Github user zasdfgbnm commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18444#discussion_r125173496
  
    --- Diff: python/pyspark/sql/types.py ---
    @@ -935,6 +936,86 @@ def _parse_datatype_json_value(json_value):
             long: LongType,
         })
     
    +# Mapping Python array types to Spark SQL DataType
    +# We should be careful here. The size of these types in python depends on C
    +# implementation. We need to make sure that this conversion does not lose 
any
    +# precision.
    +#
    +# Reference for C integer size, see:
    +# ISO/IEC 9899:201x specification, § 5.2.4.2.1 Sizes of integer types 
<limits.h>.
    +# Reference for python array typecode, see:
    +# https://docs.python.org/2/library/array.html
    +# https://docs.python.org/3.6/library/array.html
    +
    +_array_int_typecode_ctype_mappings = {
    +    'b': ctypes.c_byte,
    +    'B': ctypes.c_ubyte,
    +    'h': ctypes.c_short,
    +    'H': ctypes.c_ushort,
    +    'i': ctypes.c_int,
    +    'I': ctypes.c_uint,
    +    'l': ctypes.c_long,
    +    'L': ctypes.c_ulong
    +}
    +
    +# TODO: Uncomment this when 'q' and 'Q' are supported by 
net.razorvine.pickle
    +# Type code 'q' and 'Q' are not available at python 2
    +# if sys.version > "2":
    +#     _array_int_typecode_ctype_mappings.update({
    +#         'q': ctypes.c_longlong,
    +#         'Q': ctypes.c_ulonglong
    +#     })
    +
    +
    +def _int_size_to_type(size):
    +    """
    +    Return the Scala type from the size of integers.
    +    """
    +    if size <= 8:
    +        return ByteType
    +    if size <= 16:
    +        return ShortType
    +    if size <= 32:
    +        return IntegerType
    +    if size <= 64:
    +        return LongType
    +    raise TypeError("not supported type: integer size too large.")
    --- End diff --
    
    I don't think we should log this. This is just a helper function that helps 
to construct `_array_type_mappings`, which is a complete list of all supported 
type codes. Being filtered out here is not an error, it's by design. If users 
try to use unsupported typecode, they will see another `TypeError` due to line 
1052:
    ```python
    .....
    elif isinstance(obj, array):
        if obj.typecode in _array_type_mappings:
            return ArrayType(_array_type_mappings[obj.typecode](), True)
        else:
            raise TypeError("not supported type: array(%s)" % obj.typecode)
    .....
    ```


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