Source code for pyspark.sql.types

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import sys
import decimal
import time
import datetime
import calendar
import json
import re
import base64
from array import array
import ctypes

from py4j.protocol import register_input_converter
from py4j.java_gateway import JavaClass

from pyspark import SparkContext
from pyspark.serializers import CloudPickleSerializer

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

[docs]class DataType(object): """Base class for data types.""" 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)
[docs] @classmethod 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)
[docs] def needConversion(self): """ Does this type needs conversion between Python object and internal SQL object. This is used to avoid the unnecessary conversion for ArrayType/MapType/StructType. """ return False
[docs] def toInternal(self, obj): """ Converts a Python object into an internal SQL object. """ return obj
[docs] def fromInternal(self, obj): """ Converts an internal SQL object into a native Python object. """ return obj
# This singleton pattern does not work with pickle, you will get # another object after pickle and unpickle class DataTypeSingleton(type): """Metaclass for DataType""" _instances = {} def __call__(cls): if cls not in cls._instances: cls._instances[cls] = super(DataTypeSingleton, cls).__call__() return cls._instances[cls]
[docs]class NullType(DataType, metaclass=DataTypeSingleton): """Null type. The data type representing None, used for the types that cannot be inferred. """
[docs] @classmethod def typeName(cls): return 'void'
class AtomicType(DataType): """An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps.""" class NumericType(AtomicType): """Numeric data types. """ class IntegralType(NumericType, metaclass=DataTypeSingleton): """Integral data types. """ pass class FractionalType(NumericType): """Fractional data types. """
[docs]class StringType(AtomicType, metaclass=DataTypeSingleton): """String data type. """ pass
[docs]class BinaryType(AtomicType, metaclass=DataTypeSingleton): """Binary (byte array) data type. """ pass
[docs]class BooleanType(AtomicType, metaclass=DataTypeSingleton): """Boolean data type. """ pass
[docs]class DateType(AtomicType, metaclass=DataTypeSingleton): """Date ( data type. """ EPOCH_ORDINAL = datetime.datetime(1970, 1, 1).toordinal()
[docs] def needConversion(self): return True
[docs] def toInternal(self, d): if d is not None: return d.toordinal() - self.EPOCH_ORDINAL
[docs] def fromInternal(self, v): if v is not None: return + self.EPOCH_ORDINAL)
[docs]class TimestampType(AtomicType, metaclass=DataTypeSingleton): """Timestamp (datetime.datetime) data type. """
[docs] def needConversion(self): return True
[docs] def toInternal(self, dt): if dt is not None: seconds = (calendar.timegm(dt.utctimetuple()) if dt.tzinfo else time.mktime(dt.timetuple())) return int(seconds) * 1000000 + dt.microsecond
[docs] def fromInternal(self, ts): if ts is not None: # using int to avoid precision loss in float return datetime.datetime.fromtimestamp(ts // 1000000).replace(microsecond=ts % 1000000)
[docs]class DecimalType(FractionalType): """Decimal (decimal.Decimal) data type. The DecimalType must have fixed precision (the maximum total number of digits) and scale (the number of digits on the right of dot). For example, (5, 2) can support the value from [-999.99 to 999.99]. The precision can be up to 38, the scale must be less or equal to precision. When creating a DecimalType, the default precision and scale is (10, 0). When inferring schema from decimal.Decimal objects, it will be DecimalType(38, 18). Parameters ---------- precision : int, optional the maximum (i.e. total) number of digits (default: 10) scale : int, optional the number of digits on right side of dot. (default: 0) """ def __init__(self, precision=10, scale=0): self.precision = precision self.scale = scale self.hasPrecisionInfo = True # this is a public API
[docs] def simpleString(self): return "decimal(%d,%d)" % (self.precision, self.scale)
[docs] def jsonValue(self): return "decimal(%d,%d)" % (self.precision, self.scale)
def __repr__(self): return "DecimalType(%d,%d)" % (self.precision, self.scale)
[docs]class DoubleType(FractionalType, metaclass=DataTypeSingleton): """Double data type, representing double precision floats. """ pass
[docs]class FloatType(FractionalType, metaclass=DataTypeSingleton): """Float data type, representing single precision floats. """ pass
[docs]class ByteType(IntegralType): """Byte data type, i.e. a signed integer in a single byte. """
[docs] def simpleString(self): return 'tinyint'
[docs]class IntegerType(IntegralType): """Int data type, i.e. a signed 32-bit integer. """
[docs] def simpleString(self): return 'int'
[docs]class LongType(IntegralType): """Long data type, i.e. a signed 64-bit integer. If the values are beyond the range of [-9223372036854775808, 9223372036854775807], please use :class:`DecimalType`. """
[docs] def simpleString(self): return 'bigint'
[docs]class ShortType(IntegralType): """Short data type, i.e. a signed 16-bit integer. """
[docs] def simpleString(self): return 'smallint'
[docs]class ArrayType(DataType): """Array data type. Parameters ---------- elementType : :class:`DataType` :class:`DataType` of each element in the array. containsNull : bool, optional whether the array can contain null (None) values. Examples -------- >>> ArrayType(StringType()) == ArrayType(StringType(), True) True >>> ArrayType(StringType(), False) == ArrayType(StringType()) False """ def __init__(self, elementType, containsNull=True): assert isinstance(elementType, DataType),\ "elementType %s should be an instance of %s" % (elementType, 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] @classmethod def fromJson(cls, json): return ArrayType(_parse_datatype_json_value(json["elementType"]), json["containsNull"])
[docs] def needConversion(self): return self.elementType.needConversion()
[docs] def toInternal(self, obj): if not self.needConversion(): return obj return obj and [self.elementType.toInternal(v) for v in obj]
[docs] def fromInternal(self, obj): if not self.needConversion(): return obj return obj and [self.elementType.fromInternal(v) for v in obj]
[docs]class MapType(DataType): """Map data type. Parameters ---------- keyType : :class:`DataType` :class:`DataType` of the keys in the map. valueType : :class:`DataType` :class:`DataType` of the values in the map. valueContainsNull : bool, optional indicates whether values can contain null (None) values. Notes ----- Keys in a map data type are not allowed to be null (None). Examples -------- >>> (MapType(StringType(), IntegerType()) ... == MapType(StringType(), IntegerType(), True)) True >>> (MapType(StringType(), IntegerType(), False) ... == MapType(StringType(), FloatType())) False """ def __init__(self, keyType, valueType, valueContainsNull=True): assert isinstance(keyType, DataType),\ "keyType %s should be an instance of %s" % (keyType, DataType) assert isinstance(valueType, DataType),\ "valueType %s should be an instance of %s" % (valueType, 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] @classmethod def fromJson(cls, json): return MapType(_parse_datatype_json_value(json["keyType"]), _parse_datatype_json_value(json["valueType"]), json["valueContainsNull"])
[docs] def needConversion(self): return self.keyType.needConversion() or self.valueType.needConversion()
[docs] def toInternal(self, obj): if not self.needConversion(): return obj return obj and dict((self.keyType.toInternal(k), self.valueType.toInternal(v)) for k, v in obj.items())
[docs] def fromInternal(self, obj): if not self.needConversion(): return obj return obj and dict((self.keyType.fromInternal(k), self.valueType.fromInternal(v)) for k, v in obj.items())
[docs]class StructField(DataType): """A field in :class:`StructType`. Parameters ---------- name : str name of the field. dataType : :class:`DataType` :class:`DataType` of the field. nullable : bool, optional whether the field can be null (None) or not. metadata : dict, optional a dict from string to simple type that can be toInternald to JSON automatically Examples -------- >>> (StructField("f1", StringType(), True) ... == StructField("f1", StringType(), True)) True >>> (StructField("f1", StringType(), True) ... == StructField("f2", StringType(), True)) False """ def __init__(self, name, dataType, nullable=True, metadata=None): assert isinstance(dataType, DataType),\ "dataType %s should be an instance of %s" % (dataType, DataType) assert isinstance(name, str), "field name %s should be a string" % (name) = 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] @classmethod def fromJson(cls, json): return StructField(json["name"], _parse_datatype_json_value(json["type"]), json["nullable"], json["metadata"])
[docs] def needConversion(self): return self.dataType.needConversion()
[docs] def toInternal(self, obj): return self.dataType.toInternal(obj)
[docs] def fromInternal(self, obj): return self.dataType.fromInternal(obj)
[docs] def typeName(self): raise TypeError( "StructField does not have typeName. " "Use typeName on its type explicitly instead.")
[docs]class StructType(DataType): """Struct type, consisting of a list of :class:`StructField`. This is the data type representing a :class:`Row`. Iterating a :class:`StructType` will iterate over its :class:`StructField`\\s. A contained :class:`StructField` can be accessed by its name or position. Examples -------- >>> struct1 = StructType([StructField("f1", StringType(), True)]) >>> struct1["f1"] StructField(f1,StringType,true) >>> struct1[0] StructField(f1,StringType,true) >>> 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 """ def __init__(self, fields=None): if not fields: self.fields = [] self.names = [] else: self.fields = fields self.names = [ for f in fields] assert all(isinstance(f, StructField) for f in fields),\ "fields should be a list of StructField" # Precalculated list of fields that need conversion with fromInternal/toInternal functions self._needConversion = [f.needConversion() for f in self] self._needSerializeAnyField = any(self._needConversion)
[docs] def add(self, field, data_type=None, nullable=True, metadata=None): """ Construct a :class:`StructType` by adding new elements to it, to define the schema. The method accepts either: a) A single parameter which is a :class:`StructField` object. b) Between 2 and 4 parameters as (name, data_type, nullable (optional), metadata(optional). The data_type parameter may be either a String or a :class:`DataType` object. Parameters ---------- field : str or :class:`StructField` Either the name of the field or a :class:`StructField` object data_type : :class:`DataType`, optional If present, the DataType of the :class:`StructField` to create nullable : bool, optional Whether the field to add should be nullable (default True) metadata : dict, optional Any additional metadata (default None) Returns ------- :class:`StructType` Examples -------- >>> struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None) >>> struct2 = StructType([StructField("f1", StringType(), True), \\ ... StructField("f2", StringType(), True, None)]) >>> struct1 == struct2 True >>> struct1 = StructType().add(StructField("f1", StringType(), True)) >>> struct2 = StructType([StructField("f1", StringType(), True)]) >>> struct1 == struct2 True >>> struct1 = StructType().add("f1", "string", True) >>> struct2 = StructType([StructField("f1", StringType(), True)]) >>> struct1 == struct2 True """ if isinstance(field, StructField): self.fields.append(field) self.names.append( else: if isinstance(field, str) and data_type is None: raise ValueError("Must specify DataType if passing name of struct_field to create.") if isinstance(data_type, str): data_type_f = _parse_datatype_json_value(data_type) else: data_type_f = data_type self.fields.append(StructField(field, data_type_f, nullable, metadata)) self.names.append(field) # Precalculated list of fields that need conversion with fromInternal/toInternal functions self._needConversion = [f.needConversion() for f in self] self._needSerializeAnyField = any(self._needConversion) return self
def __iter__(self): """Iterate the fields""" return iter(self.fields) def __len__(self): """Return the number of fields.""" return len(self.fields) def __getitem__(self, key): """Access fields by name or slice.""" if isinstance(key, str): for field in self: if == key: return field raise KeyError('No StructField named {0}'.format(key)) elif isinstance(key, int): try: return self.fields[key] except IndexError: raise IndexError('StructType index out of range') elif isinstance(key, slice): return StructType(self.fields[key]) else: raise TypeError('StructType keys should be strings, integers or slices')
[docs] def simpleString(self): return 'struct<%s>' % (','.join(f.simpleString() for f in self))
def __repr__(self): return ("StructType(List(%s))" % ",".join(str(field) for field in self))
[docs] def jsonValue(self): return {"type": self.typeName(), "fields": [f.jsonValue() for f in self]}
[docs] @classmethod def fromJson(cls, json): return StructType([StructField.fromJson(f) for f in json["fields"]])
[docs] def fieldNames(self): """ Returns all field names in a list. Examples -------- >>> struct = StructType([StructField("f1", StringType(), True)]) >>> struct.fieldNames() ['f1'] """ return list(self.names)
[docs] def needConversion(self): # We need convert Row()/namedtuple into tuple() return True
[docs] def toInternal(self, obj): if obj is None: return if self._needSerializeAnyField: # Only calling toInternal function for fields that need conversion if isinstance(obj, dict): return tuple(f.toInternal(obj.get(n)) if c else obj.get(n) for n, f, c in zip(self.names, self.fields, self._needConversion)) elif isinstance(obj, (tuple, list)): return tuple(f.toInternal(v) if c else v for f, v, c in zip(self.fields, obj, self._needConversion)) elif hasattr(obj, "__dict__"): d = obj.__dict__ return tuple(f.toInternal(d.get(n)) if c else d.get(n) for n, f, c in zip(self.names, self.fields, self._needConversion)) else: raise ValueError("Unexpected tuple %r with StructType" % obj) else: if isinstance(obj, dict): return tuple(obj.get(n) for n in self.names) elif isinstance(obj, (list, tuple)): return tuple(obj) elif hasattr(obj, "__dict__"): d = obj.__dict__ return tuple(d.get(n) for n in self.names) else: raise ValueError("Unexpected tuple %r with StructType" % obj)
[docs] def fromInternal(self, obj): if obj is None: return if isinstance(obj, Row): # it's already converted by pickler return obj if self._needSerializeAnyField: # Only calling fromInternal function for fields that need conversion values = [f.fromInternal(v) if c else v for f, v, c in zip(self.fields, obj, self._needConversion)] else: values = obj return _create_row(self.names, values)
class UserDefinedType(DataType): """User-defined type (UDT). .. note:: WARN: Spark Internal Use Only """ @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 (could be '', if there is no corresponding one). """ return '' def needConversion(self): return True @classmethod def _cachedSqlType(cls): """ Cache the sqlType() into class, because it's heavily used in `toInternal`. """ if not hasattr(cls, "_cached_sql_type"): cls._cached_sql_type = cls.sqlType() return cls._cached_sql_type def toInternal(self, obj): if obj is not None: return self._cachedSqlType().toInternal(self.serialize(obj)) def fromInternal(self, obj): v = self._cachedSqlType().fromInternal(obj) if v is not None: return self.deserialize(v) def serialize(self, obj): """ Converts a user-type object into a SQL datum. """ raise NotImplementedError("UDT must implement toInternal().") def deserialize(self, datum): """ Converts a SQL datum into a user-type object. """ raise NotImplementedError("UDT must implement fromInternal().") def simpleString(self): return 'udt' def json(self): return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True) def jsonValue(self): if self.scalaUDT(): assert self.module() != '__main__', 'UDT in __main__ cannot work with ScalaUDT' schema = { "type": "udt", "class": self.scalaUDT(), "pyClass": "%s.%s" % (self.module(), type(self).__name__), "sqlType": self.sqlType().jsonValue() } else: ser = CloudPickleSerializer() b = ser.dumps(type(self)) schema = { "type": "udt", "pyClass": "%s.%s" % (self.module(), type(self).__name__), "serializedClass": base64.b64encode(b).decode('utf8'), "sqlType": self.sqlType().jsonValue() } return schema @classmethod def fromJson(cls, json): pyUDT = str(json["pyClass"]) # convert unicode to str split = pyUDT.rfind(".") pyModule = pyUDT[:split] pyClass = pyUDT[split+1:] m = __import__(pyModule, globals(), locals(), [pyClass]) if not hasattr(m, pyClass): s = base64.b64decode(json['serializedClass'].encode('utf-8')) UDT = CloudPickleSerializer().loads(s) else: UDT = getattr(m, pyClass) return UDT() def __eq__(self, other): return type(self) == type(other) _atomic_types = [StringType, BinaryType, BooleanType, DecimalType, FloatType, DoubleType, ByteType, ShortType, IntegerType, LongType, DateType, TimestampType, NullType] _all_atomic_types = dict((t.typeName(), t) for t in _atomic_types) _all_complex_types = dict((v.typeName(), v) for v in [ArrayType, MapType, StructType]) _FIXED_DECIMAL = re.compile(r"decimal\(\s*(\d+)\s*,\s*(-?\d+)\s*\)") def _parse_datatype_string(s): """ Parses the given data type string to a :class:`DataType`. The data type string format equals :class:`DataType.simpleString`, except that the top level struct type can omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use ``byte`` instead of ``tinyint`` for :class:`ByteType`. We can also use ``int`` as a short name for :class:`IntegerType`. Since Spark 2.3, this also supports a schema in a DDL-formatted string and case-insensitive strings. Examples -------- >>> _parse_datatype_string("int ") IntegerType >>> _parse_datatype_string("INT ") IntegerType >>> _parse_datatype_string("a: byte, b: decimal( 16 , 8 ) ") StructType(List(StructField(a,ByteType,true),StructField(b,DecimalType(16,8),true))) >>> _parse_datatype_string("a DOUBLE, b STRING") StructType(List(StructField(a,DoubleType,true),StructField(b,StringType,true))) >>> _parse_datatype_string("a: array< short>") StructType(List(StructField(a,ArrayType(ShortType,true),true))) >>> _parse_datatype_string(" map<string , string > ") MapType(StringType,StringType,true) >>> # Error cases >>> _parse_datatype_string("blabla") # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ParseException:... >>> _parse_datatype_string("a: int,") # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ParseException:... >>> _parse_datatype_string("array<int") # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ParseException:... >>> _parse_datatype_string("map<int, boolean>>") # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ParseException:... """ sc = SparkContext._active_spark_context def from_ddl_schema(type_str): return _parse_datatype_json_string( def from_ddl_datatype(type_str): return _parse_datatype_json_string( try: # DDL format, "fieldname datatype, fieldname datatype". return from_ddl_schema(s) except Exception as e: try: # For backwards compatibility, "integer", "struct<fieldname: datatype>" and etc. return from_ddl_datatype(s) except: try: # For backwards compatibility, "fieldname: datatype, fieldname: datatype" case. return from_ddl_datatype("struct<%s>" % s.strip()) except: raise e def _parse_datatype_json_string(json_string): """Parses the given data type JSON string. Examples -------- >>> import pickle >>> def check_datatype(datatype): ... pickled = pickle.loads(pickle.dumps(datatype)) ... assert datatype == pickled ... scala_datatype = spark._jsparkSession.parseDataType(datatype.json()) ... python_datatype = _parse_datatype_json_string(scala_datatype.json()) ... assert datatype == python_datatype >>> for cls in _all_atomic_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) """ return _parse_datatype_json_value(json.loads(json_string)) def _parse_datatype_json_value(json_value): if not isinstance(json_value, dict): if json_value in _all_atomic_types.keys(): return _all_atomic_types[json_value]() elif json_value == '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, float: DoubleType, str: StringType, bytearray: BinaryType, decimal.Decimal: DecimalType, DateType, datetime.datetime: TimestampType, datetime.time: TimestampType, bytes: BinaryType, } # 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. Also, JVM only support signed types, when converting unsigned types, # keep in mind that it require 1 more bit when stored as signed types. # # Reference for C integer size, see: # ISO/IEC 9899:201x specification, chapter Sizes of integer types <limits.h>. # Reference for python array typecode, see: # # # Reference for JVM's supported integral types: # _array_signed_int_typecode_ctype_mappings = { 'b': ctypes.c_byte, 'h': ctypes.c_short, 'i': ctypes.c_int, 'l': ctypes.c_long, } _array_unsigned_int_typecode_ctype_mappings = { 'B': ctypes.c_ubyte, 'H': ctypes.c_ushort, 'I': ctypes.c_uint, 'L': ctypes.c_ulong } def _int_size_to_type(size): """ Return the Catalyst datatype 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 # The list of all supported array typecodes, is stored here _array_type_mappings = { # Warning: Actual properties for float and double in C is not specified in C. # On almost every system supported by both python and JVM, they are IEEE 754 # single-precision binary floating-point format and IEEE 754 double-precision # binary floating-point format. And we do assume the same thing here for now. 'f': FloatType, 'd': DoubleType } # compute array typecode mappings for signed integer types for _typecode in _array_signed_int_typecode_ctype_mappings.keys(): size = ctypes.sizeof(_array_signed_int_typecode_ctype_mappings[_typecode]) * 8 dt = _int_size_to_type(size) if dt is not None: _array_type_mappings[_typecode] = dt # compute array typecode mappings for unsigned integer types for _typecode in _array_unsigned_int_typecode_ctype_mappings.keys(): # JVM does not have unsigned types, so use signed types that is at least 1 # bit larger to store size = ctypes.sizeof(_array_unsigned_int_typecode_ctype_mappings[_typecode]) * 8 + 1 dt = _int_size_to_type(size) if dt is not None: _array_type_mappings[_typecode] = dt # Type code 'u' in Python's array is deprecated since version 3.3, and will be # removed in version 4.0. See: if sys.version_info[0] < 4: _array_type_mappings['u'] = StringType def _infer_type(obj): """Infer the DataType from obj """ if obj is None: return NullType() if hasattr(obj, '__UDT__'): return obj.__UDT__ dataType = _type_mappings.get(type(obj)) if dataType is DecimalType: # the precision and scale of `obj` may be different from row to row. return DecimalType(38, 18) elif dataType is not None: return dataType() if isinstance(obj, dict): for key, value in obj.items(): if key is not None and value is not None: return MapType(_infer_type(key), _infer_type(value), True) return MapType(NullType(), NullType(), True) elif isinstance(obj, list): for v in obj: if v is not None: return ArrayType(_infer_type(obj[0]), True) return ArrayType(NullType(), True) elif isinstance(obj, array): if obj.typecode in _array_type_mappings: return ArrayType(_array_type_mappings[obj.typecode](), False) else: raise TypeError("not supported type: array(%s)" % obj.typecode) else: try: return _infer_schema(obj) except TypeError: raise TypeError("not supported type: %s" % type(obj)) def _infer_schema(row, names=None): """Infer the schema from dict/namedtuple/object""" if isinstance(row, dict): items = sorted(row.items()) elif isinstance(row, (tuple, list)): if hasattr(row, "__fields__"): # Row items = zip(row.__fields__, tuple(row)) elif hasattr(row, "_fields"): # namedtuple items = zip(row._fields, tuple(row)) else: if names is None: names = ['_%d' % i for i in range(1, len(row) + 1)] elif len(names) < len(row): names.extend('_%d' % i for i in range(len(names) + 1, len(row) + 1)) items = zip(names, row) elif hasattr(row, "__dict__"): # object items = sorted(row.__dict__.items()) else: raise TypeError("Can not infer schema for type: %s" % type(row)) fields = [] for k, v in items: try: fields.append(StructField(k, _infer_type(v), True)) except TypeError as e: raise TypeError("Unable to infer the type of the field {}.".format(k)) from e return StructType(fields) def _has_nulltype(dt): """ Return whether there is a 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, name=None): if name is None: new_msg = lambda msg: msg new_name = lambda n: "field %s" % n else: new_msg = lambda msg: "%s: %s" % (name, msg) new_name = lambda n: "field %s in %s" % (n, name) 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(new_msg("Can not merge type %s and %s" % (type(a), type(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()), name=new_name( 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, name='element in array %s' % name), True) elif isinstance(a, MapType): return MapType(_merge_type(a.keyType, b.keyType, name='key of map %s' % name), _merge_type(a.valueType, b.valueType, name='value of map %s' % name), 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 a 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: [conv(v) for v in 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.items()) 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 TypeError("Unexpected obj type: %s" % type(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 _acceptable_types = { BooleanType: (bool,), ByteType: (int,), ShortType: (int,), IntegerType: (int,), LongType: (int,), FloatType: (float,), DoubleType: (float,), DecimalType: (decimal.Decimal,), StringType: (str,), BinaryType: (bytearray, bytes), DateType: (, datetime.datetime), TimestampType: (datetime.datetime,), ArrayType: (list, tuple, array), MapType: (dict,), StructType: (tuple, list, dict), } def _make_type_verifier(dataType, nullable=True, name=None): """ Make a verifier that checks the type of obj against dataType and raises a TypeError if they do not match. This verifier also checks the value of obj against datatype and raises a ValueError if it's not within the allowed range, e.g. using 128 as ByteType will overflow. Note that, Python float is not checked, so it will become infinity when cast to Java float, if it overflows. Examples -------- >>> _make_type_verifier(StructType([]))(None) >>> _make_type_verifier(StringType())("") >>> _make_type_verifier(LongType())(0) >>> _make_type_verifier(LongType())(1 << 64) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> _make_type_verifier(ArrayType(ShortType()))(list(range(3))) >>> _make_type_verifier(ArrayType(StringType()))(set()) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... TypeError:... >>> _make_type_verifier(MapType(StringType(), IntegerType()))({}) >>> _make_type_verifier(StructType([]))(()) >>> _make_type_verifier(StructType([]))([]) >>> _make_type_verifier(StructType([]))([1]) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> # Check if numeric values are within the allowed range. >>> _make_type_verifier(ByteType())(12) >>> _make_type_verifier(ByteType())(1234) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> _make_type_verifier(ByteType(), False)(None) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> _make_type_verifier( ... ArrayType(ShortType(), False))([1, None]) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... >>> _make_type_verifier(MapType(StringType(), IntegerType()))({None: 1}) Traceback (most recent call last): ... ValueError:... >>> schema = StructType().add("a", IntegerType()).add("b", StringType(), False) >>> _make_type_verifier(schema)((1, None)) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ValueError:... """ if name is None: new_msg = lambda msg: msg new_name = lambda n: "field %s" % n else: new_msg = lambda msg: "%s: %s" % (name, msg) new_name = lambda n: "field %s in %s" % (n, name) def verify_nullability(obj): if obj is None: if nullable: return True else: raise ValueError(new_msg("This field is not nullable, but got None")) else: return False _type = type(dataType) def assert_acceptable_types(obj): assert _type in _acceptable_types, \ new_msg("unknown datatype: %s for object %r" % (dataType, obj)) def verify_acceptable_types(obj): # subclass of them can not be fromInternal in JVM if type(obj) not in _acceptable_types[_type]: raise TypeError(new_msg("%s can not accept object %r in type %s" % (dataType, obj, type(obj)))) if isinstance(dataType, StringType): # StringType can work with any types verify_value = lambda _: _ elif isinstance(dataType, UserDefinedType): verifier = _make_type_verifier(dataType.sqlType(), name=name) def verify_udf(obj): if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType): raise ValueError(new_msg("%r is not an instance of type %r" % (obj, dataType))) verifier(dataType.toInternal(obj)) verify_value = verify_udf elif isinstance(dataType, ByteType): def verify_byte(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) if obj < -128 or obj > 127: raise ValueError(new_msg("object of ByteType out of range, got: %s" % obj)) verify_value = verify_byte elif isinstance(dataType, ShortType): def verify_short(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) if obj < -32768 or obj > 32767: raise ValueError(new_msg("object of ShortType out of range, got: %s" % obj)) verify_value = verify_short elif isinstance(dataType, IntegerType): def verify_integer(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) if obj < -2147483648 or obj > 2147483647: raise ValueError( new_msg("object of IntegerType out of range, got: %s" % obj)) verify_value = verify_integer elif isinstance(dataType, LongType): def verify_long(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) if obj < -9223372036854775808 or obj > 9223372036854775807: raise ValueError( new_msg("object of LongType out of range, got: %s" % obj)) verify_value = verify_long elif isinstance(dataType, ArrayType): element_verifier = _make_type_verifier( dataType.elementType, dataType.containsNull, name="element in array %s" % name) def verify_array(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) for i in obj: element_verifier(i) verify_value = verify_array elif isinstance(dataType, MapType): key_verifier = _make_type_verifier(dataType.keyType, False, name="key of map %s" % name) value_verifier = _make_type_verifier( dataType.valueType, dataType.valueContainsNull, name="value of map %s" % name) def verify_map(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) for k, v in obj.items(): key_verifier(k) value_verifier(v) verify_value = verify_map elif isinstance(dataType, StructType): verifiers = [] for f in dataType.fields: verifier = _make_type_verifier(f.dataType, f.nullable, name=new_name( verifiers.append((, verifier)) def verify_struct(obj): assert_acceptable_types(obj) if isinstance(obj, dict): for f, verifier in verifiers: verifier(obj.get(f)) elif isinstance(obj, (tuple, list)): if len(obj) != len(verifiers): raise ValueError( new_msg("Length of object (%d) does not match with " "length of fields (%d)" % (len(obj), len(verifiers)))) for v, (_, verifier) in zip(obj, verifiers): verifier(v) elif hasattr(obj, "__dict__"): d = obj.__dict__ for f, verifier in verifiers: verifier(d.get(f)) else: raise TypeError(new_msg("StructType can not accept object %r in type %s" % (obj, type(obj)))) verify_value = verify_struct else: def verify_default(obj): assert_acceptable_types(obj) verify_acceptable_types(obj) verify_value = verify_default def verify(obj): if not verify_nullability(obj): verify_value(obj) return verify # This is used to unpickle a Row from JVM def _create_row_inbound_converter(dataType): return lambda *a: dataType.fromInternal(a) def _create_row(fields, values): row = Row(*values) row.__fields__ = fields return row
[docs]class Row(tuple): """ A row in :class:`DataFrame`. The fields in it can be accessed: * like attributes (``row.key``) * like dictionary values (``row[key]``) ``key in row`` will search through row keys. Row can be used to create a row object by using named arguments. It is not allowed to omit a named argument to represent that the value is None or missing. This should be explicitly set to None in this case. .. versionchanged:: 3.0.0 Rows created from named arguments no longer have field names sorted alphabetically and will be ordered in the position as entered. Examples -------- >>> row = Row(name="Alice", age=11) >>> row Row(name='Alice', age=11) >>> row['name'], row['age'] ('Alice', 11) >>>, row.age ('Alice', 11) >>> 'name' in row True >>> 'wrong_key' in row False 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')> >>> 'name' in Person True >>> 'wrong_key' in Person False >>> Person("Alice", 11) Row(name='Alice', age=11) This form can also be used to create rows as tuple values, i.e. with unnamed fields. >>> row1 = Row("Alice", 11) >>> row2 = Row(name="Alice", age=11) >>> row1 == row2 True """ def __new__(cls, *args, **kwargs): if args and kwargs: raise ValueError("Can not use both args " "and kwargs to create Row") if kwargs: # create row objects row = tuple.__new__(cls, list(kwargs.values())) row.__fields__ = list(kwargs.keys()) return row else: # create row class or objects return tuple.__new__(cls, args)
[docs] def asDict(self, recursive=False): """ Return as a dict Parameters ---------- recursive : bool, optional turns the nested Rows to dict (default: False). Notes ----- If a row contains duplicate field names, e.g., the rows of a join between two :class:`DataFrame` that both have the fields of same names, one of the duplicate fields will be selected by ``asDict``. ``__getitem__`` will also return one of the duplicate fields, however returned value might be different to ``asDict``. Examples -------- >>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11} True >>> row = Row(key=1, value=Row(name='a', age=2)) >>> row.asDict() == {'key': 1, 'value': Row(name='a', age=2)} True >>> row.asDict(True) == {'key': 1, 'value': {'name': 'a', 'age': 2}} True """ if not hasattr(self, "__fields__"): raise TypeError("Cannot convert a Row class into dict") if recursive: def conv(obj): if isinstance(obj, Row): return obj.asDict(True) elif isinstance(obj, list): return [conv(o) for o in obj] elif isinstance(obj, dict): return dict((k, conv(v)) for k, v in obj.items()) else: return obj return dict(zip(self.__fields__, (conv(o) for o in self))) else: return dict(zip(self.__fields__, self))
def __contains__(self, item): if hasattr(self, "__fields__"): return item in self.__fields__ else: return super(Row, self).__contains__(item) # let object acts like class def __call__(self, *args): """create new Row object""" if len(args) > len(self): raise ValueError("Can not create Row with fields %s, expected %d values " "but got %s" % (self, len(self), args)) return _create_row(self, args) def __getitem__(self, item): if isinstance(item, (int, slice)): return super(Row, self).__getitem__(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 super(Row, self).__getitem__(idx) except IndexError: raise KeyError(item) except ValueError: raise ValueError(item) 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) except ValueError: raise AttributeError(item) def __setattr__(self, key, value): if key != '__fields__': raise RuntimeError("Row is read-only") self.__dict__[key] = value def __reduce__(self): """Returns a tuple so Python knows how to pickle Row.""" if hasattr(self, "__fields__"): return (_create_row, (self.__fields__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): """Printable representation of Row used in Python REPL.""" if hasattr(self, "__fields__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) for k, v in zip(self.__fields__, tuple(self))) else: return "<Row(%s)>" % ", ".join("%r" % field for field in self)
class DateConverter(object): def can_convert(self, obj): return isinstance(obj, def convert(self, obj, gateway_client): Date = JavaClass("java.sql.Date", gateway_client) return Date.valueOf(obj.strftime("%Y-%m-%d")) class DatetimeConverter(object): def can_convert(self, obj): return isinstance(obj, datetime.datetime) def convert(self, obj, gateway_client): Timestamp = JavaClass("java.sql.Timestamp", gateway_client) seconds = (calendar.timegm(obj.utctimetuple()) if obj.tzinfo else time.mktime(obj.timetuple())) t = Timestamp(int(seconds) * 1000) t.setNanos(obj.microsecond * 1000) return t # datetime is a subclass of date, we should register DatetimeConverter first register_input_converter(DatetimeConverter()) register_input_converter(DateConverter()) def _test(): import doctest from pyspark.context import SparkContext from pyspark.sql import SparkSession globs = globals() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc globs['spark'] = SparkSession.builder.getOrCreate() (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()