This does look like it provides a good way to allow other process to access the contents of an RDD in a separate app? Is there any other general purpose mechanism for serving up RDD data? I understand that the driver app and workers all are app specific and run in separate executors but would be cool if there was some general way to create a server app based on Spark. Perhaps Spark SQL is that general way and I'll soon find out. Thanks.
From: mich...@databricks.com Date: Mon, 27 Oct 2014 14:35:46 -0700 Subject: Re: Spark to eliminate full-table scan latency To: ronalday...@live.com CC: user@spark.apache.org You can access cached data in spark through the JDBC server: http://spark.apache.org/docs/latest/sql-programming-guide.html#running-the-thrift-jdbc-server On Mon, Oct 27, 2014 at 1:47 PM, Ron Ayoub <ronalday...@live.com> wrote: We have a table containing 25 features per item id along with feature weights. A correlation matrix can be constructed for every feature pair based on co-occurrence. If a user inputs a feature they can find out the features that are correlated with a self-join requiring a single full table scan. This results in high latency for big data (10 seconds +) due to the IO involved in the full table scan. My idea is for this feature the data can be loaded into an RDD and transformations and actions can be applied to find out per query what are the correlated features. I'm pretty sure Spark can do this sort of thing. Since I'm new, what I'm not sure about is, is Spark appropriate as a server application? For instance, the drive application would have to load the RDD and then listen for request and return results, perhaps using a socket? Are there any libraries to facilitate this sort of Spark server app? So I understand how Spark can be used to grab data, run algorithms, and put results back but is it appropriate as the engine of a server app and what are the general patterns involved?
