Using the Northwind demo graph with Spark OLAP jobs 

Run OLAP queries against the Northwind demo graph data.

The Northwind demo included with the DSE demos has a script for creating a graph of the data for a fictional trading company.

In this task, you'll use the Gremlin console to create the Northwind graph, snapshot part of the graph, and run a count operation on the subgraph using the SparkGraphComputer.



  1. Start the Gremlin console using the dse gremlin-console command:
    $ dse gremlin-console
  2. Set the schema mode to Development.

    To allow modifying the schema for the connected graph database, you must set the mode to Development each session. The default schema mode for DSE Graph is Production, which doesn't allow you to modify the graph's schema.

    gremlin> schema.config().option('graph.schema_mode').set('Development')
  3. Create the Northwind graph:
    gremlin> system.graph('northwind').create()
  4. Alias the traversal to Northwind graph using the default OLTP traversal source:
    gremlin> :remote config alias g northwind.g
  5. Load the Northwind demo script using the :load command:
    gremlin> :load /usr/share/dse/demos/graph/northwind/northwind.groovy
  6. Run the script:
    gremlin> loadNorthwind(graph)
  7. Look at the schema of the northwind graph:
    gremlin> schema.describe()
  8. Alias the traversal to the Northwind analytics OLAP traversal source a. Alias g to the OLAP traversal source for one-off analytic queries:
    gremlin> :remote config alias g northwind.a
  9. Count the number of vertices using the OLAP traversal source:
    gremlin> g.V().count()

    When you alias g to the OLAP traversal source database name.a, DSE Analytics is the workload back-end.

  10. Store subgraphs into snapshots using graph.snapshot().
    When you need to run multiple OLAP queries on a graph in one session, use snapshots of the graph as the traversal source.
    gremlin> employees = graph.snapshot().vertices('employee').create()
    ==>graphtraversalsource[hadoopgraph[persistedinputrdd->persistedoutputrdd], sparkgraphcomputer]
    gremlin> categories = graph.snapshot().vertices('category').create()
    ==>graphtraversalsource[hadoopgraph[persistedinputrdd->persistedoutputrdd], sparkgraphcomputer]

    The snapshot() method returns an OLAP traversal source using the SparkGraphComputer.

  11. Run an operation on the snapshot graphs.

    Count the number of employee vertices in the snapshot graph:

    gremlin> employees.V().count()
    ==> 9

    Count the number of category vertices in the snapshot graph:

    gremlin> categories.V().count()
    ==> 8