I want to set up spark SQL to allow ad hoc querying over the last X days of processed data, where the data is processed through spark. This would also have to cache data (in memory only), so the approach I was thinking of was to build a layer that persists the appropriate RDDs and stores them in memory.
I see spark sql allows ad hoc querying through JDBC though I have never used that before. Will using JDBC offer any advantages (e.g does it have built in support for caching?) over rolling my own solution for this use case? Thanks! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Is-SparkSQL-JDBC-server-a-good-approach-for-caching-tp17196.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
