apache hadoop spark notebook #5
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)
Usage
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
exit
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
~/terasort.sh
exit
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
exit
Smoke test Spark
SSH to the Spark unit and run the SparkPi demo as follows:
juju ssh spark/0
~/sparkpi.sh
exit
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