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From Justin Uang <justin.u...@gmail.com>
Subject Re: Off-heap storage and dynamic allocation
Date Tue, 03 Nov 2015 21:13:12 GMT
Thanks for your response. I was worried about #3, vs being able to use the
objects directly. #2 seems to be the dealbreaker for my use case right?
Even if it I am using tachyon for caching, if an executor is lost, then
that partition is lost for the purposes of spark?

On Tue, Nov 3, 2015 at 5:53 PM Reynold Xin <rxin@databricks.com> wrote:

> I don't think there is any special handling w.r.t. Tachyon vs in-heap
> caching. As a matter of fact, I think the current offheap caching
> implementation is pretty bad, because:
>
> 1. There is no namespace sharing in offheap mode
> 2. Similar to 1, you cannot recover the offheap memory once Spark driver
> or executor crashes
> 3. It requires expensive serialization to go offheap
>
> It would've been simpler to just treat Tachyon as a normal file system,
> and use it that way to at least satisfy 1 and 2, and also substantially
> simplify the internals.
>
>
>
>
> On Tue, Nov 3, 2015 at 7:59 AM, Justin Uang <justin.uang@gmail.com> wrote:
>
>> Yup, but I'm wondering what happens when an executor does get removed,
>> but when we're using tachyon. Will the cached data still be available,
>> since we're using off-heap storage, so the data isn't stored in the
>> executor?
>>
>> On Tue, Nov 3, 2015 at 4:57 PM Ryan Williams <
>> ryan.blake.williams@gmail.com> wrote:
>>
>>> fwiw, I think that having cached RDD partitions prevents executors from
>>> being removed under dynamic allocation by default; see SPARK-8958
>>> <https://issues.apache.org/jira/browse/SPARK-8958>. The
>>> "spark.dynamicAllocation.cachedExecutorIdleTimeout" config
>>> <http://spark.apache.org/docs/latest/configuration.html#dynamic-allocation>
>>> controls this.
>>>
>>> On Fri, Oct 30, 2015 at 12:14 PM Justin Uang <justin.uang@gmail.com>
>>> wrote:
>>>
>>>> Hey guys,
>>>>
>>>> According to the docs for 1.5.1, when an executor is removed for
>>>> dynamic allocation, the cached data is gone. If I use off-heap storage like
>>>> tachyon, conceptually there isn't this issue anymore, but is the cached
>>>> data still available in practice? This would be great because then we would
>>>> be able to set spark.dynamicAllocation.cachedExecutorIdleTimeout to be
>>>> quite small.
>>>>
>>>> ==================
>>>> In addition to writing shuffle files, executors also cache data either
>>>> on disk or in memory. When an executor is removed, however, all cached data
>>>> will no longer be accessible. There is currently not yet a solution for
>>>> this in Spark 1.2. In future releases, the cached data may be preserved
>>>> through an off-heap storage similar in spirit to how shuffle files are
>>>> preserved through the external shuffle service.
>>>> ==================
>>>>
>>>
>

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