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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