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David Gingrich edited comment on SPARK-19507 at 2/8/17 9:37 PM: ---------------------------------------------------------------- I'd advocate making it public, but I was saving that for a separate discussion sine it's a bigger change :) {{_verfy_type}} is called from {{pyspark.sql.session.createDataFrame}} if {{verifySchema is True}}, so better debug messages would help that usecase. I think the main reason to add it is that otherwise the output isn't useful for debugging. The change is also simple (adding a {{name}} parameter that is used in the exception messages and augmented for recursive calls). I have a patch but I wanted to add tests before submitting the PR. was (Author: dgingrich): I'd advocate that it be made public, but I was saving that for a separate discussion sine it's a bigger change :) {{_verfy_type}} is called from {{pyspark.sql.session.createDataFrame}} if {{verifySchema is True}}, so better debug messages would help that usecase. I think the main reason to add it is that otherwise the output isn't useful for debugging. The change is also simple (adding a {{name}} parameter that is used in the exception messages and augmented for recursive calls. I have a patch but I wanted to add tests before submitting the PR. > pyspark.sql.types._verify_type() exceptions too broad to debug collections or > nested data > ----------------------------------------------------------------------------------------- > > Key: SPARK-19507 > URL: https://issues.apache.org/jira/browse/SPARK-19507 > Project: Spark > Issue Type: New Feature > Components: PySpark > Affects Versions: 2.1.0 > Environment: macOS Sierra 10.12.3 > Spark 2.1.0, installed via Homebrew > Reporter: David Gingrich > Priority: Trivial > > The private function pyspark.sql.types._verify_type() recursively checks an > object against a datatype, raising an exception if the object does not > satisfy the type. These messages are not specific enough to debug a data > error in a collection or nested data, for instance: > {quote} > >>> import pyspark.sql.types as typ > >>> schema = typ.StructType([typ.StructField('nest1', > >>> typ.MapType(typ.StringType(), typ.ArrayType(typ.FloatType())))]) > >>> typ._verify_type({'nest1': {'nest2': [1]}}, schema) > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > File "/Users/david/src/3p/spark/python/pyspark/sql/types.py", line 1355, in > _verify_type > _verify_type(obj.get(f.name), f.dataType, f.nullable, name=new_name) > File "/Users/david/src/3p/spark/python/pyspark/sql/types.py", line 1349, in > _verify_type > _verify_type(v, dataType.valueType, dataType.valueContainsNull, > name=new_name) > File "/Users/david/src/3p/spark/python/pyspark/sql/types.py", line 1342, in > _verify_type > _verify_type(i, dataType.elementType, dataType.containsNull, > name=new_name) > File "/Users/david/src/3p/spark/python/pyspark/sql/types.py", line 1325, in > _verify_type > % (name, dataType, obj, type(obj))) > TypeError: FloatType can not accept object 1 in type <class 'int'> > {quote} > Passing and printing a field name would make debugging easier. -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org