a45647746d
## What changes were proposed in this pull request? Currently only a list of users can be specified for view and modify acls. This change enables a group of admins/devs/users to be provisioned for viewing and modifying Spark jobs. **Changes Proposed in the fix** Three new corresponding config entries have been added where the user can specify the groups to be given access. ``` spark.admin.acls.groups spark.modify.acls.groups spark.ui.view.acls.groups ``` New config entries were added because specifying the users and groups explicitly is a better and cleaner way compared to specifying them in the existing config entry using a delimiter. A generic trait has been introduced to provide the user to group mapping which makes it pluggable to support a variety of mapping protocols - similar to the one used in hadoop. A default unix shell based implementation has been provided. Custom user to group mapping protocol can be specified and configured by the entry ```spark.user.groups.mapping``` **How the patch was Tested** We ran different spark jobs setting the config entries in combinations of admin, modify and ui acls. For modify acls we tried killing the job stages from the ui and using yarn commands. For view acls we tried accessing the UI tabs and the logs. Headless accounts were used to launch these jobs and different users tried to modify and view the jobs to ensure that the groups mapping applied correctly. Additional Unit tests have been added without modifying the existing ones. These test for different ways of setting the acls through configuration and/or API and validate the expected behavior. Author: Dhruve Ashar <dhruveashar@gmail.com> Closes #12760 from dhruve/impr/SPARK-4224. |
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api.md | ||
building-spark.md | ||
cluster-overview.md | ||
configuration.md | ||
contributing-to-spark.md | ||
ec2-scripts.md | ||
graphx-programming-guide.md | ||
hadoop-provided.md | ||
hardware-provisioning.md | ||
index.md | ||
java-programming-guide.md | ||
job-scheduling.md | ||
ml-advanced.md | ||
ml-ann.md | ||
ml-classification-regression.md | ||
ml-clustering.md | ||
ml-collaborative-filtering.md | ||
ml-decision-tree.md | ||
ml-ensembles.md | ||
ml-features.md | ||
ml-guide.md | ||
ml-linear-methods.md | ||
ml-survival-regression.md | ||
mllib-classification-regression.md | ||
mllib-clustering.md | ||
mllib-collaborative-filtering.md | ||
mllib-data-types.md | ||
mllib-decision-tree.md | ||
mllib-dimensionality-reduction.md | ||
mllib-ensembles.md | ||
mllib-evaluation-metrics.md | ||
mllib-feature-extraction.md | ||
mllib-frequent-pattern-mining.md | ||
mllib-guide.md | ||
mllib-isotonic-regression.md | ||
mllib-linear-methods.md | ||
mllib-migration-guides.md | ||
mllib-naive-bayes.md | ||
mllib-optimization.md | ||
mllib-pmml-model-export.md | ||
mllib-statistics.md | ||
monitoring.md | ||
programming-guide.md | ||
python-programming-guide.md | ||
quick-start.md | ||
README.md | ||
running-on-mesos.md | ||
running-on-yarn.md | ||
scala-programming-guide.md | ||
security.md | ||
spark-standalone.md | ||
sparkr.md | ||
sql-programming-guide.md | ||
storage-openstack-swift.md | ||
streaming-custom-receivers.md | ||
streaming-flume-integration.md | ||
streaming-kafka-integration.md | ||
streaming-kinesis-integration.md | ||
streaming-programming-guide.md | ||
submitting-applications.md | ||
tuning.md |
Welcome to the Spark documentation!
This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.apache.org/documentation.html.
Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that corresponds to whichever version of Spark you currently have checked out of revision control.
Prerequisites
The Spark documentation build uses a number of tools to build HTML docs and API docs in Scala, Python and R.
You need to have Ruby and Python installed. Also install the following libraries:
$ sudo gem install jekyll jekyll-redirect-from pygments.rb
$ sudo pip install Pygments
# Following is needed only for generating API docs
$ sudo pip install sphinx
$ Rscript -e 'install.packages(c("knitr", "devtools"), repos="http://cran.stat.ucla.edu/")'
Generating the Documentation HTML
We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as the github wiki, as the definitive documentation) to enable the documentation to evolve along with the source code and be captured by revision control (currently git). This way the code automatically includes the version of the documentation that is relevant regardless of which version or release you have checked out or downloaded.
In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can read those text files directly if you want. Start with index.md.
Execute jekyll build
from the docs/
directory to compile the site. Compiling the site with
Jekyll will create a directory called _site
containing index.html as well as the rest of the
compiled files.
$ cd docs
$ jekyll build
You can modify the default Jekyll build as follows:
# Skip generating API docs (which takes a while)
$ SKIP_API=1 jekyll build
# Serve content locally on port 4000
$ jekyll serve --watch
# Build the site with extra features used on the live page
$ PRODUCTION=1 jekyll build
API Docs (Scaladoc, Sphinx, roxygen2)
You can build just the Spark scaladoc by running build/sbt unidoc
from the SPARK_PROJECT_ROOT directory.
Similarly, you can build just the PySpark docs by running make html
from the
SPARK_PROJECT_ROOT/python/docs directory. Documentation is only generated for classes that are listed as
public in __init__.py
. The SparkR docs can be built by running SPARK_PROJECT_ROOT/R/create-docs.sh.
When you run jekyll
in the docs
directory, it will also copy over the scaladoc for the various
Spark subprojects into the docs
directory (and then also into the _site
directory). We use a
jekyll plugin to run build/sbt unidoc
before building the site so if you haven't run it (recently) it
may take some time as it generates all of the scaladoc. The jekyll plugin also generates the
PySpark docs using Sphinx.
NOTE: To skip the step of building and copying over the Scala, Python, R API docs, run SKIP_API=1 jekyll
.