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04-04-2017
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08-20-2018
01:18 PM
Right, that makes sense. What I don't understand is why a checkpoint wouldn't immediately be taken on startup, since it is well past the HDFS Maximum Checkpoint Delay.
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08-17-2018
06:25 PM
Hello all, I'm running CloudBreak through some testing, and came across a situation I'm not quite sure how to solve. We're looking to use Cloudbreak to enable us to stop clusters when no activity is expected (i.e. evenings, weekends, etc). I've noticed that after stopping a cluster for an extended period of time, that the "NameNode Last Checkpoint" alert is being thrown. I'm not sure of the expected behavior (Checkpoint on cluster startup?). Any one else is a similar situation? Thanks!
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Apache Hadoop
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Hortonworks Cloudbreak
08-10-2018
02:46 PM
Nice! Is there a ballpark estimate on when 2.8 will be available? Not looking for an exact date or anything, just a sense for when it might be out. Thanks again
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08-10-2018
02:02 PM
Is it possible to change the AMI that will be used when scaling a cluster through Cloudbreak? Here's the longer scenario: - Using a custom AMI that has various hardening changes applied, let's call it AMI-11111 - I build a cluster using this AMI, everything is good. - Over time, we apply various OS patches, primarily to address security vulnerabilities. - If we need to scale the cluster in the future, it will use the original AMI, which will have the vulnerabilities. Instead, would prefer to use an updated AMI (i.e AMI-22222) that already has the security patches applied. Is this possible? Or is there a better way to handle this scenario? Thanks!
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Hortonworks Cloudbreak
04-10-2017
05:44 PM
Thanks Kshitij - will go down this route, as it seems to be a good fit.
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04-06-2017
03:43 PM
1 Kudo
Currently on HDP 2.5, and looking at options for creating groups of users, based on experience, and limiting access to resources based these groups. For example beginner group users may get 10gb/4vcpu, intermediate gets 50gb/8vcpu, expert users get 200gb/16vcpu. The stack we are interested in is: Zeppelin -> Livy -> Spark -> YARN cluster Initially looking at doing this via YARN queues (i.e. user A can only submit jobs to the beginner queue), but from what I can tell, the Zeppelin Spark interpreter config only allows configuration of one queue, so regardless of user, all jobs would be sent to the same queue.
Another option would be to have multiple Zeppelin's, and configure different queues, but from what I can tell, if we have multiple Zeppelins all managed via Ambari, they will all get the same config. Am I off base? Is there a better way to limit resources (primarily memory) based on the user? Thanks!
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Apache Spark
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Apache YARN
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Apache Zeppelin