This is a framework for repeatedly running a suite of performance tests for the Spark cluster computing framework.

The script assumes you already have a binary distribution of Spark 1.0+ installed. It can optionally checkout a new version of Spark and copy configurations over from your existing installation.

1. Download a spark 1.0+ binary distribution.
2. Set up local SSH server/keys such that ssh localhost works on your machine without a password.
3. Git clone spark-perf (this repo) and cd spark-perf
4. Copy config/config.py.template to config/config.py
5. Set config.py options that are friendly for local execution:

• SPARK_HOME_DIR = /path/to/your/spark
• SPARK_CLUSTER_URL = "spark://%s:7077" % socket.gethostname()
• SCALE_FACTOR = .05
• SPARK_DRIVER_MEMORY = 512m
• spark.executor.memory = 2g
• uncomment at least one SPARK_TESTS entry

6. Execute bin/run

1. SSH into the machine hosting the standalone master
2. Git clone spark-perf (this repo) and cd spark-perf
3. Copy config/config.py.template to config/config.py
4. Set config.py options:

• SPARK_HOME_DIR = /path/to/your/spark/install
• SPARK_CLUSTER_URL = "spark://:7077"
• SCALE_FACTOR =
• SPARK_DRIVER_MEMORY =
• spark.executor.memory =
• uncomment at least one SPARK_TESTS entry

5. Execute bin/run

1. Launch an EC2 cluster with spark-ec2 scripts.
2. Git clone spark-perf (this repo) and cd spark-perf
3. Copy config/config.py.template to config/config.py
4. Set config.py options:

• USE_CLUSTER_SPARK = False
• SPARK_COMMIT_ID =
• SCALE_FACTOR =
• SPARK_DRIVER_MEMORY =
• spark.executor.memory =
• uncomment at least one SPARK_TESTS entry

5. Execute bin/run

The script requires Python 2.7. For earlier versions of Python, argparse might need to be installed, which can be done using easy_install argparse.

Questions or comments, contact @pwendell or @andyk.

This testing framework started as a port + heavy modifiation of a predecessor Spark performance testing framework written by Denny Britz called spark-perf.