apache hadoop spark notebook #5

Supports: trusty

Apache Hadoop with Spark and IPython Notebook

The IPython Notebook is an interactive computational environment in which you can combine code execution, rich text, mathematics, plots, and rich media to interact with your data backed by an Apache Hadoop + Spark cluster.

This bundle is a 7 node cluster designed to scale out. Built around Apache Hadoop components, it contains the following units:

  • 1 HDFS Master
  • 1 HDFS Secondary Namenode
  • 1 YARN Master
  • 3 Compute Slaves
  • 1 Spark
  • 1 Plugin (colocated on the Spark unit)
  • 1 Notebook (colocated on the Spark unit)


Deploy this bundle using juju-quickstart:

juju quickstart apache-hadoop-spark-notebook

See juju quickstart --help for deployment options, including machine constraints and how to deploy a locally modified version of the apache-hadoop-spark-notebook bundle.yaml.

Testing the deployment

Smoke test HDFS admin functionality

Once the deployment is complete and the cluster is running, ssh to the HDFS Master unit:

juju ssh hdfs-master/0

As the ubuntu user, create a temporary directory on the Hadoop file system. The steps below verify HDFS functionality:

hdfs dfs -mkdir -p /tmp/hdfs-test
hdfs dfs -chmod -R 777 /tmp/hdfs-test
hdfs dfs -ls /tmp # verify the newly created hdfs-test subdirectory exists
hdfs dfs -rm -R /tmp/hdfs-test
hdfs dfs -ls /tmp # verify the hdfs-test subdirectory has been removed

Smoke test YARN and MapReduce

Run the terasort.sh script from the Spark unit to generate and sort data. The steps below verify that Spark is communicating with the cluster via the plugin and that YARN and MapReduce are working as expected:

juju ssh spark/0

Smoke test HDFS functionality from user space

From the Spark unit, delete the MapReduce output previously generated by the terasort.sh script:

juju ssh spark/0
hdfs dfs -rm -R /user/ubuntu/tera_demo_out

Smoke test Spark

SSH to the Spark unit and run the SparkPi demo as follows:

juju ssh spark/0

Access the IPython Notebook web interface

Access the notebook web interface at http://{spark_unit_ip_address}:8880. The ip address can be found by running juju status spark/0 | grep public-address.

Scale Out Usage

This bundle was designed to scale out. To increase the amount of Compute Slaves, you can add units to the compute-slave service. To add one unit:

juju add-unit compute-slave

Or you can add multiple units at once:

juju add-unit -n4 compute-slave

Contact Information


Bundle configuration