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Hyukjin Kwon c6f3a13087 [SPARK-36626][PYTHON][FOLLOW-UP] Use datetime.tzinfo instead of datetime.tzname() 
### What changes were proposed in this pull request?

This PR is a small followup of https://github.com/apache/spark/pull/33876 which proposes to use `datetime.tzinfo` instead of `datetime.tzname` to see if timezome information is provided or not.

This way is consistent with other places such as:

9c5bcac61e/python/pyspark/sql/types.py (L182)

9c5bcac61e/python/pyspark/sql/types.py (L1662)

### Why are the changes needed?

In some cases, `datetime.tzname` can raise an exception (https://docs.python.org/3/library/datetime.html#datetime.datetime.tzname):

> ... raises an exception if the latter doesn’t return None or a string object,

I was able to reproduce this in Jenkins with setting `spark.sql.timestampType` to `TIMESTAMP_NTZ` by default:

```
======================================================================
ERROR: test_time_with_timezone (pyspark.sql.tests.test_serde.SerdeTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/sql/tests/test_serde.py", line 92, in test_time_with_timezone
...
  File "/usr/lib/pypy3/lib-python/3/datetime.py", line 979, in tzname
    raise NotImplementedError("tzinfo subclass must override tzname()")
NotImplementedError: tzinfo subclass must override tzname()
```

### Does this PR introduce _any_ user-facing change?

No to end users because it has not be released.
This is rather a safeguard to prevent potential breakage.

### How was this patch tested?

Manually tested.

Closes #33918 from HyukjinKwon/SPARK-36626-followup.

Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
2021-09-06 17:16:52 +02:00
..
docs [SPARK-36401][PYTHON] Implement Series.cov 2021-09-03 10:41:27 -07:00
lib [SPARK-34688][PYTHON] Upgrade to Py4J 0.10.9.2 2021-03-11 09:51:41 -06:00
pyspark [SPARK-36626][PYTHON][FOLLOW-UP] Use datetime.tzinfo instead of datetime.tzname() 2021-09-06 17:16:52 +02:00
test_coverage [SPARK-36092][INFRA][BUILD][PYTHON] Migrate to GitHub Actions with Codecov from Jenkins 2021-08-01 21:37:19 +09:00
test_support Spelling r common dev mlib external project streaming resource managers python 2020-11-27 10:22:45 -06:00
.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-32714][PYTHON] Initial pyspark-stubs port 2020-09-24 14:15:36 +09:00
mypy.ini [SPARK-35684][INFRA][PYTHON] Bump up mypy version in GitHub Actions 2021-07-07 13:26:28 +09:00
pylintrc [SPARK-32435][PYTHON] Remove heapq3 port from Python 3 2020-07-27 20:10:13 +09:00
README.md [SPARK-36474][PYTHON][DOCS] Mention 'pandas API on Spark' in Spark overview pages 2021-08-11 22:57:26 +09:00
run-tests [SPARK-29672][PYSPARK] update spark testing framework to use python3 2019-11-14 10:18:55 -08:00
run-tests-with-coverage [SPARK-36092][INFRA][BUILD][PYTHON] Migrate to GitHub Actions with Codecov from Jenkins 2021-08-01 21:37:19 +09:00
run-tests.py [SPARK-32194][PYTHON] Use proper exception classes instead of plain Exception 2021-05-26 11:54:40 +09:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-35759][PYTHON] Remove the upperbound for numpy for pandas-on-Spark 2021-06-15 09:59:05 +09:00

README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. 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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://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, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow). See also Dependencies for production, and dev/requirements.txt for development.