255b56f9f5
Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors.
Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result.
That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.
Based on discussion in the community, we have implemented two different APIs for relative tolerance, and absolute tolerance. It makes sense that test writers should know which one they need depending on their circumstances.
Developers also need to explicitly specify the eps, and there is no default value which will sometimes cause confusion.
When comparing against zero using relative tolerance, a exception will be raised to warn users that it's meaningless.
For relative tolerance, users can now write
assert(23.1 ~== 23.52 relTol 0.02)
assert(23.1 ~== 22.74 relTol 0.02)
assert(23.1 ~= 23.52 relTol 0.02)
assert(23.1 ~= 22.74 relTol 0.02)
assert(!(23.1 !~= 23.52 relTol 0.02))
assert(!(23.1 !~= 22.74 relTol 0.02))
// This will throw exception with the following message.
// "Did not expect 23.1 and 23.52 to be within 0.02 using relative tolerance."
assert(23.1 !~== 23.52 relTol 0.02)
// "Expected 23.1 and 22.34 to be within 0.02 using relative tolerance."
assert(23.1 ~== 22.34 relTol 0.02)
For absolute error,
assert(17.8 ~== 17.99 absTol 0.2)
assert(17.8 ~== 17.61 absTol 0.2)
assert(17.8 ~= 17.99 absTol 0.2)
assert(17.8 ~= 17.61 absTol 0.2)
assert(!(17.8 !~= 17.99 absTol 0.2))
assert(!(17.8 !~= 17.61 absTol 0.2))
// This will throw exception with the following message.
// "Did not expect 17.8 and 17.99 to be within 0.2 using absolute error."
assert(17.8 !~== 17.99 absTol 0.2)
// "Expected 17.8 and 17.59 to be within 0.2 using absolute error."
assert(17.8 ~== 17.59 absTol 0.2)
Authors:
DB Tsai <dbtsaialpinenow.com>
Marek Kolodziej <marekalpinenow.com>
Author: DB Tsai <dbtsai@alpinenow.com>
Closes #1425 from dbtsai/SPARK-2479_comparing_floating_point and squashes the following commits:
8c7cbcc [DB Tsai] Alpine Data Labs
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| bagel | ||
| bin | ||
| conf | ||
| core | ||
| data/mllib | ||
| dev | ||
| docker | ||
| docs | ||
| ec2 | ||
| examples | ||
| external | ||
| extras | ||
| graphx | ||
| mllib | ||
| project | ||
| python | ||
| repl | ||
| sbin | ||
| sbt | ||
| sql | ||
| streaming | ||
| tools | ||
| yarn | ||
| .gitignore | ||
| .rat-excludes | ||
| .travis.yml | ||
| LICENSE | ||
| make-distribution.sh | ||
| NOTICE | ||
| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
| tox.ini | ||
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLLib for machine learning, GraphX for graph processing, and Spark Streaming.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.apache.org/documentation.html. This README file only contains basic setup instructions.
Building Spark
Spark is built on Scala 2.10. To build Spark and its example programs, run:
./sbt/sbt assembly
(You do not need to do this if you downloaded a pre-built package.)
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./sbt/sbt test
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
storage systems. Because the protocols have changed in different versions of
Hadoop, you must build Spark against the same version that your cluster runs.
You can change the version by setting -Dhadoop.version when building Spark.
For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1
$ sbt/sbt -Dhadoop.version=1.2.1 assembly
# Cloudera CDH 4.2.0 with MapReduce v1
$ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly
For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
with YARN, also set -Pyarn:
# Apache Hadoop 2.0.5-alpha
$ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly
# Cloudera CDH 4.2.0 with MapReduce v2
$ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly
# Apache Hadoop 2.2.X and newer
$ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly
When developing a Spark application, specify the Hadoop version by adding the
"hadoop-client" artifact to your project's dependencies. For example, if you're
using Hadoop 1.2.1 and build your application using SBT, add this entry to
libraryDependencies:
"org.apache.hadoop" % "hadoop-client" % "1.2.1"
If your project is built with Maven, add this to your POM file's <dependencies> section:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>1.2.1</version>
</dependency>
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Contributing to Spark
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so.