Redis, perhaps? There are some Julia bindings: https://github.com/msainz/Redis.jl https://github.com/markmo/HiRedis.jl
I'm not sure which is better to use – the Redis.jl package is a pure Julia implementation of a Redis client while HiRedis.jl uses the hiredis library to interact with Redis. It would be nice to eventually have one true way to interact with Redis, but for now there's a choice. You could also try using Rif.jl <https://github.com/lgautier/Rif.jl> to call R directly from Julia. Yet another option would be to push chunks of data from Julia to R using ZeroMQ messaging. Here's a thread about that approach: https://groups.google.com/forum/#!topic/julia-users/umHiBwVLQ4g On Thu, Dec 18, 2014 at 4:26 AM, Pavel <[email protected]> wrote: > This is a fairly general question about structuring an application that > relies on both Julia and R code. The Julia part takes time series input and > performs extensive parallel computations (thank you core developers for the > efficient pmap), producing the output that is not that demanding in terms > of data storage. The R part collects some data from the web every minute > via REST APIs forming the time series for Julia input, and also uses Julia > output for further computations (not as demanding as the Julia functions so > that the R-side performance is not limiting the overall process). > > Now as I am trying to integrate everything for a production environment, > the question is about data sharing between Julia and R. Saving chunks of > time series as HDFS files works in development but is inefficient. Is there > a suitable non-relational database with both Julia and R bindings > available? Should I look into setting up Julia web-server and have R talk > to it via REST API? Any thoughts/advice on structuring my application > (planning to use Google cloud) would be helpful. Thanks for reading. >
