", the amount of memory Spark will use.  If I have a
big data set, I want to understand how I can design it so that Spark's
memory consumption falls below my available resources.  Or alternatively,
if it's even possible for Spark to process a data set over a certain size.
 And if I run into memory problems, I want to know which knobs to turn, and
how turning those knobs will affect memory consumption.

It's my understanding that between certain key stages in a Spark DAG (i.e.
group by stages), Spark will serialize all data structures necessary to
continue the computation at the next stage, including closures.  So in
theory, per machine, Spark only needs to hold the transient memory required
to process the partitions assigned to the currently active tasks.  Is my
understanding correct?  Specifically, once a key/value pair is serialized
in the shuffle stage of a task, are the references to the raw java objects
released before the next task is started.



On Tue, May 27, 2014 at 6:21 PM, Christopher Nguyen <ctn@adatao.com> wrote:

> Keith, do you mean "bound" as in (a) strictly control to some quantifiable
> limit, or (b) try to minimize the amount used by each task?
>
> If "a", then that is outside the scope of Spark's memory management, which
> you should think of as an application-level (that is, above JVM) mechanism.
> In this scope, Spark "voluntarily" tracks and limits the amount of memory
> it uses for explicitly known data structures, such as RDDs. What Spark
> cannot do is, e.g., control or manage the amount of JVM memory that a given
> piece of user code might take up. For example, I might write some closure
> code that allocates a large array of doubles unbeknownst to Spark.
>
> If "b", then your thinking is in the right direction, although quite
> imperfect, because of things like the example above. We often experience
> OOME if we're not careful with job partitioning. What I think Spark needs
> to evolve to is at least to include a mechanism for application-level hints
> about task memory requirements. We might work on this and submit a PR for
> it.
>
> --
> Christopher T. Nguyen
> Co-founder & CEO, Adatao <http://adatao.com>
> linkedin.com/in/ctnguyen
>
>
>
> On Tue, May 27, 2014 at 5:33 PM, Keith Simmons <keith@pulse.io> wrote:
>
>> I'm trying to determine how to bound my memory use in a job working with
>> more data than can simultaneously fit in RAM.  From reading the tuning
>> guide, my impression is that Spark's memory usage is roughly the following:
>>
>> (A) In-Memory RDD use + (B) In memory Shuffle use + (C) Transient memory
>> used by all currently running tasks
>>
>> I can bound A with spark.storage.memoryFraction and I can bound B with spark.shuffle.memoryFraction.
>>  I'm wondering how to bound C.
>>
>> It's been hinted at a few times on this mailing list that you can reduce
>> memory use by increasing the number of partitions.  That leads me to
>> believe that the amount of transient memory is roughly follows:
>>
>> total_data_set_size/number_of_partitions *
>> number_of_tasks_simultaneously_running_per_machine
>>
>> Does this sound right?  In other words, as I increase the number of
>> partitions, the size of each partition will decrease, and since each task
>> is processing a single partition and there are a bounded number of tasks in
>> flight, my memory use has a rough upper limit.
>>
>> Keith
>>
>
>

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