def test_hive_simple_udf_native(enable_rapids_udf_example_native):
with_spark_session(skip_if_no_hive)
def evalfn(spark):
load_hive_udf_or_skip_test(spark, "wordcount", "com.nvidia.spark.rapids.udf.hive.StringWordCount")
return gen_df(spark, data_gens)
assert_gpu_and_cpu_are_equal_sql(
evalfn,
"hive_native_udf_test_table",
"SELECT wordcount(s) FROM hive_native_udf_test_table")
def test_hive_generic_udf():
with_spark_session(skip_if_no_hive)
data_gens = [["s", StringGen('.{0,30}')]]
def evalfn(spark):
load_hive_udf_or_skip_test(spark, "urlencode", "com.nvidia.spark.rapids.udf.hive.URLEncode")
return gen_df(spark, data_gens)
assert_gpu_and_cpu_are_equal_sql(
evalfn,
"hive_generic_udf_test_table",
"SELECT urlencode(s) FROM hive_generic_udf_test_table")
def test_hive_simple_udf():
with_spark_session(skip_if_no_hive)
data_gens = [["i", int_gen], ["s", encoded_url_gen]]
def evalfn(spark):
load_hive_udf_or_skip_test(spark, "urldecode", "com.nvidia.spark.rapids.udf.hive.URLDecode")
return gen_df(spark, data_gens)
assert_gpu_and_cpu_are_equal_sql(
evalfn,
"hive_simple_udf_test_table",
"SELECT i, urldecode(s) FROM hive_simple_udf_test_table")
def test_hive_simple_udf():
with_spark_session(skip_if_no_hive)
data_gens = [["i", int_gen],
["s",
StringGen('([^%]{0,1}(%[0-9A-F][0-9A-F]){0,1}){0,30}')]]
def evalfn(spark):
load_udf_or_skip_test(spark, "urldecode",
"com.nvidia.spark.rapids.udf.URLDecode")
return gen_df(spark, data_gens)
assert_gpu_and_cpu_are_equal_sql(
evalfn, "hive_simple_udf_test_table",
"SELECT i, urldecode(s) FROM hive_simple_udf_test_table")
def test_hive_empty_generic_udf():
with_spark_session(skip_if_no_hive)
def evalfn(spark):
load_hive_udf(
spark, "emptygeneric",
"com.nvidia.spark.rapids.tests.udf.hive.EmptyHiveGenericUDF")
return gen_df(spark, [["s", string_gen]])
assert_gpu_and_cpu_are_equal_sql(
evalfn,
"hive_generic_udf_test_table",
"SELECT emptygeneric(s) FROM hive_generic_udf_test_table",
conf={'spark.rapids.sql.rowBasedUDF.enabled': 'true'})
def test_hive_empty_simple_udf():
with_spark_session(skip_if_no_hive)
data_gens = [["i", int_gen], ["s", string_gen]]
def evalfn(spark):
load_hive_udf(
spark, "emptysimple",
"com.nvidia.spark.rapids.tests.udf.hive.EmptyHiveSimpleUDF")
return gen_df(spark, data_gens)
assert_gpu_and_cpu_are_equal_sql(
evalfn,
"hive_simple_udf_test_table",
"SELECT i, emptysimple(s, 'const_string') FROM hive_simple_udf_test_table",
conf={'spark.rapids.sql.rowBasedUDF.enabled': 'true'})
lambda spark : gen_df(spark, data_gen, length=1024),
"hash_agg_table",
'select a, '
'count(*) as count_stars, '
'count(b) as count_bees, '
'sum(b) as sum_of_bees, '
'max(c) as max_seas, '
'min(c) as min_seas, '
'count(distinct c) as count_distinct_cees, '
'avg(c) as average_seas '
'from hash_agg_table group by a',
_no_nans_float_conf)
@pytest.mark.xfail(
condition=with_spark_session(lambda spark : is_spark_300()),
reason="[SPARK-32038][SQL] NormalizeFloatingNumbers should also work on distinct aggregate "
"(https://github.com/apache/spark/pull/28876) "
"Fixed in later Apache Spark releases.")
@approximate_float
@ignore_order
@pytest.mark.parametrize('data_gen', [ _grpkey_doubles_with_nan_zero_grouping_keys], ids=idfn)
def test_count_distinct_with_nan_floats(data_gen):
assert_gpu_and_cpu_are_equal_sql(
lambda spark : gen_df(spark, data_gen, length=1024),
"hash_agg_table",
'select a, count(distinct b) as count_distinct_bees from hash_agg_table group by a',
_no_nans_float_conf)
# TODO: Literal tests
# TODO: First and Last tests
assert_gpu_and_cpu_are_equal_collect(
lambda spark: spark.sql(
'select a, '
'count(*) as count_stars, '
'count(b) as count_bees, '
'sum(b) as sum_of_bees, '
'max(c) as max_seas, '
'min(c) as min_seas, '
'count(distinct c) as count_distinct_cees, '
'avg(c) as average_seas '
'from hash_agg_table group by a'),
conf=_no_nans_float_conf)
@pytest.mark.xfail(
condition=with_spark_session(lambda spark : spark.sparkContext.version == "3.0.0"),
reason="[SPARK-32038][SQL] NormalizeFloatingNumbers should also work on distinct aggregate "
"(https://github.com/apache/spark/pull/28876) "
"Fixed in later Apache Spark releases.")
@approximate_float
@ignore_order
@pytest.mark.parametrize('data_gen', [ _grpkey_doubles_with_nan_zero_grouping_keys], ids=idfn)
def test_count_distinct_with_nan_floats(data_gen):
df = with_cpu_session(
lambda spark : gen_df(spark, data_gen, length=1024))
df.createOrReplaceTempView("hash_agg_table")
assert_gpu_and_cpu_are_equal_collect(
lambda spark: spark.sql(
'select a, '
'count(distinct b) as count_distinct_bees '
'from hash_agg_table group by a'),
