f659527727
Handle executor failure with multiple containers Added a spark property spark.kubernetes.executor.checkAllContainers, with default being false. When it's true, the executor snapshot will take all containers in the executor into consideration when deciding whether the executor is in "Running" state, if the pod restart policy is "Never". Also, added the new spark property to the doc. ### What changes were proposed in this pull request? Checking of all containers in the executor pod when reporting executor status, if the `spark.kubernetes.executor.checkAllContainers` property is set to true. ### Why are the changes needed? Currently, a pod remains "running" as long as there is at least one running container. This prevents Spark from noticing when a container has failed in an executor pod with multiple containers. With this change, user can configure the behavior to be different. Namely, if any container in the executor pod has failed, either the executor process or one of its sidecars, the pod is considered to be failed, and it will be rescheduled. ### Does this PR introduce _any_ user-facing change? Yes, new spark property added. User is now able to choose whether to turn on this feature using the `spark.kubernetes.executor.checkAllContainers` property. ### How was this patch tested? Unit test was added and all passed. I tried to run integration test by following the instruction [here](https://spark.apache.org/developer-tools.html) (section "Testing K8S") and also [here](https://github.com/apache/spark/blob/master/resource-managers/kubernetes/integration-tests/README.md), but I wasn't able to run it smoothly as it fails to talk with minikube cluster. Maybe it's because my minikube version is too new (I'm using v1.13.1)...? Since I've been trying it for two days and still can't make it work, I decided to submit this PR and hopefully the Jenkins test will pass. Closes #29924 from huskysun/exec-sidecar-failure. Authored-by: Shiqi Sun <s.sun@salesforce.com> Signed-off-by: Holden Karau <hkarau@apple.com> |
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building-spark.md | ||
cloud-integration.md | ||
cluster-overview.md | ||
configuration.md | ||
contributing-to-spark.md | ||
core-migration-guide.md | ||
graphx-programming-guide.md | ||
hadoop-provided.md | ||
hardware-provisioning.md | ||
index.md | ||
job-scheduling.md | ||
migration-guide.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-linalg-guide.md | ||
ml-linear-methods.md | ||
ml-migration-guide.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-naive-bayes.md | ||
mllib-optimization.md | ||
mllib-pmml-model-export.md | ||
mllib-statistics.md | ||
monitoring.md | ||
programming-guide.md | ||
pyspark-migration-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-migration-guide.md | ||
sparkr.md | ||
sql-data-sources-avro.md | ||
sql-data-sources-binaryFile.md | ||
sql-data-sources-generic-options.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-migration-guide.md | ||
sql-migration-old.md | ||
sql-performance-tuning.md | ||
sql-programming-guide.md | ||
sql-pyspark-pandas-with-arrow.md | ||
sql-ref-ansi-compliance.md | ||
sql-ref-datatypes.md | ||
sql-ref-datetime-pattern.md | ||
sql-ref-functions-builtin.md | ||
sql-ref-functions-udf-aggregate.md | ||
sql-ref-functions-udf-hive.md | ||
sql-ref-functions-udf-scalar.md | ||
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sql-ref-syntax-aux-conf-mgmt-reset.md | ||
sql-ref-syntax-aux-conf-mgmt-set-timezone.md | ||
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sql-ref-syntax-aux-resource-mgmt-list-file.md | ||
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sql-ref-syntax-aux-resource-mgmt.md | ||
sql-ref-syntax-aux-show-columns.md | ||
sql-ref-syntax-aux-show-create-table.md | ||
sql-ref-syntax-aux-show-databases.md | ||
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sql-ref-syntax-aux-show-partitions.md | ||
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sql-ref-syntax-ddl-alter-database.md | ||
sql-ref-syntax-ddl-alter-table.md | ||
sql-ref-syntax-ddl-alter-view.md | ||
sql-ref-syntax-ddl-create-database.md | ||
sql-ref-syntax-ddl-create-function.md | ||
sql-ref-syntax-ddl-create-table-datasource.md | ||
sql-ref-syntax-ddl-create-table-hiveformat.md | ||
sql-ref-syntax-ddl-create-table-like.md | ||
sql-ref-syntax-ddl-create-table.md | ||
sql-ref-syntax-ddl-create-view.md | ||
sql-ref-syntax-ddl-drop-database.md | ||
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sql-ref-syntax-ddl-drop-view.md | ||
sql-ref-syntax-ddl-repair-table.md | ||
sql-ref-syntax-ddl-truncate-table.md | ||
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sql-ref-syntax-ddl.md | ||
sql-ref-syntax-dml-insert-into.md | ||
sql-ref-syntax-dml-insert-overwrite-directory-hive.md | ||
sql-ref-syntax-dml-insert-overwrite-directory.md | ||
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sql-ref-syntax-dml-load.md | ||
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sql-ref-syntax-qry-explain.md | ||
sql-ref-syntax-qry-select-case.md | ||
sql-ref-syntax-qry-select-clusterby.md | ||
sql-ref-syntax-qry-select-cte.md | ||
sql-ref-syntax-qry-select-distribute-by.md | ||
sql-ref-syntax-qry-select-groupby.md | ||
sql-ref-syntax-qry-select-having.md | ||
sql-ref-syntax-qry-select-hints.md | ||
sql-ref-syntax-qry-select-inline-table.md | ||
sql-ref-syntax-qry-select-join.md | ||
sql-ref-syntax-qry-select-lateral-view.md | ||
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sql-ref-syntax-qry-select-limit.md | ||
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sql-ref-syntax-qry-select-pivot.md | ||
sql-ref-syntax-qry-select-sampling.md | ||
sql-ref-syntax-qry-select-setops.md | ||
sql-ref-syntax-qry-select-sortby.md | ||
sql-ref-syntax-qry-select-subqueries.md | ||
sql-ref-syntax-qry-select-tvf.md | ||
sql-ref-syntax-qry-select-where.md | ||
sql-ref-syntax-qry-select-window.md | ||
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sql-ref.md | ||
ss-migration-guide.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 | ||
web-ui.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 rouge
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.
R Documentation
If you'd like to generate R documentation, you'll need to install Pandoc and install these libraries:
$ sudo Rscript -e 'install.packages(c("knitr", "devtools", "testthat", "rmarkdown"), repos="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("roxygen2", version = "5.0.1", repos="https://cloud.r-project.org/")'
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.
API Documentation
To generate API docs for any language, you'll need to install these libraries:
$ sudo pip install 'sphinx<3.1.0' mkdocs numpy pydata_sphinx_theme ipython nbsphinx
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 $SPARK_HOME/sql/create-docs.sh
after building Spark first.
When you run jekyll build
in the docs
directory, it will also copy over the scaladoc and javadoc for the various
Spark subprojects into the docs
directory (and then also into the _site
directory). We use a
jekyll plugin to run ./build/sbt unidoc
before building the site so if you haven't run it (recently) it
may take some time as it generates all of the scaladoc and javadoc using Unidoc.
The jekyll plugin also generates the PySpark docs using Sphinx, SparkR docs
using roxygen2 and SQL docs
using MkDocs.
NOTE: To skip the step of building and copying over the Scala, Java, Python, R and SQL API docs, run SKIP_API=1 jekyll build
. In addition, SKIP_SCALADOC=1
, SKIP_PYTHONDOC=1
, SKIP_RDOC=1
and SKIP_SQLDOC=1
can be used
to skip a single step of the corresponding language. SKIP_SCALADOC
indicates skipping both the Scala and Java docs.
Automatically Rebuilding API Docs
jekyll serve --watch
will only watch what's in docs/
, and it won't follow symlinks. That means it won't monitor your API docs under python/docs
or elsewhere.
To work around this limitation for Python, install entr
and run the following in a separate shell:
cd "$SPARK_HOME/python/docs"
find .. -type f -name '*.py' \
| entr -s 'make html && cp -r _build/html/. ../../docs/api/python'
Whenever there is a change to your Python code, entr
will automatically rebuild the Python API docs and copy them to docs/
, thus triggering a Jekyll update.