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Add conditional xfail test for DISTINCT aggregates with NaN #261

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Merged
revans2 merged 1 commit into from
Jun 24, 2020
Merged

Add conditional xfail test for DISTINCT aggregates with NaN #261

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10 changes: 6 additions & 4 deletions integration_tests/src/main/python/hash_aggregate_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
from pyspark.sql.types import *
from marks import *
import pyspark.sql.functions as f
from spark_session import with_cpu_session
from spark_session import with_cpu_session, with_spark_session

_no_nans_float_conf = {'spark.rapids.sql.variableFloatAgg.enabled': 'true',
'spark.rapids.sql.hasNans': 'false',
Expand Down Expand Up @@ -338,8 +338,11 @@ def test_hash_agg_with_nan_keys(data_gen):
conf=_no_nans_float_conf)


@pytest.mark.xfail(reason="count(distinct floats) fails when there are NaN values in the aggregation column."
"(https://github.com/NVIDIA/spark-rapids/issues/194)")
@pytest.mark.xfail(
condition=with_spark_session(lambda spark : spark.sparkContext.version == "3.0.0"),
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@abellina abellina Jun 23, 2020

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This is great 👍

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)
Expand All @@ -354,6 +357,5 @@ def test_count_distinct_with_nan_floats(data_gen):
'from hash_agg_table group by a'),
conf=_no_nans_float_conf)


# TODO: Literal tests
# TODO: First and Last tests