@Tobias, According to my understanding, your approach is to register a series of tables by using transformWith, right? And then, you can get a new Dstream (i.e., SchemaDstream), which consists of lots of SchemaRDDs.
Please correct me if my understanding is wrong. Thank you && Best Regards, Grace （Huang Jie) From: Jason Dai [mailto:jason....@gmail.com] Sent: Wednesday, March 11, 2015 10:45 PM To: Irfan Ahmad Cc: Tobias Pfeiffer; Cheng, Hao; Mohit Anchlia; user@spark.apache.org; Shao, Saisai; Dai, Jason; Huang, Jie Subject: Re: SQL with Spark Streaming Sorry typo; should be https://github.com/intel-spark/stream-sql Thanks, -Jason On Wed, Mar 11, 2015 at 10:19 PM, Irfan Ahmad <ir...@cloudphysics.com<mailto:ir...@cloudphysics.com>> wrote: Got a 404 on that link: https://github.com/Intel-bigdata/spark-streamsql Irfan Ahmad CTO | Co-Founder | CloudPhysics<http://www.cloudphysics.com> Best of VMworld Finalist Best Cloud Management Award NetworkWorld 10 Startups to Watch EMA Most Notable Vendor On Wed, Mar 11, 2015 at 6:41 AM, Jason Dai <jason....@gmail.com<mailto:jason....@gmail.com>> wrote: Yes, a previous prototype is available https://github.com/Intel-bigdata/spark-streamsql, and a talk is given at last year's Spark Summit (http://spark-summit.org/2014/talk/streamsql-on-spark-manipulating-streams-by-sql-using-spark) We are currently porting the prototype to use the latest DataFrame API, and will provide a stable version for people to try soon. Thabnks, -Jason On Wed, Mar 11, 2015 at 9:12 AM, Tobias Pfeiffer <t...@preferred.jp<mailto:t...@preferred.jp>> wrote: Hi, On Wed, Mar 11, 2015 at 9:33 AM, Cheng, Hao <hao.ch...@intel.com<mailto:hao.ch...@intel.com>> wrote: Intel has a prototype for doing this, SaiSai and Jason are the authors. Probably you can ask them for some materials. The github repository is here: https://github.com/intel-spark/stream-sql Also, what I did is writing a wrapper class SchemaDStream that internally holds a DStream[Row] and a DStream[StructType] (the latter having just one element in every RDD) and then allows to do - operations SchemaRDD => SchemaRDD using `rowStream.transformWith(schemaStream, ...)` - in particular you can register this stream's data as a table this way - and via a companion object with a method `fromSQL(sql: String): SchemaDStream` you can get a new stream from previously registered tables. However, you are limited to batch-internal operations, i.e., you can't aggregate across batches. I am not able to share the code at the moment, but will within the next months. It is not very advanced code, though, and should be easy to replicate. Also, I have no idea about the performance of transformWith.... Tobias
