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Posted to github@arrow.apache.org by "viirya (via GitHub)" <gi...@apache.org> on 2024/01/28 22:04:37 UTC
[PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
viirya opened a new pull request, #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470226144
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("index", DataType::UInt8, false),
+ Field::new("uint", DataType::UInt64, true),
+ Field::new("int", DataType::Int64, true),
+ Field::new("float", DataType::Float64, true),
+ ]));
+
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![
+ Arc::new(UInt8Array::from_iter_values([1, 2, 3])),
+ Arc::new(UInt64Array::from(vec![Some(2), Some(3), None])),
+ Arc::new(Int64Array::from(vec![Some(-2), Some(3), None])),
+ Arc::new(Float64Array::from(vec![Some(1.0), Some(3.3), None])),
+ ],
+ )?;
+
+ ctx.register_batch("data_table", batch)?;
+
+ let random_normal_udf = ScalarUDF::from(RandomUDF::new());
+ ctx.register_udf(random_normal_udf);
+
+ let result = plan_and_collect(
+ &ctx,
+ "SELECT random_udf() AS random_udf, random() AS native_random FROM data_table",
+ )
+ .await?;
+
+ assert_eq!(result.len(), 1);
+ let batch = &result[0];
+ let random_udf = batch
+ .column(0)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+ let native_random = batch
+ .column(1)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+
+ assert_eq!(random_udf.len(), native_random.len());
+
+ let mut previous = 1.0;
Review Comment:
Can could the random implementation ever actually make 1.0 (the range is `0..1.0`). Maybe we could start at -1.0 or something just to be sure this won't ever flake
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
Review Comment:
it doesn't hurt but I wonder if the example table needs 4 columns 🤔
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
Review Comment:
I think it would help to add context here on what this test is ensuring
```suggestion
/// Ensure that a volatile user defined function will be invoked for every row (not once)
#[tokio::test]
```
##########
datafusion/physical-expr/src/scalar_function.rs:
##########
@@ -149,6 +153,11 @@ impl PhysicalExpr for ScalarFunctionExpr {
{
vec![ColumnarValue::create_null_array(batch.num_rows())]
}
+ // If the function supports zero argument, we pass in a null array indicating the batch size.
Review Comment:
I never fully understood why this didn't just check `self.args.is_empty()` 🤔
##########
datafusion/physical-expr/src/scalar_function.rs:
##########
@@ -58,6 +58,8 @@ pub struct ScalarFunctionExpr {
// and it specifies the effect of an increase or decrease in
// the corresponding `arg` to the function value.
monotonicity: Option<FuncMonotonicity>,
+ // Signature of the function
+ signature: Signature,
Review Comment:
It seems like only one field is ever read. I wonder if it would be better to copy just this field rather than the entire signature (which is both larger with several allocations, but also might be misleading that this signature information was used somehow more in execution plans.
I worry that the signature information might start being referred to in physical planning
So perhaps something like
```suggestion
// Does this function need to be invoked with zero arguments ?
supports_zero_argument: bool,
```
```rust
self.signature.type_signature.supports_zero_argument
```
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470043292
##########
datafusion/proto/tests/cases/roundtrip_physical_plan.rs:
##########
@@ -578,8 +578,9 @@ fn roundtrip_builtin_scalar_function() -> Result<()> {
"acos",
fun_expr,
vec![col("a", &schema)?],
- DataType::Int64,
+ DataType::Float64,
Review Comment:
The existing test is not correct at all. `acos` built-in scalar function's return type should be `Float64`.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470303689
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
Review Comment:
Added the comment with a little more info.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#issuecomment-1913756154
Fix clippy at https://github.com/apache/arrow-datafusion/pull/9034
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470303456
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
Review Comment:
Reduced to one column.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470043292
##########
datafusion/proto/tests/cases/roundtrip_physical_plan.rs:
##########
@@ -578,8 +578,9 @@ fn roundtrip_builtin_scalar_function() -> Result<()> {
"acos",
fun_expr,
vec![col("a", &schema)?],
- DataType::Int64,
+ DataType::Float64,
Review Comment:
The existing test is not correct at all. `acos` built-in scalar function's return type should be `Float64`.
Previously the roundtrip test passes because `from_proto` simply takes serde return type and uses it as parameter to `ScalarFunctionExpr`.
But in this PR, `from_proto` calls `create_physical_expr` which gets return type directly from `BuiltinScalarFunction`. So with the PR, this test issue is found.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470242498
##########
datafusion/physical-expr/src/scalar_function.rs:
##########
@@ -58,6 +58,8 @@ pub struct ScalarFunctionExpr {
// and it specifies the effect of an increase or decrease in
// the corresponding `arg` to the function value.
monotonicity: Option<FuncMonotonicity>,
+ // Signature of the function
+ signature: Signature,
Review Comment:
Ok
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470302950
##########
datafusion/physical-expr/src/scalar_function.rs:
##########
@@ -149,6 +153,11 @@ impl PhysicalExpr for ScalarFunctionExpr {
{
vec![ColumnarValue::create_null_array(batch.num_rows())]
}
+ // If the function supports zero argument, we pass in a null array indicating the batch size.
Review Comment:
Good idea. Changed to `self.args.is_empty()`.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470241531
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("index", DataType::UInt8, false),
+ Field::new("uint", DataType::UInt64, true),
+ Field::new("int", DataType::Int64, true),
+ Field::new("float", DataType::Float64, true),
+ ]));
+
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![
+ Arc::new(UInt8Array::from_iter_values([1, 2, 3])),
+ Arc::new(UInt64Array::from(vec![Some(2), Some(3), None])),
+ Arc::new(Int64Array::from(vec![Some(-2), Some(3), None])),
+ Arc::new(Float64Array::from(vec![Some(1.0), Some(3.3), None])),
+ ],
+ )?;
+
+ ctx.register_batch("data_table", batch)?;
+
+ let random_normal_udf = ScalarUDF::from(RandomUDF::new());
+ ctx.register_udf(random_normal_udf);
+
+ let result = plan_and_collect(
+ &ctx,
+ "SELECT random_udf() AS random_udf, random() AS native_random FROM data_table",
+ )
+ .await?;
+
+ assert_eq!(result.len(), 1);
+ let batch = &result[0];
+ let random_udf = batch
+ .column(0)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+ let native_random = batch
+ .column(1)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+
+ assert_eq!(random_udf.len(), native_random.len());
+
+ let mut previous = 1.0;
Review Comment:
I think the range `0..1.0` is exclusive on the end point?
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470288307
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("index", DataType::UInt8, false),
+ Field::new("uint", DataType::UInt64, true),
+ Field::new("int", DataType::Int64, true),
+ Field::new("float", DataType::Float64, true),
+ ]));
+
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![
+ Arc::new(UInt8Array::from_iter_values([1, 2, 3])),
+ Arc::new(UInt64Array::from(vec![Some(2), Some(3), None])),
+ Arc::new(Int64Array::from(vec![Some(-2), Some(3), None])),
+ Arc::new(Float64Array::from(vec![Some(1.0), Some(3.3), None])),
+ ],
+ )?;
+
+ ctx.register_batch("data_table", batch)?;
+
+ let random_normal_udf = ScalarUDF::from(RandomUDF::new());
+ ctx.register_udf(random_normal_udf);
+
+ let result = plan_and_collect(
+ &ctx,
+ "SELECT random_udf() AS random_udf, random() AS native_random FROM data_table",
+ )
+ .await?;
+
+ assert_eq!(result.len(), 1);
+ let batch = &result[0];
+ let random_udf = batch
+ .column(0)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+ let native_random = batch
+ .column(1)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+
+ assert_eq!(random_udf.len(), native_random.len());
+
+ let mut previous = 1.0;
Review Comment:
> I think the range 0..1.0 is exclusive on the end point?
If so that this is fine!
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470043292
##########
datafusion/proto/tests/cases/roundtrip_physical_plan.rs:
##########
@@ -578,8 +578,9 @@ fn roundtrip_builtin_scalar_function() -> Result<()> {
"acos",
fun_expr,
vec![col("a", &schema)?],
- DataType::Int64,
+ DataType::Float64,
Review Comment:
`acos` built-in scalar function's return type should be `Float64`.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470303232
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("index", DataType::UInt8, false),
+ Field::new("uint", DataType::UInt64, true),
+ Field::new("int", DataType::Int64, true),
+ Field::new("float", DataType::Float64, true),
+ ]));
+
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![
+ Arc::new(UInt8Array::from_iter_values([1, 2, 3])),
+ Arc::new(UInt64Array::from(vec![Some(2), Some(3), None])),
+ Arc::new(Int64Array::from(vec![Some(-2), Some(3), None])),
+ Arc::new(Float64Array::from(vec![Some(1.0), Some(3.3), None])),
+ ],
+ )?;
+
+ ctx.register_batch("data_table", batch)?;
+
+ let random_normal_udf = ScalarUDF::from(RandomUDF::new());
+ ctx.register_udf(random_normal_udf);
+
+ let result = plan_and_collect(
+ &ctx,
+ "SELECT random_udf() AS random_udf, random() AS native_random FROM data_table",
+ )
+ .await?;
+
+ assert_eq!(result.len(), 1);
+ let batch = &result[0];
+ let random_udf = batch
+ .column(0)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+ let native_random = batch
+ .column(1)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+
+ assert_eq!(random_udf.len(), native_random.len());
+
+ let mut previous = 1.0;
Review Comment:
Changed to `-1.0` to make it more clear.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470240693
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
Review Comment:
I just copied the reported test case. I think we can reduce the columns.
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Re: [PR] ScalarUDF with zero arguments should be provided with one null array as parameter [arrow-datafusion]
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #9031:
URL: https://github.com/apache/arrow-datafusion/pull/9031#discussion_r1470241743
##########
datafusion/core/tests/user_defined/user_defined_scalar_functions.rs:
##########
@@ -392,6 +396,112 @@ async fn test_user_defined_functions_with_alias() -> Result<()> {
Ok(())
}
+#[derive(Debug)]
+pub struct RandomUDF {
+ signature: Signature,
+}
+
+impl RandomUDF {
+ pub fn new() -> Self {
+ Self {
+ signature: Signature::one_of(
+ vec![Any(0), Variadic(vec![Float64])],
+ Volatility::Volatile,
+ ),
+ }
+ }
+}
+
+impl ScalarUDFImpl for RandomUDF {
+ fn as_any(&self) -> &dyn std::any::Any {
+ self
+ }
+
+ fn name(&self) -> &str {
+ "random_udf"
+ }
+
+ fn signature(&self) -> &Signature {
+ &self.signature
+ }
+
+ fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+ Ok(Float64)
+ }
+
+ fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let len: usize = match &args[0] {
+ ColumnarValue::Array(array) => array.len(),
+ _ => {
+ return Err(datafusion::error::DataFusionError::Internal(
+ "Invalid argument type".to_string(),
+ ))
+ }
+ };
+ let mut rng = thread_rng();
+ let values = iter::repeat_with(|| rng.gen_range(0.1..1.0)).take(len);
+ let array = Float64Array::from_iter_values(values);
+ Ok(ColumnarValue::Array(Arc::new(array)))
+ }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_zero_argument() -> Result<()> {
+ let ctx = SessionContext::new();
+
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("index", DataType::UInt8, false),
+ Field::new("uint", DataType::UInt64, true),
+ Field::new("int", DataType::Int64, true),
+ Field::new("float", DataType::Float64, true),
+ ]));
+
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![
+ Arc::new(UInt8Array::from_iter_values([1, 2, 3])),
+ Arc::new(UInt64Array::from(vec![Some(2), Some(3), None])),
+ Arc::new(Int64Array::from(vec![Some(-2), Some(3), None])),
+ Arc::new(Float64Array::from(vec![Some(1.0), Some(3.3), None])),
+ ],
+ )?;
+
+ ctx.register_batch("data_table", batch)?;
+
+ let random_normal_udf = ScalarUDF::from(RandomUDF::new());
+ ctx.register_udf(random_normal_udf);
+
+ let result = plan_and_collect(
+ &ctx,
+ "SELECT random_udf() AS random_udf, random() AS native_random FROM data_table",
+ )
+ .await?;
+
+ assert_eq!(result.len(), 1);
+ let batch = &result[0];
+ let random_udf = batch
+ .column(0)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+ let native_random = batch
+ .column(1)
+ .as_any()
+ .downcast_ref::<Float64Array>()
+ .unwrap();
+
+ assert_eq!(random_udf.len(), native_random.len());
+
+ let mut previous = 1.0;
Review Comment:
But `-1.0` is also good.
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