8f8dc45f6d
resubmit of https://github.com/apache/spark/pull/636 for a totally different algorithm
https://issues.apache.org/jira/browse/SPARK-1706
In current implementation, the user has to start multiple workers in a server for starting multiple executors in a server, which introduces additional overhead due to the more JVM processes...
In this patch, I changed the scheduling logic in master to enable the user to start multiple executor processes within the same JVM process.
1. user configure spark.executor.maxCoreNumPerExecutor to suggest the maximum core he/she would like to allocate to each executor
2. Master assigns the executors to the workers with the major consideration on the memoryPerExecutor and the worker.freeMemory, and tries to allocate as many as possible cores to the executor ```min(min(memoryPerExecutor, worker.freeCore), maxLeftCoreToAssign)``` where ```maxLeftCoreToAssign = maxExecutorCanAssign * maxCoreNumPerExecutor```
---------------------------------------
Other small changes include
change memoryPerSlave in ApplicationDescription to memoryPerExecutor, as "Slave" is overrided to represent both worker and executor in the documents... (we have some discussion on this before?)
Author: CodingCat <zhunansjtu@gmail.com>
Closes #731 from CodingCat/SPARK-1706-2 and squashes the following commits:
6dee808 [CodingCat] change filter predicate
fbeb7e5 [CodingCat] address the comments
940cb42 [CodingCat] avoid unnecessary allocation
b8ca561 [CodingCat] revert a change
45967b4 [CodingCat] remove unused method
2eeff77 [CodingCat] stylistic fixes
12a1b32 [CodingCat] change the semantic of coresPerExecutor to exact core number
f035423 [CodingCat] stylistic fix
d9c1685 [CodingCat] remove unused var
f595bd6 [CodingCat] recover some unintentional changes
63b3df9 [CodingCat] change the description of the parameter in the submit script
4cf61f1 [CodingCat] improve the code and docs
ff011e2 [CodingCat] start multiple executors on the worker by rewriting startExeuctor logic
2c2bcc5 [CodingCat] fix wrong usage info
497ec2c [CodingCat] address andrew's comments
878402c [CodingCat] change the launching executor code
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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-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-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
.
knitr, devtools
SparkR documentation is written using roxygen2
and we use knitr
, devtools
to generate
documentation. To install these packages you can run install.packages(c("knitr", "devtools"))
from a
R console.
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
.