I see. I could not find any explanation about this. Could you tell me
what sort of portability issue is this? Isn't JVM supposed to give the
abstraction of that?
Thanks!
On 12.07.2016 20:04, Reynold Xin wrote:
Platform as a general word, eg language platforms, OS, different JVM
versions, different JVM vendors, different Spark versions...
On Tuesday, July 12, 2016, Salih Gedik <m...@salih.xyz
<mailto:m...@salih.xyz>> wrote:
Hi Reynold,
I was wondering if you meant cross language or cross platform? Thanks
On 12.07.2016 19:57, Reynold Xin wrote:
Also Java serialization isn't great for cross platform
compatibility.
On Tuesday, July 12, 2016, aka.fe2s <aka.f...@gmail.com
<javascript:_e(%7B%7D,'cvml','aka.f...@gmail.com');>> wrote:
Okay, I think I found an answer on my question. Some models
(for instance
org.apache.spark.mllib.recommendation.MatrixFactorizationModel)
hold RDDs, so just serializing these objects will not work.
--
Oleksiy Dyagilev
On Tue, Jul 12, 2016 at 5:40 PM, aka.fe2s
<aka.f...@gmail.com> wrote:
What is the reason Spark has an individual
implementations of read/write routines for every model in
mllib and ml? (Saveable and MLWritable trait impls)
Wouldn't a generic implementation via Java serialization
mechanism work? I would like to use it to store the
models to a custom storage.
--
Oleksiy
--
Salih Gedik
--
Salih Gedik
