Because this was a maintenance release, we should not have introduced any binary backwards or forwards incompatibilities. Therefore, applications that were written and compiled against 1.1.0 should still work against a 1.1.1 cluster, and vice versa.
On Wed, Dec 3, 2014 at 1:30 PM, Andrew Or <and...@databricks.com> wrote: > By the Spark server do you mean the standalone Master? It is best if they > are upgraded together because there have been changes to the Master in > 1.1.1. Although it might "just work", it's highly recommended to restart > your cluster manager too. > > 2014-12-03 13:19 GMT-08:00 Romi Kuntsman <r...@totango.com>: > > About version compatibility and upgrade path - can the Java application >> dependencies and the Spark server be upgraded separately (i.e. will 1.1.0 >> library work with 1.1.1 server, and vice versa), or do they need to be >> upgraded together? >> >> Thanks! >> >> *Romi Kuntsman*, *Big Data Engineer* >> http://www.totango.com >> >> On Tue, Dec 2, 2014 at 11:36 PM, Andrew Or <and...@databricks.com> wrote: >> >>> I am happy to announce the availability of Spark 1.1.1! This is a >>> maintenance release with many bug fixes, most of which are concentrated in >>> the core. This list includes various fixes to sort-based shuffle, memory >>> leak, and spilling issues. Contributions from this release came from 55 >>> developers. >>> >>> Visit the release notes [1] to read about the new features, or >>> download [2] the release today. >>> >>> [1] http://spark.apache.org/releases/spark-release-1-1-1.html >>> [2] http://spark.apache.org/downloads.html >>> >>> Please e-mail me directly for any typo's in the release notes or name >>> listing. >>> >>> Thanks for everyone who contributed, and congratulations! >>> -Andrew >>> >> >> >
