sparkgraph | Spark-Gremlin is an implementation of the Blueprints Graph

by kellrott Scala Version: Current License: No License

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kandi X-RAY | sparkgraph Summary

sparkgraph is a Scala library typically used in Big Data, Spark applications. sparkgraph has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Spark-Gremlin is an implementation of the Blueprints Graph API (and the Gremlin Graph Traversal Language (implmented using Spark (

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Support

sparkgraph has a low active ecosystem.

It has 15 star(s) with 4 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sparkgraph is current.

Quality

sparkgraph has no bugs reported.

Security

sparkgraph has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

sparkgraph does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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sparkgraph releases are not available. You will need to build from source code and install.

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sparkgraph Key Features

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sparkgraph Examples and Code Snippets

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Community Discussions

Trending Discussions on sparkgraph

Spark GraphFrames High Shuffle read/write

QUESTION

Spark GraphFrames High Shuffle read/write

Asked 2020-Jun-21 at 20:51

Hi I have created Graph using vertex and edge files. Size of graph is 600GB. I am querying this graph using motif feature of Spark GraphFrames. I have setup an AWS EMR cluster for querying graph.

cluster details:- 1 master and 8 slaves

Master Node:

...

ANSWER

Answered 2020-Jun-21 at 20:51

The problem with the implementation of Graphframes is that it makes self joins of the internal dataframes as many times as you use on the motifs. That means that you will have more a more shuffle as the length of the chain increases

You can see more details at https://www.waitingforcode.com/apache-spark-graphframes/motifs-finding-graphframes/read

I have also tried a similar approach and have seen that when the length of the chain is greater than 12 Spark starts being not responsive and connections are lost with executors, even if I increased resources.

If you are trying to do that, I would recommend using a graph database instead.

Hope this helps

Source https://stackoverflow.com/questions/62498702

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Vulnerabilities

No vulnerabilities reported

Install sparkgraph

You can download it from GitHub.

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