def test_alias__long_bigint():
schema = StructType.fromDDL('i1: long, i2: bigint')
assert schema == StructType([
StructField('i1', LongType(), True),
StructField('i2', LongType(), True),
])
assert str(schema) == 'StructType(List(StructField(i1,LongType,true),StructField(i2,LongType,true)))'
def test_double_string():
schema = StructType.fromDDL("a DOUBLE, b STRING")
assert schema == StructType([
StructField('a', DoubleType(), True),
StructField('b', StringType(), True),
])
assert str(schema) == 'StructType(List(StructField(a,DoubleType,true),StructField(b,StringType,true)))'
def test_byte_decimal():
schema = StructType.fromDDL("a: byte, b: decimal( 16 , 8 ) ")
assert schema == StructType([
StructField('a', ByteType(), True),
StructField('b', DecimalType(16, 8), True),
])
assert str(schema) == 'StructType(List(StructField(a,ByteType,true),StructField(b,DecimalType(16,8),true)))'
def test_nested_array():
schema = StructType.fromDDL('some_str: string, arr: array<array<string>>')
assert schema == StructType([
StructField('some_str', StringType(), True),
StructField('arr', ArrayType(ArrayType(StringType())), True),
])
assert str(schema) == 'StructType(List(' \
'StructField(some_str,StringType,true),' \
'StructField(arr,ArrayType(ArrayType(StringType,true),true),true)' \
'))'
def test_basic_entries():
schema = StructType.fromDDL('some_str: string, some_int: integer, some_date: date')
assert schema == StructType([
StructField('some_str', StringType(), True),
StructField('some_int', IntegerType(), True),
StructField('some_date', DateType(), True),
])
assert str(schema) == (
'StructType(List('
'StructField(some_str,StringType,true),'
'StructField(some_int,IntegerType,true),'
'StructField(some_date,DateType,true)'
'))'
)
def test_session_create_data_frame_from_pandas_data_frame(self):
try:
# Pandas is an optional dependency
# pylint: disable=import-outside-toplevel
import pandas as pd
except ImportError as e:
raise ImportError("pandas is not importable") from e
pdf = pd.DataFrame([(1, "one"), (2, "two"), (3, "three")])
df = self.spark.createDataFrame(pdf)
self.assertEqual(df.count(), 3)
self.assertListEqual(df.collect(), [
Row(**{
"0": 1,
"1": 'one'
}),
Row(**{
"0": 2,
"1": 'two'
}),
Row(**{
"0": 3,
"2": 'three'
})
])
self.assertEqual(
df.schema,
StructType([
StructField("0", LongType(), True),
StructField("1", StringType(), True)
]))
def test_session_create_data_frame_from_list_with_schema(self):
schema = StructType(
[StructField("map", MapType(StringType(), IntegerType()), True)])
df = self.spark.createDataFrame([({'a': 1}, )], schema=schema)
self.assertEqual(df.count(), 1)
self.assertListEqual(df.collect(), [Row(map={'a': 1})])
self.assertEqual(df.schema, schema)
def test_csv_read_with_given_schema(self):
schema = StructType([
StructField("permalink", StringType()),
StructField("company", StringType()),
StructField("numEmps", IntegerType()),
StructField("category", StringType()),
StructField("city", StringType()),
StructField("state", StringType()),
StructField("fundedDate", DateType()),
StructField("raisedAmt", IntegerType()),
StructField("raisedCurrency", StringType()),
StructField("round", StringType())
])
df = spark.read.schema(schema).csv(os.path.join(
os.path.dirname(os.path.realpath(__file__)), "data/fundings/"),
header=True)
self.assertEqual([Row(**r.asDict()) for r in df.collect()], [
Row(permalink='mycityfaces',
company='MyCityFaces',
numEmps=7,
category='web',
city='Scottsdale',
state='AZ',
fundedDate=datetime.date(2008, 1, 1),
raisedAmt=50000,
raisedCurrency='USD',
round='seed'),
Row(permalink='flypaper',
company='Flypaper',
numEmps=None,
category='web',
city='Phoenix',
state='AZ',
fundedDate=datetime.date(2008, 2, 1),
raisedAmt=3000000,
raisedCurrency='USD',
round='a'),
Row(permalink='chosenlist-com',
company='ChosenList.com',
numEmps=5,
category='web',
city='Scottsdale',
state='AZ',
fundedDate=datetime.date(2008, 1, 25),
raisedAmt=233750,
raisedCurrency='USD',
round='angel'),
Row(permalink='digg',
company='Digg',
numEmps=60,
category='web',
city='San Francisco',
state='CA',
fundedDate=datetime.date(2006, 12, 1),
raisedAmt=8500000,
raisedCurrency='USD',
round='b')
])
def test_session_create_data_frame_from_list_with_col_names(self):
df = self.spark.createDataFrame([(0.0, [1.0, 0.8]), (1.0, [0.0, 0.0]),
(2.0, [0.5, 0.5])],
["label", "features"])
self.assertEqual(df.count(), 3)
self.assertListEqual(df.collect(), [
row_from_keyed_values([("label", 0.0), ("features", [1.0, 0.8])]),
row_from_keyed_values([("label", 1.0), ("features", [0.0, 0.0])]),
row_from_keyed_values([("label", 2.0), ("features", [0.5, 0.5])]),
])
self.assertEqual(
df.schema,
StructType([
StructField("label", DoubleType(), True),
StructField("features", ArrayType(DoubleType(), True), True)
]))
def test_session_create_data_frame_from_list(self):
df = self.spark.createDataFrame([
(1, "one"),
(2, "two"),
(3, "three"),
])
self.assertEqual(df.count(), 3)
self.assertListEqual(
df.collect(),
[Row(_1=1, _2='one'),
Row(_1=2, _2='two'),
Row(_1=3, _2='three')])
self.assertEqual(
df.schema,
StructType([
StructField("_1", LongType(), True),
StructField("_2", StringType(), True)
]))
def test_column_stat_helper():
"""
Expected quantile values come from use of org.apache.spark.sql.catalyst.util.QuantileSummaries
"""
schema = StructType([StructField("value", IntegerType())])
helper = ColumnStatHelper(col("value"))
for i in range(1, 100001):
helper.merge(Row(value=i), schema)
helper.finalize()
assert helper.count == 100000
assert helper.min == 1
assert helper.max == 100000
assert helper.mean == 50000.5
assert helper.stddev == 28867.65779668774 # sample standard deviation
assert helper.get_quantile(0) == 1
assert helper.get_quantile(0.25) == 24998
assert helper.get_quantile(0.5) == 50000
assert helper.get_quantile(0.75) == 74993
assert helper.get_quantile(1) == 100000
DecimalType(10, 0),
"Array<string>":
ArrayType(StringType()),
"Array<doublE>":
ArrayType(DoubleType()),
"Array<int>":
ArrayType(IntegerType()),
"Array<map<int, tinYint>>":
ArrayType(MapType(IntegerType(), ByteType())),
"Map<string, int>":
MapType(StringType(), IntegerType()),
"Map < integer, String >":
MapType(IntegerType(), StringType()),
"Struct<name: string, age: int>":
StructType([
StructField(name="name", dataType=StringType()),
StructField(name="age", dataType=IntegerType()),
]),
"array<struct<tinYint:tinyint>>":
ArrayType(StructType([StructField('tinYint', ByteType())]), ),
"MAp<int, ARRAY<double>>":
MapType(IntegerType(), ArrayType(DoubleType())),
"MAP<int, struct<varchar:string>>":
MapType(IntegerType(), StructType([StructField("varchar", StringType())])),
"struct<intType: int, ts:timestamp>":
StructType([
StructField("intType", IntegerType()),
StructField("ts", TimestampType())
]),
"Struct<int: int, timestamp:timestamp>":
StructType([
StructField("int", IntegerType()),
def test_too_much_closed_map():
with pytest.raises(ParseException):
StructType.fromDDL("map<int, boolean>>")
def test_wrong_type():
with pytest.raises(ParseException):
StructType.fromDDL("blabla")
def test_array_short():
schema = StructType.fromDDL("a: array< short>")
assert schema == StructType([
StructField('a', ArrayType(ShortType()), True),
])
assert str(schema) == 'StructType(List(StructField(a,ArrayType(ShortType,true),true)))'
def test_comma_at_end():
with pytest.raises(ParseException):
print(StructType.fromDDL("a: int,"))
def test_csv_read_without_schema(self):
df = spark.read.csv(os.path.join(
os.path.dirname(os.path.realpath(__file__)), "data/fundings/"),
header=True)
self.assertEqual(df.count(), 4)
self.assertEqual(
df.schema,
StructType([
StructField("permalink", StringType()),
StructField("company", StringType()),
StructField("numEmps", StringType()),
StructField("category", StringType()),
StructField("city", StringType()),
StructField("state", StringType()),
StructField("fundedDate", StringType()),
StructField("raisedAmt", StringType()),
StructField("raisedCurrency", StringType()),
StructField("round", StringType())
]))
self.assertListEqual([Row(**r.asDict()) for r in df.collect()], [
Row(permalink='mycityfaces',
company='MyCityFaces',
numEmps='7',
category='web',
city='Scottsdale',
state='AZ',
fundedDate='2008-01-01',
raisedAmt='50000',
raisedCurrency='USD',
round='seed'),
Row(permalink='flypaper',
company='Flypaper',
numEmps=None,
category='web',
city='Phoenix',
state='AZ',
fundedDate='2008-02-01',
raisedAmt='3000000',
raisedCurrency='USD',
round='a'),
Row(permalink='chosenlist-com',
company='ChosenList.com',
numEmps='5',
category='web',
city='Scottsdale',
state='AZ',
fundedDate='2008-01-25',
raisedAmt='233750',
raisedCurrency='USD',
round='angel'),
Row(permalink='digg',
company='Digg',
numEmps='60',
category='web',
city='San Francisco',
state='CA',
fundedDate='2006-12-01',
raisedAmt='8500000',
raisedCurrency='USD',
round='b')
])
import pytest
from gelanis import Row
from gelanis.sql._ast.ast_to_python import parse_expression
from gelanis.sql.types import IntegerType, StructField, StructType
ROW = Row(a=1, b=2, c=3)
SCHEMA = StructType([
StructField("a", IntegerType()),
StructField("b", IntegerType()),
])
SCENARIOS = {
'Least(-1,0,1)': ('least', 'least(-1, 0, 1)', -1),
'GREATEST(-1,0,1)': ('greatest', 'greatest(-1, 0, 1)', 1),
'shiftRight ( 42, 1 )': ('shiftright', 'shiftright(42, 1)', 21),
'ShiftLeft ( 42, 1 )': ('shiftleft', 'shiftleft(42, 1)', 84),
"concat_ws('/', a, b )": ('concat_ws', 'concat_ws(/, a, b)', "1/2"),
'instr(a, a)': ('instr', 'instr(a, a)', 1), # rely on columns
'instr(a, b)': ('instr', 'instr(a, b)', 0), # rely on columns
"instr('abc', 'c')": ('instr', 'instr(abc, c)', 3), # rely on lit
}
@pytest.mark.parametrize('string, expected', SCENARIOS.items())
def test_functions(string, expected):
operator, expected_parsed, expected_result = expected
actual_parsed = parse_expression(string, True)
assert expected_parsed == str(actual_parsed)
assert operator == actual_parsed.pretty_name
