isabella232 / layer-apache-zeppelin

The layer to build Apache Zeppelin

Overview

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

Usage

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.

Verify the deployment

Status and Smoke Test

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>

Limitations

Spark Interpreter Settings

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:

  • spark.executor.memory

Contact Information

• [email protected]

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