Are you asking about Spark Streaming support? On Sun, Nov 1, 2015 at 4:42 AM, Sisyphuss <[email protected]> wrote:
> http://dl.acm.org/citation.cfm?id=2228301 > > On Saturday, October 31, 2015 at 5:18:01 PM UTC+1, Jey Kottalam wrote: >> >> Could you please define "streams of RDDs"? >> >> On Sat, Oct 31, 2015 at 12:59 AM, <[email protected]> wrote: >> >>> Is there any implementation with streams of RDDs for Julia ? >>> >>> >>> On Monday, April 20, 2015 at 11:54:10 AM UTC-7, [email protected] wrote: >>>> >>>> Unfortunately, Spark.jl is an incorrect RDD implementation. Instead of >>>> creating transformations as independent abstraction operations with a lazy >>>> evaluation, the package has all transformations immediately executed upon >>>> their call. This is completely undermines whole purpose of RDD as >>>> fault-tolerant parallel data structure. >>>> >>>> On Saturday, April 18, 2015 at 4:04:23 AM UTC-4, Tanmay K. Mohapatra >>>> wrote: >>>>> >>>>> There was some attempt made towards a pure Julia RDD in Spark.jl ( >>>>> https://github.com/d9w/Spark.jl). >>>>> We also have DistributedArrays ( >>>>> https://github.com/JuliaParallel/DistributedArrays.jl), Blocks ( >>>>> https://github.com/JuliaParallel/Blocks.jl) and ( >>>>> https://github.com/JuliaStats/DataFrames.jl). >>>>> >>>>> I wonder if it is possible to leverage any of these for a pure Julia >>>>> RDD. >>>>> And MachineLearning.jl >>>>> <https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fbenhamner%2FMachineLearning.jl&sa=D&sntz=1&usg=AFQjCNEBun6ioX809NFBqVDu3eMKWzrZBQ> >>>>> or >>>>> something similar could probably be the equivalent of MLib. >>>>> >>>>> >>>>> On Friday, April 17, 2015 at 9:24:03 PM UTC+5:30, [email protected] >>>>> wrote: >>>>>> >>>>>> Of course, a Spark data access infrastructure is unbeatable, due to >>>>>> mature JVM-based libraries for accessing various data sources and formats >>>>>> (avro, parquet, hdfs). That includes SQL support as well. But, look at >>>>>> Python and R bindings, these are just facades for JVM calls. MLLib is >>>>>> written in Scala, Streaming API as well, and then all this called from >>>>>> Python or R, all data transformations happen on JVM level. It would be >>>>>> more >>>>>> efficient write code in Scala then use any non-JVM bindings. Think of >>>>>> overhead for RPC and data serialization over huge volumes of data needed >>>>>> to >>>>>> be processed and you'll understand why Dpark exists. BTW, machine >>>>>> learning >>>>>> libraries in JVM, good luck. It only works because of large computational >>>>>> resources used, but even that has its limits. >>>>>> >>>>>> On Thursday, April 16, 2015 at 6:29:58 PM UTC-4, Andrei Zh wrote: >>>>>>> >>>>>>> Julia bindings for Spark would provide much more than just RDD, they >>>>>>> will give us access to multiple big data components for streaming, >>>>>>> machine >>>>>>> learning, SQL capabilities and much more. >>>>>>> >>>>>>> On Friday, April 17, 2015 at 12:54:32 AM UTC+3, [email protected] >>>>>>> wrote: >>>>>>>> >>>>>>>> However, I wonder, how hard it would be to implement RDD in Julia? >>>>>>>> It looks straight forward from a RDD paper >>>>>>>> <https://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf> >>>>>>>> how to implement it. It is a robust abstraction that can be used in any >>>>>>>> parallel computation. >>>>>>>> >>>>>>>> On Thursday, April 16, 2015 at 3:32:32 AM UTC-4, Steven Sagaert >>>>>>>> wrote: >>>>>>>>> >>>>>>>>> yes that's a solid approach. For my personal julia - java >>>>>>>>> integrations I also run the JVM in a separate process. >>>>>>>>> >>>>>>>>> On Wednesday, April 15, 2015 at 9:30:28 PM UTC+2, [email protected] >>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>> 1) simply wrap the Spark java API via JavaCall. This is the low >>>>>>>>>>> level approach. BTW I've experimented with javaCall and found it was >>>>>>>>>>> unstable & also lacking functionality (e.g. there's no way to >>>>>>>>>>> shutdown the >>>>>>>>>>> jvm or create a pool of JVM analogous to DB connections) so that >>>>>>>>>>> might need >>>>>>>>>>> some work before trying the Spark integration. >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Using JavaCall is not an option, especially when JVM became >>>>>>>>>> close-sourced, see https://github.com/aviks/JavaCall.jl/issues/7. >>>>>>>>>> >>>>>>>>>> Python bindings are done through Py4J, which is RPC to JVM. If >>>>>>>>>> you look at the sparkR >>>>>>>>>> <https://github.com/apache/spark/tree/master/R>, it is done in a >>>>>>>>>> same way. sparkR uses a RPC interface to communicate with a >>>>>>>>>> Netty-based >>>>>>>>>> Spark JVM backend that translates R calls into JVM calls, keeps >>>>>>>>>> SparkContext on a JVM side, and ships serialized data to/from R. >>>>>>>>>> >>>>>>>>>> So it is just a matter of writing Julia RPC to JVM and wrapping >>>>>>>>>> necessary Spark methods in a Julia friendly way. >>>>>>>>>> >>>>>>>>> >>
