If a shared cluster is being used by development team in Databricks, how is isolation done to ensure that one developer does not impact another developer's work by installing a particular package. Does CONDA help in isolation?
When you're working on Databricks you have multiple levels of libraries (doc):
Cluster level libraries that are installed using cluster UI or REST API - these libraries are shared by all users of the cluster
For Python & R there is support for notebook-level libraries. For Python, libraries installed with %pip install are installed to a virtual environment specific to a given notebook. So different users can install different libraries or different versions of the same library on the same cluster without breaking other's work.
Related
I have a library installed as a cluster library, so that it's available for all notebooks and everyone working on the cluster. However, for a specific use case (notebook), I need to uninstall that library, since it conflicts with another one.
Is there any way to do this locally, avoiding uninstalling it for the whole cluster?
What is the best practice in automatically installing dev cluster libraries on a running production cluster in Azure databricks?
I found a certain solution of notebook-scoped libraries but I want to retain the libraries on the cluster and not have to update the notebooks libraries each time.
I have to setup a small size Hadoop cluster on Linux (Ubuntu) machines. For that I have to install JDK, python and some other linux utilities on all systems. After that I have to configure Hadoop for each system one by one. Is there any tool available so that I can install all these tools from a single system. For example if I have to install jdk on some system, that tool should install to that. I prefer the tool is web based .
Apache Ambari or Cloudera Manager are purposely built to accomplish these tasks for Hadoop
They also monitor the cluster, and provision extra services that communicate with it like Kafka, Hbase, Spark, etc
That only gets you so far, though, and you'll want to have something like Ansible to deploy custom configurations (AWX is a web UI for Ansible). Puppet & Chef are alternatives too
I have started a course on Hadoop on Udemy. Now here the instructor is using windows OS and installs a virtual box and then runs a Horton Sandbox image for using Hadoop.
I am using LinuxMint and after doing some research on install hadoop on Linux I found(click for ref) out that we can install the VM on linux and download the Horton Sandbox image run it.
I also found another method which does not uses the VM (click for ref). I am confused as to which is the best way for install hadoop.
Should I use the VM or the second method. Which is better for learning and development?
Thanks a lot for help!
can install the VM on linux
You can use a VM on any host OS... That's the point of a VM.
The last link is only Hadoop, where Hortonworks has much, much more like Spark, Hive, Hbase, Pig, etc. Things you'd need to additionally install and configure yourself otherwise
Which is better for learning and development?
I would strongly suggest using a VM (or containers) overall
1) rather than messing up your local OS trying to get Hadoop working
2) The Hortonworks documentation has lots of tutorials that can really only be ran in the sandbox with the pre installed datasets
I have created an Azure HDInsight cluster using PowerShell. Now I need to install some custom software on the worker nodes that is required for the mappers I will be running using Hadoop streaming. I haven't found any PowerShell command that could help me with this task. I can prepare a custom job that will setup all the workers, but I'm not convinced that this is the best solution. Are there better options?
edit:
With AWS Elastic MapReduce there is an option to install additional software in a bootstrap action that is defined when you create a cluster. I was looking for something similar.
You can use a bootstrap action to install additional software and to change the configuration of applications on the cluster. Bootstrap actions are scripts that are run on the cluster nodes when Amazon EMR launches the cluster. They run before Hadoop starts and before the node begins processing data.
from: Create Bootstrap Actions to Install Additional Software
The short answer is that you don't. It's not ideal from a caching perspective, but you ought to be able to bundle all your job dependencies into the map reduce jar which is distributed across the cluster for you by YARN (part of Hadoop). This is broadly speaking transparent to the end user, as it's all handled through the job submission process.
If you need something large which is a shared dependency across many jobs, and you don't want it copied out every time, you can keep it on wasb:// storage, and reference that in a class path, but that might cause you complexity if you are for instance using the .NET Streaming API.
I've just heard from a collage that I need to update my Azure PS because recently a new Cmdlet Add-AzureHDInsightScriptAction was added and it does just that.
Customize HDInsight clusters using Script Action