Spark Streaming takes care of restarting receivers if it fails. Regarding the fault-tolerance properties and deployment options, we made some improvements in the upcoming Spark 1.2. Here is a staged version of the Spark Streaming programming guide that you can read for the up-to-date explanation of streaming fault-tolerance semantics.
http://people.apache.org/~tdas/spark-1.2-temp/ On Thu, Dec 11, 2014 at 4:03 PM, twizansk <twiza...@gmail.com> wrote: > Hi, > > I'm looking for resources and examples for the deployment of spark streaming > in production. Specifically, I would like to know how high availability and > fault tolerance of receivers is typically achieved. > > The workers are managed by the spark framework and are therefore fault > tolerant out of the box but it seems like the receiver deployment and > management is up to me. Is that correct? > > Thanks > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-in-Production-tp20644.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 > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
