[jeremystan]

To persist sparse models to the DB, especially if you use L1 regularization (like Lasso) then many coefficients will be 0, and don't need to be stored or processed. Insofar as you store coefficients and features in a "tall" format (e.g., user, feature_key, feature_value), then space is conserved. Scoring can be done in DB with joins and group by, or in another language with similar operations.

Changing variables frequently can be versioned in the feature and model coefficients tables, but takes care.

I haven't used Postgres JSONB, but if you have problems with JSON in R check out the tidyjson package (I wrote when dealing with Mongo data previously).

Scoring in another language is best avoided if you can. But supporting R "real time" services will also come with many complications. Hence, we use Python when we really want that.

SparkR was completely unreliable when I first tested it over a year ago, but may have improved. Though the Spark Python API has some limitations compared to Scala, so I would guess the latest SparkR is even further behind, but we haven't tested it. Long term I'd love for that to be the answer to these questions.