Interesting, you saying StreamContext checkpoint can regenerate DAG stuff?
Best Regards Jun Feng Liu IBM China Systems & Technology Laboratory in Beijing Phone: 86-10-82452683 E-mail: liuj...@cn.ibm.com BLD 28,ZGC Software Park No.8 Rd.Dong Bei Wang West, Dist.Haidian Beijing 100193 China Tathagata Das <tathagata.das1...@gmail.com> 2014/12/11 20:20 To Jun Feng Liu/China/IBM@IBMCN, cc Sandy Ryza <sandy.r...@cloudera.com>, "dev@spark.apache.org" <dev@spark.apache.org>, Reynold Xin <r...@databricks.com> Subject Re: HA support for Spark Spark Streaming essentially does this by saving the DAG of DStreams, which can deterministically regenerate the DAG of RDDs upon recovery from failure. Along with that the progress information (which batches have finished, which batches are queued, etc.) is also saved, so that upon recovery the system can restart from where it was before failure. This was conceptually easy to do because the RDDs are very deterministically generated in every batch. Extending this to a very general Spark program with arbitrary RDD computations is definitely conceptually possible but not that easy to do. On Wed, Dec 10, 2014 at 7:34 PM, Jun Feng Liu <liuj...@cn.ibm.com> wrote: Right, perhaps also need preserve some DAG information? I am wondering if there is any work around this. Sandy Ryza ---2014-12-11 01:36:35---Sandy Ryza <sandy.r...@cloudera.com> Sandy Ryza <sandy.r...@cloudera.com> 2014-12-11 01:34 To Jun Feng Liu/China/IBM@IBMCN, cc Reynold Xin <r...@databricks.com>, "dev@spark.apache.org" < dev@spark.apache.org> Subject Re: HA support for Spark I think that if we were able to maintain the full set of created RDDs as well as some scheduler and block manager state, it would be enough for most apps to recover. On Wed, Dec 10, 2014 at 5:30 AM, Jun Feng Liu <liuj...@cn.ibm.com> wrote: > Well, it should not be mission impossible thinking there are so many HA > solution existing today. I would interest to know if there is any specific > difficult. > > Best Regards > > > *Jun Feng Liu* > IBM China Systems & Technology Laboratory in Beijing > > ------------------------------ > [image: 2D barcode - encoded with contact information] *Phone: *86-10-82452683 > > * E-mail:* *liuj...@cn.ibm.com* <liuj...@cn.ibm.com> > [image: IBM] > > BLD 28,ZGC Software Park > No.8 Rd.Dong Bei Wang West, Dist.Haidian Beijing 100193 > China > > > > > > *Reynold Xin <r...@databricks.com <r...@databricks.com>>* > > 2014/12/10 16:30 > To > Jun Feng Liu/China/IBM@IBMCN, > cc > "dev@spark.apache.org" <dev@spark.apache.org> > Subject > Re: HA support for Spark > > > > > This would be plausible for specific purposes such as Spark streaming or > Spark SQL, but I don't think it is doable for general Spark driver since it > is just a normal JVM process with arbitrary program state. > > On Wed, Dec 10, 2014 at 12:25 AM, Jun Feng Liu <liuj...@cn.ibm.com> wrote: > > > Do we have any high availability support in Spark driver level? For > > example, if we want spark drive can move to another node continue > execution > > when failure happen. I can see the RDD checkpoint can help to > serialization > > the status of RDD. I can image to load the check point from another node > > when error happen, but seems like will lost track all tasks status or > even > > executor information that maintain in spark context. I am not sure if > there > > is any existing stuff I can leverage to do that. thanks for any suggests > > > > Best Regards > > > > > > *Jun Feng Liu* > > IBM China Systems & Technology Laboratory in Beijing > > > > ------------------------------ > > [image: 2D barcode - encoded with contact information] *Phone: > *86-10-82452683 > > > > * E-mail:* *liuj...@cn.ibm.com* <liuj...@cn.ibm.com> > > [image: IBM] > > > > BLD 28,ZGC Software Park > > No.8 Rd.Dong Bei Wang West, Dist.Haidian Beijing 100193 > > China > > > > > > > > > > > >
