cbad616d4c
## What changes were proposed in this pull request? In this PR, we implements a complete process of GPU-aware resources scheduling in Standalone. The whole process looks like: Worker sets up isolated resources when it starts up and registers to master along with its resources. And, Master picks up usable workers according to driver/executor's resource requirements to launch driver/executor on them. Then, Worker launches the driver/executor after preparing resources file, which is created under driver/executor's working directory, with specified resource addresses(told by master). When driver/executor finished, their resources could be recycled to worker. Finally, if a worker stops, it should always release its resources firstly. For the case of Workers and Drivers in **client** mode run on the same host, we introduce a config option named `spark.resources.coordinate.enable`(default true) to indicate whether Spark should coordinate resources for user. If `spark.resources.coordinate.enable=false`, user should be responsible for configuring different resources for Workers and Drivers when use resourcesFile or discovery script. If true, Spark would help user to assign different resources for Workers and Drivers. The solution for Spark to coordinate resources among Workers and Drivers is: Generally, use a shared file named *____allocated_resources____.json* to sync allocated resources info among Workers and Drivers on the same host. After a Worker or Driver found all resources using the configured resourcesFile and/or discovery script during launching, it should filter out available resources by excluding resources already allocated in *____allocated_resources____.json* and acquire resources from available resources according to its own requirement. After that, it should write its allocated resources along with its process id (pid) into *____allocated_resources____.json*. Pid (proposed by tgravescs) here used to check whether the allocated resources are still valid in case of Worker or Driver crashes and doesn't release resources properly. And when a Worker or Driver finished, normally, it would always clean up its own allocated resources in *____allocated_resources____.json*. Note that we'll always get a file lock before any access to file *____allocated_resources____.json* and release the lock finally. Futhermore, we appended resources info in `WorkerSchedulerStateResponse` to work around master change behaviour in HA mode. ## How was this patch tested? Added unit tests in WorkerSuite, MasterSuite, SparkContextSuite. Manually tested with client/cluster mode (e.g. multiple workers) in a single node Standalone. Closes #25047 from Ngone51/SPARK-27371. Authored-by: wuyi <ngone_5451@163.com> Signed-off-by: Thomas Graves <tgraves@apache.org> |
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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-datasource.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-data-sources-avro.md | ||
sql-data-sources-binaryFile.md | ||
sql-data-sources-hive-tables.md | ||
sql-data-sources-jdbc.md | ||
sql-data-sources-json.md | ||
sql-data-sources-load-save-functions.md | ||
sql-data-sources-orc.md | ||
sql-data-sources-parquet.md | ||
sql-data-sources-troubleshooting.md | ||
sql-data-sources.md | ||
sql-distributed-sql-engine.md | ||
sql-getting-started.md | ||
sql-keywords.md | ||
sql-migration-guide-hive-compatibility.md | ||
sql-migration-guide-upgrade.md | ||
sql-migration-guide.md | ||
sql-performance-tuning.md | ||
sql-programming-guide.md | ||
sql-pyspark-pandas-with-arrow.md | ||
sql-reference.md | ||
storage-openstack-swift.md | ||
streaming-custom-receivers.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 |
license |
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Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
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="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("roxygen2", version = "5.0.1", repos="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("testthat", version = "1.0.2", repos="https://cloud.r-project.org/")'
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.