baaa756dee
### What changes were proposed in this pull request? This is a follow up PR to #29328 to apply the same constraint where `path` option cannot coexist with path parameter to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`. ### Why are the changes needed? The current behavior silently overwrites the `path` option if path parameter is passed to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`. For example, ``` Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2") ``` will write the result to `/tmp/path2`. ### Does this PR introduce _any_ user-facing change? Yes, if `path` option coexists with path parameter to any of the above methods, it will throw `AnalysisException`: ``` scala> Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2") org.apache.spark.sql.AnalysisException: There is a 'path' option set and save() is called with a path parameter. Either remove the path option, or call save() without the parameter. To ignore this check, set 'spark.sql.legacy.pathOptionBehavior.enabled' to 'true'.; ``` The user can restore the previous behavior by setting `spark.sql.legacy.pathOptionBehavior.enabled` to `true`. ### How was this patch tested? Added new tests. Closes #29543 from imback82/path_option. Authored-by: Terry Kim <yuminkim@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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docs | ||
lib | ||
pyspark | ||
test_coverage | ||
test_support | ||
.coveragerc | ||
.gitignore | ||
MANIFEST.in | ||
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).