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Latest Docker releases are stricter in their enforcement of build argument scope. The location of the `ARG spark_uid` declaration in the Python and R Dockerfiles means the variable is out of scope by the time it is used in a `USER` declaration resulting in a container running as root rather than the default/configured UID. Also with some of the refactoring of the script that has happened since my PR that introduced the configurable UID it turns out the `-u <uid>` argument is not being properly passed to the Python and R image builds when those are opted into ## What changes were proposed in this pull request? This commit moves the `ARG` declaration to just before the argument is used such that it is in scope. It also ensures that Python and R image builds receive the build arguments that include the `spark_uid` argument where relevant ## How was this patch tested? Prior to the patch images are produced where the Python and R images ignore the default/configured UID: ``` > docker run -it --entrypoint /bin/bash rvesse/spark-py:uid456 bash-4.4# whoami root bash-4.4# id -u 0 bash-4.4# exit > docker run -it --entrypoint /bin/bash rvesse/spark:uid456 bash-4.4$ id -u 456 bash-4.4$ exit ``` Note that the Python image is still running as `root` having ignored the configured UID of 456 while the base image has the correct UID because the relevant `ARG` declaration is correctly in scope. After the patch the correct UID is observed: ``` > docker run -it --entrypoint /bin/bash rvesse/spark-r:uid456 bash-4.4$ id -u 456 bash-4.4$ exit exit > docker run -it --entrypoint /bin/bash rvesse/spark-py:uid456 bash-4.4$ id -u 456 bash-4.4$ exit exit > docker run -it --entrypoint /bin/bash rvesse/spark:uid456 bash-4.4$ id -u 456 bash-4.4$ exit ``` Closes #23611 from rvesse/SPARK-26685. Authored-by: Rob Vesse <rvesse@dotnetrdf.org> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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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 and Enabling YARN" 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.