Github user szalai1 commented on a diff in the pull request: https://github.com/apache/spark/pull/17435#discussion_r108348472 --- Diff: python/pyspark/sql/types.py --- @@ -57,7 +57,25 @@ def __ne__(self, other): @classmethod def typeName(cls): - return cls.__name__[:-4].lower() + typeTypeNameMap = {"DataType": "data", + "NullType": "null", + "StringType": "string", + "BinaryType": "binary", + "BooleanType": "boolean", + "DateType": "date", + "TimestampType": "timestamp", + "DecimalType": "decimal", + "DoubleType": "double", + "FloatType": "float", + "ByteType": "byte", + "IntegerType": "integer", + "LongType": "long", + "ShortType": "short", + "ArrayType": "array", + "MapType": "map", + "StructField": "struct", --- End diff -- The reason I called `typeName ` is, that I wanted to generate a HIVE table dynamically from data and to do this I need the type of each column. ``` >>> sqlContext = SQLContext(sc) >>> df = sqlContext.read.json('path_to_a_json_doc') >>> cols = [] >>> for i in df.schema: ... cols.append("`" + i.name + "`" + "\t" + i.typeName()) >>> ",\n".join(cols) ``` In the real case, I converted the type name to a hive compatible form.
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