Source code for pyspark.sql.types

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import decimal
import datetime
import keyword
import warnings
import json
import re
import weakref
from array import array
from operator import itemgetter

__all__ = [
    "DataType", "NullType", "StringType", "BinaryType", "BooleanType", "DateType",
    "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType",
    "LongType", "ShortType", "ArrayType", "MapType", "StructField", "StructType"]

[docs]class DataType(object): """Spark SQL DataType""" def __repr__(self): return self.__class__.__name__ def __hash__(self): return hash(str(self)) def __eq__(self, other): return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not self.__eq__(other) @classmethod
[docs] def typeName(cls): return cls.__name__[:-4].lower()
[docs] def simpleString(self): return self.typeName()
[docs] def jsonValue(self): return self.typeName()
[docs] def json(self): return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True) # This singleton pattern does not work with pickle, you will get # another object after pickle and unpickle
class PrimitiveTypeSingleton(type): """Metaclass for PrimitiveType""" _instances = {} def __call__(cls): if cls not in cls._instances: cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__() return cls._instances[cls] class PrimitiveType(DataType): """Spark SQL PrimitiveType""" __metaclass__ = PrimitiveTypeSingleton
[docs]class NullType(PrimitiveType): """Spark SQL NullType The data type representing None, used for the types which has not been inferred. """
[docs]class StringType(PrimitiveType): """Spark SQL StringType The data type representing string values. """
[docs]class BinaryType(PrimitiveType): """Spark SQL BinaryType The data type representing bytearray values. """
[docs]class BooleanType(PrimitiveType): """Spark SQL BooleanType The data type representing bool values. """
[docs]class DateType(PrimitiveType): """Spark SQL DateType The data type representing values. """
[docs]class TimestampType(PrimitiveType): """Spark SQL TimestampType The data type representing datetime.datetime values. """
[docs]class DecimalType(DataType): """Spark SQL DecimalType The data type representing decimal.Decimal values. """ def __init__(self, precision=None, scale=None): self.precision = precision self.scale = scale self.hasPrecisionInfo = precision is not None
[docs] def simpleString(self): if self.hasPrecisionInfo: return "decimal(%d,%d)" % (self.precision, self.scale) else: return "decimal(10,0)"
[docs] def jsonValue(self): if self.hasPrecisionInfo: return "decimal(%d,%d)" % (self.precision, self.scale) else: return "decimal"
def __repr__(self): if self.hasPrecisionInfo: return "DecimalType(%d,%d)" % (self.precision, self.scale) else: return "DecimalType()"
[docs]class DoubleType(PrimitiveType): """Spark SQL DoubleType The data type representing float values. """
[docs]class FloatType(PrimitiveType): """Spark SQL FloatType The data type representing single precision floating-point values. """
[docs]class ByteType(PrimitiveType): """Spark SQL ByteType The data type representing int values with 1 singed byte. """
[docs] def simpleString(self): return 'tinyint'
[docs]class IntegerType(PrimitiveType): """Spark SQL IntegerType The data type representing int values. """
[docs] def simpleString(self): return 'int'
[docs]class LongType(PrimitiveType): """Spark SQL LongType The data type representing long values. If the any value is beyond the range of [-9223372036854775808, 9223372036854775807], please use DecimalType. """
[docs] def simpleString(self): return 'bigint'
[docs]class ShortType(PrimitiveType): """Spark SQL ShortType The data type representing int values with 2 signed bytes. """
[docs] def simpleString(self): return 'smallint'
[docs]class ArrayType(DataType): """Spark SQL ArrayType The data type representing list values. An ArrayType object comprises two fields, elementType (a DataType) and containsNull (a bool). The field of elementType is used to specify the type of array elements. The field of containsNull is used to specify if the array has None values. """ def __init__(self, elementType, containsNull=True): """Creates an ArrayType :param elementType: the data type of elements. :param containsNull: indicates whether the list contains None values. >>> ArrayType(StringType()) == ArrayType(StringType(), True) True >>> ArrayType(StringType(), False) == ArrayType(StringType()) False """ assert isinstance(elementType, DataType), "elementType should be DataType" self.elementType = elementType self.containsNull = containsNull
[docs] def simpleString(self): return 'array<%s>' % self.elementType.simpleString()
def __repr__(self): return "ArrayType(%s,%s)" % (self.elementType, str(self.containsNull).lower())
[docs] def jsonValue(self): return {"type": self.typeName(), "elementType": self.elementType.jsonValue(), "containsNull": self.containsNull}
[docs] def fromJson(cls, json): return ArrayType(_parse_datatype_json_value(json["elementType"]), json["containsNull"])
[docs]class MapType(DataType): """Spark SQL MapType The data type representing dict values. A MapType object comprises three fields, keyType (a DataType), valueType (a DataType) and valueContainsNull (a bool). The field of keyType is used to specify the type of keys in the map. The field of valueType is used to specify the type of values in the map. The field of valueContainsNull is used to specify if values of this map has None values. For values of a MapType column, keys are not allowed to have None values. """ def __init__(self, keyType, valueType, valueContainsNull=True): """Creates a MapType :param keyType: the data type of keys. :param valueType: the data type of values. :param valueContainsNull: indicates whether values contains null values. >>> (MapType(StringType(), IntegerType()) ... == MapType(StringType(), IntegerType(), True)) True >>> (MapType(StringType(), IntegerType(), False) ... == MapType(StringType(), FloatType())) False """ assert isinstance(keyType, DataType), "keyType should be DataType" assert isinstance(valueType, DataType), "valueType should be DataType" self.keyType = keyType self.valueType = valueType self.valueContainsNull = valueContainsNull
[docs] def simpleString(self): return 'map<%s,%s>' % (self.keyType.simpleString(), self.valueType.simpleString())
def __repr__(self): return "MapType(%s,%s,%s)" % (self.keyType, self.valueType, str(self.valueContainsNull).lower())
[docs] def jsonValue(self): return {"type": self.typeName(), "keyType": self.keyType.jsonValue(), "valueType": self.valueType.jsonValue(), "valueContainsNull": self.valueContainsNull}
[docs] def fromJson(cls, json): return MapType(_parse_datatype_json_value(json["keyType"]), _parse_datatype_json_value(json["valueType"]), json["valueContainsNull"])
[docs]class StructField(DataType): """Spark SQL StructField Represents a field in a StructType. A StructField object comprises three fields, name (a string), dataType (a DataType) and nullable (a bool). The field of name is the name of a StructField. The field of dataType specifies the data type of a StructField. The field of nullable specifies if values of a StructField can contain None values. """ def __init__(self, name, dataType, nullable=True, metadata=None): """Creates a StructField :param name: the name of this field. :param dataType: the data type of this field. :param nullable: indicates whether values of this field can be null. :param metadata: metadata of this field, which is a map from string to simple type that can be serialized to JSON automatically >>> (StructField("f1", StringType(), True) ... == StructField("f1", StringType(), True)) True >>> (StructField("f1", StringType(), True) ... == StructField("f2", StringType(), True)) False """ assert isinstance(dataType, DataType), "dataType should be DataType" = name self.dataType = dataType self.nullable = nullable self.metadata = metadata or {}
[docs] def simpleString(self): return '%s:%s' % (, self.dataType.simpleString())
def __repr__(self): return "StructField(%s,%s,%s)" % (, self.dataType, str(self.nullable).lower())
[docs] def jsonValue(self): return {"name":, "type": self.dataType.jsonValue(), "nullable": self.nullable, "metadata": self.metadata}
[docs] def fromJson(cls, json): return StructField(json["name"], _parse_datatype_json_value(json["type"]), json["nullable"], json["metadata"])
[docs]class StructType(DataType): """Spark SQL StructType The data type representing rows. A StructType object comprises a list of L{StructField}. """ def __init__(self, fields): """Creates a StructType >>> struct1 = StructType([StructField("f1", StringType(), True)]) >>> struct2 = StructType([StructField("f1", StringType(), True)]) >>> struct1 == struct2 True >>> struct1 = StructType([StructField("f1", StringType(), True)]) >>> struct2 = StructType([StructField("f1", StringType(), True), ... StructField("f2", IntegerType(), False)]) >>> struct1 == struct2 False """ assert all(isinstance(f, DataType) for f in fields), "fields should be a list of DataType" self.fields = fields
[docs] def simpleString(self): return 'struct<%s>' % (','.join(f.simpleString() for f in self.fields))
def __repr__(self): return ("StructType(List(%s))" % ",".join(str(field) for field in self.fields))
[docs] def jsonValue(self): return {"type": self.typeName(), "fields": [f.jsonValue() for f in self.fields]}
[docs] def fromJson(cls, json): return StructType([StructField.fromJson(f) for f in json["fields"]])
class UserDefinedType(DataType): """ .. note:: WARN: Spark Internal Use Only SQL User-Defined Type (UDT). """ @classmethod def typeName(cls): return cls.__name__.lower() @classmethod def sqlType(cls): """ Underlying SQL storage type for this UDT. """ raise NotImplementedError("UDT must implement sqlType().") @classmethod def module(cls): """ The Python module of the UDT. """ raise NotImplementedError("UDT must implement module().") @classmethod def scalaUDT(cls): """ The class name of the paired Scala UDT. """ raise NotImplementedError("UDT must have a paired Scala UDT.") def serialize(self, obj): """ Converts the a user-type object into a SQL datum. """ raise NotImplementedError("UDT must implement serialize().") def deserialize(self, datum): """ Converts a SQL datum into a user-type object. """ raise NotImplementedError("UDT must implement deserialize().") def simpleString(self): return 'udt' def json(self): return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True) def jsonValue(self): schema = { "type": "udt", "class": self.scalaUDT(), "pyClass": "%s.%s" % (self.module(), type(self).__name__), "sqlType": self.sqlType().jsonValue() } return schema @classmethod def fromJson(cls, json): pyUDT = json["pyClass"] split = pyUDT.rfind(".") pyModule = pyUDT[:split] pyClass = pyUDT[split+1:] m = __import__(pyModule, globals(), locals(), [pyClass], -1) UDT = getattr(m, pyClass) return UDT() def __eq__(self, other): return type(self) == type(other) _all_primitive_types = dict((v.typeName(), v) for v in globals().itervalues() if type(v) is PrimitiveTypeSingleton and v.__base__ == PrimitiveType) _all_complex_types = dict((v.typeName(), v) for v in [ArrayType, MapType, StructType]) def _parse_datatype_json_string(json_string): """Parses the given data type JSON string. >>> import pickle >>> def check_datatype(datatype): ... pickled = pickle.loads(pickle.dumps(datatype)) ... assert datatype == pickled ... scala_datatype = sqlCtx._ssql_ctx.parseDataType(datatype.json()) ... python_datatype = _parse_datatype_json_string(scala_datatype.json()) ... assert datatype == python_datatype >>> for cls in _all_primitive_types.values(): ... check_datatype(cls()) >>> # Simple ArrayType. >>> simple_arraytype = ArrayType(StringType(), True) >>> check_datatype(simple_arraytype) >>> # Simple MapType. >>> simple_maptype = MapType(StringType(), LongType()) >>> check_datatype(simple_maptype) >>> # Simple StructType. >>> simple_structtype = StructType([ ... StructField("a", DecimalType(), False), ... StructField("b", BooleanType(), True), ... StructField("c", LongType(), True), ... StructField("d", BinaryType(), False)]) >>> check_datatype(simple_structtype) >>> # Complex StructType. >>> complex_structtype = StructType([ ... StructField("simpleArray", simple_arraytype, True), ... StructField("simpleMap", simple_maptype, True), ... StructField("simpleStruct", simple_structtype, True), ... StructField("boolean", BooleanType(), False), ... StructField("withMeta", DoubleType(), False, {"name": "age"})]) >>> check_datatype(complex_structtype) >>> # Complex ArrayType. >>> complex_arraytype = ArrayType(complex_structtype, True) >>> check_datatype(complex_arraytype) >>> # Complex MapType. >>> complex_maptype = MapType(complex_structtype, ... complex_arraytype, False) >>> check_datatype(complex_maptype) >>> check_datatype(ExamplePointUDT()) >>> structtype_with_udt = StructType([StructField("label", DoubleType(), False), ... StructField("point", ExamplePointUDT(), False)]) >>> check_datatype(structtype_with_udt) """ return _parse_datatype_json_value(json.loads(json_string)) _FIXED_DECIMAL = re.compile("decimal\\((\\d+),(\\d+)\\)") def _parse_datatype_json_value(json_value): if type(json_value) is unicode: if json_value in _all_primitive_types.keys(): return _all_primitive_types[json_value]() elif json_value == u'decimal': return DecimalType() elif _FIXED_DECIMAL.match(json_value): m = _FIXED_DECIMAL.match(json_value) return DecimalType(int(, int( else: raise ValueError("Could not parse datatype: %s" % json_value) else: tpe = json_value["type"] if tpe in _all_complex_types: return _all_complex_types[tpe].fromJson(json_value) elif tpe == 'udt': return UserDefinedType.fromJson(json_value) else: raise ValueError("not supported type: %s" % tpe) # Mapping Python types to Spark SQL DataType _type_mappings = { type(None): NullType, bool: BooleanType, int: LongType, long: LongType, float: DoubleType, str: StringType, unicode: StringType, bytearray: BinaryType, decimal.Decimal: DecimalType, DateType, datetime.datetime: TimestampType, datetime.time: TimestampType, } def _infer_type(obj): """Infer the DataType from obj >>> p = ExamplePoint(1.0, 2.0) >>> _infer_type(p) ExamplePointUDT """ if obj is None: return NullType() if hasattr(obj, '__UDT__'): return obj.__UDT__ dataType = _type_mappings.get(type(obj)) if dataType is not None: return dataType() if isinstance(obj, dict): for key, value in obj.iteritems(): if key is not None and value is not None: return MapType(_infer_type(key), _infer_type(value), True) else: return MapType(NullType(), NullType(), True) elif isinstance(obj, (list, array)): for v in obj: if v is not None: return ArrayType(_infer_type(obj[0]), True) else: return ArrayType(NullType(), True) else: try: return _infer_schema(obj) except ValueError: raise ValueError("not supported type: %s" % type(obj)) def _infer_schema(row): """Infer the schema from dict/namedtuple/object""" if isinstance(row, dict): items = sorted(row.items()) elif isinstance(row, (tuple, list)): if hasattr(row, "_fields"): # namedtuple items = zip(row._fields, tuple(row)) elif hasattr(row, "__FIELDS__"): # Row items = zip(row.__FIELDS__, tuple(row)) else: names = ['_%d' % i for i in range(1, len(row) + 1)] items = zip(names, row) elif hasattr(row, "__dict__"): # object items = sorted(row.__dict__.items()) else: raise ValueError("Can not infer schema for type: %s" % type(row)) fields = [StructField(k, _infer_type(v), True) for k, v in items] return StructType(fields) def _need_python_to_sql_conversion(dataType): """ Checks whether we need python to sql conversion for the given type. For now, only UDTs need this conversion. >>> _need_python_to_sql_conversion(DoubleType()) False >>> schema0 = StructType([StructField("indices", ArrayType(IntegerType(), False), False), ... StructField("values", ArrayType(DoubleType(), False), False)]) >>> _need_python_to_sql_conversion(schema0) False >>> _need_python_to_sql_conversion(ExamplePointUDT()) True >>> schema1 = ArrayType(ExamplePointUDT(), False) >>> _need_python_to_sql_conversion(schema1) True >>> schema2 = StructType([StructField("label", DoubleType(), False), ... StructField("point", ExamplePointUDT(), False)]) >>> _need_python_to_sql_conversion(schema2) True """ if isinstance(dataType, StructType): return any([_need_python_to_sql_conversion(f.dataType) for f in dataType.fields]) elif isinstance(dataType, ArrayType): return _need_python_to_sql_conversion(dataType.elementType) elif isinstance(dataType, MapType): return _need_python_to_sql_conversion(dataType.keyType) or \ _need_python_to_sql_conversion(dataType.valueType) elif isinstance(dataType, UserDefinedType): return True else: return False def _python_to_sql_converter(dataType): """ Returns a converter that converts a Python object into a SQL datum for the given type. >>> conv = _python_to_sql_converter(DoubleType()) >>> conv(1.0) 1.0 >>> conv = _python_to_sql_converter(ArrayType(DoubleType(), False)) >>> conv([1.0, 2.0]) [1.0, 2.0] >>> conv = _python_to_sql_converter(ExamplePointUDT()) >>> conv(ExamplePoint(1.0, 2.0)) [1.0, 2.0] >>> schema = StructType([StructField("label", DoubleType(), False), ... StructField("point", ExamplePointUDT(), False)]) >>> conv = _python_to_sql_converter(schema) >>> conv((1.0, ExamplePoint(1.0, 2.0))) (1.0, [1.0, 2.0]) """ if not _need_python_to_sql_conversion(dataType): return lambda x: x if isinstance(dataType, StructType): names, types = zip(*[(, f.dataType) for f in dataType.fields]) converters = map(_python_to_sql_converter, types) def converter(obj): if isinstance(obj, dict): return tuple(c(obj.get(n)) for n, c in zip(names, converters)) elif isinstance(obj, tuple): if hasattr(obj, "_fields") or hasattr(obj, "__FIELDS__"): return tuple(c(v) for c, v in zip(converters, obj)) elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): # k-v pairs d = dict(obj) return tuple(c(d.get(n)) for n, c in zip(names, converters)) else: return tuple(c(v) for c, v in zip(converters, obj)) else: raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType)) return converter elif isinstance(dataType, ArrayType): element_converter = _python_to_sql_converter(dataType.elementType) return lambda a: [element_converter(v) for v in a] elif isinstance(dataType, MapType): key_converter = _python_to_sql_converter(dataType.keyType) value_converter = _python_to_sql_converter(dataType.valueType) return lambda m: dict([(key_converter(k), value_converter(v)) for k, v in m.items()]) elif isinstance(dataType, UserDefinedType): return lambda obj: dataType.serialize(obj) else: raise ValueError("Unexpected type %r" % dataType) def _has_nulltype(dt): """ Return whether there is NullType in `dt` or not """ if isinstance(dt, StructType): return any(_has_nulltype(f.dataType) for f in dt.fields) elif isinstance(dt, ArrayType): return _has_nulltype((dt.elementType)) elif isinstance(dt, MapType): return _has_nulltype(dt.keyType) or _has_nulltype(dt.valueType) else: return isinstance(dt, NullType) def _merge_type(a, b): if isinstance(a, NullType): return b elif isinstance(b, NullType): return a elif type(a) is not type(b): # TODO: type cast (such as int -> long) raise TypeError("Can not merge type %s and %s" % (a, b)) # same type if isinstance(a, StructType): nfs = dict((, f.dataType) for f in b.fields) fields = [StructField(, _merge_type(f.dataType, nfs.get(, NullType()))) for f in a.fields] names = set([ for f in fields]) for n in nfs: if n not in names: fields.append(StructField(n, nfs[n])) return StructType(fields) elif isinstance(a, ArrayType): return ArrayType(_merge_type(a.elementType, b.elementType), True) elif isinstance(a, MapType): return MapType(_merge_type(a.keyType, b.keyType), _merge_type(a.valueType, b.valueType), True) else: return a def _need_converter(dataType): if isinstance(dataType, StructType): return True elif isinstance(dataType, ArrayType): return _need_converter(dataType.elementType) elif isinstance(dataType, MapType): return _need_converter(dataType.keyType) or _need_converter(dataType.valueType) elif isinstance(dataType, NullType): return True else: return False def _create_converter(dataType): """Create an converter to drop the names of fields in obj """ if not _need_converter(dataType): return lambda x: x if isinstance(dataType, ArrayType): conv = _create_converter(dataType.elementType) return lambda row: map(conv, row) elif isinstance(dataType, MapType): kconv = _create_converter(dataType.keyType) vconv = _create_converter(dataType.valueType) return lambda row: dict((kconv(k), vconv(v)) for k, v in row.iteritems()) elif isinstance(dataType, NullType): return lambda x: None elif not isinstance(dataType, StructType): return lambda x: x # dataType must be StructType names = [ for f in dataType.fields] converters = [_create_converter(f.dataType) for f in dataType.fields] convert_fields = any(_need_converter(f.dataType) for f in dataType.fields) def convert_struct(obj): if obj is None: return if isinstance(obj, (tuple, list)): if convert_fields: return tuple(conv(v) for v, conv in zip(obj, converters)) else: return tuple(obj) if isinstance(obj, dict): d = obj elif hasattr(obj, "__dict__"): # object d = obj.__dict__ else: raise ValueError("Unexpected obj: %s" % obj) if convert_fields: return tuple([conv(d.get(name)) for name, conv in zip(names, converters)]) else: return tuple([d.get(name) for name in names]) return convert_struct _BRACKETS = {'(': ')', '[': ']', '{': '}'} def _split_schema_abstract(s): """ split the schema abstract into fields >>> _split_schema_abstract("a b c") ['a', 'b', 'c'] >>> _split_schema_abstract("a(a b)") ['a(a b)'] >>> _split_schema_abstract("a b[] c{a b}") ['a', 'b[]', 'c{a b}'] >>> _split_schema_abstract(" ") [] """ r = [] w = '' brackets = [] for c in s: if c == ' ' and not brackets: if w: r.append(w) w = '' else: w += c if c in _BRACKETS: brackets.append(c) elif c in _BRACKETS.values(): if not brackets or c != _BRACKETS[brackets.pop()]: raise ValueError("unexpected " + c) if brackets: raise ValueError("brackets not closed: %s" % brackets) if w: r.append(w) return r def _parse_field_abstract(s): """ Parse a field in schema abstract >>> _parse_field_abstract("a") StructField(a,NullType,true) >>> _parse_field_abstract("b(c d)") StructField(b,StructType(...c,NullType,true),StructField(d... >>> _parse_field_abstract("a[]") StructField(a,ArrayType(NullType,true),true) >>> _parse_field_abstract("a{[]}") StructField(a,MapType(NullType,ArrayType(NullType,true),true),true) """ if set(_BRACKETS.keys()) & set(s): idx = min((s.index(c) for c in _BRACKETS if c in s)) name = s[:idx] return StructField(name, _parse_schema_abstract(s[idx:]), True) else: return StructField(s, NullType(), True) def _parse_schema_abstract(s): """ parse abstract into schema >>> _parse_schema_abstract("a b c") StructType...a...b...c... >>> _parse_schema_abstract("a[b c] b{}") StructType...a,ArrayType...b...c...b,MapType... >>> _parse_schema_abstract("c{} d{a b}") StructType...c,MapType...d,MapType...a...b... >>> _parse_schema_abstract("a b(t)").fields[1] StructField(b,StructType(List(StructField(t,NullType,true))),true) """ s = s.strip() if not s: return NullType() elif s.startswith('('): return _parse_schema_abstract(s[1:-1]) elif s.startswith('['): return ArrayType(_parse_schema_abstract(s[1:-1]), True) elif s.startswith('{'): return MapType(NullType(), _parse_schema_abstract(s[1:-1])) parts = _split_schema_abstract(s) fields = [_parse_field_abstract(p) for p in parts] return StructType(fields) def _infer_schema_type(obj, dataType): """ Fill the dataType with types inferred from obj >>> schema = _parse_schema_abstract("a b c d") >>> row = (1, 1.0, "str",, 10, 10)) >>> _infer_schema_type(row, schema) StructType...LongType...DoubleType...StringType...DateType... >>> row = [[1], {"key": (1, 2.0)}] >>> schema = _parse_schema_abstract("a[] b{c d}") >>> _infer_schema_type(row, schema) StructType...a,ArrayType...b,MapType(StringType,...c,LongType... """ if dataType is NullType(): return _infer_type(obj) if not obj: return NullType() if isinstance(dataType, ArrayType): eType = _infer_schema_type(obj[0], dataType.elementType) return ArrayType(eType, True) elif isinstance(dataType, MapType): k, v = obj.iteritems().next() return MapType(_infer_schema_type(k, dataType.keyType), _infer_schema_type(v, dataType.valueType)) elif isinstance(dataType, StructType): fs = dataType.fields assert len(fs) == len(obj), \ "Obj(%s) have different length with fields(%s)" % (obj, fs) fields = [StructField(, _infer_schema_type(o, f.dataType), True) for o, f in zip(obj, fs)] return StructType(fields) else: raise ValueError("Unexpected dataType: %s" % dataType) _acceptable_types = { BooleanType: (bool,), ByteType: (int, long), ShortType: (int, long), IntegerType: (int, long), LongType: (int, long), FloatType: (float,), DoubleType: (float,), DecimalType: (decimal.Decimal,), StringType: (str, unicode), BinaryType: (bytearray,), DateType: (,), TimestampType: (datetime.datetime,), ArrayType: (list, tuple, array), MapType: (dict,), StructType: (tuple, list), } def _verify_type(obj, dataType): """ Verify the type of obj against dataType, raise an exception if they do not match. >>> _verify_type(None, StructType([])) >>> _verify_type("", StringType()) >>> _verify_type(0, LongType()) >>> _verify_type(range(3), ArrayType(ShortType())) >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TypeError:... >>> _verify_type({}, MapType(StringType(), IntegerType())) >>> _verify_type((), StructType([])) >>> _verify_type([], StructType([])) >>> _verify_type([1], StructType([])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> _verify_type(ExamplePoint(1.0, 2.0), ExamplePointUDT()) >>> _verify_type([1.0, 2.0], ExamplePointUDT()) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... """ # all objects are nullable if obj is None: return if isinstance(dataType, UserDefinedType): if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType): raise ValueError("%r is not an instance of type %r" % (obj, dataType)) _verify_type(dataType.serialize(obj), dataType.sqlType()) return _type = type(dataType) assert _type in _acceptable_types, "unknown datatype: %s" % dataType # subclass of them can not be deserialized in JVM if type(obj) not in _acceptable_types[_type]: raise TypeError("%s can not accept object in type %s" % (dataType, type(obj))) if isinstance(dataType, ArrayType): for i in obj: _verify_type(i, dataType.elementType) elif isinstance(dataType, MapType): for k, v in obj.iteritems(): _verify_type(k, dataType.keyType) _verify_type(v, dataType.valueType) elif isinstance(dataType, StructType): if len(obj) != len(dataType.fields): raise ValueError("Length of object (%d) does not match with " "length of fields (%d)" % (len(obj), len(dataType.fields))) for v, f in zip(obj, dataType.fields): _verify_type(v, f.dataType) _cached_cls = weakref.WeakValueDictionary() def _restore_object(dataType, obj): """ Restore object during unpickling. """ # use id(dataType) as key to speed up lookup in dict # Because of batched pickling, dataType will be the # same object in most cases. k = id(dataType) cls = _cached_cls.get(k) if cls is None: # use dataType as key to avoid create multiple class cls = _cached_cls.get(dataType) if cls is None: cls = _create_cls(dataType) _cached_cls[dataType] = cls _cached_cls[k] = cls return cls(obj) def _create_object(cls, v): """ Create an customized object with class `cls`. """ # would be deserialized as datetime.datetime # from java type, so we need to set it back. if cls is and isinstance(v, datetime.datetime): return return cls(v) if v is not None else v def _create_getter(dt, i): """ Create a getter for item `i` with schema """ cls = _create_cls(dt) def getter(self): return _create_object(cls, self[i]) return getter def _has_struct_or_date(dt): """Return whether `dt` is or has StructType/DateType in it""" if isinstance(dt, StructType): return True elif isinstance(dt, ArrayType): return _has_struct_or_date(dt.elementType) elif isinstance(dt, MapType): return _has_struct_or_date(dt.keyType) or _has_struct_or_date(dt.valueType) elif isinstance(dt, DateType): return True elif isinstance(dt, UserDefinedType): return True return False def _create_properties(fields): """Create properties according to fields""" ps = {} for i, f in enumerate(fields): name = if (name.startswith("__") and name.endswith("__") or keyword.iskeyword(name)): warnings.warn("field name %s can not be accessed in Python," "use position to access it instead" % name) if _has_struct_or_date(f.dataType): # delay creating object until accessing it getter = _create_getter(f.dataType, i) else: getter = itemgetter(i) ps[name] = property(getter) return ps def _create_cls(dataType): """ Create an class by dataType The created class is similar to namedtuple, but can have nested schema. >>> schema = _parse_schema_abstract("a b c") >>> row = (1, 1.0, "str") >>> schema = _infer_schema_type(row, schema) >>> obj = _create_cls(schema)(row) >>> import pickle >>> pickle.loads(pickle.dumps(obj)) Row(a=1, b=1.0, c='str') >>> row = [[1], {"key": (1, 2.0)}] >>> schema = _parse_schema_abstract("a[] b{c d}") >>> schema = _infer_schema_type(row, schema) >>> obj = _create_cls(schema)(row) >>> pickle.loads(pickle.dumps(obj)) Row(a=[1], b={'key': Row(c=1, d=2.0)}) >>> pickle.loads(pickle.dumps(obj.a)) [1] >>> pickle.loads(pickle.dumps(obj.b)) {'key': Row(c=1, d=2.0)} """ if isinstance(dataType, ArrayType): cls = _create_cls(dataType.elementType) def List(l): if l is None: return return [_create_object(cls, v) for v in l] return List elif isinstance(dataType, MapType): kcls = _create_cls(dataType.keyType) vcls = _create_cls(dataType.valueType) def Dict(d): if d is None: return return dict((_create_object(kcls, k), _create_object(vcls, v)) for k, v in d.items()) return Dict elif isinstance(dataType, DateType): return elif isinstance(dataType, UserDefinedType): return lambda datum: dataType.deserialize(datum) elif not isinstance(dataType, StructType): # no wrapper for primitive types return lambda x: x class Row(tuple): """ Row in DataFrame """ __DATATYPE__ = dataType __FIELDS__ = tuple( for f in dataType.fields) __slots__ = () # create property for fast access locals().update(_create_properties(dataType.fields)) def asDict(self): """ Return as a dict """ return dict((n, getattr(self, n)) for n in self.__FIELDS__) def __repr__(self): # call collect __repr__ for nested objects return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n)) for n in self.__FIELDS__)) def __reduce__(self): return (_restore_object, (self.__DATATYPE__, tuple(self))) return Row def _create_row(fields, values): row = Row(*values) row.__FIELDS__ = fields return row class Row(tuple): """ A row in L{DataFrame}. The fields in it can be accessed like attributes. Row can be used to create a row object by using named arguments, the fields will be sorted by names. >>> row = Row(name="Alice", age=11) >>> row Row(age=11, name='Alice') >>>, row.age ('Alice', 11) Row also can be used to create another Row like class, then it could be used to create Row objects, such as >>> Person = Row("name", "age") >>> Person <Row(name, age)> >>> Person("Alice", 11) Row(name='Alice', age=11) """ def __new__(self, *args, **kwargs): if args and kwargs: raise ValueError("Can not use both args " "and kwargs to create Row") if args: # create row class or objects return tuple.__new__(self, args) elif kwargs: # create row objects names = sorted(kwargs.keys()) row = tuple.__new__(self, [kwargs[n] for n in names]) row.__FIELDS__ = names return row else: raise ValueError("No args or kwargs") def asDict(self): """ Return as an dict """ if not hasattr(self, "__FIELDS__"): raise TypeError("Cannot convert a Row class into dict") return dict(zip(self.__FIELDS__, self)) # let obect acs like class def __call__(self, *args): """create new Row object""" return _create_row(self, args) def __getattr__(self, item): if item.startswith("__"): raise AttributeError(item) try: # it will be slow when it has many fields, # but this will not be used in normal cases idx = self.__FIELDS__.index(item) return self[idx] except IndexError: raise AttributeError(item) def __reduce__(self): if hasattr(self, "__FIELDS__"): return (_create_row, (self.__FIELDS__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): if hasattr(self, "__FIELDS__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) for k, v in zip(self.__FIELDS__, self)) else: return "<Row(%s)>" % ", ".join(self) def _test(): import doctest from pyspark.context import SparkContext # let doctest run in pyspark.sql.types, so DataTypes can be picklable import pyspark.sql.types from pyspark.sql import Row, SQLContext from pyspark.sql.tests import ExamplePoint, ExamplePointUDT globs = pyspark.sql.types.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc globs['sqlCtx'] = sqlCtx = SQLContext(sc) globs['ExamplePoint'] = ExamplePoint globs['ExamplePointUDT'] = ExamplePointUDT (failure_count, test_count) = doctest.testmod( pyspark.sql.types, globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()