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[SPARK-21773][BUILD][DOCS] Installs mkdocs if missing in the path in SQL documentation build

hyukjinkwon authored 7 years ago

## What changes were proposed in this pull request?

This PR proposes to install `mkdocs` by `pip install` if missing in the path. Mainly to fix Jenkins's documentation build failure in `spark-master-docs`. See https://amplab.cs.berkeley.edu/jenkins/job/spark-master-docs/3580/console.

It also adds `mkdocs` as requirements in `docs/README.md`.

## How was this patch tested?

I manually ran `jekyll build` under `docs` directory after manually removing `mkdocs` via `pip uninstall mkdocs`.

Also, tested this in the same way but on CentOS Linux release 7.3.1611 (Core) where I built Spark few times but never built documentation before and `mkdocs` is not installed.

```
...
Moving back into docs dir.
Moving to SQL directory and building docs.
Missing mkdocs in your path, trying to install mkdocs for SQL documentation generation.
Collecting mkdocs
  Downloading mkdocs-0.16.3-py2.py3-none-any.whl (1.2MB)
    100% |████████████████████████████████| 1.2MB 574kB/s
Requirement already satisfied: PyYAML>=3.10 in /usr/lib64/python2.7/site-packages (from mkdocs)
Collecting livereload>=2.5.1 (from mkdocs)
  Downloading livereload-2.5.1-py2-none-any.whl
Collecting tornado>=4.1 (from mkdocs)
  Downloading tornado-4.5.1.tar.gz (483kB)
    100% |████████████████████████████████| 491kB 1.4MB/s
Collecting Markdown>=2.3.1 (from mkdocs)
  Downloading Markdown-2.6.9.tar.gz (271kB)
    100% |████████████████████████████████| 276kB 2.4MB/s
Collecting click>=3.3 (from mkdocs)
  Downloading click-6.7-py2.py3-none-any.whl (71kB)
    100% |████████████████████████████████| 71kB 2.8MB/s
Requirement already satisfied: Jinja2>=2.7.1 in /usr/lib/python2.7/site-packages (from mkdocs)
Requirement already satisfied: six in /usr/lib/python2.7/site-packages (from livereload>=2.5.1->mkdocs)
Requirement already satisfied: backports.ssl_match_hostname in /usr/lib/python2.7/site-packages (from tornado>=4.1->mkdocs)
Collecting singledispatch (from tornado>=4.1->mkdocs)
  Downloading singledispatch-3.4.0.3-py2.py3-none-any.whl
Collecting certifi (from tornado>=4.1->mkdocs)
  Downloading certifi-2017.7.27.1-py2.py3-none-any.whl (349kB)
    100% |████████████████████████████████| 358kB 2.1MB/s
Collecting backports_abc>=0.4 (from tornado>=4.1->mkdocs)
  Downloading backports_abc-0.5-py2.py3-none-any.whl
Requirement already satisfied: MarkupSafe>=0.23 in /usr/lib/python2.7/site-packages (from Jinja2>=2.7.1->mkdocs)
Building wheels for collected packages: tornado, Markdown
  Running setup.py bdist_wheel for tornado ... done
  Stored in directory: /root/.cache/pip/wheels/84/83/cd/6a04602633457269d161344755e6766d24307189b7a67ff4b7
  Running setup.py bdist_wheel for Markdown ... done
  Stored in directory: /root/.cache/pip/wheels/bf/46/10/c93e17ae86ae3b3a919c7b39dad3b5ccf09aeb066419e5c1e5
Successfully built tornado Markdown
Installing collected packages: singledispatch, certifi, backports-abc, tornado, livereload, Markdown, click, mkdocs
Successfully installed Markdown-2.6.9 backports-abc-0.5 certifi-2017.7.27.1 click-6.7 livereload-2.5.1 mkdocs-0.16.3 singledispatch-3.4.0.3 tornado-4.5.1
Generating markdown files for SQL documentation.
Generating HTML files for SQL documentation.
INFO    -  Cleaning site directory
INFO    -  Building documentation to directory: .../spark/sql/site
Moving back into docs dir.
Making directory api/sql
cp -r ../sql/site/. api/sql
            Source: .../spark/docs
       Destination: .../spark/docs/_site
      Generating...
                    done.
 Auto-regeneration: disabled. Use --watch to enable.
 ```

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18984 from HyukjinKwon/sql-doc-mkdocs.

41e0eb71

41e0eb71 7 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.