[jira] [Created] (SPARK-18541) Add pyspark.sql.Column.aliasWithMetadata to allow dynamic metadata management in pyspark SQL API

["Shea Parkes (JIRA)" <ji...@apache.org>]

Shea Parkes created SPARK-18541:
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             Summary: Add pyspark.sql.Column.aliasWithMetadata to allow dynamic metadata management in pyspark SQL API
                 Key: SPARK-18541
                 URL: https://issues.apache.org/jira/browse/SPARK-18541
             Project: Spark
          Issue Type: Improvement
          Components: PySpark, SQL
    Affects Versions: 2.0.2
         Environment: all
            Reporter: Shea Parkes
            Priority: Minor


In the Scala SQL API, you can pass in new metadata when you alias a field.  That functionality is not available in the Python API.   Right now, you have to painfully utilize {{SparkSession.createDataFrame}} to manipulate the metadata for even a single column.  I would propose to add the following method to {{pyspark.sql.Column}}:

{code}
def aliasWithMetadata(self, name, metadata):
    """
    Make a new Column that has the provided alias and metadata.
    Metadata will be processed with json.dumps()
    """
    _context = pyspark.SparkContext._active_spark_context
    _metadata_str = json.dumps(metadata)
    _metadata_jvm = _context._jvm.org.apache.spark.sql.types.Metadata.fromJson(_metadata_str)
    _new_java_column = getattr(self._jc, 'as')(name, _metadata_jvm)
    return Column(_new_java_column)
{code}

I can likely complete this request myself if there is any interest for it.  Just have to dust off my knowledge of doctest and the location of the python tests.



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