f825847c82
Whew, lots of work to track down again all the license requirements, but this ought to be a pretty good pass. Below, find a writeup on how I approached it for future reference. - LICENSE and NOTICE and licenses/ now reflect the *source* release - LICENSE-binary and NOTICE-binary and licenses-binary now reflect the binary release - Recreated all the license info from scratch - Added notes about how this was constructed for next time - License-oriented info was moved from NOTICE to LICENSE, esp. for Cat B deps - Some seemingly superfluous or stale license info was removed, especially for test-scope deps - Updated release script to put binary-oriented versions in binary releases ---- # Principles ASF projects distribute source and binary code under the Apache License 2.0. However these project distributions frequently include copies of source or binary code from third parties, under possibly other license terms. This triggers conditions of those licenses, which essentially amount to including license information in a LICENSE and/or NOTICE file, and including copies of license texts (here, in a directory called `license/`). See http://www.apache.org/dev/licensing-howto.html and https://www.apache.org/legal/resolved.html#required-third-party-notices # In Spark Spark produces source releases, and also binary releases of that code. Spark source code may contain source from third parties, possibly modified. This is true in Scala, Java, Python and R, and in the UI's JavaScript and CSS files. These must be handled appropriately per above in a LICENSE and NOTICE file created for the source release. Separately, the binary releases may contain binary code from third parties. This is very much true for Scala and Java, as Spark produces an 'assembly' binary release which includes all transitive binary dependencies of this part of Spark. With perhaps the exception of py4j, this doesn't occur in the same way for Python or R because of the way these ecosystems work. (Note that the JS and CSS for the UI will be in both 'source' and 'binary' releases.) These must also be handled in a separate LICENSE and NOTICE file for the binary release. # Binary Release License ## Transitive Maven Dependencies We'll first tackle the binary release, and that almost entirely means assessing the transitive dependencies of the Scala/Java backbone of Spark. Run `project-info-reports:dependencies` with essentially all profiles: a set that would bring in all different possible transitive dependencies. However, don't activate any of the '-lgpl' profiles as these would bring in LGPL-licensed dependencies that are explicitly excluded from Spark binary releases. ``` mvn -Phadoop-2.7 -Pyarn -Phive -Pmesos -Pkubernetes -Pflume -Pkinesis-asl -Pdocker-integration-tests -Phive-thriftserver -Pkafka-0-8 -Ddependency.locations.enabled=false project-info-reports:dependencies ``` Open `assembly/target/site/dependencies.html`. Find "Project Transitive Dependencies", and find "compile" and "runtime" (if exists). This is a list of all the dependencies that Spark is going to ship in its binary "assembly" distro and therefore whose licenses need to be appropriately considered in LICENSE and NOTICE. Copy this table into a spreadsheet for easy management. Next job is to fill in some blanks, as a few projects will not have clearly declared their licenses in a POM. Sort by license. This is a good time to verify all the dependencies are at least Cat A/B licenses, and not Cat X! http://www.apache.org/legal/resolved.html ### Apache License 2 The Apache License 2 variants are typically easiest to deal with as they will not require you to modify LICENSE, nor add to license/. It's still good form to list the ALv2 dependencies in LICENSE for completeness, but optional. They may require you to propagate bits from NOTICE. It's tedious to track down all the NOTICE files and evaluate what if anything needs to be copied to NOTICE. Fortunately, this can be made easier as the assembly module can be temporarily modified to produce a NOTICE file that concatenates all NOTICE files bundled with transitive dependencies. First change the packaging of `assembly/spark-assembly_2.11/pom.xml` to `<packaging>jar</packaging>`. Next add this stanza somewhere in the body of the same POM file: ``` <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> <configuration> <shadedArtifactAttached>false</shadedArtifactAttached> <artifactSet> <includes> <include>*:*</include> </includes> </artifactSet> </configuration> <executions> <execution> <phase>package</phase> <goals> <goal>shade</goal> </goals> <configuration> <transformers> <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/> </transformers> </configuration> </execution> </executions> </plugin> ``` Finally execute `mvn ... package` with all of the same `-P` profile flags as above. In the JAR file at `assembly/target/spark-assembly_2.11....jar` you'll find a file `META-INF/NOTICE` that concatenates all NOTICE files bundled with transitive dependencies. This should be the starting point for the binary release's NOTICE file. Some elements in the file are from Spark itself, like: ``` Spark Project Assembly Copyright 2018 The Apache Software Foundation Spark Project Core Copyright 2018 The Apache Software Foundation ``` These can be removed. Remove elements of the combined NOTICE file that aren't relevant to Spark. It's actually rare that we are sure that some element is completely irrelevant to Spark, because each transitive dependency includes all its transitive dependencies. So there may be nothing that can be done here. Of course, some projects may not publish NOTICE in their Maven artifacts. Ideally, search for the NOTICE file of projects that don't seem to have produced any text in NOTICE, but, there is some argument that projects that don't produce a NOTICE in their Maven artifacts don't entail an obligation on projects that depend solely on their Maven artifacts. ### Other Licenses Next are "Cat A" permissively licensed (BSD 2-Clause, BSD 3-Clause, MIT) components. List the components grouped by their license type in LICENSE. Then add the text of the license to licenses/. For example if you list "foo bar" as a BSD-licensed dependency, add its license text as licenses/LICENSE-foo-bar.txt. Public domain and similar works are treated like permissively licensed dependencies. And the same goes for all Cat B licenses too, like CDDL. However these additional require at least a URL pointer to the project's page. Use the artifact hyperlink in your spreadsheet if possible; if non-existent or doesn't resolve, do your best to determine a URL for the project's source. ### Shaded third-party dependencies Some third party dependencies actually copy in other dependencies rather than depend on them as Maven artifacts. This means they don't show up in the process above. These can be quite hard to track down, but are rare. A key example is reflectasm, embedded in kryo. ### Examples module The above _almost_ considers everything bundled in a Spark binary release. The main assembly won't include examples. The same must be done for dependencies marked as 'compile' for the examples module. See `examples/target/site/dependencies.html`. At the time of this writing however this just adds one dependency: `scopt`. ### provided scope Above we considered just compile and runtime scope dependencies, which makes sense as they are the ones that are packaged. However, for complicated reasons (shading), a few components that Spark does bundle are not marked as compile dependencies in the assembly. Therefore it's also necessary to consider 'provided' dependencies from `assembly/target/site/dependencies.html` actually! Right now that's just Jetty and JPMML artifacts. ## Python, R Don't forget that Py4J is also distributed in the binary release, actually. There should be no other R, Python code in the binary release. That's it. ## Sense checking Compare the contents of `jars/`, `examples/jars/` and `python/lib` from a recent binary release to see if anything appears there that doesn't seem to have been covered above. These additional components will have to be handled manually, but should be few or none of this type. # Source Release License While there are relatively fewer third-party source artifacts included as source code, there is no automated way to detect it, really. It requires some degree of manual auditing. Most third party source comes from included JS and CSS files. At the time of this writing, some places to look or consider: `build/sbt-launch-lib.bash`, `python/lib`, third party source in `python/pyspark` like `heapq3.py`, `docs/js/vendor`, and `core/src/main/resources/org/apache/spark/ui/static`. The principles are the same as above. Remember some JS files copy in other JS files! Look out for Modernizr. # One More Thing: JS and CSS in Binary Release Now that you've got a handle on source licenses, recall that all the JS and CSS source code will *also* be part of the binary release. Copy that info from source to binary license files accordingly. Author: Sean Owen <srowen@gmail.com> Closes #21640 from srowen/SPARK-24654. |
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, 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 DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
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" 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:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
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.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.