graph-notebook

• Added new dataset for DiningByFriends, and associated notebook (Link to PR)
• Added new Neptune ML Sample Application for People Analytics (Link to PR)
• Added graph customization support for SPARQL queries (Link to PR)
• Added graph reset and search refinement buttons to the graph output tab (Link to PR)
• Added support for setting custom edge and node tooltips (Link to PR)
• Added edge tooltips, and options for specifying edge label length (Link to PR)
• Updated NeptuneML pre-trained model resources for CN regions (Link to PR)
• Fixed inaccurate help message being displayed for certain GremlinServerErrors (Link to PR)
• Fixed error causing query autocompletion to fail (Link to PR)
• Fixed Jupyter start script for cases where the nbconfig directory is missing (Link to PR)

• Added support for specifying the Gremlin traversal source (Link to PR)
• Added edge tooltips, and options for specifying edge label length (Link to PR)
• Fixed configuration options missing when using a CN region Neptune host (Link to PR)
• Correct naming of ID parameter for NeptuneML Endpoint command (Link to PR)

• Added full support for NeptuneML API command parameters to %neptune_ml (Link to PR)
• Allow %%neptune_ml to accept JSON blob as parameter input for most phases (Link to PR)
• Added --silent option for suppressing query output (PR #1) (PR #2)
• Added all parserConfiguration options to %load (Link to PR)
• Upgraded to Gremlin-Python 3.5 and Jupyter Notebook 6.x (Link to PR)
• Resolved smart indent bug in openCypher magic cells (Link to PR)
• Removed default /sparql path suffix from non-Neptune SPARQL requests (Link to PR)

• Added a new %stream_viewer magic that allows interactive exploration of the Neptune CDC stream (if enabled). (Link to PR)
• Added support for multi-property values in vertex and edge labels (Link to PR)
• Added new visualization physics options, toggle button (Link to PR)
• Fixed TypeError thrown for certain OC list type results (Link to PR
• Documentation fixes for additional databases (Link to PR)

• Disabled SigV4 signing for non-IAM AWS requests (Link to PR)
• Added new --nopoll option to %load to disable status polling (Link to PR)
• Made Neptune specific parameters optional for %%graph_notebook_config (Link to PR)
• Upgraded Jupyter Notebook dependency to 5.7.13 for security fix (Link to PR)
• Improved usability of %load Edge IDs option (Link to PR)

• Gremlin visualization bugfixes (PR #1) (PR #2) (PR #3)
• Updated the airport data loadable via %seed to the latest version (Link to PR)
• Added support for Gremlin Profile API parameters (Link to PR)
• Improved %seed so that the progress bar is seen to complete (Link to PR)
• Added helper functions to neptune_ml utils to get node embeddings, model predictions and performance metrics (Link to PR)
• Changed visualization behavior to add all group-less nodes to a default group (Link to PR)
• Fixed a bug causing ML Export requests to fail (Link to PR)

• Added new Knowledge Graph use case notebook for openCypher usage (Link to PR)
• Fixed incorrect visualizations of some Gremlin results returned by valueMap (Link to PR)
• Pin RDFLib version in README (Link to PR)
• Fixed inconsistent node tooltips in openCypher visualizations (Link to PR)

openCypher Support:

With the release of support for the openCypher query language in Amazon Neptune's lab mode, graph-notebook can now be used to execute and visualize openCypher queries with any compatible graph database.

Two new magic commands have been added:

• %%oc/%%opencypher
• %%oc_status/%%opencypher_status

These openCypher magic commands inherit the majority of the query and visualization customization features that are already available in the Gremlin and SPARQL magics.

For more detailed information and examples of how you can execute and visualize openCypher queries through graph-notebook, please refer to the new Air-Routes-openCypher and EPL-openCypher sample notebooks.

(Link to PR)

Other Updates:

• Added visualization support for elementMap Gremlin step (Link to PR)
• Added support for additional customization of edge node labels in Gremlin (Link to PR)
• Refactored %load form display code for flexibility; fixes some descriptions being cut off (Link to PR)
• Overhauled Gremlin visualization notebooks with example usage of new customization options and elementMap step (Link to PR)
• Updated Neptune ML notebooks, utils, and pretrained models config (Link to PR)
• Added support for modeltransform commands in %neptune_ml (Link to PR)
• Included index operations metrics in metadata results tab for Gremlin Profile queries(Link to PR)
• Added new notebook to explain Identity Graph data modeling (Link to PR)
• Various bugfixes and documentation updates

• Support for additional customization of graph node labels in Gremlin (Link to PR)

• Support dictionary value access in variable injection (Link to PR)

• Pin gremlinpython to <3.5.* (Link to PR)
• Add support for notebook variables in Sparql/Gremlin magic queries (Link to PR)
• Add support for grouping by different properties per label in Gremlin (Link to PR)
• Fix missing Boto3 dependency in setup.py (Link to PR)
• Update %load execution time to HH:MM:SS format if over a minute (Link to PR)

• Fix bug in %neptune_ml export ... logic where the iam setting for the exporter endpoint wasn't getting picked up properly

• Add support for Mode, queueRequest, and Dependencies parameters when running %load command (Link to PR)
• Add support for list and dict as map keys in Python Gremlin (Link to PR)
• Refactor modules that call to Neptune or other SPARQL/Gremlin endpoints to use a unified client object (Link to PR)
• Added an additional notebook under 02-Visualization demonstrating how to use the visualzation grouping and coloring options in Gremlin. (Link to PR)
• Add metadata output tab for magic queries (Link to PR)

• Add default parameters for get_load_status
• Add ipython as a dependency in setup.py (Link to PT)
• Add parameters in load_status for details, errors, page, and errorsPerPage

• Print execution time when running %load command (Link to PR)

• New datasets and notebooks for Sample applications in Gremlin including:
  • Fraud Graph
  • Knowledge Graph
  • Identity Graph

• Added What’s Next sections to 01-Getting-Started notebooks to point users to next suggested notebook tutorials after finishing one notebook.

• Add missing __init__ to notebook directories to they get installed correctly
• Update list of available magics in notebook documentation

Gremlin Visualization

• Enhanced Gremlin Visualization output to group vertices and color-code them based on groups. When not specified it will group by the label (if it exists). You can also specify the property to groupby using the switch --groupby or -g followed by the property name
• Added the functionality to sort the values in the details box by key
• Updated Air-Routes-Visualization notebook to discuss the group by functionality

NeptuneML

• Add tutorial notebooks for NeptuneML functionality

• Integration with NeptuneML feature set in AWS Neptune
• Add helper library to perform Sigv4 signing for %neptune_ml export ..., we will move our other signing at a later date.
• Swap how credentials are obtained for ROLE iam credentials provider such that it uses a botocore session now instead of calling the ec2 metadata service. This should make the module more usable outside of Sagemaker.
• Add sub-configuration for sparql to allow specifying path to sparql endpoint

New Line magics:

• %neptune_ml export status
• %neptune_ml dataprocessing start
• %neptune_ml dataprocessing status
• %neptune_ml training start
• %neptune_ml training status
• %neptune_ml endpoint create
• %neptune_ml endpoint status

New Cell magics:

• %%neptune_ml export start
• %%neptune_ml dataprocessing start
• %%neptune_ml training start
• %%neptune_ml endpoint create

NOTE: If a cell magic is used, its line inputs for specifying parts of the command will be ignore such as --job-id as a line-param.

Inject variable as cell input:
Currently this will only work for our new cell magic commands details above. You can now specify a variable to use as the cell input received by our neptune_ml magics using the syntax ${var_name}. For example...

# in one notebook cell:
foo = {'foo', 'bar'}

# in another notebook cell:
%%neptune_ml export start

${foo}

NOTE: The above will only work if it is the sole content of the cell body. You cannot inline multiple variables at this time.