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https://issues.apache.org/jira/browse/TINKERPOP-2294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16934651#comment-16934651
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Wolfgang Fahl commented on TINKERPOP-2294:
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IMHO there is a need to get the following issues solved by a companion API to 
GLV - which then would not only be available to gremlin-python but also to 
other languages:
 * selecting a provider
 * selecting a graph e.g. by name/alias
 * listing available graphs
 * authenticating
 * getting meta-information
 * checking the availability of a server
 * ...

In traditional database APIs like JDBC these needs are all handled by some 
means or another. For GLVs this is IMHO much harder than necessary. E.g. if you 
want to use data from two or three graph databases at the same time it get's 
very awkward. I have many usecase where i need adhoc in-memory graphdatabases 
and only the computational results should be stored in another graph database 
that is backed by some provider. Should there be a ticket for each of the 
improvement wishes - are any of these things already addressed somewhere? 
Should the discussion first happen in a forum before this ticket is refined? I 
don't know what the proper procedure is to get to an improved version fo 
TINKERPOP ...

> Gremlin-Python usability and tutorial
> -------------------------------------
>
>                 Key: TINKERPOP-2294
>                 URL: https://issues.apache.org/jira/browse/TINKERPOP-2294
>             Project: TinkerPop
>          Issue Type: Improvement
>          Components: python
>    Affects Versions: 3.4.3
>            Reporter: Wolfgang Fahl
>            Priority: Major
>
> The Gremlin-Python environment is not useable "out of the box" at this time 
> and hard to configure. It needs a specially configured gremlin-server for 
> each use case.
> The handling of the server configuration and startup could be simplified if 
> it would be done by some kind of API instead of the YAML file. Especially it 
> is important to make graph io available to be able to create python specific 
> tutorials that are based on typical databases.  The handling of typical 
> backend database configurations should be simplified.
>  
>  



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