About bwilson

bwilson · ‎10-06-2016

Specifically, I am trying to remove the spark.yarn.principal and spark.yarn.keytab parameters from a configuration group so my Livy server can run successfully. I've been able to manually copy the configs, remove these parameters, and point Livy at the custom configs and everything works. Simply setting them to be empty strings does not work, they need to be fully removed. I would like Ambari to manage this instead of being something I have to keep in-sync if more spark configs change. Is there a way to do this either via the UI or down in the internals of Ambari?

bwilson · ‎09-01-2016

Hi @Aengus Rooney and @Tamas Bihari, I just ran into the same issue. I tried to deploy with two different unique stack names and both times I got a failure during cbdinit. For giggles I tried again but this time removed the non-alphanumeric characters ('&' and '*') from my password and it succeeded. Seems like there is an implicit character set limit on the password. Would be nice to either remove this limit [preferable] or at least make a note on the form that only alphanumeric characters are acceptable.

bwilson · ‎08-30-2016

Hi @Sami Ahmad, You can install NiFi using the steps documented in the installation guide here.

bwilson · ‎08-24-2016

The title really says it all. Is there a point at which HBase will fall over if I take too many snapshots for an individual table or a point at which I will notice issues restoring/exporting snapshots if I take too many? So, for example, if I take daily snapshots then do I need to start cleaning them up after 30 days, 6 months, a year?

bwilson · ‎08-24-2016

I know we can do an all or nothing approach to locking down the ResourceManager UI but I am looking for a slightly more granular control. I want to allow user X to see the UI and only be able to view their running jobs. Is this possible through existing RM configurations? I have seen a related question but it is actually the opposite of my requirement above.

bwilson · ‎08-23-2016

Hi @Johnny Fugers, What you are talking about sounds like you are trying to build a recommender system. I assume what you are trying to do is understand that users that do X and Y also tend to do Z. From your example, customers that buy diapers and beer also tend to buy milk. Within HDP, you would be looking at Apache Spark and the machine learning capabilities that it offers. There is a clear example of leveraging Collaborative Filtering for this type of problem on the Spark site. You should be able to run this directly on your HDP sandbox or cluster.

bwilson · ‎08-22-2016

Hi @Smart Solutions, I think this would be sufficient to certify that the libraries are installed and your applications will be able to find them. You can find several examples that are ready to run under /usr/hdp/current/spark-client/examples/src/main/python/mllib. You can substitute python with your preferred language to find examples that correspond to the appropriate API. In terms of optimized configurations, it is hard to tune that upfront as it will be highly dependent upon on your application, dataset, and cluster.

bwilson · ‎08-19-2016

I think you should grant this role to the user BEFORE restarting HS2 with the new Authorization enforced. I would revert your Hive configuration back to the original settings. Then execute the GRANT. Then re-enable the SQL authorization via the configs and service restart.

bwilson · ‎08-19-2016

In the link I posted abouve there is a step requiring the admin user to grant admin privileges to itself: GRANT admin TO USER adf_admin;

bwilson · ‎08-19-2016

Hi @John Smith, At first glance, I am wondering if you granted the appropriate admin privilege to the adf_admin user. There are a few more details on setting up SQL standard auth in a SysAdmin guider here. Notice the step where the admin user must grant themselves admin rights.

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