Spark Standalone 1.3.x cluster
Apache Spark™ is a fast and general purpose engine for large-scale data processing.
The IPython Notebook is an interactive computational environment, in which you
can combine code execution, rich text, mathematics, plots and rich media.
Speed: Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing.
Ease of Use: Write applications quickly in Java, Scala or Python.
Spark offers over 80 high-level operators that make it easy to build parallel apps.
And you can use it interactively from the Scala and Python shells.
General Purpose Engine: Combine SQL, streaming, and complex analytics.
Spark powers a stack of high-level tools including Shark for SQL, MLlib for
machine learning, GraphX, and Spark Streaming. You can combine these frameworks
seamlessly in the same application.
from bundle's home directory: juju quickstart bundles.yaml
Scale Out Usage
In order to increase the amount of spark slaves, you just add units, to add one
unit to spark-slave nodes (current bundle has 4 spark-slave):
juju add-unit -n4 spark-slave
Smoke tests after deployment
# Spark admins use ssh to access spark console from master node 1) juju ssh spark-master/0 <<= ssh to spark master 2) Use spark-submit to run your application: spark-submit --class org.apache.spark.examples.SparkPi /usr/lib/spark/lib/spark-examples*.jar 10 you should get pi = 3.14 or execute demo.sh from /home/ubuntu 3) Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in either Scala or Python. Start it by running the following in the Spark directory: $spark-shell <== for interaction using scala $pyspark <== for interaction using python