On 17 Oct 2016, at 22:11, Michael Segel <michael_se...@hotmail.com<mailto:michael_se...@hotmail.com>> wrote:
@Steve you are going to have to explain what you mean by ‘turn Kerberos on’. Taken one way… it could mean making cluster B secure and running Kerberos and then you’d have to create some sort of trust between B and C, I'd imagined making cluster B a kerberized cluster. I don't think you need to go near trust relations though —ideally you'd just want the same accounts everywhere if you can, if not, the main thing is that the user submitting the job can get a credential for that far NN at job submission time, and that credential is propagated all the way to the executors. Did you mean turn on kerberos on the nodes in Cluster B so that each node becomes a trusted client that can connect to C OR Did you mean to turn on kerberos on the master node (eg edge node) where the data persists if you collect() it so its off the cluster on to a single machine and then push it from there so that only that machine has to have kerberos running and is a trusted server to Cluster C? Note: In option 3, I hope I said it correctly, but I believe that you would be collecting the data to a client (edge node) before pushing it out to the secured cluster. Does that make sense? On Oct 14, 2016, at 1:32 PM, Steve Loughran <ste...@hortonworks.com<mailto:ste...@hortonworks.com>> wrote: On 13 Oct 2016, at 10:50, dbolshak <bolshakov.de...@gmail.com<mailto:bolshakov.de...@gmail.com>> wrote: Hello community, We've a challenge and no ideas how to solve it. The problem, Say we have the following environment: 1. `cluster A`, the cluster does not use kerberos and we use it as a source of data, important thing is - we don't manage this cluster. 2. `cluster B`, small cluster where our spark application is running and performing some logic. (we manage this cluster and it does not have kerberos). 3. `cluster C`, the cluster uses kerberos and we use it to keep results of our spark application, we manage this cluster Our requrements and conditions that are not mentioned yet: 1. All clusters are in a single data center, but in the different subnetworks. 2. We cannot turn on kerberos on `cluster A` 3. We cannot turn off kerberos on `cluster C` 4. We can turn on/off kerberos on `cluster B`, currently it's turned off. 5. Spark app is built on top of RDD and does not depend on spark-sql. Does anybody know how to write data using RDD api to remote cluster which is running with Kerberos? If you want to talk to the secure clsuter, C, from code running in cluster B, you'll need to turn kerberos on there. Maybe, maybe, you could just get away with kerberos being turned off, but you, the user, launching the application while logged in to kerberos yourself and so trusted by Cluster C. one of the problems you are likely to hit with Spark here is that it's only going to collect the tokens you need to talk to HDFS at the time you launch the application, and by default, it only knows about the cluster FS. You will need to tell spark about the other filesystem at launch time, so it will know to authenticate with it as you, then collect the tokens needed for the application itself to work with kerberos. spark.yarn.access.namenodes=hdfs://cluster-c:8080 -Steve ps: https://steveloughran.gitbooks.io/kerberos_and_hadoop/content/
