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Takuya UESHIN 11a849b3a7 [SPARK-22370][SQL][PYSPARK][FOLLOW-UP] Fix a test failure when xmlrunner is installed. 
## What changes were proposed in this pull request?

This is a follow-up pr of #19587.

If `xmlrunner` is installed, `VectorizedUDFTests.test_vectorized_udf_check_config` fails by the following error because the `self` which is a subclass of `unittest.TestCase` in the UDF `check_records_per_batch` can't be pickled anymore.

```
PicklingError: Cannot pickle files that are not opened for reading: w
```

This changes the UDF not to refer the `self`.

## How was this patch tested?

Tested locally.

Author: Takuya UESHIN <ueshin@databricks.com>

Closes #20115 from ueshin/issues/SPARK-22370_fup1.
2017-12-29 23:04:28 +09:00
..
docs [SPARK-21866][ML][PYSPARK] Adding spark image reader 2017-11-22 15:45:45 -08:00
lib [SPARK-21278][PYSPARK] Upgrade to Py4J 0.10.6 2017-07-05 16:33:23 -07:00
pyspark [SPARK-22370][SQL][PYSPARK][FOLLOW-UP] Fix a test failure when xmlrunner is installed. 2017-12-29 23:04:28 +09:00
test_support [SPARK-19610][SQL] Support parsing multiline CSV files 2017-02-28 13:34:33 -08:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-18652][PYTHON] Include the example data and third-party licenses in pyspark package. 2016-12-07 06:09:27 +08:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [SPARK-22324][SQL][PYTHON][FOLLOW-UP] Update setup.py file. 2017-12-27 20:51:26 +09:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests.py [SPARK-14280][BUILD][WIP] Update change-version.sh and pom.xml to add Scala 2.12 profiles and enable 2.12 compilation 2017-09-01 19:21:21 +01:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-22324][SQL][PYTHON][FOLLOW-UP] Update setup.py file. 2017-12-27 20:51:26 +09:00

README.md

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

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to setup your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.6), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).