testdata/workloads/functional-query/queries/QueryTest/parquet-stats.test - impala - Git at Google

 ====
 ---- QUERY
 select id, bool_col from functional_parquet.alltypessmall where int_col < 0
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 set explain_level=2;
 explain select id, bool_col from functional_parquet.alltypessmall where int_col < 0;
 ---- RESULTS: VERIFY_IS_SUBSET
 '   parquet statistics predicates: int_col < CAST(0 AS INT)'
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col < 0
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where smallint_col < 0
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where int_col < 0
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where bigint_col < 0
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where float_col < 0
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where double_col < 0
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 # Test with inverted predicate
 select id, bool_col from functional_parquet.alltypessmall where -1 > int_col
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col > 9
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where smallint_col > 9
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select id, bool_col from functional_parquet.alltypessmall where int_col > 9
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where bigint_col > 90
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where float_col > 9.9
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where double_col > 99
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col >= 10
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col <= 0
 ---- RESULTS
 12
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col >= 9
 ---- RESULTS
 8
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col = -1
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where tinyint_col = 10
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 set explain_level=2;
 explain select count(*) from functional_parquet.alltypessmall where tinyint_col = 10
 ---- RESULTS: VERIFY_IS_SUBSET
 '   parquet statistics predicates: tinyint_col = CAST(10 AS TINYINT)'
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where id >= 30 and id <= 80
 ---- RESULTS
 51
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 # Mix with partitioning columns
 select count(*) from functional_parquet.alltypessmall where int_col < 0 and year < 2012
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select id, bool_col from functional_parquet.alltypessmall where int_col < 3 - 3
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select id, bool_col from functional_parquet.alltypessmall where int_col < 3 - 3
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 # Test that without expr rewrite and thus without constant folding, constant exprs still
 # can be used to prune row groups.
 set enable_expr_rewrites=0;
 select id, bool_col from functional_parquet.alltypessmall where int_col < 3 - 3
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select id, bool_col from functional_parquet.alltypessmall where 5 + 5 < int_col
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 # Test that without expr rewrite and thus without constant folding, constant exprs still
 # can be used to prune row groups.
 set enable_expr_rewrites=0;
 select id, bool_col from functional_parquet.alltypessmall where 5 + 5 < int_col
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 # Test name based column resolution
 create table name_resolve stored as parquet as select * from functional_parquet.alltypessmall;
 alter table name_resolve replace columns (int_col int, bool_col boolean, tinyint_col tinyint, smallint_col smallint, id int);
 set parquet_fallback_schema_resolution=NAME;
 # If this picks up the stats from int_col, then it will filter all row groups and return
 # an incorrect result.
 select count(*) from name_resolve where id > 10;
 ---- RESULTS
 89
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 # Query that has an implicit cast to a larger integer type
 select count(*) from functional_parquet.alltypessmall where tinyint_col > 1000000000000
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 # Predicates with explicit casts are not supported when evaluating parquet::Statistics.
 select count(*) from functional_parquet.alltypessmall where '0' > cast(tinyint_col as string)
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 # Explicit casts between numerical types can violate the transitivity of "min()", so they
 # are not supported when evaluating parquet::Statistics.
 select count(*) from functional_parquet.alltypes where cast(id as tinyint) < 10;
 ---- RESULTS
 3878
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 24
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 select count(*) from functional_parquet.complextypestbl.int_array where pos < 5;
 ---- RESULTS
 9
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 # Test the conversion of constant IN lists to min/max predicats
 select count(*) from functional_parquet.alltypes where int_col in (-1,-2,-3,-4);
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 24
 aggregation(SUM, NumStatsFilteredRowGroups): 24
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypes where id IN (1,25,49);
 ---- RESULTS
 3
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 24
 aggregation(SUM, NumStatsFilteredRowGroups): 23
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where string_col < "0"
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where string_col <= "/"
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where string_col < "1"
 ---- RESULTS
 12
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where string_col >= "9"
 ---- RESULTS
 8
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where string_col > ":"
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where timestamp_col < "2009-01-01 00:00:00"
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where timestamp_col <= "2009-01-01 00:00:00"
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 3
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where timestamp_col = "2009-01-01 00:00:00"
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 3
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where timestamp_col > "2009-04-03 00:24:00.96"
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 4
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where timestamp_col >= "2009-04-03 00:24:00.96"
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 3
 ====
 ---- QUERY
 select count(*) from functional_parquet.alltypessmall where timestamp_col = "2009-04-03 00:24:00.96"
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 4
 aggregation(SUM, NumStatsFilteredRowGroups): 3
 ====
 ---- QUERY
 select count(*) from functional_parquet.date_tbl
 where date_part in ("2017-11-27", "1399-06-27") and date_col < '0001-06-21';
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 2
 ====
 ---- QUERY
 select count(*) from functional_parquet.date_tbl
 where date_part in ("2017-11-27", "1399-06-27") and date_col <= '0001-06-21';
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.date_tbl
 where date_part in ("2017-11-27", "1399-06-27") and date_col = '0001-06-21';
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.date_tbl
 where date_part in ("2017-11-27", "1399-06-27") and date_col > '2018-12-31';
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 2
 ====
 ---- QUERY
 select count(*) from functional_parquet.date_tbl
 where date_part in ("2017-11-27", "1399-06-27") and date_col >= '2018-12-31';
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.date_tbl
 where date_part in ("2017-11-27", "1399-06-27") and date_col = '2018-12-31';
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d1 < 1234
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d3 < 1.23456789
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d3 = 1.23456788
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d3 = 1.23456789
 ---- RESULTS
 1
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d4 > 0.123456789
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d4 >= 0.12345678
 ---- RESULTS
 5
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 select count(*) from functional_parquet.decimal_tbl where d4 >= 0.12345679
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 # Test that stats are disabled for CHAR type columns.
 create table chars (id int, c char(4)) stored as parquet;
 insert into chars values (1, cast("abaa" as char(4))), (2, cast("abab" as char(4)));
 select count(*) from chars;
 ---- RESULTS
 2
 ====
 ---- QUERY
 select count(*) from chars where c <= "aaaa"
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 # IMPALA-4988: Test that stats support can be disabled using the parquet_read_statistics
 # query option.
 set parquet_read_statistics=0;
 select count(*) from functional_parquet.alltypes where id < 0;
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 24
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 # Check that all the row groups are skipped using null_count stat
 create table table_for_null_count_test (i int, j int) stored as parquet;
 insert into table_for_null_count_test values (1, NULL), (2, NULL), (3, NULL);
 select count(*) from table_for_null_count_test where j < 3;
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 # Insert another row group where not all the 'j' values are NULL
 insert into table_for_null_count_test values (4, 1), (5, NULL);
 select i from table_for_null_count_test where j < 3;
 ---- RESULTS
 4
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 2
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 # Turning off parquet stats and verifying that no row groups are skipped
 set PARQUET_READ_STATISTICS=0;
 create table table_for_null_count_test2 (i int, j int) stored as parquet;
 insert into table_for_null_count_test2 values (1, NULL), (2, NULL), (3, NULL);
 select count(*) from table_for_null_count_test2 where j < 3;
 ---- RESULTS
 0
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 0
 ====
 ---- QUERY
 # IMPALA-6527: NaN values lead to incorrect filtering.
 # When the first value is NaN in a column chunk, Impala chose it as min_value and
 # max_value for statistics. Test if it is no longer the case.
 create table test_nan(val double) stored as parquet;
 insert into test_nan values (cast('NaN' as double)), (42);
 select * from test_nan where val > 0
 ---- RESULTS
 42
 ====
 ---- QUERY
 # IMPALA-6527: NaN values lead to incorrect filtering
 # Test if '<' predicate produces expected results as well.
 select * from test_nan where val < 100
 ---- RESULTS
 42
 ====
 ---- QUERY
 # IMPALA-6527: NaN values lead to incorrect filtering
 # Test if valid statistics are written. The column chunk should be filtered out by
 # the min filter.
 select * from test_nan where val < 10
 ---- RESULTS
 ---- RUNTIME_PROFILE
 aggregation(SUM, NumRowGroups): 1
 aggregation(SUM, NumStatsFilteredRowGroups): 1
 ====
 ---- QUERY
 # IMPALA-6527: NaN values lead to incorrect filtering
 # Test predicate that is true for NaN.
 select * from test_nan where not val >= 0
 ---- RESULTS
 NaN
 ====
 ---- QUERY
 # IMPALA-6527: NaN values lead to incorrect filtering
 # Test predicate that is true for NaN.
 select * from test_nan where val != 0
 ---- RESULTS
 NaN
 42
 ====
 ---- QUERY
 # Statistics filtering must not filter out row groups if predicate can be true for NaN.
 create table test_nan_true_predicate(val double) stored as parquet;
 insert into test_nan_true_predicate values (10), (20), (cast('NaN' as double));
 select * from test_nan_true_predicate where not val >= 0
 ---- RESULTS
 NaN
 ====
 ---- QUERY
 # NaN is the last element, predicate is true for NaN and value.
 select * from test_nan_true_predicate where not val >= 20
 ---- RESULTS
 10
 NaN
 ====
 ---- QUERY
 # NaN is the last element, predicate is true for NaN and value.
 select * from test_nan_true_predicate where val != 10
 ---- RESULTS
 20
 NaN
 ====
 ---- QUERY
 # Test the case when NaN is inserted between two values.
 # Test predicate true for NaN and false for the values.
 create table test_nan_in_the_middle(val double) stored as parquet;
 insert into test_nan_in_the_middle values (10), (cast('NaN' as double)), (20);
 select * from test_nan_in_the_middle where not val >= 0
 ---- RESULTS
 NaN
 ====
 ---- QUERY
 # NaN in the middle, predicate true for NaN and value.
 select * from test_nan_in_the_middle where not val >= 20
 ---- RESULTS
 10
 NaN
 ====
 ---- QUERY
 # NaN in the middle, '!=' should return NaN and value.
 select * from test_nan_in_the_middle where val != 10
 ---- RESULTS
 NaN
 20
 ====
 ---- QUERY
 # Test the case when there are only NaNs in the column chunk.
 # Test predicate true for NaN
 create table test_nan_only(val double) stored as parquet;
 insert into test_nan_only values (cast('NaN' as double)), (cast('NaN' as double)),
     (cast('NaN' as double));
 select * from test_nan_only where not val >= 0
 ---- RESULTS
 NaN
 NaN
 NaN
 ====
 ---- QUERY
 # There are only NaN values, predicate is false for NaN
 select * from test_nan_only where val >= 20
 ---- RESULTS
 ====
 ---- QUERY
 # Test the case when a number is following multiple NaNs.
 # Test predicate true for NaN, false for the inserted number
 create table test_multiple_nans(val double) stored as parquet;
 insert into test_multiple_nans values (cast('NaN' as double)), (cast('NaN' as double)),
     (cast('NaN' as double)), (20);
 select * from test_multiple_nans where not val >= 0
 ---- RESULTS
 NaN
 NaN
 NaN
 ====
 ---- QUERY
 # Multiple NaNs followed by a number, predicate is false for NaN and true for the number
 select * from test_multiple_nans where val >= 20
 ---- RESULTS
 20
 ====
 ---- QUERY
 # Multiple NaNs followed by a number, predicate is true for NaN and true for the number
 select * from test_multiple_nans where not val > 20
 ---- RESULTS
 NaN
 NaN
 NaN
 20
 ====
 ---- QUERY
 # Multiple NaNs followed by a number, predicate is false for NaN and false for the number
 select * from test_multiple_nans where val > 20
 ---- RESULTS
 ====
	====
	---- QUERY
	select id, bool_col from functional_parquet.alltypessmall where int_col < 0
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	set explain_level=2;
	explain select id, bool_col from functional_parquet.alltypessmall where int_col < 0;
	---- RESULTS: VERIFY_IS_SUBSET
	' parquet statistics predicates: int_col < CAST(0 AS INT)'
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col < 0
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where smallint_col < 0
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where int_col < 0
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where bigint_col < 0
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where float_col < 0
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where double_col < 0
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	# Test with inverted predicate
	select id, bool_col from functional_parquet.alltypessmall where -1 > int_col
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col > 9
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where smallint_col > 9
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select id, bool_col from functional_parquet.alltypessmall where int_col > 9
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where bigint_col > 90
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where float_col > 9.9
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where double_col > 99
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col >= 10
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col <= 0
	---- RESULTS
	12
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col >= 9
	---- RESULTS
	8
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col = -1
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where tinyint_col = 10
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	set explain_level=2;
	explain select count(*) from functional_parquet.alltypessmall where tinyint_col = 10
	---- RESULTS: VERIFY_IS_SUBSET
	' parquet statistics predicates: tinyint_col = CAST(10 AS TINYINT)'
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where id >= 30 and id <= 80
	---- RESULTS
	51
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	# Mix with partitioning columns
	select count(*) from functional_parquet.alltypessmall where int_col < 0 and year < 2012
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select id, bool_col from functional_parquet.alltypessmall where int_col < 3 - 3
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select id, bool_col from functional_parquet.alltypessmall where int_col < 3 - 3
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	# Test that without expr rewrite and thus without constant folding, constant exprs still
	# can be used to prune row groups.
	set enable_expr_rewrites=0;
	select id, bool_col from functional_parquet.alltypessmall where int_col < 3 - 3
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select id, bool_col from functional_parquet.alltypessmall where 5 + 5 < int_col
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	# Test that without expr rewrite and thus without constant folding, constant exprs still
	# can be used to prune row groups.
	set enable_expr_rewrites=0;
	select id, bool_col from functional_parquet.alltypessmall where 5 + 5 < int_col
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	# Test name based column resolution
	create table name_resolve stored as parquet as select * from functional_parquet.alltypessmall;
	alter table name_resolve replace columns (int_col int, bool_col boolean, tinyint_col tinyint, smallint_col smallint, id int);
	set parquet_fallback_schema_resolution=NAME;
	# If this picks up the stats from int_col, then it will filter all row groups and return
	# an incorrect result.
	select count(*) from name_resolve where id > 10;
	---- RESULTS
	89
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	# Query that has an implicit cast to a larger integer type
	select count(*) from functional_parquet.alltypessmall where tinyint_col > 1000000000000
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	# Predicates with explicit casts are not supported when evaluating parquet::Statistics.
	select count(*) from functional_parquet.alltypessmall where '0' > cast(tinyint_col as string)
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	# Explicit casts between numerical types can violate the transitivity of "min()", so they
	# are not supported when evaluating parquet::Statistics.
	select count(*) from functional_parquet.alltypes where cast(id as tinyint) < 10;
	---- RESULTS
	3878
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 24
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	select count(*) from functional_parquet.complextypestbl.int_array where pos < 5;
	---- RESULTS
	9
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	# Test the conversion of constant IN lists to min/max predicats
	select count(*) from functional_parquet.alltypes where int_col in (-1,-2,-3,-4);
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 24
	aggregation(SUM, NumStatsFilteredRowGroups): 24
	====
	---- QUERY
	select count(*) from functional_parquet.alltypes where id IN (1,25,49);
	---- RESULTS
	3
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 24
	aggregation(SUM, NumStatsFilteredRowGroups): 23
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where string_col < "0"
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where string_col <= "/"
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where string_col < "1"
	---- RESULTS
	12
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where string_col >= "9"
	---- RESULTS
	8
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where string_col > ":"
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where timestamp_col < "2009-01-01 00:00:00"
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where timestamp_col <= "2009-01-01 00:00:00"
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 3
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where timestamp_col = "2009-01-01 00:00:00"
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 3
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where timestamp_col > "2009-04-03 00:24:00.96"
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 4
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where timestamp_col >= "2009-04-03 00:24:00.96"
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 3
	====
	---- QUERY
	select count(*) from functional_parquet.alltypessmall where timestamp_col = "2009-04-03 00:24:00.96"
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 4
	aggregation(SUM, NumStatsFilteredRowGroups): 3
	====
	---- QUERY
	select count(*) from functional_parquet.date_tbl
	where date_part in ("2017-11-27", "1399-06-27") and date_col < '0001-06-21';
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 2
	====
	---- QUERY
	select count(*) from functional_parquet.date_tbl
	where date_part in ("2017-11-27", "1399-06-27") and date_col <= '0001-06-21';
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.date_tbl
	where date_part in ("2017-11-27", "1399-06-27") and date_col = '0001-06-21';
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.date_tbl
	where date_part in ("2017-11-27", "1399-06-27") and date_col > '2018-12-31';
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 2
	====
	---- QUERY
	select count(*) from functional_parquet.date_tbl
	where date_part in ("2017-11-27", "1399-06-27") and date_col >= '2018-12-31';
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.date_tbl
	where date_part in ("2017-11-27", "1399-06-27") and date_col = '2018-12-31';
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d1 < 1234
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d3 < 1.23456789
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d3 = 1.23456788
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d3 = 1.23456789
	---- RESULTS
	1
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d4 > 0.123456789
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d4 >= 0.12345678
	---- RESULTS
	5
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	select count(*) from functional_parquet.decimal_tbl where d4 >= 0.12345679
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	# Test that stats are disabled for CHAR type columns.
	create table chars (id int, c char(4)) stored as parquet;
	insert into chars values (1, cast("abaa" as char(4))), (2, cast("abab" as char(4)));
	select count(*) from chars;
	---- RESULTS
	2
	====
	---- QUERY
	select count(*) from chars where c <= "aaaa"
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	# IMPALA-4988: Test that stats support can be disabled using the parquet_read_statistics
	# query option.
	set parquet_read_statistics=0;
	select count(*) from functional_parquet.alltypes where id < 0;
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 24
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	# Check that all the row groups are skipped using null_count stat
	create table table_for_null_count_test (i int, j int) stored as parquet;
	insert into table_for_null_count_test values (1, NULL), (2, NULL), (3, NULL);
	select count(*) from table_for_null_count_test where j < 3;
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	# Insert another row group where not all the 'j' values are NULL
	insert into table_for_null_count_test values (4, 1), (5, NULL);
	select i from table_for_null_count_test where j < 3;
	---- RESULTS
	4
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 2
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	# Turning off parquet stats and verifying that no row groups are skipped
	set PARQUET_READ_STATISTICS=0;
	create table table_for_null_count_test2 (i int, j int) stored as parquet;
	insert into table_for_null_count_test2 values (1, NULL), (2, NULL), (3, NULL);
	select count(*) from table_for_null_count_test2 where j < 3;
	---- RESULTS
	0
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 0
	====
	---- QUERY
	# IMPALA-6527: NaN values lead to incorrect filtering.
	# When the first value is NaN in a column chunk, Impala chose it as min_value and
	# max_value for statistics. Test if it is no longer the case.
	create table test_nan(val double) stored as parquet;
	insert into test_nan values (cast('NaN' as double)), (42);
	select * from test_nan where val > 0
	---- RESULTS
	42
	====
	---- QUERY
	# IMPALA-6527: NaN values lead to incorrect filtering
	# Test if '<' predicate produces expected results as well.
	select * from test_nan where val < 100
	---- RESULTS
	42
	====
	---- QUERY
	# IMPALA-6527: NaN values lead to incorrect filtering
	# Test if valid statistics are written. The column chunk should be filtered out by
	# the min filter.
	select * from test_nan where val < 10
	---- RESULTS
	---- RUNTIME_PROFILE
	aggregation(SUM, NumRowGroups): 1
	aggregation(SUM, NumStatsFilteredRowGroups): 1
	====
	---- QUERY
	# IMPALA-6527: NaN values lead to incorrect filtering
	# Test predicate that is true for NaN.
	select * from test_nan where not val >= 0
	---- RESULTS
	NaN
	====
	---- QUERY
	# IMPALA-6527: NaN values lead to incorrect filtering
	# Test predicate that is true for NaN.
	select * from test_nan where val != 0
	---- RESULTS
	NaN
	42
	====
	---- QUERY
	# Statistics filtering must not filter out row groups if predicate can be true for NaN.
	create table test_nan_true_predicate(val double) stored as parquet;
	insert into test_nan_true_predicate values (10), (20), (cast('NaN' as double));
	select * from test_nan_true_predicate where not val >= 0
	---- RESULTS
	NaN
	====
	---- QUERY
	# NaN is the last element, predicate is true for NaN and value.
	select * from test_nan_true_predicate where not val >= 20
	---- RESULTS
	10
	NaN
	====
	---- QUERY
	# NaN is the last element, predicate is true for NaN and value.
	select * from test_nan_true_predicate where val != 10
	---- RESULTS
	20
	NaN
	====
	---- QUERY
	# Test the case when NaN is inserted between two values.
	# Test predicate true for NaN and false for the values.
	create table test_nan_in_the_middle(val double) stored as parquet;
	insert into test_nan_in_the_middle values (10), (cast('NaN' as double)), (20);
	select * from test_nan_in_the_middle where not val >= 0
	---- RESULTS
	NaN
	====
	---- QUERY
	# NaN in the middle, predicate true for NaN and value.
	select * from test_nan_in_the_middle where not val >= 20
	---- RESULTS
	10
	NaN
	====
	---- QUERY
	# NaN in the middle, '!=' should return NaN and value.
	select * from test_nan_in_the_middle where val != 10
	---- RESULTS
	NaN
	20
	====
	---- QUERY
	# Test the case when there are only NaNs in the column chunk.
	# Test predicate true for NaN
	create table test_nan_only(val double) stored as parquet;
	insert into test_nan_only values (cast('NaN' as double)), (cast('NaN' as double)),
	(cast('NaN' as double));
	select * from test_nan_only where not val >= 0
	---- RESULTS
	NaN
	NaN
	NaN
	====
	---- QUERY
	# There are only NaN values, predicate is false for NaN
	select * from test_nan_only where val >= 20
	---- RESULTS
	====
	---- QUERY
	# Test the case when a number is following multiple NaNs.
	# Test predicate true for NaN, false for the inserted number
	create table test_multiple_nans(val double) stored as parquet;
	insert into test_multiple_nans values (cast('NaN' as double)), (cast('NaN' as double)),
	(cast('NaN' as double)), (20);
	select * from test_multiple_nans where not val >= 0
	---- RESULTS
	NaN
	NaN
	NaN
	====
	---- QUERY
	# Multiple NaNs followed by a number, predicate is false for NaN and true for the number
	select * from test_multiple_nans where val >= 20
	---- RESULTS
	20
	====
	---- QUERY
	# Multiple NaNs followed by a number, predicate is true for NaN and true for the number
	select * from test_multiple_nans where not val > 20
	---- RESULTS
	NaN
	NaN
	NaN
	20
	====
	---- QUERY
	# Multiple NaNs followed by a number, predicate is false for NaN and false for the number
	select * from test_multiple_nans where val > 20
	---- RESULTS
	====