0812d6c17c
### What changes were proposed in this pull request? This improves error handling when a failure in conversion from Pandas to Arrow occurs. And fixes tests to be compatible with upcoming Arrow 2.0.0 release. ### Why are the changes needed? Current tests will fail with Arrow 2.0.0 because of a change in error message when the schema is invalid. For these cases, the current error message also includes information on disabling safe conversion config, which is mainly meant for floating point truncation and overflow. The tests have been updated to use a message that is show for past Arrow versions, and upcoming. If the user enters an invalid schema, the error produced by pyarrow is not consistent and either `TypeError` or `ArrowInvalid`, with the latter being caught, and raised as a `RuntimeError` with the extra info. The error handling is improved by: - narrowing the exception type to `TypeError`s, which `ArrowInvalid` is a subclass and what is raised on safe conversion failures. - The exception is only raised with additional information on disabling "spark.sql.execution.pandas.convertToArrowArraySafely" if it is enabled in the first place. - The original exception is chained to better show it to the user. ### Does this PR introduce _any_ user-facing change? Yes, the error re-raised changes from a RuntimeError to a ValueError, which better categorizes this type of error and in-line with the original Arrow error. ### How was this patch tested? Existing tests, using pyarrow 1.0.1 and 2.0.0-snapshot Closes #29951 from BryanCutler/arrow-better-handle-pandas-errors-SPARK-33073. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
||
|---|---|---|
| .. | ||
| docs | ||
| lib | ||
| pyspark | ||
| test_coverage | ||
| test_support | ||
| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| mypy.ini | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
| run-tests-with-coverage | ||
| run-tests.py | ||
| setup.cfg | ||
| setup.py | ||
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, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
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).