4a6d90e187
### What changes were proposed in this pull request? This PR proposes to introduce the strategy on mismatched offset for start offset timestamp on Kafka data source. Please read the section `Why are the changes needed?` to understand the rationalization of the functionality. This would be pretty much helpful for the case where there's a skew between partitions and some partitions have older records. * AS-IS: Spark simply fails the query and end users have to deal with workarounds requiring manual steps. * TO-BE: Spark will assign the latest offset for these partitions, so that Spark can read newer records from these partitions in further micro-batches. To retain the existing behavior and also give some help for the proposed "TO-BE" behavior, we'd like to introduce the strategy on mismatched offset for start offset timestamp to let end users choose from them. The strategy will be added as source option, to ensure end users set the behavior explicitly (otherwise simply "known" default value). * New source option to be added: startingOffsetsByTimestampStrategy * Available values: `error` (fail the query as referred as AS-IS), `latest` (set the offset to the latest as referred as TO-BE) Doc changes are following: ![ES-106042-doc-screenshot-1](https://user-images.githubusercontent.com/1317309/120472697-2c1ba800-c3e1-11eb-884f-f28152168053.png) ![ES-106042-doc-screenshot-2](https://user-images.githubusercontent.com/1317309/120472719-33db4c80-c3e1-11eb-9851-939be8a3ddb7.png) ### Why are the changes needed? We encountered a real-world case Spark fails the query if some of the partitions don't have matching offset by timestamp. This is intended behavior to avoid bring unintended output for some cases like: * timestamp 2 is presented as timestamp-offset, but the some of partitions don't have the record yet * record with timestamp 1 comes "later" in the following micro-batch which is possible since Kafka allows to specify the timestamp in record. Here the unintended output we talked about was the risk of reading record with timestamp 1 in the next micro-batch despite the option specifying timestamp 2. But for many cases end users just suppose timestamp is increasing monotonically with wall clocks are all in sync, and current behavior blocks these cases to make progress. ### Does this PR introduce _any_ user-facing change? Yes, but not a breaking change. It's up to end users to choose the behavior which the default value is "error" (current behavior). And it's a source option (not config) so they need to explicitly set the behavior to let the functionality takes effect. ### How was this patch tested? New UTs. Closes #32747 from HeartSaVioR/SPARK-35611. Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com> Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com> |
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building-spark.md | ||
cloud-integration.md | ||
cluster-overview.md | ||
configuration.md | ||
contributing-to-spark.md | ||
core-migration-guide.md | ||
Gemfile | ||
Gemfile.lock | ||
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 | ||
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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 | ||
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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 | ||
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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-csv.md | ||
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sql-distributed-sql-engine.md | ||
sql-getting-started.md | ||
sql-migration-guide.md | ||
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sql-ref-syntax-ddl-drop-database.md | ||
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sql-ref-syntax-ddl-repair-table.md | ||
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sql-ref-syntax-dml-insert-overwrite-directory-hive.md | ||
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sql-ref-syntax-dml-insert-overwrite-table.md | ||
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sql-ref-syntax-dml-load.md | ||
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sql-ref-syntax-hive-format.md | ||
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 | ||
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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 | ||
sql-ref-syntax-qry-select-like.md | ||
sql-ref-syntax-qry-select-limit.md | ||
sql-ref-syntax-qry-select-orderby.md | ||
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-transform.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. Make sure the bundle
command is available, if not install the Gem containing it:
$ sudo gem install bundler
After this all the required ruby dependencies can be installed from the docs/
directory via the Bundler:
$ cd docs
$ bundle install
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 = "7.1.1", 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 7.1.1, 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 numpydoc 'jinja2<3.0.0'
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 bundle exec 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
$ bundle exec jekyll build
You can modify the default Jekyll build as follows:
# Skip generating API docs (which takes a while)
$ SKIP_API=1 bundle exec jekyll build
# Serve content locally on port 4000
$ bundle exec jekyll serve --watch
# Build the site with extra features used on the live page
$ PRODUCTION=1 bundle exec 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 $SPARK_HOME/sql/create-docs.sh
after building Spark first.
When you run bundle exec jekyll build
in the docs
directory, it will also copy over the scaladoc and javadoc for the various
Spark subprojects into the docs
directory (and then also into the _site
directory). We use a
jekyll plugin to run ./build/sbt unidoc
before building the site so if you haven't run it (recently) it
may take some time as it generates all of the scaladoc and javadoc using Unidoc.
The jekyll plugin also generates the PySpark docs using Sphinx, SparkR docs
using roxygen2 and SQL docs
using MkDocs.
NOTE: To skip the step of building and copying over the Scala, Java, Python, R and SQL API docs, run SKIP_API=1 bundle exec jekyll build
. In addition, SKIP_SCALADOC=1
, SKIP_PYTHONDOC=1
, SKIP_RDOC=1
and SKIP_SQLDOC=1
can be used
to skip a single step of the corresponding language. SKIP_SCALADOC
indicates skipping both the Scala and Java docs.
Automatically Rebuilding API Docs
bundle exec jekyll serve --watch
will only watch what's in docs/
, and it won't follow symlinks. That means it won't monitor your API docs under python/docs
or elsewhere.
To work around this limitation for Python, install entr
and run the following in a separate shell:
cd "$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.