How does Julia interact with spark. I would be interested, mainly because I seem to find scala syntax a little obscure and it would be great to see actual numbers comparing scala, Python, Julia workloads.
> On Feb 1, 2014, at 16:08, Aureliano Buendia <[email protected]> wrote: > > A much (much) better solution than python, (and also scala, if that doesn't > make you upset) is julia. > > Libraries like numpy and scipy are bloated when compared with julia c-like > performance. Julia comes with eveything that numpy+scipy come with + more - > performance hit. > > I hope we can see an official support of julia on spark very soon. > > >> On Thu, Jan 30, 2014 at 4:30 PM, nileshc <[email protected]> wrote: >> Hi there, >> >> *Background:* >> I need to do some matrix multiplication stuff inside the mappers, and trying >> to choose between Python and Scala for writing the Spark MR jobs. I'm >> equally fluent with Python and Java, and find Scala pretty easy too for what >> it's worth. Going with Python would let me use numpy + scipy, which is >> blazing fast when compared to Java libraries like Colt etc. Configuring Java >> with BLAS seems to be a pain when compared to scipy (direct apt-get >> installs, or pip). >> >> *Question:* >> I posted a couple of comments on this answer at StackOverflow: >> http://stackoverflow.com/questions/17236936/api-compatibility-between-scala-and-python. >> Basically it states that as of Spark 0.7.2, the Python API would be slower >> than Scala. What's the performance scenario now? The fork issue seems to be >> fixed. How about serialization? Can it match Java/Scala Writable-like >> serialization (having knowledge of object type beforehand, reducing I/O) >> performance? Also, a probably silly question - loops seem to be slow in >> Python in general, do you think this can turn out to be an issue? >> >> Bottomline, should I choose Python for computation-intensive algorithms like >> PageRank? Scipy gives me an edge, but does the framework kill it? >> >> Any help, insights, benchmarks will be much appreciated. :) >> >> Cheers, >> Nilesh >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/Python-API-Performance-tp1048.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >
