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hyukjinkwon a72d118cd9 [SPARK-25473][PYTHON][SS][TEST] ForeachWriter tests failed on Python 3.6 and macOS High Sierra 
## What changes were proposed in this pull request?

This PR does not fix the problem itself but just target to add few comments to run PySpark tests on Python 3.6 and macOS High Serria since it actually blocks to run tests on this enviornment.

it does not target to fix the problem yet.

The problem here looks because we fork python workers and the forked workers somehow call Objective-C libraries in some codes at CPython's implementation. After debugging a while, I suspect `pickle` in Python 3.6 has some changes:

58419b9267/python/pyspark/serializers.py (L577)

in particular, it looks also related to which objects are serialized or not as well.

This link (http://sealiesoftware.com/blog/archive/2017/6/5/Objective-C_and_fork_in_macOS_1013.html) and this link (https://blog.phusion.nl/2017/10/13/why-ruby-app-servers-break-on-macos-high-sierra-and-what-can-be-done-about-it/) were helpful for me to understand this.

I am still debugging this but my guts say it's difficult to fix or workaround within Spark side.

## How was this patch tested?

Manually tested:

Before `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`:

```
/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/subprocess.py:766: ResourceWarning: subprocess 27563 is still running
  ResourceWarning, source=self)
[Stage 0:>                                                          (0 + 1) / 1]objc[27586]: +[__NSPlaceholderDictionary initialize] may have been in progress in another thread when fork() was called.
objc[27586]: +[__NSPlaceholderDictionary initialize] may have been in progress in another thread when fork() was called. We cannot safely call it or ignore it in the fork() child process. Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.
ERROR

======================================================================
ERROR: test_streaming_foreach_with_simple_function (pyspark.sql.tests.SQLTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/.../spark/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/.../spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o54.processAllAvailable.
: org.apache.spark.sql.streaming.StreamingQueryException: Writing job aborted.
=== Streaming Query ===
Identifier: [id = f508d634-407c-4232-806b-70e54b055c42, runId = 08d1435b-5358-4fb6-b167-811584a3163e]
Current Committed Offsets: {}
Current Available Offsets: {FileStreamSource[file:/var/folders/71/484zt4z10ks1vydt03bhp6hr0000gp/T/tmpolebys1s]: {"logOffset":0}}

Current State: ACTIVE
Thread State: RUNNABLE

Logical Plan:
FileStreamSource[file:/var/folders/71/484zt4z10ks1vydt03bhp6hr0000gp/T/tmpolebys1s]
	at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
	at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: org.apache.spark.SparkException: Writing job aborted.
	at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:91)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
```

After `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`:

```
test_streaming_foreach_with_simple_function (pyspark.sql.tests.SQLTests) ...
ok
```

Closes #22480 from HyukjinKwon/SPARK-25473.

Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
2018-09-23 11:14:27 +08:00
..
docs [SPARK-24530][PYTHON] Add a control to force Python version in Sphinx via environment variable, SPHINXPYTHON 2018-07-11 10:10:07 +08:00
lib [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
pyspark [SPARK-25473][PYTHON][SS][TEST] ForeachWriter tests failed on Python 3.6 and macOS High Sierra 2018-09-23 11:14:27 +08:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.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 [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
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-with-coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
run-tests.py [SPARK-25238][PYTHON] lint-python: Fix W605 warnings for pycodestyle 2.4 2018-09-13 11:19:43 +08:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-23698][PYTHON][FOLLOWUP] Resolve undefiend names in setup.py 2018-08-27 10:02:31 +08:00

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 set up 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 0.10.7), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).