RE: Spark JDBC connection - data writing success or failure cases

o:divya.htco...@gmail.com] Sent: 21 February 2016 00:09 To: Mich Talebzadeh <m...@peridale.co.uk> Cc: user @spark <user@spark.apache.org>; Russell Jurney <russell.jur...@gmail.com>; Jörn Franke <jornfra...@gmail.com> Subject: RE: Spark JDBC connection - data writing success or fa

RE: Spark JDBC connection - data writing success or failure cases

t;jornfra...@gmail.com> Cc: Divya Gehlot <divya.htco...@gmail.com>; user @spark <user@spark.apache.org> Subject: Re: Spark JDBC connection - data writing success or failure cases Oracle is a perfectly reasonable endpoint for publishing data processed in Spark. I've got to assum

Re: Spark JDBC connection - data writing success or failure cases

Oracle is a perfectly reasonable endpoint for publishing data processed in Spark. I've got to assume he's using it that way and not as a stand in for HDFS? On Friday, February 19, 2016, Jörn Franke wrote: > Generally oracle db should not be used as a storage layer for

Re: Spark JDBC connection - data writing success or failure cases

Generally oracle db should not be used as a storage layer for spark due to performance reasons. You should consider HDFS. This will help you also with fault - tolerance. > On 19 Feb 2016, at 03:35, Divya Gehlot wrote: > > Hi, > I am a Spark job which connects to

RE: Spark JDBC connection - data writing success or failure cases

l.com] Sent: 19 February 2016 02:36 To: user @spark <user@spark.apache.org> Subject: Spark JDBC connection - data writing success or failure cases Hi, I am a Spark job which connects to RDBMS (in mycase its Oracle). How can we check that complete data writing is successful? Can I

Spark JDBC connection - data writing success or failure cases

Hi, I am a Spark job which connects to RDBMS (in mycase its Oracle). How can we check that complete data writing is successful? Can I use commit in case of success or rollback in case of failure ? Thanks, Divya