Apache Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive, and collaborative documents with SQL, Scala and more.
As a Multi-purpose Notebook, Apache Zeppelin is the place for interactive:
- Data Ingestion
- Data Discovery
- Data Analytics
- Data Visualization & Collaboration
This is a subordinate charm that requires the apache-spark interface. This
means that you will need to deploy a base Apache Spark cluster to use
Zeppelin. An easy way to deploy the recommended environment is to use the
apache-hadoop-spark-zeppelin
bundle. This will deploy the Apache Hadoop platform with an Apache Spark +
Zeppelin unit that communicates with the cluster by relating to the
apache-hadoop-plugin subordinate charm:
juju-quickstart apache-hadoop-spark-zeppelin
Once deployment is complete, expose Zeppelin:
juju expose zeppelin
You may now access the web interface at
http://{spark_unit_ip_address}:9090. The ip address can be found by running
juju status spark | grep public-address.
The services provide extended status reporting to indicate when they are ready:
juju status --format=tabular
This is particularly useful when combined with watch to track the on-going
progress of the deployment:
watch -n 0.5 juju status --format=tabular
The message for each unit will provide information about that unit's state.
Once they all indicate that they are ready, you can perform a "smoke test"
to verify that Zeppelin is working as expected using the built-in smoke-test
action:
juju action do zeppelin/0 smoke-test
After a few seconds or so, you can check the results of the smoke test:
juju action status
You will see status: completed if the smoke test was successful, or
status: failed if it was not. You can get more information on why it failed
via:
juju action fetch <action-id>
Zeppelin Spark interpreter configuration is set according to environment
variable values at deploy time. If you alter these variables post
deployment (e.g., juju set spark spark_execution_mode=NEW_VALUE), you will
need to edit Zeppelin's Spark interpreter to match the new value. Do this on
the Interpreter tab of the Zeppelin web interface.
-
Affected Spark Interpreter configuration includes:
- master
- spark.executor.memory