Add Decimal support for In, InSet, AtLeastNNonNulls, GetArrayItem, GetStructField, and GenerateExec

docs/supported_ops.md

@@ -233,11 +233,11 @@ Accelerator supports are described below.
 <td>S</td>
 <td>S*</td>
 <td>S</td>
+<td>S*</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
-<td><b>NS</b></td>
-<td><em>PS* (Only literal arrays and the output of the array function are supported; missing nested DECIMAL, NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
+<td><em>PS* (Only literal arrays and the output of the array function are supported; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
@@ -1601,13 +1601,13 @@ Accelerator support is described below.
 <td>S</td>
 <td>S*</td>
 <td>S</td>
-<td><b>NS</b></td>
+<td>S*</td>
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
-<td><em>PS* (missing nested DECIMAL, BINARY, CALENDAR, UDT)</em></td>
-<td><em>PS* (missing nested DECIMAL, BINARY, CALENDAR, UDT)</em></td>
-<td><em>PS* (missing nested DECIMAL, BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
 <td><b>NS</b></td>
 </tr>
 <tr>
@@ -4925,7 +4925,7 @@ Accelerator support is described below.
 <td> </td>
 <td> </td>
 <td> </td>
-<td><em>PS* (missing nested DECIMAL, BINARY, CALENDAR, MAP, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
 <td> </td>
 <td> </td>
 <td> </td>
@@ -4963,13 +4963,13 @@ Accelerator support is described below.
 <td>S</td>
 <td>S*</td>
 <td>S</td>
-<td><b>NS</b></td>
+<td>S*</td>
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
-<td><em>PS* (missing nested DECIMAL, BINARY, CALENDAR, MAP, UDT)</em></td>
-<td><b>NS</b></td>
-<td><em>PS* (missing nested DECIMAL, BINARY, CALENDAR, MAP, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
 <td><b>NS</b></td>
 </tr>
 <tr>
@@ -5191,7 +5191,7 @@ Accelerator support is described below.
 <td> </td>
 <td> </td>
 <td> </td>
-<td><em>PS* (missing nested DECIMAL, NULL, BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
 <td> </td>
 </tr>
 <tr>
@@ -5206,13 +5206,13 @@ Accelerator support is described below.
 <td>S</td>
 <td>S*</td>
 <td>S</td>
+<td>S*</td>
+<td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
-<td><b>NS</b></td>
-<td><b>NS</b></td>
-<td><em>PS* (missing nested DECIMAL, NULL, BINARY, CALENDAR, UDT)</em></td>
-<td><em>PS* (missing nested DECIMAL, NULL, BINARY, CALENDAR, UDT)</em></td>
-<td><em>PS* (missing nested DECIMAL, NULL, BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, UDT)</em></td>
 <td><b>NS</b></td>
 </tr>
 <tr>
@@ -5893,7 +5893,7 @@ Accelerator support is described below.
 <td>S</td>
 <td>S*</td>
 <td>S</td>
-<td><b>NS</b></td>
+<td>S*</td>
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
@@ -5914,7 +5914,7 @@ Accelerator support is described below.
 <td><em>PS (Literal value only)</em></td>
 <td><em>PS* (Literal value only)</em></td>
 <td><em>PS (Literal value only)</em></td>
-<td><b>NS</b></td>
+<td><em>PS* (Literal value only)</em></td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
@@ -6025,7 +6025,7 @@ Accelerator support is described below.
 <td>S</td>
 <td>S*</td>
 <td>S</td>
-<td><b>NS</b></td>
+<td>S*</td>
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>

integration_tests/src/main/python/array_test.py

@@ -33,7 +33,8 @@ def test_array_index(data_gen):
                 'a[null]',
                 'a[3]',
                 'a[50]',
-                'a[-1]'))
+                'a[-1]'),
+            conf=allow_negative_scale_of_decimal_conf)
 
 # Once we support arrays as literals then we can support a[null] for
 # all array gens. See test_array_index for more info

integration_tests/src/main/python/cmp_test.py

@@ -105,39 +105,44 @@ def test_gte(data_gen):
                 f.col('b') >= f.lit(None).cast(data_type),
                 f.col('a') >= f.col('b')), conf=allow_negative_scale_of_decimal_conf)
 
-@pytest.mark.parametrize('data_gen', eq_gens + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
 def test_isnull(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
             lambda spark : unary_op_df(spark, data_gen).select(
-                f.isnull(f.col('a'))))
+                f.isnull(f.col('a'))),
+            conf=allow_negative_scale_of_decimal_conf)
 
 @pytest.mark.parametrize('data_gen', [FloatGen(), DoubleGen()], ids=idfn)
 def test_isnan(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
             lambda spark : unary_op_df(spark, data_gen).select(
                 f.isnan(f.col('a'))))
 
-@pytest.mark.parametrize('data_gen', eq_gens + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
 def test_dropna_any(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
-            lambda spark : binary_op_df(spark, data_gen).dropna())
+            lambda spark : binary_op_df(spark, data_gen).dropna(),
+            conf=allow_negative_scale_of_decimal_conf)
 
-@pytest.mark.parametrize('data_gen', eq_gens + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
 def test_dropna_all(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
-            lambda spark : binary_op_df(spark, data_gen).dropna(how='all'))
+            lambda spark : binary_op_df(spark, data_gen).dropna(how='all'),
+            conf=allow_negative_scale_of_decimal_conf)
 
 #dropna is really a filter along with a test for null, but lets do an explicit filter test too
-@pytest.mark.parametrize('data_gen', eq_gens + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
 def test_filter(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
-            lambda spark : three_col_df(spark, BooleanGen(), data_gen, data_gen).filter(f.col('a')))
+            lambda spark : three_col_df(spark, BooleanGen(), data_gen, data_gen).filter(f.col('a')),
+            conf=allow_negative_scale_of_decimal_conf)
 
 # coalesce batch happens after a filter, but only if something else happens on the GPU after that
-@pytest.mark.parametrize('data_gen', eq_gens + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen + array_gens_sample + struct_gens_sample + map_gens_sample, ids=idfn)
 def test_filter_with_project(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
-            lambda spark : two_col_df(spark, BooleanGen(), data_gen).filter(f.col('a')).selectExpr('*', 'a as a2'))
+            lambda spark : two_col_df(spark, BooleanGen(), data_gen).filter(f.col('a')).selectExpr('*', 'a as a2'),
+            conf=allow_negative_scale_of_decimal_conf)
 
 @pytest.mark.parametrize('expr', [f.lit(True), f.lit(False), f.lit(None).cast('boolean')], ids=idfn)
 def test_filter_with_lit(expr):
@@ -146,21 +151,27 @@ def test_filter_with_lit(expr):
 
 # Spark supports two different versions of 'IN', and it depends on the spark.sql.optimizer.inSetConversionThreshold conf
 # This is to test entries under that value.
-@pytest.mark.parametrize('data_gen', eq_gens, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen, ids=idfn)
 def test_in(data_gen):
     # nulls are not supported for in on the GPU yet
     num_entries = int(with_cpu_session(lambda spark: spark.conf.get('spark.sql.optimizer.inSetConversionThreshold'))) - 1
-    scalars = list(gen_scalars(data_gen, num_entries, force_no_nulls=not isinstance(data_gen, NullGen)))
+    # we have to make the scalars in a session so negative scales in decimals are supported
+    scalars = with_cpu_session(lambda spark: list(gen_scalars(data_gen, num_entries, force_no_nulls=not isinstance(data_gen, NullGen))),
+            conf=allow_negative_scale_of_decimal_conf)
     assert_gpu_and_cpu_are_equal_collect(
-            lambda spark : unary_op_df(spark, data_gen).select(f.col('a').isin(scalars)))
+            lambda spark : unary_op_df(spark, data_gen).select(f.col('a').isin(scalars)),
+            conf=allow_negative_scale_of_decimal_conf)
 
 # Spark supports two different versions of 'IN', and it depends on the spark.sql.optimizer.inSetConversionThreshold conf
 # This is to test entries over that value.
-@pytest.mark.parametrize('data_gen', eq_gens, ids=idfn)
+@pytest.mark.parametrize('data_gen', eq_gens_with_decimal_gen, ids=idfn)
 def test_in_set(data_gen):
     # nulls are not supported for in on the GPU yet
     num_entries = int(with_cpu_session(lambda spark: spark.conf.get('spark.sql.optimizer.inSetConversionThreshold'))) + 1
-    scalars = list(gen_scalars(data_gen, num_entries, force_no_nulls=not isinstance(data_gen, NullGen)))
+    # we have to make the scalars in a session so negative scales in decimals are supported
+    scalars = with_cpu_session(lambda spark: list(gen_scalars(data_gen, num_entries, force_no_nulls=not isinstance(data_gen, NullGen))),
+            conf=allow_negative_scale_of_decimal_conf)
     assert_gpu_and_cpu_are_equal_collect(
-            lambda spark : unary_op_df(spark, data_gen).select(f.col('a').isin(scalars)))
+            lambda spark : unary_op_df(spark, data_gen).select(f.col('a').isin(scalars)),
+            conf=allow_negative_scale_of_decimal_conf)
 

integration_tests/src/main/python/data_gen.py

@@ -589,11 +589,15 @@ def gen_df(spark, data_gen, length=2048, seed=0):
     data = [src.gen() for index in range(0, length)]
     return spark.createDataFrame(data, src.data_type)
 
-def _mark_as_lit(data):
+def _mark_as_lit(data, data_type = None):
     # Sadly you cannot create a literal from just an array in pyspark
     if isinstance(data, list):
         return f.array([_mark_as_lit(x) for x in data])
-    return f.lit(data)
+    if data_type is None:
+        return f.lit(data)
+    else:
+        # lit does not take a data type so we might have to cast it
+        return f.lit(data).cast(data_type)
 
 def _gen_scalars_common(data_gen, count, seed=0):
     if isinstance(data_gen, list):
@@ -614,7 +618,8 @@ def gen_scalars(data_gen, count, seed=0, force_no_nulls=False):
     if force_no_nulls:
         assert(not isinstance(data_gen, NullGen))
     src = _gen_scalars_common(data_gen, count, seed=seed)
-    return (_mark_as_lit(src.gen(force_no_nulls=force_no_nulls)) for i in range(0, count))
+    data_type = src.data_type
+    return (_mark_as_lit(src.gen(force_no_nulls=force_no_nulls), data_type) for i in range(0, count))
 
 def gen_scalar(data_gen, seed=0, force_no_nulls=False):
     """Generate a single scalar value."""
@@ -761,7 +766,7 @@ def gen_scalars_for_sql(data_gen, count, seed=0, force_no_nulls=False):
 
 boolean_gens = [boolean_gen]
 
-single_level_array_gens = [ArrayGen(sub_gen) for sub_gen in all_basic_gens]
+single_level_array_gens = [ArrayGen(sub_gen) for sub_gen in all_basic_gens + decimal_gens + [null_gen]]
 
 # Be careful to not make these too large of data generation takes for ever
 # This is only a few nested array gens, because nesting can be very deep

integration_tests/src/main/python/generate_expr_test.py

@@ -29,7 +29,9 @@ def four_op_df(spark, gen, length=2048, seed=0):
         ('d', gen)], nullable=False), length=length, seed=seed)
 
 all_gen = [StringGen(), ByteGen(), ShortGen(), IntegerGen(), LongGen(), 
-         FloatGen(), DoubleGen(), BooleanGen(), DateGen(), TimestampGen()]
+         FloatGen(), DoubleGen(), BooleanGen(), DateGen(), TimestampGen(),
+         decimal_gen_default, decimal_gen_scale_precision, decimal_gen_same_scale_precision,
+         decimal_gen_64bit]
 
 #sort locally because of https://github.com/NVIDIA/spark-rapids/issues/84
 @ignore_order(local=True)

integration_tests/src/main/python/struct_test.py

@@ -23,7 +23,8 @@
 @pytest.mark.parametrize('data_gen', [StructGen([["first", boolean_gen], ["second", byte_gen], ["third", float_gen]]),
     StructGen([["first", short_gen], ["second", int_gen], ["third", long_gen]]),
     StructGen([["first", double_gen], ["second", date_gen], ["third", timestamp_gen]]),
-    StructGen([["first", string_gen], ["second", ArrayGen(byte_gen)], ["third", simple_string_to_string_map_gen]])], ids=idfn)
+    StructGen([["first", string_gen], ["second", ArrayGen(byte_gen)], ["third", simple_string_to_string_map_gen]]),
+    StructGen([["first", decimal_gen_default], ["second", decimal_gen_scale_precision], ["third", decimal_gen_same_scale_precision]])], ids=idfn)
 def test_struct_get_item(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
             lambda spark : unary_op_df(spark, data_gen).selectExpr(

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuInSet.scala

@@ -80,9 +80,18 @@ case class GpuInSet(
       ColumnVector.timestampMicroSecondsFromBoxedLongs(timestamps:_*)
     case StringType =>
       val strings = values.asInstanceOf[Seq[UTF8String]]
-      val builder = HostColumnVector.builder(DType.STRING, strings.size)
-      strings.foreach(s => builder.appendUTF8String(s.getBytes))
-      builder.buildAndPutOnDevice()
+      withResource(HostColumnVector.builder(DType.STRING, strings.size)) { builder =>
+        strings.foreach(s => builder.appendUTF8String(s.getBytes))
+        builder.buildAndPutOnDevice()
+      }
+    case t: DecimalType =>
+      val decs = values.asInstanceOf[Seq[Decimal]]
+      // When we support DECIMAL32 this will need to change to support that
+      withResource(HostColumnVector.builder(DType.create(DType.DTypeEnum.DECIMAL64, - t.scale),
+        decs.size)) { builder =>
+        decs.foreach(d => builder.appendUnscaledDecimal(d.toUnscaledLong))
+        builder.buildAndPutOnDevice()
+      }
     case _ =>
       throw new UnsupportedOperationException(s"Unsupported list type: ${child.dataType}")
     }

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala

@@ -969,7 +969,7 @@ object GpuOverrides {
       "Checks if number of non null/Nan values is greater than a given value",
       ExprChecks.projectNotLambda(TypeSig.BOOLEAN, TypeSig.BOOLEAN,
         repeatingParamCheck = Some(RepeatingParamCheck("input",
-          (TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.MAP + TypeSig.ARRAY +
+          (TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL + TypeSig.MAP + TypeSig.ARRAY +
               TypeSig.STRUCT).nested(),
           TypeSig.all))),
       (a, conf, p, r) => new ExprMeta[AtLeastNNonNulls](a, conf, p, r) {
@@ -1466,8 +1466,10 @@ object GpuOverrides {
     expr[In](
       "IN operator",
       ExprChecks.projectNotLambda(TypeSig.BOOLEAN, TypeSig.BOOLEAN,
-        Seq(ParamCheck("value", TypeSig.commonCudfTypes + TypeSig.NULL, TypeSig.all)),
-        Some(RepeatingParamCheck("list", TypeSig.commonCudfTypes.withAllLit(), TypeSig.all))),
+        Seq(ParamCheck("value", TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL,
+          TypeSig.all)),
+        Some(RepeatingParamCheck("list", (TypeSig.commonCudfTypes + TypeSig.DECIMAL).withAllLit(),
+          TypeSig.all))),
       (in, conf, p, r) => new ExprMeta[In](in, conf, p, r) {
         override def tagExprForGpu(): Unit = {
           val unaliased = in.list.map(extractLit)
@@ -1485,7 +1487,7 @@ object GpuOverrides {
     expr[InSet](
       "INSET operator",
       ExprChecks.unaryProjectNotLambda(TypeSig.BOOLEAN, TypeSig.BOOLEAN,
-        TypeSig.commonCudfTypes + TypeSig.NULL, TypeSig.all),
+        TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL, TypeSig.all),
       (in, conf, p, r) => new ExprMeta[InSet](in, conf, p, r) {
         override def tagExprForGpu(): Unit = {
           if (in.hset.contains(null)) {
@@ -1864,10 +1866,11 @@ object GpuOverrides {
     expr[GetStructField](
       "Gets the named field of the struct",
       ExprChecks.unaryProjectNotLambda(
-        (TypeSig.commonCudfTypes + TypeSig.ARRAY + TypeSig.STRUCT + TypeSig.MAP).nested(),
+        (TypeSig.commonCudfTypes + TypeSig.ARRAY + TypeSig.STRUCT + TypeSig.MAP + TypeSig.NULL +
+            TypeSig.DECIMAL).nested(),
         TypeSig.all,
         TypeSig.STRUCT.nested(TypeSig.commonCudfTypes + TypeSig.ARRAY +
-            TypeSig.STRUCT + TypeSig.MAP),
+            TypeSig.STRUCT + TypeSig.MAP + TypeSig.NULL + TypeSig.DECIMAL),
         TypeSig.STRUCT.nested(TypeSig.all)),
       (expr, conf, p, r) => new UnaryExprMeta[GetStructField](expr, conf, p, r) {
         override def convertToGpu(arr: Expression): GpuExpression =
@@ -1876,10 +1879,11 @@ object GpuOverrides {
     expr[GetArrayItem](
       "Gets the field at `ordinal` in the Array",
       ExprChecks.binaryProjectNotLambda(
-        (TypeSig.commonCudfTypes + TypeSig.ARRAY + TypeSig.STRUCT + TypeSig.NULL).nested(),
+        (TypeSig.commonCudfTypes + TypeSig.ARRAY + TypeSig.STRUCT + TypeSig.NULL +
+            TypeSig.DECIMAL + TypeSig.MAP).nested(),
         TypeSig.all,
         ("array", TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.ARRAY +
-            TypeSig.STRUCT + TypeSig.NULL),
+            TypeSig.STRUCT + TypeSig.NULL + TypeSig.DECIMAL + TypeSig.MAP),
             TypeSig.ARRAY.nested(TypeSig.all)),
         ("ordinal", TypeSig.lit(TypeEnum.INT), TypeSig.INT)),
       (in, conf, p, r) => new GpuGetArrayItemMeta(in, conf, p, r)),
@@ -2165,10 +2169,10 @@ object GpuOverrides {
     exec[GenerateExec] (
       "The backend for operations that generate more output rows than input rows like explode",
       ExecChecks(
-        TypeSig.commonCudfTypes
+        TypeSig.commonCudfTypes + TypeSig.DECIMAL
             .withPsNote(TypeEnum.ARRAY,
               "Only literal arrays and the output of the array function are supported")
-            .nested(TypeSig.commonCudfTypes),
+            .nested(TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL),
         TypeSig.all),
       (gen, conf, p, r) => new GpuGenerateExecSparkPlanMeta(gen, conf, p, r)),
     exec[ProjectExec](