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. >>>>>>>> >>>>>>>
