tree: 0a60142070df7b861e967163026b81ff0173485b [path history] [tgz]
  1. api/
  2. css/
  3. img/
  4. js/
  5. api.html
  6. bagel-programming-guide.html
  7. building-spark.html
  8. building-with-maven.html
  9. cluster-overview.html
  10. configuration.html
  11. contributing-to-spark.html
  12. ec2-scripts.html
  13. graphx-programming-guide.html
  14. hadoop-provided.html
  15. hadoop-third-party-distributions.html
  16. hardware-provisioning.html
  17. index.html
  18. java-programming-guide.html
  19. job-scheduling.html
  20. ml-ann.html
  21. ml-decision-tree.html
  22. ml-ensembles.html
  23. ml-features.html
  24. ml-guide.html
  25. ml-linear-methods.html
  26. mllib-classification-regression.html
  27. mllib-clustering.html
  28. mllib-collaborative-filtering.html
  29. mllib-data-types.html
  30. mllib-decision-tree.html
  31. mllib-dimensionality-reduction.html
  32. mllib-ensembles.html
  33. mllib-evaluation-metrics.html
  34. mllib-feature-extraction.html
  35. mllib-frequent-pattern-mining.html
  36. mllib-guide.html
  37. mllib-isotonic-regression.html
  38. mllib-linear-methods.html
  39. mllib-migration-guides.html
  40. mllib-naive-bayes.html
  41. mllib-optimization.html
  42. mllib-pmml-model-export.html
  43. mllib-statistics.html
  44. monitoring.html
  45. programming-guide.html
  46. python-programming-guide.html
  47. quick-start.html
  48. README.md
  49. running-on-mesos.html
  50. running-on-yarn.html
  51. scala-programming-guide.html
  52. security.html
  53. spark-standalone.html
  54. sparkr.html
  55. sql-programming-guide.html
  56. storage-openstack-swift.html
  57. streaming-custom-receivers.html
  58. streaming-flume-integration.html
  59. streaming-kafka-integration.html
  60. streaming-kinesis-integration.html
  61. streaming-programming-guide.html
  62. submitting-applications.html
  63. tuning.html
site/docs/1.5.2/README.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. To get started you can run the following commands

$ sudo gem install jekyll
$ sudo gem install jekyll-redirect-from
$ sudo pip install Pygments
$ 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 Sphinx.

NOTE: To skip the step of building and copying over the Scala, Python, R API docs, run SKIP_API=1 jekyll.