That might be a good alternative to what we are looking for. But I wonder
if this would be as efficient as we want to. For instance, will RDDs of the
same size usually get partitioned to the same machines - thus not
triggering any cross machine aligning, etc. We'll explore it, but I would
still very much like to see more direct worker memory management besides
RDDs.
On Mon, Apr 28, 2014 at 10:26 AM, Tom Vacek <minnesota...@gmail.com> wrote:
> Right---They are zipped at each iteration.
>
>
> On Mon, Apr 28, 2014 at 11:56 AM, Chester Chen <chesterxgc...@yahoo.com>wrote:
>
>> Tom,
>> Are you suggesting two RDDs, one with loss and another for the rest
>> info, using zip to tie them together, but do update on loss RDD (copy) ?
>>
>> Chester
>>
>> Sent from my iPhone
>>
>> On Apr 28, 2014, at 9:45 AM, Tom Vacek <minnesota...@gmail.com> wrote:
>>
>> I'm not sure what I said came through. RDD zip is not hacky at all, as
>> it only depends on a user not changing the partitioning. Basically, you
>> would keep your losses as an RDD[Double] and zip whose with the RDD of
>> examples, and update the losses. You're doing a copy (and GC) on the RDD
>> of losses each time, but this is negligible.
>>
>>
>> On Mon, Apr 28, 2014 at 11:33 AM, Sung Hwan Chung <
>> coded...@cs.stanford.edu> wrote:
>>
>>> Yes, this is what we've done as of now (if you read earlier threads).
>>> And we were saying that we'd prefer if Spark supported persistent worker
>>> memory management in a little bit less hacky way ;)
>>>
>>>
>>> On Mon, Apr 28, 2014 at 8:44 AM, Ian O'Connell <i...@ianoconnell.com>wrote:
>>>
>>>> A mutable map in an object should do what your looking for then I
>>>> believe. You just reference the object as an object in your closure so it
>>>> won't be swept up when your closure is serialized and you can reference
>>>> variables of the object on the remote host then. e.g.:
>>>>
>>>> object MyObject {
>>>> val mmap = scala.collection.mutable.Map[Long, Long]()
>>>> }
>>>>
>>>> rdd.map { ele =>
>>>> MyObject.mmap.getOrElseUpdate(ele, 1L)
>>>> ...
>>>> }.map {ele =>
>>>> require(MyObject.mmap(ele) == 1L)
>>>>
>>>> }.count
>>>>
>>>> Along with the data loss just be careful with thread safety and
>>>> multiple threads/partitions on one host so the map should be viewed as
>>>> shared amongst a larger space.
>>>>
>>>>
>>>>
>>>> Also with your exact description it sounds like your data should be
>>>> encoded into the RDD if its per-record/per-row: RDD[(MyBaseData,
>>>> LastIterationSideValues)]
>>>>
>>>>
>>>>
>>>> On Mon, Apr 28, 2014 at 1:51 AM, Sung Hwan Chung <
>>>> coded...@cs.stanford.edu> wrote:
>>>>
>>>>> In our case, we'd like to keep memory content from one iteration to
>>>>> the next, and not just during a single mapPartition call because then we
>>>>> can do more efficient computations using the values from the previous
>>>>> iteration.
>>>>>
>>>>> So essentially, we need to declare objects outside the scope of the
>>>>> map/reduce calls (but residing in individual workers), then those can be
>>>>> accessed from the map/reduce calls.
>>>>>
>>>>> We'd be making some assumptions as you said, such as - RDD partition
>>>>> is statically located and can't move from worker to another worker unless
>>>>> the worker crashes.
>>>>>
>>>>>
>>>>>
>>>>> On Mon, Apr 28, 2014 at 1:35 AM, Sean Owen <so...@cloudera.com> wrote:
>>>>>
>>>>>> On Mon, Apr 28, 2014 at 9:30 AM, Sung Hwan Chung <
>>>>>> coded...@cs.stanford.edu> wrote:
>>>>>>
>>>>>>> Actually, I do not know how to do something like this or whether
>>>>>>> this is possible - thus my suggestive statement.
>>>>>>>
>>>>>>> Can you already declare persistent memory objects per worker? I
>>>>>>> tried something like constructing a singleton object within map
>>>>>>> functions,
>>>>>>> but that didn't work as it seemed to actually serialize singletons and
>>>>>>> pass
>>>>>>> it back and forth in a weird manner.
>>>>>>>
>>>>>>>
>>>>>> Does it need to be persistent across operations, or just persist for
>>>>>> the lifetime of processing of one partition in one mapPartition? The
>>>>>> latter
>>>>>> is quite easy and might give most of the speedup.
>>>>>>
>>>>>> Maybe that's 'enough', even if it means you re-cache values several
>>>>>> times in a repeated iterative computation. It would certainly avoid
>>>>>> managing a lot of complexity in trying to keep that state alive remotely
>>>>>> across operations. I'd also be interested if there is any reliable way to
>>>>>> do that, though it seems hard since it means you embed assumptions about
>>>>>> where particular data is going to be processed.
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
