I'm putting together a big data system centered around using Spark 
Streaming for data ingest and Spark SQL for querying the stored data.  I've 
been investigating what options there are for implementing Spark 
applications using Clojure.  It's been close to a decade since sparkling or 
flambo have received any updates and it doesn't look like either will 
accommodate recent distributions of Spark.  I've found powderkeg an 
interesting option, and I like how it supports remote REPLs and the use of 
tranducers rather than wrapped Scala fns.  However, it looks like it's also 
seen a few years without commits and I've heard loose talk that the 
developers have moved on to other pursuits.

Part of the problem seems to be Spark.  The project seem unapologetic about 
breaking interfaces and seems willing to sacrifice third-party code that 
tries to track Spark's development.

So my options seem to be the following:

1. Deploy an older version of Spark that's compatible with one of the above 
mentioned libraries.  While we don't need to be bleeding edge, deploying a 
three year old version just to accommodate my preferred language is hard to 
justify.

2. Create a merge to update one of those libraries to more recent versions 
of Spark and be prepared to maintain it internally for the lifespan of this 
project.  This may be vastly overestimating my personal heroics.

3. Code my own solution from scratch using Java/Scala interop, sketching 
out just enough of a Clojure wrapper to suit my ends.

4. Learn Scala.

I realize that Spark isn't the only game in town (Onyx, for example).  
However, I'm working with a team of developers who are not familiar with 
Clojure (though I'm working to be an advocate). I choose Spark as an 
established solution that supports multiple languages and handles both 
streaming and batch processing.

Any insights?  Any solutions I'm overlooking?



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