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Hide JVM traceback for SparkUpgradeException
Following PR will result into
from pyspark.sql.functions import to_date, unix_timestamp, from_unixtime
df2 = df.select('date_str',to_date(from_unixtime(unix_timestamp('date_str', 'yyyy-dd-aa'))))
df2.show(1, False)
raise converted from None
pyspark.sql.utils.SparkUpgradeException: You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyy-dd-aa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
Why are the changes needed?
This change will remove JVM traceback for pyspark in SparkUpgradeException. This will help to have stack trace more pythonic way
Does this PR introduce any user-facing change?
Yes user will be able to see only python stacktrace
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What changes were proposed in this pull request?
Hide JVM traceback for SparkUpgradeException
Following PR will result into
Why are the changes needed?
This change will remove JVM traceback for pyspark in SparkUpgradeException. This will help to have stack trace more pythonic way
Does this PR introduce any user-facing change?
Yes user will be able to see only python stacktrace
How was this patch tested?
unit tests