9a7ce70eab
This patch introduces a new configuration option, `spark.extraListeners`, that allows SparkListeners to be specified in SparkConf and registered before the SparkContext is initialized. From the configuration documentation: > A comma-separated list of classes that implement SparkListener; when initializing SparkContext, instances of these classes will be created and registered with Spark's listener bus. If a class has a single-argument constructor that accepts a SparkConf, that constructor will be called; otherwise, a zero-argument constructor will be called. If no valid constructor can be found, the SparkContext creation will fail with an exception. This motivation for this patch is to allow monitoring code to be easily injected into existing Spark programs without having to modify those programs' code. Author: Josh Rosen <joshrosen@databricks.com> Closes #4111 from JoshRosen/SPARK-5190-register-sparklistener-in-sc-constructor and squashes the following commits: 8370839 [Josh Rosen] Two minor fixes after merging with master 6e0122c [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-5190-register-sparklistener-in-sc-constructor 1a5b9a0 [Josh Rosen] Remove SPARK_EXTRA_LISTENERS environment variable. 2daff9b [Josh Rosen] Add a couple of explanatory comments for SPARK_EXTRA_LISTENERS. b9973da [Josh Rosen] Add test to ensure that conf and env var settings are merged, not overriden. d6f3113 [Josh Rosen] Use getConstructors() instead of try-catch to find right constructor. d0d276d [Josh Rosen] Move code into setupAndStartListenerBus() method b22b379 [Josh Rosen] Instantiate SparkListeners from classes listed in configurations. 9c0d8f1 [Josh Rosen] Revert "[SPARK-5190] Allow SparkListeners to be registered before SparkContext starts." 217ecc0 [Josh Rosen] Revert "Add addSparkListener to JavaSparkContext" 25988f3 [Josh Rosen] Add addSparkListener to JavaSparkContext 163ba19 [Josh Rosen] [SPARK-5190] Allow SparkListeners to be registered before SparkContext starts. |
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.. | ||
_layouts | ||
_plugins | ||
css | ||
img | ||
js | ||
_config.yml | ||
api.md | ||
bagel-programming-guide.md | ||
building-spark.md | ||
cluster-overview.md | ||
configuration.md | ||
contributing-to-spark.md | ||
ec2-scripts.md | ||
graphx-programming-guide.md | ||
hadoop-third-party-distributions.md | ||
hardware-provisioning.md | ||
index.md | ||
java-programming-guide.md | ||
job-scheduling.md | ||
ml-guide.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-feature-extraction.md | ||
mllib-guide.md | ||
mllib-linear-methods.md | ||
mllib-naive-bayes.md | ||
mllib-optimization.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 | ||
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.
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.
The markdown code can be compiled to HTML using the Jekyll tool.
Jekyll
and a few dependencies must be installed for this to work. We recommend
installing via the Ruby Gem dependency manager. Since the exact HTML output
varies between versions of Jekyll and its dependencies, we list specific versions here
in some cases:
$ sudo gem install jekyll
$ sudo gem install jekyll-redirect-from
Execute jekyll
from the docs/
directory. Compiling the site with Jekyll will create a directory
called _site
containing index.html as well as the rest of the compiled files.
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
Pygments
We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages,
so you will also need to install that (it requires Python) by running sudo pip install Pygments
.
To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile phase, use the following sytax:
{% highlight scala %}
// Your scala code goes here, you can replace scala with many other
// supported languages too.
{% endhighlight %}
Sphinx
We use Sphinx to generate Python API docs, so you will need to install it by running
sudo pip install sphinx
.
API Docs (Scaladoc and Sphinx)
You can build just the Spark scaladoc by running build/sbt doc
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
.
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 doc
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 and Python API docs, run SKIP_API=1 jekyll
.