question

Hi @baid_manish_187433,

DseGraphFrames are an extension of Spark DataFrames. They are similar to Spark GraphFrames with some additional methods to support Tinkerpop graph traversals.

A DseGraphFrame represents a Graph as two virtual tables: a Vertex DataFrame and an Edge DataFrame (just as you can consider a graph as a set of two sets, one for all vertices, one for all edges).

The principles of Spark apply to reading DseGraphFrames.

An important aspect with Spark is that setting the DseGraphFrame (or DataFrame etc) does not mean that anything is read. Spark does not read anything until certain actions are called, like count or show. Once one of these actions is called, Spark loads data in chunks, more accurately known as partitions.

Spark also divides a job into transformation stages, and applies a stage of the job only to the currently loaded partition. Once this is done, Spark keeps the output and then loads more partitions, and reapplies the stage transformation, until all the partitions have been read and processed, and then on to the next stage, and so on.

So, via the DseGraphFrames, you get access to the entire graph database, but it is not all read at that time.

Spark is an extensive topic, and if you want to understand more about the Spark internals, let me refer you to the official Spark documentation.

https://spark.apache.org/docs/latest/quick-start.html

However, you can get started with DseGraphFrames without the detailed Spark knowledge. This is a good page for first experiments.

https://docs.datastax.com/en/dse/6.8/dse-dev/datastax_enterprise/graph/graphAnalytics/dseGraphFrameOverview.html

And this is still a good blog about DseGraphFrames:

https://www.datastax.com/blog/2017/05/introducing-dse-graph-frames#Dse_Graph_Frame

And yes, via traversals on these graphframes you can then construct vertex and edge tables of subgraphs.

I hope this answers your question.