da6fa3828b
## What changes were proposed in this pull request? The default behaviour of Spark on K8S currently is to create `emptyDir` volumes to back `SPARK_LOCAL_DIRS`. In some environments e.g. diskless compute nodes this may actually hurt performance because these are backed by the Kubelet's node storage which on a diskless node will typically be some remote network storage. Even if this is enterprise grade storage connected via a high speed interconnect the way Spark uses these directories as scratch space (lots of relatively small short lived files) has been observed to cause serious performance degradation. Therefore we would like to provide the option to use K8S's ability to instead back these `emptyDir` volumes with `tmpfs`. Therefore this PR adds a configuration option that enables `SPARK_LOCAL_DIRS` to be backed by Memory backed `emptyDir` volumes rather than the default. Documentation is added to describe both the default behaviour plus this new option and its implications. One of which is that scratch space then counts towards your pods memory limits and therefore users will need to adjust their memory requests accordingly. *NB* - This is an alternative version of PR #22256 reduced to just the `tmpfs` piece ## How was this patch tested? Ran with this option in our diskless compute environments to verify functionality Author: Rob Vesse <rvesse@dotnetrdf.org> Closes #22323 from rvesse/SPARK-25262-tmpfs. |
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_data | ||
_includes | ||
_layouts | ||
_plugins | ||
css | ||
img | ||
js | ||
_config.yml | ||
api.md | ||
avro-data-source-guide.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-kubernetes.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 https://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 correspond 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", "rmarkdown"), repos="http://cran.stat.ucla.edu/")'
$ sudo Rscript -e 'devtools::install_version("roxygen2", version = "5.0.1", repos="http://cran.stat.ucla.edu/")'
$ sudo Rscript -e 'devtools::install_version("testthat", version = "1.0.2", 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.
Note: Other versions of roxygen2 might work in SparkR documentation generation but RoxygenNote
field in $SPARK_HOME/R/pkg/DESCRIPTION
is 5.0.1, which is updated if the version is mismatched.
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.