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Morpheus build in offline environment. #941

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horndog opened this issue Nov 13, 2019 · 3 comments
Open

Morpheus build in offline environment. #941

horndog opened this issue Nov 13, 2019 · 3 comments
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@horndog
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horndog commented Nov 13, 2019

Hi,

Not sure if correct place to ask so feel free to move.

Is there any material on installing morpheus in an offline environment? I can build and install at home locally but currently tasked with setting up a POC in enterprise environment with locked down machines to internet.

Many Thanks

@Mats-SX
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Mats-SX commented Nov 15, 2019

Hello @horndog and thanks for reaching out!

Are you thinking about a use case for deploying updates? If not, and you just want to start with the current version and go from there, then I would advise that you just bring the all JAR (download from here) on a USB stick into the offline environment and put on the classpath of the Spark instance. Would that work?

Regards
Mats

@horndog
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horndog commented Nov 25, 2019

Hi Matt,

At the moment it is very much just POC. I suppose when it is rolled up into Spark 3.0 it will be easier to manage.

I was able to pull the jar in but am I right in that it doesn't contain any of the morpheus-examples classes. Is that jar available prepackaged also?

Also when trying to compile in in windows I have run into issues with neo4j test instances not starting. I have installed it locally on windows but no luck. Seems to be fine in a Linux enviornment at home that has access to internet.

FAILURE: Build failed with an exception.

  • What went wrong:
    Execution failed for task ':okapi-neo4j-io-testing:neo4jStart'.

java.lang.NoSuchFieldException: pid

Thanks,
David

@Mats-SX
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Mats-SX commented Dec 19, 2019

Building locally on Windows is not really something we've invested much time in, I'm afraid.

No, the examples only exist in their form in this repository. You should be able to copy-paste almost all of the code from them to run an equivalent process as custom Scala code in your Spark shell or notebook.

The examples are not designed so much to add value as executables, but to serve mainly two purposes:

  • act as acceptance tests for intended workflows
  • act as documentation for how to use the library

So hopefully you can study the code in the examples and try to replicate it yourself in your Scala app or notebook to do the graph processing in the Spark cluster.

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