#
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# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#
"""
Unit tests for pyspark.sql; additional tests are implemented as doctests in
individual modules.
"""
import os
import sys
import pydoc
import shutil
import tempfile
import pickle
import functools
import datetime
import py4j
if sys.version_info[:2] <= (2, 6):
try:
import unittest2 as unittest
except ImportError:
sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
sys.exit(1)
else:
import unittest
from pyspark.sql import SQLContext, HiveContext, Column, Row
from pyspark.sql.types import *
from pyspark.sql.types import UserDefinedType, _infer_type
from pyspark.tests import ReusedPySparkTestCase
from pyspark.sql.functions import UserDefinedFunction
class ExamplePointUDT(UserDefinedType):
"""
User-defined type (UDT) for ExamplePoint.
"""
@classmethod
def sqlType(self):
return ArrayType(DoubleType(), False)
@classmethod
def module(cls):
return 'pyspark.tests'
@classmethod
def scalaUDT(cls):
return 'org.apache.spark.sql.test.ExamplePointUDT'
def serialize(self, obj):
return [obj.x, obj.y]
def deserialize(self, datum):
return ExamplePoint(datum[0], datum[1])
class ExamplePoint:
"""
An example class to demonstrate UDT in Scala, Java, and Python.
"""
__UDT__ = ExamplePointUDT()
def __init__(self, x, y):
self.x = x
self.y = y
def __repr__(self):
return "ExamplePoint(%s,%s)" % (self.x, self.y)
def __str__(self):
return "(%s,%s)" % (self.x, self.y)
def __eq__(self, other):
return isinstance(other, ExamplePoint) and \
other.x == self.x and other.y == self.y
class DataTypeTests(unittest.TestCase):
# regression test for SPARK-6055
def test_data_type_eq(self):
lt = LongType()
lt2 = pickle.loads(pickle.dumps(LongType()))
self.assertEquals(lt, lt2)
class SQLTests(ReusedPySparkTestCase):
@classmethod
def setUpClass(cls):
ReusedPySparkTestCase.setUpClass()
cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
os.unlink(cls.tempdir.name)
cls.sqlCtx = SQLContext(cls.sc)
cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
rdd = cls.sc.parallelize(cls.testData)
cls.df = rdd.toDF()
@classmethod
def tearDownClass(cls):
ReusedPySparkTestCase.tearDownClass()
shutil.rmtree(cls.tempdir.name, ignore_errors=True)
def test_udf_with_callable(self):
d = [Row(number=i, squared=i**2) for i in range(10)]
rdd = self.sc.parallelize(d)
data = self.sqlCtx.createDataFrame(rdd)
class PlusFour:
def __call__(self, col):
if col is not None:
return col + 4
call = PlusFour()
pudf = UserDefinedFunction(call, LongType())
res = data.select(pudf(data['number']).alias('plus_four'))
self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85)
def test_udf_with_partial_function(self):
d = [Row(number=i, squared=i**2) for i in range(10)]
rdd = self.sc.parallelize(d)
data = self.sqlCtx.createDataFrame(rdd)
def some_func(col, param):
if col is not None:
return col + param
pfunc = functools.partial(some_func, param=4)
pudf = UserDefinedFunction(pfunc, LongType())
res = data.select(pudf(data['number']).alias('plus_four'))
self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85)
def test_udf(self):
self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
[row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect()
self.assertEqual(row[0], 5)
def test_udf2(self):
self.sqlCtx.registerFunction("strlen", lambda string: len(string), IntegerType())
self.sqlCtx.createDataFrame(self.sc.parallelize([Row(a="test")])).registerTempTable("test")
[res] = self.sqlCtx.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect()
self.assertEqual(4, res[0])
def test_udf_with_array_type(self):
d = [Row(l=list(range(3)), d={"key": list(range(5))})]
rdd = self.sc.parallelize(d)
self.sqlCtx.createDataFrame(rdd).registerTempTable("test")
self.sqlCtx.registerFunction("copylist", lambda l: list(l), ArrayType(IntegerType()))
self.sqlCtx.registerFunction("maplen", lambda d: len(d), IntegerType())
[(l1, l2)] = self.sqlCtx.sql("select copylist(l), maplen(d) from test").collect()
self.assertEqual(list(range(3)), l1)
self.assertEqual(1, l2)
def test_broadcast_in_udf(self):
bar = {"a": "aa", "b": "bb", "c": "abc"}
foo = self.sc.broadcast(bar)
self.sqlCtx.registerFunction("MYUDF", lambda x: foo.value[x] if x else '')
[res] = self.sqlCtx.sql("SELECT MYUDF('c')").collect()
self.assertEqual("abc", res[0])
[res] = self.sqlCtx.sql("SELECT MYUDF('')").collect()
self.assertEqual("", res[0])
def test_basic_functions(self):
rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
df = self.sqlCtx.jsonRDD(rdd)
df.count()
df.collect()
df.schema
# cache and checkpoint
self.assertFalse(df.is_cached)
df.persist()
df.unpersist()
df.cache()
self.assertTrue(df.is_cached)
self.assertEqual(2, df.count())
df.registerTempTable("temp")
df = self.sqlCtx.sql("select foo from temp")
df.count()
df.collect()
def test_apply_schema_to_row(self):
df = self.sqlCtx.jsonRDD(self.sc.parallelize(["""{"a":2}"""]))
df2 = self.sqlCtx.createDataFrame(df.map(lambda x: x), df.schema)
self.assertEqual(df.collect(), df2.collect())
rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x))
df3 = self.sqlCtx.createDataFrame(rdd, df.schema)
self.assertEqual(10, df3.count())
def test_serialize_nested_array_and_map(self):
d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})]
rdd = self.sc.parallelize(d)
df = self.sqlCtx.createDataFrame(rdd)
row = df.head()
self.assertEqual(1, len(row.l))
self.assertEqual(1, row.l[0].a)
self.assertEqual("2", row.d["key"].d)
l = df.map(lambda x: x.l).first()
self.assertEqual(1, len(l))
self.assertEqual('s', l[0].b)
d = df.map(lambda x: x.d).first()
self.assertEqual(1, len(d))
self.assertEqual(1.0, d["key"].c)
row = df.map(lambda x: x.d["key"]).first()
self.assertEqual(1.0, row.c)
self.assertEqual("2", row.d)
def test_infer_schema(self):
d = [Row(l=[], d={}, s=None),
Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}, s="")]
rdd = self.sc.parallelize(d)
df = self.sqlCtx.createDataFrame(rdd)
self.assertEqual([], df.map(lambda r: r.l).first())
self.assertEqual([None, ""], df.map(lambda r: r.s).collect())
df.registerTempTable("test")
result = self.sqlCtx.sql("SELECT l[0].a from test where d['key'].d = '2'")
self.assertEqual(1, result.head()[0])
df2 = self.sqlCtx.createDataFrame(rdd, samplingRatio=1.0)
self.assertEqual(df.schema, df2.schema)
self.assertEqual({}, df2.map(lambda r: r.d).first())
self.assertEqual([None, ""], df2.map(lambda r: r.s).collect())
df2.registerTempTable("test2")
result = self.sqlCtx.sql("SELECT l[0].a from test2 where d['key'].d = '2'")
self.assertEqual(1, result.head()[0])
def test_infer_nested_schema(self):
NestedRow = Row("f1", "f2")
nestedRdd1 = self.sc.parallelize([NestedRow([1, 2], {"row1": 1.0}),
NestedRow([2, 3], {"row2": 2.0})])
df = self.sqlCtx.inferSchema(nestedRdd1)
self.assertEqual(Row(f1=[1, 2], f2={u'row1': 1.0}), df.collect()[0])
nestedRdd2 = self.sc.parallelize([NestedRow([[1, 2], [2, 3]], [1, 2]),
NestedRow([[2, 3], [3, 4]], [2, 3])])
df = self.sqlCtx.inferSchema(nestedRdd2)
self.assertEqual(Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), df.collect()[0])
from collections import namedtuple
CustomRow = namedtuple('CustomRow', 'field1 field2')
rdd = self.sc.parallelize([CustomRow(field1=1, field2="row1"),
CustomRow(field1=2, field2="row2"),
CustomRow(field1=3, field2="row3")])
df = self.sqlCtx.inferSchema(rdd)
self.assertEquals(Row(field1=1, field2=u'row1'), df.first())
def test_apply_schema(self):
from datetime import date, datetime
rdd = self.sc.parallelize([(127, -128, -32768, 32767, 2147483647, 1.0,
date(2010, 1, 1), datetime(2010, 1, 1, 1, 1, 1),
{"a": 1}, (2,), [1, 2, 3], None)])
schema = StructType([
StructField("byte1", ByteType(), False),
StructField("byte2", ByteType(), False),
StructField("short1", ShortType(), False),
StructField("short2", ShortType(), False),
StructField("int1", IntegerType(), False),
StructField("float1", FloatType(), False),
StructField("date1", DateType(), False),
StructField("time1", TimestampType(), False),
StructField("map1", MapType(StringType(), IntegerType(), False), False),
StructField("struct1", StructType([StructField("b", ShortType(), False)]), False),
StructField("list1", ArrayType(ByteType(), False), False),
StructField("null1", DoubleType(), True)])
df = self.sqlCtx.createDataFrame(rdd, schema)
results = df.map(lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int1, x.float1, x.date1,
x.time1, x.map1["a"], x.struct1.b, x.list1, x.null1))
r = (127, -128, -32768, 32767, 2147483647, 1.0, date(2010, 1, 1),
datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None)
self.assertEqual(r, results.first())
df.registerTempTable("table2")
r = self.sqlCtx.sql("SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " +
"short1 + 1 AS short1, short2 - 1 AS short2, int1 - 1 AS int1, " +
"float1 + 1.5 as float1 FROM table2").first()
self.assertEqual((126, -127, -32767, 32766, 2147483646, 2.5), tuple(r))
from pyspark.sql.types import _parse_schema_abstract, _infer_schema_type
rdd = self.sc.parallelize([(127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1),
{"a": 1}, (2,), [1, 2, 3])])
abstract = "byte1 short1 float1 time1 map1{} struct1(b) list1[]"
schema = _parse_schema_abstract(abstract)
typedSchema = _infer_schema_type(rdd.first(), schema)
df = self.sqlCtx.applySchema(rdd, typedSchema)
r = (127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1), {"a": 1}, Row(b=2), [1, 2, 3])
self.assertEqual(r, tuple(df.first()))
def test_struct_in_map(self):
d = [Row(m={Row(i=1): Row(s="")})]
df = self.sc.parallelize(d).toDF()
k, v = list(df.head().m.items())[0]
self.assertEqual(1, k.i)
self.assertEqual("", v.s)
def test_convert_row_to_dict(self):
row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})
self.assertEqual(1, row.asDict()['l'][0].a)
df = self.sc.parallelize([row]).toDF()
df.registerTempTable("test")
row = self.sqlCtx.sql("select l, d from test").head()
self.assertEqual(1, row.asDict()["l"][0].a)
self.assertEqual(1.0, row.asDict()['d']['key'].c)
def test_infer_schema_with_udt(self):
from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
df = self.sc.parallelize([row]).toDF()
schema = df.schema
field = [f for f in schema.fields if f.name == "point"][0]
self.assertEqual(type(field.dataType), ExamplePointUDT)
df.registerTempTable("labeled_point")
point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point
self.assertEqual(point, ExamplePoint(1.0, 2.0))
def test_apply_schema_with_udt(self):
from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
row = (1.0, ExamplePoint(1.0, 2.0))
rdd = self.sc.parallelize([row])
schema = StructType([StructField("label", DoubleType(), False),
StructField("point", ExamplePointUDT(), False)])
df = rdd.toDF(schema)
point = df.head().point
self.assertEquals(point, ExamplePoint(1.0, 2.0))
def test_parquet_with_udt(self):
from pyspark.sql.tests import ExamplePoint
row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
df0 = self.sc.parallelize([row]).toDF()
output_dir = os.path.join(self.tempdir.name, "labeled_point")
df0.saveAsParquetFile(output_dir)
df1 = self.sqlCtx.parquetFile(output_dir)
point = df1.head().point
self.assertEquals(point, ExamplePoint(1.0, 2.0))
def test_column_operators(self):
ci = self.df.key
cs = self.df.value
c = ci == cs
self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
self.assertTrue(all(isinstance(c, Column) for c in rcc))
cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs]
self.assertTrue(all(isinstance(c, Column) for c in cb))
cbool = (ci & ci), (ci | ci), (~ci)
self.assertTrue(all(isinstance(c, Column) for c in cbool))
css = cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(), cs.startswith('a'), cs.endswith('a')
self.assertTrue(all(isinstance(c, Column) for c in css))
self.assertTrue(isinstance(ci.cast(LongType()), Column))
def test_column_select(self):
df = self.df
self.assertEqual(self.testData, df.select("*").collect())
self.assertEqual(self.testData, df.select(df.key, df.value).collect())
self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect())
def test_aggregator(self):
df = self.df
g = df.groupBy()
self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0]))
self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
from pyspark.sql import functions
self.assertEqual((0, u'99'),
tuple(g.agg(functions.first(df.key), functions.last(df.value)).first()))
self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0])
self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0])
def test_save_and_load(self):
df = self.df
tmpPath = tempfile.mkdtemp()
shutil.rmtree(tmpPath)
df.save(tmpPath, "org.apache.spark.sql.json", "error")
actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json")
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
schema = StructType([StructField("value", StringType(), True)])
actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json", schema)
self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect()))
df.save(tmpPath, "org.apache.spark.sql.json", "overwrite")
actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json")
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
df.save(source="org.apache.spark.sql.json", mode="overwrite", path=tmpPath,
noUse="this options will not be used in save.")
actual = self.sqlCtx.load(source="org.apache.spark.sql.json", path=tmpPath,
noUse="this options will not be used in load.")
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default",
"org.apache.spark.sql.parquet")
self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
actual = self.sqlCtx.load(path=tmpPath)
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
shutil.rmtree(tmpPath)
def test_help_command(self):
# Regression test for SPARK-5464
rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
df = self.sqlCtx.jsonRDD(rdd)
# render_doc() reproduces the help() exception without printing output
pydoc.render_doc(df)
pydoc.render_doc(df.foo)
pydoc.render_doc(df.take(1))
def test_access_column(self):
df = self.df
self.assertTrue(isinstance(df.key, Column))
self.assertTrue(isinstance(df['key'], Column))
self.assertTrue(isinstance(df[0], Column))
self.assertRaises(IndexError, lambda: df[2])
self.assertRaises(IndexError, lambda: df["bad_key"])
self.assertRaises(TypeError, lambda: df[{}])
def test_access_nested_types(self):
df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
self.assertEqual(1, df.select(df.l[0]).first()[0])
self.assertEqual(1, df.select(df.l.getItem(0)).first()[0])
self.assertEqual(1, df.select(df.r.a).first()[0])
self.assertEqual("b", df.select(df.r.getField("b")).first()[0])
self.assertEqual("v", df.select(df.d["k"]).first()[0])
self.assertEqual("v", df.select(df.d.getItem("k")).first()[0])
def test_infer_long_type(self):
longrow = [Row(f1='a', f2=100000000000000)]
df = self.sc.parallelize(longrow).toDF()
self.assertEqual(df.schema.fields[1].dataType, LongType())
# this saving as Parquet caused issues as well.
output_dir = os.path.join(self.tempdir.name, "infer_long_type")
df.saveAsParquetFile(output_dir)
df1 = self.sqlCtx.parquetFile(output_dir)
self.assertEquals('a', df1.first().f1)
self.assertEquals(100000000000000, df1.first().f2)
self.assertEqual(_infer_type(1), LongType())
self.assertEqual(_infer_type(2**10), LongType())
self.assertEqual(_infer_type(2**20), LongType())
self.assertEqual(_infer_type(2**31 - 1), LongType())
self.assertEqual(_infer_type(2**31), LongType())
self.assertEqual(_infer_type(2**61), LongType())
self.assertEqual(_infer_type(2**71), LongType())
def test_filter_with_datetime(self):
time = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000)
date = time.date()
row = Row(date=date, time=time)
df = self.sqlCtx.createDataFrame([row])
self.assertEqual(1, df.filter(df.date == date).count())
self.assertEqual(1, df.filter(df.time == time).count())
self.assertEqual(0, df.filter(df.date > date).count())
self.assertEqual(0, df.filter(df.time > time).count())
def test_dropna(self):
schema = StructType([
StructField("name", StringType(), True),
StructField("age", IntegerType(), True),
StructField("height", DoubleType(), True)])
# shouldn't drop a non-null row
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', 50, 80.1)], schema).dropna().count(),
1)
# dropping rows with a single null value
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, 80.1)], schema).dropna().count(),
0)
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, 80.1)], schema).dropna(how='any').count(),
0)
# if how = 'all', only drop rows if all values are null
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, 80.1)], schema).dropna(how='all').count(),
1)
self.assertEqual(self.sqlCtx.createDataFrame(
[(None, None, None)], schema).dropna(how='all').count(),
0)
# how and subset
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', 50, None)], schema).dropna(how='any', subset=['name', 'age']).count(),
1)
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, None)], schema).dropna(how='any', subset=['name', 'age']).count(),
0)
# threshold
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(),
1)
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, None)], schema).dropna(thresh=2).count(),
0)
# threshold and subset
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', 50, None)], schema).dropna(thresh=2, subset=['name', 'age']).count(),
1)
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', None, 180.9)], schema).dropna(thresh=2, subset=['name', 'age']).count(),
0)
# thresh should take precedence over how
self.assertEqual(self.sqlCtx.createDataFrame(
[(u'Alice', 50, None)], schema).dropna(
how='any', thresh=2, subset=['name', 'age']).count(),
1)
def test_fillna(self):
schema = StructType([
StructField("name", StringType(), True),
StructField("age", IntegerType(), True),
StructField("height", DoubleType(), True)])
# fillna shouldn't change non-null values
row = self.sqlCtx.createDataFrame([(u'Alice', 10, 80.1)], schema).fillna(50).first()
self.assertEqual(row.age, 10)
# fillna with int
row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50).first()
self.assertEqual(row.age, 50)
self.assertEqual(row.height, 50.0)
# fillna with double
row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50.1).first()
self.assertEqual(row.age, 50)
self.assertEqual(row.height, 50.1)
# fillna with string
row = self.sqlCtx.createDataFrame([(None, None, None)], schema).fillna("hello").first()
self.assertEqual(row.name, u"hello")
self.assertEqual(row.age, None)
# fillna with subset specified for numeric cols
row = self.sqlCtx.createDataFrame(
[(None, None, None)], schema).fillna(50, subset=['name', 'age']).first()
self.assertEqual(row.name, None)
self.assertEqual(row.age, 50)
self.assertEqual(row.height, None)
# fillna with subset specified for numeric cols
row = self.sqlCtx.createDataFrame(
[(None, None, None)], schema).fillna("haha", subset=['name', 'age']).first()
self.assertEqual(row.name, "haha")
self.assertEqual(row.age, None)
self.assertEqual(row.height, None)
class HiveContextSQLTests(ReusedPySparkTestCase):
@classmethod
def setUpClass(cls):
ReusedPySparkTestCase.setUpClass()
cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
try:
cls.sc._jvm.org.apache.hadoop.hive.conf.HiveConf()
except py4j.protocol.Py4JError:
cls.sqlCtx = None
return
except TypeError:
cls.sqlCtx = None
return
os.unlink(cls.tempdir.name)
_scala_HiveContext =\
cls.sc._jvm.org.apache.spark.sql.hive.test.TestHiveContext(cls.sc._jsc.sc())
cls.sqlCtx = HiveContext(cls.sc, _scala_HiveContext)
cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
cls.df = cls.sc.parallelize(cls.testData).toDF()
@classmethod
def tearDownClass(cls):
ReusedPySparkTestCase.tearDownClass()
shutil.rmtree(cls.tempdir.name, ignore_errors=True)
def test_save_and_load_table(self):
if self.sqlCtx is None:
return # no hive available, skipped
df = self.df
tmpPath = tempfile.mkdtemp()
shutil.rmtree(tmpPath)
df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "append", path=tmpPath)
actual = self.sqlCtx.createExternalTable("externalJsonTable", tmpPath,
"org.apache.spark.sql.json")
self.assertTrue(
sorted(df.collect()) ==
sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect()))
self.assertTrue(
sorted(df.collect()) ==
sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect()))
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
self.sqlCtx.sql("DROP TABLE externalJsonTable")
df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "overwrite", path=tmpPath)
schema = StructType([StructField("value", StringType(), True)])
actual = self.sqlCtx.createExternalTable("externalJsonTable",
source="org.apache.spark.sql.json",
schema=schema, path=tmpPath,
noUse="this options will not be used")
self.assertTrue(
sorted(df.collect()) ==
sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect()))
self.assertTrue(
sorted(df.select("value").collect()) ==
sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect()))
self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect()))
self.sqlCtx.sql("DROP TABLE savedJsonTable")
self.sqlCtx.sql("DROP TABLE externalJsonTable")
defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default",
"org.apache.spark.sql.parquet")
self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
df.saveAsTable("savedJsonTable", path=tmpPath, mode="overwrite")
actual = self.sqlCtx.createExternalTable("externalJsonTable", path=tmpPath)
self.assertTrue(
sorted(df.collect()) ==
sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect()))
self.assertTrue(
sorted(df.collect()) ==
sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect()))
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
self.sqlCtx.sql("DROP TABLE savedJsonTable")
self.sqlCtx.sql("DROP TABLE externalJsonTable")
self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
shutil.rmtree(tmpPath)
if __name__ == "__main__":
unittest.main()