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