Spark Performance Tests

This is a performance testing framework for Apache Spark 1.0+.

Features

• Suites of performance tests for Spark, PySpark, Spark Streaming, and MLlib.
• Parameterized test configurations:
  • Sweeps sets of parameters to test against multiple Spark and test configurations.
• Automatically downloads and builds Spark:
  • Maintains a cache of successful builds to enable rapid testing against multiple Spark versions.
• [...]

For questions, bug reports, or feature requests, please open an issue on GitHub.

Coverage

• Spark Core RDD
  • list coming soon
• SQL and DataFrames
  • coming soon
• Machine Learning
  • glm-regression: Generalized Linear Regression Model
  • glm-classification: Generalized Linear Classification Model
  • naive-bayes: Naive Bayes
  • naive-bayes-bernoulli: Bernoulli Naive Bayes
  • decision-tree: Decision Tree
  • als: Alternating Least Squares
  • kmeans: K-Means clustering
  • gmm: Gaussian Mixture Model
  • svd: Singular Value Decomposition
  • pca: Principal Component Analysis
  • summary-statistics: Summary Statistics (min, max, ...)
  • block-matrix-mult: Matrix Multiplication
  • pearson: Pearson's Correlation
  • spearman: Spearman's Correlation
  • chi-sq-feature/gof/mat: Chi-square Tests
  • word2vec: Word2Vec distributed presentation of words
  • fp-growth: FP-growth frequent item sets
  • python-glm-classification: Generalized Linear Classification Model
  • python-glm-regression: Generalized Linear Regression Model
  • python-naive-bayes: Naive Bayes
  • python-als: Alternating Least Squares
  • python-kmeans: K-Means clustering
  • python-pearson: Pearson's Correlation
  • python-spearman: Spearman's Correlation

Dependencies

The spark-perf scripts require Python 2.7+. If you're using an earlier version of Python, you may need to install the argparse library using easy_install argparse.

Support for automatically building Spark requires Maven. On spark-ec2 clusters, this can be installed using the ./bin/spark-ec2/install-maven script from this project.

Configuration

To configure spark-perf, copy config/config.py.template to config/config.py and edit that file. See config.py.template for detailed configuration instructions. After editing config.py, execute ./bin/run to run performance tests. You can pass the --config option to use a custom configuration file.

The following sections describe some additional settings to change for certain test environments:

Running locally

1. Set up local SSH server/keys such that ssh localhost works on your machine without a password.

2. 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
   

3. Uncomment at least one SPARK_TESTS entry.

Running on an existing Spark cluster

1. SSH into the machine hosting the standalone master

2. Set config.py options:

   SPARK_HOME_DIR = /path/to/your/spark/install
   SPARK_CLUSTER_URL = "spark://<your-master-hostname>:7077"
   SCALE_FACTOR = <depends on your hardware>
   SPARK_DRIVER_MEMORY = <depends on your hardware>
   spark.executor.memory = <depends on your hardware>
   

3. Uncomment at least one SPARK_TESTS entry.

Running on a spark-ec2 cluster with a custom Spark version

1. Launch an EC2 cluster with Spark's EC2 scripts.

2. Set config.py options:

   USE_CLUSTER_SPARK = False
   SPARK_COMMIT_ID = <what you want test>
   SCALE_FACTOR = <depends on your hardware>
   SPARK_DRIVER_MEMORY = <depends on your hardware>
   spark.executor.memory = <depends on your hardware>
   

3. Uncomment at least one SPARK_TESTS entry.

License

This project is licensed under the Apache 2.0 License. See LICENSE for full license text.