Graphframes package for Spark provides the capabilities of both the GraphX and the Spark DataFrames.
The recent Apache Spark 3.0 release has several enhancements with respect to the features enablement and performance, especially, for the Spark DataFrames.
Since the GraphFrames are based on Spark DataFrames, there is a direct advantage in using the GraphFrames compared to GraphX, as all the enhancements to DataFrames apply to GraphFrames.
This requirement is for the biggest IBM's bank client in India where the data size is humongous and hence utilizing GraphFrames would definitely help to better the performance.
This extended functionality includes motif finding, DataFrame-based serialization, and highly expressive graph queries.
Currently, in CPD, it is not possible to customize the Spark for Scala/Python environment, and also it is difficult to add a custom library.
|Who would benefit from this IDEA?||Data Scientist will be able to utilize GraphFrames package in building graph applications|
How should it work?
Add GraphFrames package/jar along with the jars shipped with the respective spark version.
Currently, in CPD, it is not possible to customize the Spark for Scala/Python environment and also it is difficult to add a custom library.
|Priority Justification||This requirement is for the biggest IBM's bank client in India where the data size is humongous and hence utilizing GraphFrames would definitely help to better the performance.|
|Client Name||State Bank of India|
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