5a07aca4d4
## What changes were proposed in this pull request? The HTTP Strict-Transport-Security response header (often abbreviated as HSTS) is a security feature that lets a web site tell browsers that it should only be communicated with using HTTPS, instead of using HTTP. Note: The Strict-Transport-Security header is ignored by the browser when your site is accessed using HTTP; this is because an attacker may intercept HTTP connections and inject the header or remove it. When your site is accessed over HTTPS with no certificate errors, the browser knows your site is HTTPS capable and will honor the Strict-Transport-Security header. The HTTP X-XSS-Protection response header is a feature of Internet Explorer, Chrome and Safari that stops pages from loading when they detect reflected cross-site scripting (XSS) attacks. The HTTP X-Content-Type-Options response header is used to protect against MIME sniffing vulnerabilities. ## How was this patch tested? Checked on my system locally. <img width="750" alt="screen shot 2017-10-03 at 6 49 20 pm" src="https://user-images.githubusercontent.com/6433184/31127234-eadf7c0c-a86b-11e7-8e5d-f6ea3f97b210.png"> Author: krishna-pandey <krish.pandey21@gmail.com> Author: Krishna Pandey <krish.pandey21@gmail.com> Closes #19419 from krishna-pandey/SPARK-22188. |
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_data | ||
_includes | ||
_layouts | ||
_plugins | ||
css | ||
img | ||
js | ||
_config.yml | ||
api.md | ||
building-spark.md | ||
cloud-integration.md | ||
cluster-overview.md | ||
configuration.md | ||
contributing-to-spark.md | ||
graphx-programming-guide.md | ||
hadoop-provided.md | ||
hardware-provisioning.md | ||
index.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-frequent-pattern-mining.md | ||
ml-guide.md | ||
ml-linear-methods.md | ||
ml-migration-guides.md | ||
ml-pipeline.md | ||
ml-statistics.md | ||
ml-survival-regression.md | ||
ml-tuning.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 | ||
quick-start.md | ||
rdd-programming-guide.md | ||
README.md | ||
running-on-mesos.md | ||
running-on-yarn.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-0-8-integration.md | ||
streaming-kafka-0-10-integration.md | ||
streaming-kafka-integration.md | ||
streaming-kinesis-integration.md | ||
streaming-programming-guide.md | ||
structured-streaming-kafka-integration.md | ||
structured-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, Java, Python, R and SQL.
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 pypandoc mkdocs
$ sudo Rscript -e 'install.packages(c("knitr", "devtools", "roxygen2", "testthat", "rmarkdown"), repos="http://cran.stat.ucla.edu/")'
(Note: If you are on a system with both Ruby 1.9 and Ruby 2.0 you may need to replace gem with gem2.0)
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 text files 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, Javadoc, Sphinx, roxygen2, MkDocs)
You can build just the Spark scaladoc and javadoc by running build/sbt unidoc
from the SPARK_HOME
directory.
Similarly, you can build just the PySpark docs by running make html
from the
SPARK_HOME/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_HOME/R/create-docs.sh
, and
the SQL docs can be built by running SPARK_HOME/sql/create-docs.sh
after building Spark first.
When you run jekyll build
in the docs
directory, it will also copy over the scaladoc and javadoc 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 and javadoc using Unidoc.
The jekyll plugin also generates the PySpark docs using Sphinx, SparkR docs
using roxygen2 and SQL docs
using MkDocs.
NOTE: To skip the step of building and copying over the Scala, Java, Python, R and SQL API docs, run SKIP_API=1 jekyll build
. In addition, SKIP_SCALADOC=1
, SKIP_PYTHONDOC=1
, SKIP_RDOC=1
and SKIP_SQLDOC=1
can be used
to skip a single step of the corresponding language. SKIP_SCALADOC
indicates skipping both the Scala and Java docs.