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 <r...@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.u...@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.willi...@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.u...@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. >>>> ================== >>>> >>> >
