def test_data_type_ops(self):
_mock_spark_type = DataType()
_mock_dtype = ExtensionDtype()
_mappings = (
(CategoricalDtype(), _mock_spark_type, CategoricalOps),
(_mock_dtype, DecimalType(), DecimalOps),
(_mock_dtype, FractionalType(), FractionalOps),
(_mock_dtype, IntegralType(), IntegralOps),
(_mock_dtype, StringType(), StringOps),
(_mock_dtype, BooleanType(), BooleanOps),
(_mock_dtype, TimestampType(), DatetimeOps),
(_mock_dtype, TimestampNTZType(), DatetimeNTZOps),
(_mock_dtype, DateType(), DateOps),
(_mock_dtype, DayTimeIntervalType(), TimedeltaOps),
(_mock_dtype, BinaryType(), BinaryOps),
(_mock_dtype, ArrayType(StringType()), ArrayOps),
(_mock_dtype, MapType(StringType(), IntegralType()), MapOps),
(_mock_dtype, StructType(), StructOps),
(_mock_dtype, NullType(), NullOps),
(_mock_dtype, UserDefinedType(), UDTOps),
)
for _dtype, _spark_type, _ops in _mappings:
self.assertIsInstance(DataTypeOps(_dtype, _spark_type), _ops)
_unknow_spark_type = _mock_spark_type
self.assertRaises(TypeError, DataTypeOps, BooleanType(),
_unknow_spark_type)
def setUpClass(cls):
from datetime import date, datetime
from decimal import Decimal
super(ArrowTests, cls).setUpClass()
cls.warnings_lock = threading.Lock()
# Synchronize default timezone between Python and Java
cls.tz_prev = os.environ.get("TZ", None) # save current tz if set
tz = "America/Los_Angeles"
os.environ["TZ"] = tz
time.tzset()
cls.spark.conf.set("spark.sql.session.timeZone", tz)
# Test fallback
cls.spark.conf.set("spark.sql.execution.arrow.enabled", "false")
assert cls.spark.conf.get(
"spark.sql.execution.arrow.pyspark.enabled") == "false"
cls.spark.conf.set("spark.sql.execution.arrow.enabled", "true")
assert cls.spark.conf.get(
"spark.sql.execution.arrow.pyspark.enabled") == "true"
cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled",
"true")
assert cls.spark.conf.get(
"spark.sql.execution.arrow.pyspark.fallback.enabled") == "true"
cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled",
"false")
assert cls.spark.conf.get(
"spark.sql.execution.arrow.pyspark.fallback.enabled") == "false"
# Enable Arrow optimization in this tests.
cls.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
# Disable fallback by default to easily detect the failures.
cls.spark.conf.set(
"spark.sql.execution.arrow.pyspark.fallback.enabled", "false")
cls.schema_wo_null = StructType([
StructField("1_str_t", StringType(), True),
StructField("2_int_t", IntegerType(), True),
StructField("3_long_t", LongType(), True),
StructField("4_float_t", FloatType(), True),
StructField("5_double_t", DoubleType(), True),
StructField("6_decimal_t", DecimalType(38, 18), True),
StructField("7_date_t", DateType(), True),
StructField("8_timestamp_t", TimestampType(), True),
StructField("9_binary_t", BinaryType(), True)
])
cls.schema = cls.schema_wo_null.add("10_null_t", NullType(), True)
cls.data_wo_null = [
(u"a", 1, 10, 0.2, 2.0, Decimal("2.0"), date(1969, 1, 1),
datetime(1969, 1, 1, 1, 1, 1), bytearray(b"a")),
(u"b", 2, 20, 0.4, 4.0, Decimal("4.0"), date(2012, 2, 2),
datetime(2012, 2, 2, 2, 2, 2), bytearray(b"bb")),
(u"c", 3, 30, 0.8, 6.0, Decimal("6.0"), date(2100, 3, 3),
datetime(2100, 3, 3, 3, 3, 3), bytearray(b"ccc")),
(u"d", 4, 40, 1.0, 8.0, Decimal("8.0"), date(2262, 4, 12),
datetime(2262, 3, 3, 3, 3, 3), bytearray(b"dddd")),
]
cls.data = [tuple(list(d) + [None]) for d in cls.data_wo_null]
def makeSchema(columns):
struct_field_map = {'string': StringType(),
'date': TimestampType(),
'double': DoubleType(),
'int': IntegerType(),
'none': NullType()}
fields = [StructField(k, struct_field_map[v], True) for k, v in columns]
return StructType(fields)
def from_arrow_type(at):
""" Convert pyarrow type to Spark data type.
"""
from distutils.version import LooseVersion
import pyarrow as pa
import pyarrow.types as types
if types.is_boolean(at):
spark_type = BooleanType()
elif types.is_int8(at):
spark_type = ByteType()
elif types.is_int16(at):
spark_type = ShortType()
elif types.is_int32(at):
spark_type = IntegerType()
elif types.is_int64(at):
spark_type = LongType()
elif types.is_float32(at):
spark_type = FloatType()
elif types.is_float64(at):
spark_type = DoubleType()
elif types.is_decimal(at):
spark_type = DecimalType(precision=at.precision, scale=at.scale)
elif types.is_string(at):
spark_type = StringType()
elif types.is_binary(at):
spark_type = BinaryType()
elif types.is_date32(at):
spark_type = DateType()
elif types.is_timestamp(at):
spark_type = TimestampType()
elif types.is_list(at):
if types.is_timestamp(at.value_type):
raise TypeError("Unsupported type in conversion from Arrow: " + str(at))
spark_type = ArrayType(from_arrow_type(at.value_type))
elif types.is_map(at):
if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
raise TypeError("MapType is only supported with pyarrow 2.0.0 and above")
if types.is_timestamp(at.key_type) or types.is_timestamp(at.item_type):
raise TypeError("Unsupported type in conversion from Arrow: " + str(at))
spark_type = MapType(from_arrow_type(at.key_type), from_arrow_type(at.item_type))
elif types.is_struct(at):
if any(types.is_struct(field.type) for field in at):
raise TypeError("Nested StructType not supported in conversion from Arrow: " + str(at))
return StructType(
[StructField(field.name, from_arrow_type(field.type), nullable=field.nullable)
for field in at])
elif types.is_dictionary(at):
spark_type = from_arrow_type(at.value_type)
elif types.is_null(at):
spark_type = NullType()
else:
raise TypeError("Unsupported type in conversion from Arrow: " + str(at))
return spark_type
def test_unsupported_types(self):
common_err_msg = 'Invalid return type.*grouped map Pandas UDF.*'
unsupported_types = [
StructField('arr_ts', ArrayType(TimestampType())),
StructField('null', NullType()),
StructField('struct', StructType([StructField('l', LongType())])),
]
for unsupported_type in unsupported_types:
schema = StructType([StructField('id', LongType(), True), unsupported_type])
with QuietTest(self.sc):
with self.assertRaisesRegex(NotImplementedError, common_err_msg):
pandas_udf(lambda x: x, schema, PandasUDFType.GROUPED_MAP)
def test_get_col_info(self):
with spark_session('test_get_col_info') as spark:
data = [[
0,
0.0,
None,
[1, 1],
DenseVector([1.0, 1.0]),
SparseVector(2, {1: 1.0}),
DenseVector([1.0, 1.0])
], [
1,
None,
None,
[1, 1],
DenseVector([1.0, 1.0]),
SparseVector(2, {1: 1.0}),
SparseVector(2, {1: 1.0})
]]
schema = StructType([
StructField('int', IntegerType()),
StructField('float', FloatType()),
StructField('null', NullType()),
StructField('array', ArrayType(IntegerType())),
StructField('dense', VectorUDT()),
StructField('sparse', VectorUDT()),
StructField('mixed', VectorUDT())
])
df = create_test_data_from_schema(spark, data, schema)
all_col_types, col_shapes, col_max_sizes = util._get_col_info(df)
expected = [
('int', {int}, 1, 1),
('float', {float, NullType}, 1, 1),
('null', {NullType}, 1, 1),
('array', {list}, 2, 2),
('dense', {DenseVector}, 2, 2),
('sparse', {SparseVector}, 2, 1),
('mixed', {DenseVector, SparseVector}, 2, 2)
]
for expected_col_info in expected:
col_name, col_types, col_shape, col_size = expected_col_info
assert all_col_types[col_name] == col_types, col_name
assert col_shapes[col_name] == col_shape, col_name
assert col_max_sizes[col_name] == col_size, col_name
def test_toPandas_empty_df_arrow_enabled(self):
# SPARK-30537 test that toPandas() on an empty dataframe has the correct dtypes
# when arrow is enabled
from datetime import date
from decimal import Decimal
schema = StructType([
StructField("a", StringType(), True),
StructField("a", IntegerType(), True),
StructField("c", TimestampType(), True),
StructField("d", NullType(), True),
StructField("e", LongType(), True),
StructField("f", FloatType(), True),
StructField("g", DateType(), True),
StructField("h", BinaryType(), True),
StructField("i", DecimalType(38, 18), True),
StructField("k", TimestampNTZType(), True),
StructField("L", DayTimeIntervalType(0, 3), True),
])
df = self.spark.createDataFrame(self.spark.sparkContext.emptyRDD(),
schema=schema)
non_empty_df = self.spark.createDataFrame(
[(
"a",
1,
datetime.datetime(1969, 1, 1, 1, 1, 1),
None,
10,
0.2,
date(1969, 1, 1),
bytearray(b"a"),
Decimal("2.0"),
datetime.datetime(1969, 1, 1, 1, 1, 1),
datetime.timedelta(microseconds=123),
)],
schema=schema,
)
pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
pdf_non_empty, pdf_arrow_non_empty = self._toPandas_arrow_toggle(
non_empty_df)
assert_frame_equal(pdf, pdf_arrow)
self.assertTrue(pdf_arrow.dtypes.equals(pdf_arrow_non_empty.dtypes))
self.assertTrue(pdf_arrow.dtypes.equals(pdf_non_empty.dtypes))
package_id_product_id_map = entity_package_data_frame.dropna(
subset=['PACKAGE_FDC_ID', 'ESD_PRODUCT_FDC_ID']).set_index(
'PACKAGE_FDC_ID')['ESD_PRODUCT_FDC_ID'].to_dict()
# In[ ]:
# package_id_product_id_map
# In[ ]:
from pyspark.sql.types import NullType
audit_data_frame = audit_data_frame.dropna(subset=['PACKAGE_FDC_ID'])
get_product_id = spark_session.udf.register('get_product_id',
lambda package_id: package_id_product_id_map[package_id] \
if package_id in package_id_product_id_map else NullType())
audit_data_frame = audit_data_frame.withColumn(
'PRODUCT_FDC_ID', get_product_id('PACKAGE_FDC_ID'))
audit_data_frame = audit_data_frame.dropna(subset=['PRODUCT_FDC_ID'])
audit_data_frame.head(1)
# In[ ]:
audit_data_frame.count()
# In[ ]:
from pyspark.ml.feature import StringIndexer
str_indexer = StringIndexer(inputCol='PRODUCT_FDC_ID', outputCol='label')
audit_data_frame = str_indexer.fit(audit_data_frame).transform(
audit_data_frame)
def test_supported_types(self):
values = [
1, 2, 3, 4, 5, 1.1, 2.2,
Decimal(1.123), [1, 2, 2], True, 'hello',
bytearray([0x01, 0x02]), None
]
output_fields = [('id', IntegerType()), ('byte', ByteType()),
('short', ShortType()), ('int', IntegerType()),
('long', LongType()), ('float', FloatType()),
('double', DoubleType()),
('decim', DecimalType(10, 3)),
('array', ArrayType(IntegerType())),
('bool', BooleanType()), ('str', StringType()),
('bin', BinaryType()), ('null', NullType())]
output_schema = StructType([StructField(*x) for x in output_fields])
df = self.spark.createDataFrame([values], schema=output_schema)
# Different forms of group map pandas UDF, results of these are the same
udf1 = pandas_udf(
lambda pdf: pdf.assign(byte=pdf.byte * 2,
short=pdf.short * 2,
int=pdf.int * 2,
long=pdf.long * 2,
float=pdf.float * 2,
double=pdf.double * 2,
decim=pdf.decim * 2,
bool=False if pdf.bool else True,
str=pdf.str + 'there',
array=pdf.array,
bin=pdf.bin,
null=pdf.null), output_schema,
PandasUDFType.GROUPED_MAP)
udf2 = pandas_udf(
lambda _, pdf: pdf.assign(byte=pdf.byte * 2,
short=pdf.short * 2,
int=pdf.int * 2,
long=pdf.long * 2,
float=pdf.float * 2,
double=pdf.double * 2,
decim=pdf.decim * 2,
bool=False if pdf.bool else True,
str=pdf.str + 'there',
array=pdf.array,
bin=pdf.bin,
null=pdf.null), output_schema,
PandasUDFType.GROUPED_MAP)
udf3 = pandas_udf(
lambda key, pdf: pdf.assign(id=key[0],
byte=pdf.byte * 2,
short=pdf.short * 2,
int=pdf.int * 2,
long=pdf.long * 2,
float=pdf.float * 2,
double=pdf.double * 2,
decim=pdf.decim * 2,
bool=False if pdf.bool else True,
str=pdf.str + 'there',
array=pdf.array,
bin=pdf.bin,
null=pdf.null), output_schema,
PandasUDFType.GROUPED_MAP)
result1 = df.groupby('id').apply(udf1).sort('id').toPandas()
expected1 = df.toPandas().groupby('id').apply(
udf1.func).reset_index(drop=True)
result2 = df.groupby('id').apply(udf2).sort('id').toPandas()
expected2 = expected1
result3 = df.groupby('id').apply(udf3).sort('id').toPandas()
expected3 = expected1
assert_frame_equal(expected1, result1)
assert_frame_equal(expected2, result2)
assert_frame_equal(expected3, result3)
"null": "null",
"vector": "vector",
"timestamp": "datetime"
}
SPARK_DTYPES_DICT = {"string": StringType, "int": IntegerType, "float": FloatType,
"double": DoubleType, "boolean": BooleanType, "struct": StructType, "array": ArrayType,
"bigint": LongType, "date": DateType, "byte": ByteType, "short": ShortType,
"datetime": TimestampType, "binary": BinaryType, "null": NullType, "vector": VectorUDT
}
SPARK_DTYPES_DICT_OBJECTS = \
{"string": StringType(), "int": IntegerType(), "float": FloatType(),
"double": DoubleType(), "boolean": BooleanType(), "struct": StructType(), "array": ArrayType(StringType()),
"bigint": LongType(), "date": DateType(), "byte": ByteType(), "short": ShortType(),
"datetime": TimestampType(), "binary": BinaryType(), "null": NullType()
}
# Profiler
PROFILER_COLUMN_TYPES = {"categorical", "numeric", "date", "null", "array", "binary"}
SPARK_DTYPES_TO_PROFILER = {"int": ["smallint", "tinyint", "bigint", "int"], "decimal": ["float", "double"],
"string": "string", "date": {"date", "timestamp"}, "boolean": "boolean", "binary": "binary",
"array": "array", "object": "object", "null": "null", "missing": "missing"}
from enum import Enum
class Actions(Enum):
"""
Actions that modify a columns.
def test_supported_types(self):
values = [
1,
2,
3,
4,
5,
1.1,
2.2,
Decimal(1.123),
[1, 2, 2],
True,
"hello",
bytearray([0x01, 0x02]),
None,
]
output_fields = [
("id", IntegerType()),
("byte", ByteType()),
("short", ShortType()),
("int", IntegerType()),
("long", LongType()),
("float", FloatType()),
("double", DoubleType()),
("decim", DecimalType(10, 3)),
("array", ArrayType(IntegerType())),
("bool", BooleanType()),
("str", StringType()),
("bin", BinaryType()),
("null", NullType()),
]
output_schema = StructType([StructField(*x) for x in output_fields])
df = self.spark.createDataFrame([values], schema=output_schema)
# Different forms of group map pandas UDF, results of these are the same
udf1 = pandas_udf(
lambda pdf: pdf.assign(
byte=pdf.byte * 2,
short=pdf.short * 2,
int=pdf.int * 2,
long=pdf.long * 2,
float=pdf.float * 2,
double=pdf.double * 2,
decim=pdf.decim * 2,
bool=False if pdf.bool else True,
str=pdf.str + "there",
array=pdf.array,
bin=pdf.bin,
null=pdf.null,
),
output_schema,
PandasUDFType.GROUPED_MAP,
)
udf2 = pandas_udf(
lambda _, pdf: pdf.assign(
byte=pdf.byte * 2,
short=pdf.short * 2,
int=pdf.int * 2,
long=pdf.long * 2,
float=pdf.float * 2,
double=pdf.double * 2,
decim=pdf.decim * 2,
bool=False if pdf.bool else True,
str=pdf.str + "there",
array=pdf.array,
bin=pdf.bin,
null=pdf.null,
),
output_schema,
PandasUDFType.GROUPED_MAP,
)
udf3 = pandas_udf(
lambda key, pdf: pdf.assign(
id=key[0],
byte=pdf.byte * 2,
short=pdf.short * 2,
int=pdf.int * 2,
long=pdf.long * 2,
float=pdf.float * 2,
double=pdf.double * 2,
decim=pdf.decim * 2,
bool=False if pdf.bool else True,
str=pdf.str + "there",
array=pdf.array,
bin=pdf.bin,
null=pdf.null,
),
output_schema,
PandasUDFType.GROUPED_MAP,
)
result1 = df.groupby("id").apply(udf1).sort("id").toPandas()
expected1 = df.toPandas().groupby("id").apply(udf1.func).reset_index(drop=True)
result2 = df.groupby("id").apply(udf2).sort("id").toPandas()
expected2 = expected1
result3 = df.groupby("id").apply(udf3).sort("id").toPandas()
expected3 = expected1
assert_frame_equal(expected1, result1)
assert_frame_equal(expected2, result2)
assert_frame_equal(expected3, result3)
def sql_types_example(spark):
# DataType
dp = DataType()
python_obj = dp.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = dp.toInternal(1)
print(sql_obj, type(sql_obj))
print(dp.json())
print(dp.jsonValue())
print(dp.needConversion())
print(dp.simpleString())
print(DataType.typeName())
# NullType
nt = NullType()
python_obj = nt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = nt.toInternal(1)
print(sql_obj, type(sql_obj))
print(nt.json())
print(nt.jsonValue())
print(nt.needConversion())
print(nt.simpleString())
print(NullType.typeName())
# AtomicType
at = AtomicType()
python_obj = at.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = at.toInternal(1)
print(sql_obj, type(sql_obj))
print(at.json())
print(at.jsonValue())
print(at.needConversion())
print(at.simpleString())
print(AtomicType.typeName())
# NumericType
nt = NumericType()
python_obj = nt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = nt.toInternal(1)
print(sql_obj, type(sql_obj))
print(nt.json())
print(nt.jsonValue())
print(nt.needConversion())
print(nt.simpleString())
print(NumericType.typeName())
# IntegralType
it = IntegralType()
python_obj = it.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = it.toInternal(1)
print(sql_obj, type(sql_obj))
print(it.json())
print(it.jsonValue())
print(it.needConversion())
print(it.simpleString())
print(IntegralType.typeName())
# FractionalType
ft = FractionalType()
python_obj = ft.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = ft.toInternal(1)
print(sql_obj, type(sql_obj))
print(ft.json())
print(ft.jsonValue())
print(ft.needConversion())
print(ft.simpleString())
print(FractionalType.typeName())
# StringType
st = StringType()
python_obj = st.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = st.toInternal(1)
print(sql_obj, type(sql_obj))
print(st.json())
print(st.jsonValue())
print(st.needConversion())
print(st.simpleString())
print(StringType.typeName())
# BinaryType
bt = BinaryType()
python_obj = bt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = bt.toInternal(1)
print(sql_obj, type(sql_obj))
print(bt.json())
print(bt.jsonValue())
print(bt.needConversion())
print(bt.simpleString())
print(BinaryType.typeName())
# BooleanType
bt = BooleanType()
python_obj = bt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = bt.toInternal(1)
print(sql_obj, type(sql_obj))
print(bt.json())
print(bt.jsonValue())
print(bt.needConversion())
print(bt.simpleString())
print(BooleanType.typeName())
# DateType
from datetime import datetime
dt = DateType()
python_obj = dt.fromInternal(1000)
print(python_obj, type(python_obj))
today = datetime.today()
sql_obj = dt.toInternal(today)
print(sql_obj, type(sql_obj))
print(dt.json())
print(dt.jsonValue())
print(dt.needConversion())
print(dt.simpleString())
print(DateType.typeName())
# TimestampType
tt = TimestampType()
python_obj = tt.fromInternal(365000000)
print(python_obj, type(python_obj))
today = datetime.today()
sql_obj = tt.toInternal(today)
print(sql_obj, type(sql_obj))
print(tt.json())
print(tt.jsonValue())
print(tt.needConversion())
print(tt.simpleString())
print(TimestampType.typeName())
# DecimalType
dt = DecimalType()
python_obj = dt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = dt.toInternal(1)
print(sql_obj, type(sql_obj))
print(dt.json())
print(dt.jsonValue())
print(dt.needConversion())
print(dt.simpleString())
print(DecimalType.typeName())
# DoubleType
dt = DoubleType()
python_obj = dt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = dt.toInternal(1)
print(sql_obj, type(sql_obj))
print(dt.json())
print(dt.jsonValue())
print(dt.needConversion())
print(dt.simpleString())
print(DoubleType.typeName())
# FloatType
ft = FloatType()
python_obj = ft.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = ft.toInternal(1)
print(sql_obj, type(sql_obj))
print(ft.json())
print(ft.jsonValue())
print(ft.needConversion())
print(ft.simpleString())
print(FloatType.typeName())
# ByteType
bt = ByteType()
python_obj = bt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = bt.toInternal(1)
print(sql_obj, type(sql_obj))
print(bt.json())
print(bt.jsonValue())
print(bt.needConversion())
print(bt.simpleString())
print(ByteType.typeName())
# IntegerType
it = IntegerType()
python_obj = it.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = it.toInternal(1)
print(sql_obj, type(sql_obj))
print(it.json())
print(it.jsonValue())
print(it.needConversion())
print(it.simpleString())
print(IntegerType.typeName())
# LongType
lt = LongType()
python_obj = lt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = lt.toInternal(1)
print(sql_obj, type(sql_obj))
print(lt.json())
print(lt.jsonValue())
print(lt.needConversion())
print(lt.simpleString())
print(LongType.typeName())
# ShortType
st = ShortType()
python_obj = st.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = st.toInternal(1)
print(sql_obj, type(sql_obj))
print(st.json())
print(st.jsonValue())
print(st.needConversion())
print(st.simpleString())
print(ShortType.typeName())
# ArrayType
dt = DataType()
at = ArrayType(dt)
python_obj = at.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = at.toInternal(1)
print(sql_obj, type(sql_obj))
print(at.json())
print(at.jsonValue())
print(at.needConversion())
print(at.simpleString())
print(ArrayType.typeName())
print(ArrayType.fromJson({"containsNull": True, "elementType": "string"}))
# MapType
key_type = DataType()
value_type = DataType()
mt = MapType(key_type, value_type)
python_obj = mt.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = mt.toInternal(1)
print(sql_obj, type(sql_obj))
print(mt.json())
print(mt.jsonValue())
print(mt.needConversion())
print(mt.simpleString())
print(MapType.typeName())
print(MapType.fromJson({"valueContainsNull": True, "keyType": "string", "valueType": "integer"}))
# StructField
dt = DataType()
sf = StructField("first_struct", dt)
python_obj = sf.fromInternal(1)
print(python_obj, type(python_obj))
sql_obj = sf.toInternal(1)
print(sql_obj, type(sql_obj))
print(sf.json())
print(sf.jsonValue())
print(sf.needConversion())
print(sf.simpleString())
print(StructField.fromJson({"metadata": None, "nullable": True, "name": "first_struct", "type": "string"}))
# StructType
string_type = StringType()
st = StructType([StructField("first_struct", StringType()), StructField("second_struct", DataType())])
print("------")
print(st.names)
print(st.fields)
print(st._needConversion)
print(st._needSerializeAnyField)
python_obj = st.fromInternal(["first_struct", "second_struct"])
print(python_obj, type(python_obj))
sql_obj = st.toInternal(["first_struct", "second_struct"])
print(sql_obj, type(sql_obj))
print(st.json())
print(st.jsonValue())
print(st.needConversion())
print(st.simpleString())
print(st.fieldNames())
fields = {
"fields": [
{"metadata": None, "nullable": True, "name": "first", "type": "string"},
{"metadata": None, "nullable": True, "name": "second", "type": "integer"}
]
}
print(st.fromJson(fields))
st.add(StructField("first_struct", StringType()))
print("st.add success!")
print("Finish running types module API")
def test_merge_type(self):
self.assertEqual(_merge_type(LongType(), NullType()), LongType())
self.assertEqual(_merge_type(NullType(), LongType()), LongType())
self.assertEqual(_merge_type(LongType(), LongType()), LongType())
self.assertEqual(
_merge_type(ArrayType(LongType()), ArrayType(LongType())),
ArrayType(LongType()))
with self.assertRaisesRegexp(TypeError, 'element in array'):
_merge_type(ArrayType(LongType()), ArrayType(DoubleType()))
self.assertEqual(
_merge_type(MapType(StringType(), LongType()),
MapType(StringType(), LongType())),
MapType(StringType(), LongType()))
with self.assertRaisesRegexp(TypeError, 'key of map'):
_merge_type(MapType(StringType(), LongType()),
MapType(DoubleType(), LongType()))
with self.assertRaisesRegexp(TypeError, 'value of map'):
_merge_type(MapType(StringType(), LongType()),
MapType(StringType(), DoubleType()))
self.assertEqual(
_merge_type(
StructType([
StructField("f1", LongType()),
StructField("f2", StringType())
]),
StructType([
StructField("f1", LongType()),
StructField("f2", StringType())
])),
StructType([
StructField("f1", LongType()),
StructField("f2", StringType())
]))
with self.assertRaisesRegexp(TypeError, 'field f1'):
_merge_type(
StructType([
StructField("f1", LongType()),
StructField("f2", StringType())
]),
StructType([
StructField("f1", DoubleType()),
StructField("f2", StringType())
]))
self.assertEqual(
_merge_type(
StructType([
StructField("f1",
StructType([StructField("f2", LongType())]))
]),
StructType([
StructField("f1",
StructType([StructField("f2", LongType())]))
])),
StructType([
StructField("f1", StructType([StructField("f2", LongType())]))
]))
with self.assertRaisesRegexp(TypeError, 'field f2 in field f1'):
_merge_type(
StructType([
StructField("f1",
StructType([StructField("f2", LongType())]))
]),
StructType([
StructField("f1",
StructType([StructField("f2", StringType())]))
]))
self.assertEqual(
_merge_type(
StructType([
StructField("f1", ArrayType(LongType())),
StructField("f2", StringType())
]),
StructType([
StructField("f1", ArrayType(LongType())),
StructField("f2", StringType())
])),
StructType([
StructField("f1", ArrayType(LongType())),
StructField("f2", StringType())
]))
with self.assertRaisesRegexp(TypeError, 'element in array field f1'):
_merge_type(
StructType([
StructField("f1", ArrayType(LongType())),
StructField("f2", StringType())
]),
StructType([
StructField("f1", ArrayType(DoubleType())),
StructField("f2", StringType())
]))
self.assertEqual(
_merge_type(
StructType([
StructField("f1", MapType(StringType(), LongType())),
StructField("f2", StringType())
]),
StructType([
StructField("f1", MapType(StringType(), LongType())),
StructField("f2", StringType())
])),
StructType([
StructField("f1", MapType(StringType(), LongType())),
StructField("f2", StringType())
]))
with self.assertRaisesRegexp(TypeError, 'value of map field f1'):
_merge_type(
StructType([
StructField("f1", MapType(StringType(), LongType())),
StructField("f2", StringType())
]),
StructType([
StructField("f1", MapType(StringType(), DoubleType())),
StructField("f2", StringType())
]))
self.assertEqual(
_merge_type(
StructType([
StructField("f1",
ArrayType(MapType(StringType(), LongType())))
]),
StructType([
StructField("f1",
ArrayType(MapType(StringType(), LongType())))
])),
StructType([
StructField("f1", ArrayType(MapType(StringType(), LongType())))
]))
with self.assertRaisesRegexp(TypeError,
'key of map element in array field f1'):
_merge_type(
StructType([
StructField("f1",
ArrayType(MapType(StringType(), LongType())))
]),
StructType([
StructField("f1",
ArrayType(MapType(DoubleType(), LongType())))
]))
spark = SparkSession.builder.master("local[2]").appName(
'link-prediction').getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
# Create spark context
sc = spark.sparkContext
# Create a dataframe from training_set and testing_set
trainingRDD = sc.textFile("training_set.txt").map(
lambda x: x.strip().split(' '))
trainingDF = trainingRDD.toDF(['from_node_id', 'to_node_id',
'label']).sample(False, 0.3, 10)
predRDD = sc.textFile("testing_set.txt").map(
lambda x: x.strip().split(' '))
predDF = predRDD.toDF(['from_node_id', 'to_node_id'])
predictDF = predDF.withColumn('label', lit(None).cast(NullType()))
combinedDF = trainingDF.union(predictDF)
print("Input DataFrame contains %d elements" % trainingDF.count())
print("To predict DataFrame contains %d elements" % predictDF.count())
print("The combined DataFrame contains %d elements" % combinedDF.count())
# Create a dataframe for paper information (title, authors, abstract, etc)
infoRDD = sc.textFile("node_information.csv")
infoRDD = infoRDD.mapPartitions(lambda x: csv.reader(x))
infoDF = infoRDD.toDF(
['node_id', 'year', 'title', 'authors', 'journal', 'abstract'])
infoDF.printSchema()
infoDF.show(5)
raw_input("Press enter ... ")
'_TIMESTAMP_TYPE',
'_DATETIME_TYPES',
'_DECIMAL_10_0_TYPE',
'_DECIMAL_38_18_TYPE',
'_DECIMAL_TYPE_PREFIX',
'_ARRAY_TYPE_PREFIX',
'_MAP_TYPE_PREFIX',
'_STRUCT_TYPE_PREFIX',
'_VECTOR_TYPE',
)
__null_type: NullType = NullType()
_NULL_TYPE: str = __null_type.simpleString()
assert _NULL_TYPE == __null_type.typeName()
__bool_type: BooleanType = BooleanType()
_BOOL_TYPE: str = __bool_type.simpleString()
assert _BOOL_TYPE == __bool_type.typeName()
__str_type: StringType = StringType()
_STR_TYPE: str = __str_type.simpleString()
assert _STR_TYPE == __str_type.typeName()
__binary_type: BinaryType = BinaryType()
# | |
# Complex Types # | |
ArrayType, # array<...> | tuple, list, array |
MapType, # map<...> | dict |
StructField, # ... : ... | |
StructType, # struct<...> | tuple, list, dict |
_atomic_types,
_all_atomic_types,
_all_complex_types,
_type_mappings,
_array_signed_int_typecode_ctype_mappings,
_array_unsigned_int_typecode_ctype_mappings,
_array_type_mappings,
_acceptable_types)
__null_type = NullType()
_NULL_TYPE = __null_type.simpleString()
assert _NULL_TYPE == __null_type.typeName()
__bool_type = BooleanType()
_BOOL_TYPE = __bool_type.simpleString()
assert _BOOL_TYPE == __bool_type.typeName()
__str_type = StringType()
_STR_TYPE = __str_type.simpleString()
assert _STR_TYPE == __str_type.typeName()
__binary_type = BinaryType()
_BINARY_TYPE = __binary_type.simpleString()
assert _BINARY_TYPE == __binary_type.typeName()
