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impala/testdata/workloads/functional-query/queries/QueryTest/udf.test
Tim Armstrong d4648e87b4 IMPALA-4356,IMPALA-7331: codegen all ScalarExprs 
Based on initial draft patch by Pooja Nilangekar.

Codegen'd expressions can be executed in two ways - either by
being called directly from a fully codegend function, or from
interpreted code via a function pointer (previously
ScalarFnCall::scalar_fn_wrapper_).

This change moves the function pointer from ScalarFnCall to its
base class ScalarExpr, so the full expr tree can be codegen'd, not
just the ScalarFnCall subtrees. The key refactoring and improvements
are:
* ScalarExpr::Get*Val() switches between interpreted and the codegen'd
  function pointer code paths in an inline function, avoiding a
  virtual function call to ScalarFnCal::Get*Val().
* Boilerplate logic is moved to ScalarExpr::GetCodegendComputeFn(),
  which calls a virtual function GetCodegenComputeFnImpl().
* ScalarFnCall's logic for deciding whether to interpret or codegen is
  better abstracted and exposed to ScalarExpr as IsInterpretable()
  and ShouldCodegen() methods.
* The ScalarExpr::codegend_compute_fn_ function pointer is only
  populated for expressions that are "codegen entry points". These
  include the roots of expr trees and non-root expressions
  where the parent expression calls Get*Val() from the
  pseudo-codegend GetCodegendComputeFnWrapper().
* ScalarFnCall is always initialised for interpreted execution.
  Otherwise the function pointer is needed for non-root expressions,
  e.g. to support ScalarExprEvaluator::GetConstantVal().
* Latent bugs/gaps for codegen of CollectionVal are fixed. CollectionVal
  is modified to use the StringVal memory layout to allow code sharing
  with StringVal. These fixes allowed simplification of
  IsNotEmptyPredicate codegen (from IMPALA-7657).

I chose to tackle two problems in one change - adding support for
generating codegen'd function pointers for all ScalarExprs, and adding
the "entry point" concept - to avoid a blow-up in the number of
codegen'd entry points that could lead to longer codegen times and/or
worse code because of inlining changes.

IMPALA-7331 (CHAR codegen support functions) is also fixed because
it was simpler to enable CHAR codegen within ScalarExpr than to carry
forward the exiting CHAR workarounds from ScalarFnCall. The
CHAR-specific codegen support required in the scalar expr subsystem is
very limited.  StringVal intermediates are used everywhere. Only
SlotRef actually operates on the different tuple layout, and the
required codegen support for SlotRef already exists for UDA
intermediates anyway.

Testing:
* Ran exhaustive tests.

Perf:
* Ran a basic insert benchmark, which went from 10.1s to 7.6s
  create table foo stored as parquet as
  select case when l_orderkey % 2 = 0 then 'aaa' else 'bbb' end
  from tpch30_parquet.lineitem;
* Ran a basic CHAR expr test:
  set num_nodes=1;
  set mt_dop=1;
  select count(*) from lineitem
  where cast(l_linestatus as CHAR(2)) = 'O ' and
        cast(l_returnflag as CHAR(2)) = 'N '
  The time spent in the scan went from 520ms to 220ms.
* Added perf regression test to tpcds-insert, similar to the manual
  benchmark.
* Ran single-node TPC-H with large and small scale factors, to estimate
  impact on execution perf and query startup time, respectively.

+----------+-----------------------+---------+------------+------------+----------------+
| Workload | File Format           | Avg (s) | Delta(Avg) | GeoMean(s) | Delta(GeoMean) |
+----------+-----------------------+---------+------------+------------+----------------+
| TPCH(30) | parquet / none / none | 6.84    | -0.18%     | 4.49       | -0.31%         |
+----------+-----------------------+---------+------------+------------+----------------+

+----------+----------+-----------------------+--------+-------------+------------+-----------+----------------+-------+----------------+---------+--------+
| Workload | Query    | File Format           | Avg(s) | Base Avg(s) | Delta(Avg) | StdDev(%) | Base StdDev(%) | Iters | Median Diff(%) | MW Zval | Tval   |
+----------+----------+-----------------------+--------+-------------+------------+-----------+----------------+-------+----------------+---------+--------+
| TPCH(30) | TPCH-Q20 | parquet / none / none | 2.58   | 2.47        |   +4.18%   |   1.29%   |   0.88%        | 5     |   +4.12%       | 2.31    | 5.81   |
| TPCH(30) | TPCH-Q17 | parquet / none / none | 4.81   | 4.61        |   +4.33%   |   2.18%   |   2.15%        | 5     |   +3.91%       | 1.73    | 3.09   |
| TPCH(30) | TPCH-Q21 | parquet / none / none | 26.45  | 26.16       |   +1.09%   |   0.37%   |   0.50%        | 5     |   +1.36%       | 2.02    | 3.94   |
| TPCH(30) | TPCH-Q9  | parquet / none / none | 15.92  | 15.75       |   +1.09%   |   2.87%   |   1.65%        | 5     |   +0.88%       | 0.29    | 0.73   |
| TPCH(30) | TPCH-Q12 | parquet / none / none | 2.38   | 2.35        |   +1.12%   |   1.64%   |   1.11%        | 5     |   +0.80%       | 1.15    | 1.26   |
| TPCH(30) | TPCH-Q14 | parquet / none / none | 2.94   | 2.91        |   +1.13%   |   7.68%   |   5.37%        | 5     |   -0.34%       | -0.29   | 0.27   |
| TPCH(30) | TPCH-Q18 | parquet / none / none | 18.10  | 18.02       |   +0.42%   |   2.70%   |   0.56%        | 5     |   +0.28%       | 0.29    | 0.34   |
| TPCH(30) | TPCH-Q8  | parquet / none / none | 4.72   | 4.72        |   -0.04%   |   1.20%   |   1.65%        | 5     |   +0.05%       | 0.00    | -0.04  |
| TPCH(30) | TPCH-Q19 | parquet / none / none | 3.92   | 3.93        |   -0.26%   |   1.08%   |   2.36%        | 5     |   +0.20%       | 0.58    | -0.23  |
| TPCH(30) | TPCH-Q6  | parquet / none / none | 1.27   | 1.27        |   -0.28%   |   0.22%   |   0.88%        | 5     |   +0.09%       | 0.29    | -0.68  |
| TPCH(30) | TPCH-Q16 | parquet / none / none | 2.64   | 2.65        |   -0.45%   |   1.65%   |   0.65%        | 5     |   -0.24%       | -0.58   | -0.57  |
| TPCH(30) | TPCH-Q22 | parquet / none / none | 3.10   | 3.13        |   -0.76%   |   1.47%   |   1.12%        | 5     |   -0.21%       | -0.29   | -0.93  |
| TPCH(30) | TPCH-Q2  | parquet / none / none | 1.20   | 1.21        |   -0.80%   |   2.26%   |   2.47%        | 5     |   -0.82%       | -1.15   | -0.53  |
| TPCH(30) | TPCH-Q4  | parquet / none / none | 1.97   | 1.99        |   -1.37%   |   1.84%   |   3.21%        | 5     |   -0.47%       | -0.58   | -0.83  |
| TPCH(30) | TPCH-Q13 | parquet / none / none | 11.53  | 11.63       |   -0.91%   |   0.46%   |   0.49%        | 5     |   -0.95%       | -2.02   | -3.08  |
| TPCH(30) | TPCH-Q10 | parquet / none / none | 5.13   | 5.21        |   -1.51%   |   2.24%   |   4.05%        | 5     |   -0.94%       | -0.58   | -0.73  |
| TPCH(30) | TPCH-Q5  | parquet / none / none | 3.61   | 3.66        |   -1.40%   |   0.66%   |   0.79%        | 5     |   -1.33%       | -1.73   | -3.05  |
| TPCH(30) | TPCH-Q7  | parquet / none / none | 19.42  | 19.71       |   -1.52%   |   1.34%   |   1.39%        | 5     |   -1.22%       | -1.44   | -1.76  |
| TPCH(30) | TPCH-Q3  | parquet / none / none | 5.08   | 5.15        |   -1.49%   |   1.34%   |   0.73%        | 5     |   -1.35%       | -1.44   | -2.20  |
| TPCH(30) | TPCH-Q15 | parquet / none / none | 3.42   | 3.49        |   -1.92%   |   0.93%   |   1.47%        | 5     |   -1.53%       | -1.15   | -2.49  |
| TPCH(30) | TPCH-Q11 | parquet / none / none | 1.15   | 1.19        |   -3.17%   |   2.27%   |   1.95%        | 5     |   -4.21%       | -1.15   | -2.41  |
| TPCH(30) | TPCH-Q1  | parquet / none / none | 9.26   | 9.63        |   -3.85%   |   0.62%   |   0.59%        | 5     |   -3.78%       | -2.31   | -10.25 |
+----------+----------+-----------------------+--------+-------------+------------+-----------+----------------+-------+----------------+---------+--------+

Cluster Name: UNKNOWN
Lab Run Info: UNKNOWN
Impala Version:          impalad version 3.2.0-SNAPSHOT RELEASE ()
Baseline Impala Version: impalad version 3.2.0-SNAPSHOT RELEASE (2019-03-19)

+----------+-----------------------+---------+------------+------------+----------------+
| Workload | File Format           | Avg (s) | Delta(Avg) | GeoMean(s) | Delta(GeoMean) |
+----------+-----------------------+---------+------------+------------+----------------+
| TPCH(2)  | parquet / none / none | 0.90    | -0.08%     | 0.80       | -0.05%         |
+----------+-----------------------+---------+------------+------------+----------------+

+----------+----------+-----------------------+--------+-------------+------------+-----------+----------------+-------+----------------+---------+-------+
| Workload | Query    | File Format           | Avg(s) | Base Avg(s) | Delta(Avg) | StdDev(%) | Base StdDev(%) | Iters | Median Diff(%) | MW Zval | Tval  |
+----------+----------+-----------------------+--------+-------------+------------+-----------+----------------+-------+----------------+---------+-------+
| TPCH(2)  | TPCH-Q18 | parquet / none / none | 1.22   | 1.19        |   +1.93%   |   3.81%   |   4.46%        | 20    |   +3.34%       | 1.62    | 1.46  |
| TPCH(2)  | TPCH-Q10 | parquet / none / none | 0.74   | 0.73        |   +1.97%   |   3.36%   |   2.94%        | 20    |   +0.97%       | 1.88    | 1.95  |
| TPCH(2)  | TPCH-Q11 | parquet / none / none | 0.49   | 0.48        |   +1.91%   |   6.19%   |   4.64%        | 20    |   +0.25%       | 0.95    | 1.09  |
| TPCH(2)  | TPCH-Q4  | parquet / none / none | 0.43   | 0.43        |   +1.99%   |   6.26%   |   5.86%        | 20    |   +0.15%       | 0.92    | 1.03  |
| TPCH(2)  | TPCH-Q15 | parquet / none / none | 0.50   | 0.49        |   +1.82%   |   7.32%   |   6.35%        | 20    |   +0.26%       | 1.01    | 0.83  |
| TPCH(2)  | TPCH-Q1  | parquet / none / none | 0.98   | 0.97        |   +0.79%   |   4.64%   |   2.73%        | 20    |   +0.36%       | 0.77    | 0.65  |
| TPCH(2)  | TPCH-Q19 | parquet / none / none | 0.83   | 0.83        |   +0.65%   |   3.33%   |   2.80%        | 20    |   +0.44%       | 2.18    | 0.67  |
| TPCH(2)  | TPCH-Q14 | parquet / none / none | 0.62   | 0.62        |   +0.97%   |   2.86%   |   1.00%        | 20    |   +0.04%       | 0.13    | 1.42  |
| TPCH(2)  | TPCH-Q3  | parquet / none / none | 0.88   | 0.87        |   +0.57%   |   2.17%   |   1.74%        | 20    |   +0.29%       | 1.15    | 0.92  |
| TPCH(2)  | TPCH-Q12 | parquet / none / none | 0.53   | 0.53        |   +0.27%   |   4.58%   |   5.78%        | 20    |   +0.46%       | 1.47    | 0.16  |
| TPCH(2)  | TPCH-Q17 | parquet / none / none | 0.72   | 0.72        |   +0.15%   |   3.64%   |   5.55%        | 20    |   +0.21%       | 0.86    | 0.10  |
| TPCH(2)  | TPCH-Q21 | parquet / none / none | 2.05   | 2.05        |   +0.21%   |   1.99%   |   2.37%        | 20    |   +0.01%       | 0.25    | 0.30  |
| TPCH(2)  | TPCH-Q5  | parquet / none / none | 1.28   | 1.27        |   +0.24%   |   1.61%   |   1.80%        | 20    |   -0.02%       | -0.57   | 0.44  |
| TPCH(2)  | TPCH-Q13 | parquet / none / none | 1.27   | 1.27        |   -0.34%   |   1.69%   |   1.83%        | 20    |   -0.20%       | -1.65   | -0.61 |
| TPCH(2)  | TPCH-Q7  | parquet / none / none | 1.72   | 1.73        |   -0.55%   |   2.40%   |   1.69%        | 20    |   -0.03%       | -0.42   | -0.83 |
| TPCH(2)  | TPCH-Q8  | parquet / none / none | 1.27   | 1.28        |   -0.68%   |   3.10%   |   3.89%        | 20    |   -0.06%       | -0.54   | -0.62 |
| TPCH(2)  | TPCH-Q6  | parquet / none / none | 0.36   | 0.36        |   -0.84%   |   0.79%   |   3.51%        | 20    |   -0.07%       | -0.36   | -1.04 |
| TPCH(2)  | TPCH-Q2  | parquet / none / none | 0.65   | 0.65        |   -1.17%   |   4.76%   |   5.99%        | 20    |   -0.05%       | -0.25   | -0.69 |
| TPCH(2)  | TPCH-Q9  | parquet / none / none | 1.59   | 1.62        |   -2.01%   |   1.45%   |   5.12%        | 20    |   -0.16%       | -1.24   | -1.69 |
| TPCH(2)  | TPCH-Q20 | parquet / none / none | 0.68   | 0.69        |   -1.73%   |   4.35%   |   4.43%        | 20    |   -0.49%       | -1.74   | -1.25 |
| TPCH(2)  | TPCH-Q22 | parquet / none / none | 0.38   | 0.40        |   -2.89%   |   7.42%   |   6.39%        | 20    |   -0.21%       | -0.66   | -1.34 |
| TPCH(2)  | TPCH-Q16 | parquet / none / none | 0.59   | 0.62        |   -4.01%   |   6.33%   |   5.83%        | 20    |   -4.72%       | -1.39   | -2.13 |
+----------+----------+-----------------------+--------+-------------+------------+-----------+----------------+-------+----------------+---------+-------+

Change-Id: I839d7a3a2f5e1309c33a1f66013ef11628c5dc11
Reviewed-on: http://gerrit.cloudera.org:8080/12797
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
2019-05-15 22:34:28 +00:00

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====
---- QUERY
# Test identity functions
select identity(true);
---- TYPES
boolean
---- RESULTS
true
====
---- QUERY
select identity(cast(10 as tinyint));
---- TYPES
tinyint
---- RESULTS
10
====
---- QUERY
select identity(cast(10 as smallint));
---- TYPES
smallint
---- RESULTS
10
====
---- QUERY
select identity(cast(10 as int));
---- TYPES
int
---- RESULTS
10
====
---- QUERY
select identity(cast(10 as bigint));
---- TYPES
bigint
---- RESULTS
10
====
---- QUERY
select identity(cast(10.0 as float));
---- TYPES
float
---- RESULTS
10
====
---- QUERY
select identity(cast(10.0 as double));
---- TYPES
double
---- RESULTS
10
====
---- QUERY
select identity("why hello there");
---- TYPES
string
---- RESULTS
'why hello there'
====
---- QUERY
select identity(now());
---- TYPES
timestamp
====
---- QUERY
select identity(date '2019-02-20');
---- TYPES
date
---- RESULTS
2019-02-20
====
---- QUERY
select identity(cast(1 as decimal(9,0)));
---- TYPES
decimal
---- RESULTS
1
====
---- QUERY
select identity(cast(1 as decimal(18,1)));
---- TYPES
decimal
---- RESULTS
1.0
====
---- QUERY
select identity(cast(1 as decimal(38,10)));
---- TYPES
decimal
---- RESULTS
1.0000000000
====
---- QUERY
select identity(NULL);
---- TYPES
boolean
---- RESULTS
NULL
====
---- QUERY
select constant_timestamp();
---- TYPES
timestamp
---- RESULTS
2013-10-09 00:00:00.000000001
====
---- QUERY
select constant_date();
---- TYPES
date
---- RESULTS
2013-10-09
====
---- QUERY
# This provides coverage for ScalarExprEvaluator::GetConstValue(), which will interpret
# constant_timestamp(). This means that for both native and IR UDFs, constant_timestamp()
# needs to support evaluation from interpreted code.
select from_utc_timestamp(constant_timestamp(), "UTC");
---- TYPES
timestamp
---- RESULTS
2013-10-09 00:00:00.000000001
====
---- QUERY
# Test UDFs with different arguments
select all_types_fn("1", true, 2, 3, 4, 5, 6.0, 7.0, cast(8 as decimal(2,0)),
date '1970-01-10');
---- TYPES
int
---- RESULTS
46
====
---- QUERY
select no_args();
---- TYPES
string
---- RESULTS
'string'
====
---- QUERY
# Test UDFs over tables
select sum(identity(bigint_col)) from functional.alltypes
---- TYPES
bigint
---- RESULTS
328500
====
---- QUERY
select identity(a) from functional.tinytable;
---- TYPES
string
---- RESULTS
'aaaaaaa'
'ccccc'
'eeeeeeee'
====
---- QUERY
select identity(d1),
identity(cast(d3 as decimal(38,10))), identity(cast(d5 as decimal(38,10)))
from functional.decimal_tbl;
---- TYPES
decimal,decimal,decimal
---- RESULTS
1234,1.2345678900,12345.7890000000
2345,12.3456789000,3.1410000000
12345,123.4567890000,11.2200000000
12345,1234.5678900000,0.1000000000
132842,12345.6789000000,0.7788900000
====
---- QUERY
select identity(date_part), identity(date_col)
from functional.date_tbl;
---- TYPES
DATE, DATE
---- RESULTS
0001-01-01,0001-01-01
0001-01-01,0001-12-31
0001-01-01,0002-01-01
0001-01-01,1399-12-31
0001-01-01,2017-11-28
0001-01-01,9999-12-31
0001-01-01,NULL
1399-06-27,2017-11-28
1399-06-27,2018-12-31
1399-06-27,NULL
2017-11-27,0001-06-21
2017-11-27,0001-06-22
2017-11-27,0001-06-23
2017-11-27,0001-06-24
2017-11-27,0001-06-25
2017-11-27,0001-06-26
2017-11-27,0001-06-27
2017-11-27,0001-06-28
2017-11-27,0001-06-29
2017-11-27,2017-11-28
9999-12-31,9999-12-01
9999-12-31,9999-12-31
====
---- QUERY
select identity(bool_col), identity(tinyint_col),
identity(smallint_col), identity(int_col),
identity(bigint_col), identity(float_col),
identity(double_col), identity(string_col),
identity(timestamp_col), identity(year)
from functional.alltypestiny;
---- TYPES
boolean, tinyint, smallint, int, bigint, float, double, string, timestamp, int
---- RESULTS
true,0,0,0,0,0,0,'0',2009-02-01 00:00:00,2009
false,1,1,1,10,1.100000023841858,10.1,'1',2009-02-01 00:01:00,2009
true,0,0,0,0,0,0,'0',2009-04-01 00:00:00,2009
false,1,1,1,10,1.100000023841858,10.1,'1',2009-04-01 00:01:00,2009
true,0,0,0,0,0,0,'0',2009-01-01 00:00:00,2009
false,1,1,1,10,1.100000023841858,10.1,'1',2009-01-01 00:01:00,2009
true,0,0,0,0,0,0,'0',2009-03-01 00:00:00,2009
false,1,1,1,10,1.100000023841858,10.1,'1',2009-03-01 00:01:00,2009
====
---- QUERY
select sum(all_types_fn(
string_col, bool_col, tinyint_col, smallint_col,
int_col, bigint_col, float_col, double_col, cast(tinyint_col as decimal(2,0)),
cast(adddate('1970-01-01', tinyint_col) as date)))
from functional.alltypes;
---- TYPES
bigint
---- RESULTS
# Verify with 'select sum(length(string_col)) + sum(cast(bool_col as int))
# + sum(tinyint_col) + sum(smallint_col) + sum(int_col) + sum(bigint_col)
# + sum(cast(float_col as bigint)) + sum(cast(double_col as bigint)) + sum(tinyint_col)
# + sum(tinyint_col)
# from functional.alltypes;'
865050
====
---- QUERY
select no_args() from functional.alltypes limit 1;
---- TYPES
string
---- RESULTS
'string'
====
---- QUERY
# Chain UDFs/exprs together to test glue
select identity(no_args());
---- TYPES
string
---- RESULTS
'string'
====
---- QUERY
select identity(cast(identity(3.0) as bigint));
---- TYPES
bigint
---- RESULTS
3
====
---- QUERY
select count(*) from functional.alltypessmall having identity(count(*)) > 1
---- TYPES
bigint
---- RESULTS
100
====
---- QUERY
select count(identity(id)) from functional.alltypessmall
having identity(count(*)) > 1
---- TYPES
bigint
---- RESULTS
100
====
---- QUERY
select count(identity(id)) from functional.alltypessmall
group by identity(int_col)
having identity(count(*)) > 10
---- TYPES
bigint
---- RESULTS
12
12
12
12
12
====
---- QUERY
select identity(a.tinyint_col),
identity(b.id),
identity(a.string_col)
from functional.alltypesagg a join functional.alltypessmall b on
(identity(a.tinyint_col) = identity(b.id))
and identity(a.tinyint_col + b.tinyint_col) < 5
where identity(a.month) = identity(1)
and identity(a.day) = identity(1)
and identity(a.string_col) > identity('88')
and identity(b.bool_col) = identity(false)
order by identity(a.string_col)
limit 5
---- TYPES
tinyint, int, string
---- RESULTS
1,1,'881'
1,1,'891'
1,1,'901'
1,1,'91'
1,1,'911'
====
---- QUERY
select identity(int_col),
identity(min(identity(bool_col))),
identity(max(identity(tinyint_col))),
identity(max(identity(smallint_col))),
identity(max(identity(int_col))),
identity(max(identity(bigint_col))),
identity(max(identity(float_col))),
identity(max(identity(double_col))),
identity(max(identity(string_col))),
identity(max(identity(timestamp_col)))
from functional.alltypesagg
where identity(identity(tinyint_col) > identity(1))
group by identity(int_col)
having identity(identity(int_col) > identity(998))
---- TYPES
int,boolean,tinyint,smallint,int,bigint,float,double,string,timestamp
---- RESULTS
999,false,9,99,999,9990,1098.900024414062,10089.9,'999',2010-01-10 18:02:05.100000000
====
---- QUERY
select identity(year),
identity(min(identity(month))),
identity(min(string_col)),
identity(max(timestamp_col))
from functional.alltypesagg group by identity(year)
having identity(identity(year) = identity(2010))
---- TYPES
int,int,string,timestamp
---- RESULTS
2010,1,'0',2010-01-10 18:02:05.100000000
====
---- QUERY
select min(identity(int_col)) from functional.alltypesagg where int_col is null;
---- TYPES
int
---- RESULTS
NULL
====
---- QUERY
select var_sum(NULL, NULL, NULL)
---- TYPES
int
---- RESULTS
NULL
====
---- QUERY
select var_and(true, false, true)
---- TYPES
boolean
---- RESULTS
false
====
---- QUERY
select var_and(true, true, true, true, true)
---- TYPES
boolean
---- RESULTS
true
====
---- QUERY
select var_sum(1, 2, 3, 4, 5, 6)
---- TYPES
int
---- RESULTS
21
====
---- QUERY
select var_sum(1.0, 2.0, 3.0)
---- TYPES
decimal
---- RESULTS
6.00
====
---- QUERY
select var_sum("Hello", "World", "Foo", "Bar")
---- TYPES
int
---- RESULTS
16
====
---- QUERY
select var_sum(cast(1 as decimal(4,2)), cast(2 as decimal(4,2)), cast(3 as decimal(4,2)));
---- TYPES
decimal
---- RESULTS
6.00
====
---- QUERY
# More complicated arguments
select var_sum(
cast(1 as decimal(4,2)), cast(2 as decimal(4,2)),
cast(3 as decimal(3,2)) + cast("1.1" as decimal(3,2)));
---- TYPES
decimal
---- RESULTS
7.10
====
---- QUERY
select tinyint_col, int_col, var_sum(tinyint_col, int_col)
from functional.alltypestiny
---- TYPES
tinyint, int, int
---- RESULTS
0,0,0
1,1,2
0,0,0
1,1,2
0,0,0
1,1,2
0,0,0
1,1,2
====
---- QUERY
select var_sum_multiply(NULL, 1, 2)
---- TYPES
double
---- RESULTS
NULL
====
---- QUERY
select var_sum_multiply(1.0, 1, 2, NULL, 3)
---- TYPES
double
---- RESULTS
6
====
---- QUERY
select var_sum_multiply(5.0, 1, 2, 3, 4, 5, 6)
---- TYPES
double
---- RESULTS
105
====
---- QUERY
select var_sum_multiply2(5.0, 1, 2, 3, 4, 5, 6)
---- TYPES
double
---- RESULTS
105
====
---- QUERY
select to_lower("HELLO")
---- TYPES
string
---- RESULTS
'hello'
====
---- QUERY
select to_upper("foobar")
---- TYPES
string
---- RESULTS
'FOOBAR'
====
---- QUERY
select tinyint_col, int_col, var_sum_multiply(2, tinyint_col, int_col)
from functional.alltypestiny
---- TYPES
tinyint, int, double
---- RESULTS
0,0,0
1,1,4
0,0,0
1,1,4
0,0,0
1,1,4
0,0,0
1,1,4
====
---- QUERY
# Test UDFs that are evaluated in the planner (doesn't take cols as input)
# and returns a string.
select count(*) from functional.alltypessmall where No_Args() = 'string'
---- TYPES
BIGINT
---- RESULTS
100
====
---- QUERY
select count(*) from functional.alltypessmall where No_Args() != 'string'
---- TYPES
BIGINT
---- RESULTS
0
====
---- QUERY
select validate_arg_type("dummy")
---- TYPES
BOOLEAN
---- RESULTS
true
====
---- QUERY
select constant_arg(1), constant_arg(int_col) from functional.alltypestiny limit 1;
---- TYPES
int,int
---- RESULTS
1,NULL
====
---- QUERY
# Test applying a UDF on a partition column predicate (IMPALA-887)
select * from functional.alltypestiny where identity(year) = 2009 and identity(month) = 1;
---- RESULTS
0,true,0,0,0,0,0,0,'01/01/09','0',2009-01-01 00:00:00,2009,1
1,false,1,1,1,10,1.100000023841858,10.1,'01/01/09','1',2009-01-01 00:01:00,2009,1
---- TYPES
INT, BOOLEAN, TINYINT, SMALLINT, INT, BIGINT, FLOAT, DOUBLE, STRING, STRING, TIMESTAMP, INT, INT
====
---- QUERY
select mem_test(100);
---- TYPES
bigint
---- RESULTS
100
====
---- QUERY
# Make sure rand() is non-constant
select constant_arg(cast(rand() as int));
---- TYPES
INT
---- RESULTS
NULL
====
---- QUERY
select four_args(1,2,3,4);
---- TYPES
INT
---- RESULTS
10
====
---- QUERY
select five_args(1,2,3,4,5);
---- TYPES
INT
---- RESULTS
15
====
---- QUERY
select six_args(1,2,3,4,5,6);
---- TYPES
INT
---- RESULTS
21
====
---- QUERY
select seven_args(1,2,3,4,5,6,7);
---- TYPES
INT
---- RESULTS
28
====
---- QUERY
select eight_args(1,2,3,4,5,6,7,8);
---- TYPES
INT
---- RESULTS
36
====
---- QUERY
select twenty_args(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20);
---- TYPES
INT
---- RESULTS
210
====
---- QUERY
select pow(3,2), xpow(3,2);
---- TYPES
DOUBLE, DOUBLE
---- RESULTS
9,9
====
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