These are the instructions for installing and using the DataStax Graph Labs preview using Docker images.
For the downloadable tarball version of the Graph preview, refer to the DataStax Labs website and download DataStax Graph (DSG). Then follow the instructions with the included README.md from the download.
The new, experimental graph engine included in this Labs package allows for users to work seamlessly across Cassandra and Graph APIs while accessing the data stored in Cassandra. This is possible because the experimental graph engine has been embedded into Cassandra's data model metadata. DataStax is calling this novel approach to graph data management, DataStax's OneModel.
Because this is an experimental preview of a new graph engine in DataStax Graph, a subset of documentation is available that describes the new concepts included in DataStax Graph.
Please review the graph-docs directory file for an overview of the features, behaviors, and functionality of the experimental and new graph engine.
In addition to the documentation included in the ./graph-docs directory, DataStax is providing a set of Getting Started Studio Notebooks to help users understand the key concepts of the experimental graph engine build sample applications quickly.
Three different Getting Started notebooks are provided in this package:
- DataStax Graph Gremlin - start here for a pure Gremlin experience
- DataStax Graph CQL as Graph - start here to use CQL to create a new graph.
- DataStax Graph CQL Conversion to Graph - start here to see how to convert an existing CQL keyspace into a graph.
The Studio sample notebooks are already embedded in the Studio Docker image and will be visible once the container is running.
The DataStax Graph engine available in DSE 6.7 and below is now referred to as DataStax's Classic graph engine. If you are interested in learning how to migrate from an existing graph built using the Classic graph engine into a graph that's compatible with the new, experimental graph engine included in DataStax Labs, please contact a DataStax Services professional and a member of DataStax's Graph Practice will be in touch to discuss how DataStax can help.
A simple migration example is available for review in this repo. Please review the contents under .graph-migration.
To download and run the DataStax Enterprise Labs Docker image:
docker run -e DS_LICENSE=accept --name my-dse -d datastax/labs-dse-graph-experimental -s -k -g
This will start DataStax Enterprise Server with Graph, Search and Analytics running (the recommended combination).
After a few minutes, DSE should be running. You can confirm this with a nodetool status command:
docker exec -it my-dse dsetool status
In the status message you should see confirmation that the single node is running and that Graph mode is enabled:
DC: dc1 Workload: SearchAnalytics Graph: yes Analytics Master: 172.17.0.2
====================================================================================
Status=Up/Down
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Effective-Ownership Token Rack Health [0,1]
UN 172.17.0.2 124.07 KiB 100.00% -2702044001711757463 rack1 0.20
Refer to datastax/dse-server for more details about running DataStax Enterprise Server in a Docker container.
To download and run the DataStax Studio Labs Docker image:
docker run -e DS_LICENSE=accept --link my-dse --name my-studio -p 9091:9091 -d datastax/labs-dse-graph-studio-experimental
This will start DataStax Studio and connect it with the running DataStax Enterprise Server Docker container.
Once Studio has started it should be viewable in a browser at: http://DOCKER_HOST_IP:9091
Refer to datastax/dse-studio for more details about running DataStax Studio in a Docker container.
To write your own applications which work with the DataStax Graph Labs
preview, you'll need special Labs versions of the Java and Python
drivers. Visit the DataStax Labs website and download DataStax
Graph (DSG). Then expand the graph-labs.zip file and continue with
the appropriate section below.
To use the Java driver with your application, follow these instructions.
Find the dse-java-driver.tar.gz file and expand it. You should see
the following contents under the newly-created dse-java-driver
folder:
# Core DSE driver
dse-java-driver-core-1.8.1.20190510-LABS.jar
dse-java-driver-core-1.8.1.20190510-LABS-shaded.jar # shaded deps
dse-java-driver-core-1.8.1.20190510-LABS.pom
# Graph fluent API
dse-java-driver-graph-1.8.1.20190510-LABS.jar
dse-java-driver-graph-1.8.1.20190510-LABS.pom
# Object mapper
dse-java-driver-mapping-1.8.1.20190510-LABS.jar
dse-java-driver-mapping-1.8.1.20190510-LABS.pom
You can then reference the above jars directly by placing them in your application's classpath.
Alternatively, you can install the above jars and poms in your local Maven repository, so that they can be declared as regular dependencies in your Maven or Gradle build. Refer to Maven's Guide to installing 3rd party JARs for further information.
To use the Python driver with your application, follow these instructions.
Find the dse-java-driver.tar.gz file and expand it. You should see
dse-driver*.tar.gz anddse-graph*.tar.gz files. These do not need
to be expanded.
A virtual environment is recommended, especially so that these Labs drivers don't mix or conflict with any released drivers on the same system. For example to use pyenv with the virtualenv plugin you would follow these steps to create a virtual environment:
pyenv virtualenv 2.7.15 labs
pyenv activate labs
Next install the main Python driver and the DataStax Graph enhancements driver:
pip install --upgrade pip
pip install dse-driver-2.8.1.20190509+labs.tar.gz
pip install dse-graph-1.6.0.20190509+labs.tar.gz
If the install completes without any errors, you can confirm that both drivers are loadable:
python -c 'import dse_graph; print dse_graph.__version__'
python -c 'import dse; print dse.__version__'
We want to hear your feedback! Go to the Labs section of the new DataStax Community forums.
You can also reach out on the Labs forums for any help needed with these installation steps.