aad2125475
## Problem Description
When pyspark is listed as a dependency of another package, installing
the other package will cause an install failure in pyspark. When the
other package is being installed, pyspark's setup_requires requirements
are installed including pypandoc. Thus, the exception handling on
setup.py:152 does not work because the pypandoc module is indeed
available. However, the pypandoc.convert() function fails if pandoc
itself is not installed (in our use cases it is not). This raises an
OSError that is not handled, and setup fails.
The following is a sample failure:
```
$ which pandoc
$ pip freeze | grep pypandoc
pypandoc==1.4
$ pip install pyspark
Collecting pyspark
Downloading pyspark-2.2.0.post0.tar.gz (188.3MB)
100% |████████████████████████████████| 188.3MB 16.8MB/s
Complete output from command python setup.py egg_info:
Maybe try:
sudo apt-get install pandoc
See http://johnmacfarlane.net/pandoc/installing.html
for installation options
---------------------------------------------------------------
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-build-mfnizcwa/pyspark/setup.py", line 151, in <module>
long_description = pypandoc.convert('README.md', 'rst')
File "/home/tbeck/.virtualenvs/cem/lib/python3.5/site-packages/pypandoc/__init__.py", line 69, in convert
outputfile=outputfile, filters=filters)
File "/home/tbeck/.virtualenvs/cem/lib/python3.5/site-packages/pypandoc/__init__.py", line 260, in _convert_input
_ensure_pandoc_path()
File "/home/tbeck/.virtualenvs/cem/lib/python3.5/site-packages/pypandoc/__init__.py", line 544, in _ensure_pandoc_path
raise OSError("No pandoc was found: either install pandoc and add it\n"
OSError: No pandoc was found: either install pandoc and add it
to your PATH or or call pypandoc.download_pandoc(...) or
install pypandoc wheels with included pandoc.
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-mfnizcwa/pyspark/
```
## What changes were proposed in this pull request?
This change simply adds an additional exception handler for the OSError
that is raised. This allows pyspark to be installed client-side without requiring pandoc to be installed.
## How was this patch tested?
I tested this by building a wheel package of pyspark with the change applied. Then, in a clean virtual environment with pypandoc installed but pandoc not available on the system, I installed pyspark from the wheel.
Here is the output
```
$ pip freeze | grep pypandoc
pypandoc==1.4
$ which pandoc
$ pip install --no-cache-dir ../spark/python/dist/pyspark-2.3.0.dev0-py2.py3-none-any.whl
Processing /home/tbeck/work/spark/python/dist/pyspark-2.3.0.dev0-py2.py3-none-any.whl
Requirement already satisfied: py4j==0.10.6 in /home/tbeck/.virtualenvs/cem/lib/python3.5/site-packages (from pyspark==2.3.0.dev0)
Installing collected packages: pyspark
Successfully installed pyspark-2.3.0.dev0
```
Author: Tucker Beck <tucker.beck@rentrakmail.com>
Closes #18981 from dusktreader/dusktreader/fix-pandoc-dependency-issue-in-setup_py.
|
||
|---|---|---|
| .github | ||
| assembly | ||
| bin | ||
| build | ||
| common | ||
| conf | ||
| core | ||
| data | ||
| dev | ||
| docs | ||
| examples | ||
| external | ||
| graphx | ||
| hadoop-cloud | ||
| launcher | ||
| licenses | ||
| mllib | ||
| mllib-local | ||
| project | ||
| python | ||
| R | ||
| repl | ||
| resource-managers | ||
| sbin | ||
| sql | ||
| streaming | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .travis.yml | ||
| appveyor.yml | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| NOTICE | ||
| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
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.
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.