747fe7282c
### What changes were proposed in this pull request? https://github.com/apache/spark/pull/30309 added a configuration (disabled by default) that simplifies the error messages from Python UDFS, which removed internal stacktrace from Python workers: ```python from pyspark.sql.functions import udf; spark.range(10).select(udf(lambda x: x/0)("id")).collect() ``` **Before** ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/.../python/pyspark/sql/dataframe.py", line 427, in show print(self._jdf.showString(n, 20, vertical)) File "/.../python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__ File "/.../python/pyspark/sql/utils.py", line 127, in deco raise_from(converted) File "<string>", line 3, in raise_from pyspark.sql.utils.PythonException: An exception was thrown from Python worker in the executor: Traceback (most recent call last): File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 605, in main process() File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 597, in process serializer.dump_stream(out_iter, outfile) File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream self.serializer.dump_stream(self._batched(iterator), stream) File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream for obj in iterator: File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched for item in iterator: File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs) File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr> result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs) File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda> return lambda *a: f(*a) File "/.../python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper return f(*args, **kwargs) File "<stdin>", line 1, in <lambda> ZeroDivisionError: division by zero ``` **After** ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/.../python/pyspark/sql/dataframe.py", line 427, in show print(self._jdf.showString(n, 20, vertical)) File "/.../python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__ File "/.../python/pyspark/sql/utils.py", line 127, in deco raise_from(converted) File "<string>", line 3, in raise_from pyspark.sql.utils.PythonException: An exception was thrown from Python worker in the executor: Traceback (most recent call last): File "<stdin>", line 1, in <lambda> ZeroDivisionError: division by zero ``` Note that the traceback (`return f(*args, **kwargs)`) is almost always same - I would say more than 99%. For 1% case, we can guide developers to enable this configuration for further debugging. In Databricks, it has been enabled for around 6 months, and I have had zero negative feedback on it. ### Why are the changes needed? To show simplified exception messages to end users. ### Does this PR introduce _any_ user-facing change? Yes, it will hide the internal Python worker traceback. ### How was this patch tested? Existing test cases should cover. Closes #32569 from HyukjinKwon/SPARK-35419. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org> |
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| .. | ||
| 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).