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Posted to github@arrow.apache.org by "alamb (via GitHub)" <gi...@apache.org> on 2023/06/01 11:16:58 UTC
[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #6384: feat: New functions and operations for working with arrays
alamb commented on code in PR #6384:
URL: https://github.com/apache/arrow-datafusion/pull/6384#discussion_r1212990411
##########
datafusion/physical-expr/src/array_expressions.rs:
##########
@@ -77,11 +81,40 @@ fn array_array(args: &[ArrayRef]) -> Result<ArrayRef> {
// do not accept 0 arguments.
if args.is_empty() {
return Err(DataFusionError::Internal(
- "array requires at least one argument".to_string(),
+ "Array requires at least one argument".to_string(),
));
}
- let res = match args[0].data_type() {
+ let data_type = args[0].data_type();
+ let res = match data_type {
+ DataType::List(..) => {
+ let arrays =
Review Comment:
@tustvold can you offer some suggestions on using the arrow-rs API to build list arrays? Is this the best way to use that API?
##########
datafusion/core/tests/sqllogictests/test_files/array.slt:
##########
@@ -0,0 +1,206 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+#############
+## Array expressions Tests
+#############
+
+# array scalar function #1
Review Comment:
These are great @izveigor -- thank you so much
the only thing I recommend is adding some additional tests that have `null` in the lists.
##########
datafusion/expr/src/function.rs:
##########
@@ -322,10 +404,21 @@ pub fn signature(fun: &BuiltinScalarFunction) -> Signature {
// for now, the list is small, as we do not have many built-in functions.
match fun {
- BuiltinScalarFunction::MakeArray => Signature::variadic(
- array_expressions::SUPPORTED_ARRAY_TYPES.to_vec(),
- fun.volatility(),
- ),
+ BuiltinScalarFunction::ArrayAppend => Signature::any(2, fun.volatility()),
Review Comment:
🤔 Given the element type of the list is part of its `DataType`, you probably can'y use the existing Signatures
Perhaps you could add a new `Signature::any_list` or something that would only check that the datatype matched `DataType::list` 🤔
##########
datafusion/physical-expr/src/array_expressions.rs:
##########
@@ -115,3 +149,1560 @@ pub fn array(values: &[ColumnarValue]) -> Result<ColumnarValue> {
.collect();
Ok(ColumnarValue::Array(array_array(arrays.as_slice())?))
}
+
+macro_rules! downcast_arg {
+ ($ARG:expr, $ARRAY_TYPE:ident) => {{
+ $ARG.as_any().downcast_ref::<$ARRAY_TYPE>().ok_or_else(|| {
+ DataFusionError::Internal(format!(
+ "could not cast to {}",
+ type_name::<$ARRAY_TYPE>()
+ ))
+ })?
+ }};
+}
+
+macro_rules! append {
+ ($ARRAY:expr, $ELEMENT:expr, $ARRAY_TYPE:ident) => {{
+ let child_array =
+ downcast_arg!(downcast_arg!($ARRAY, ListArray).values(), $ARRAY_TYPE);
+ let element = downcast_arg!($ELEMENT, $ARRAY_TYPE);
+ let concat = compute::concat(&[child_array, element])?;
+ let mut scalars = vec![];
+ for i in 0..concat.len() {
+ scalars.push(ColumnarValue::Scalar(ScalarValue::try_from_array(
+ &concat, i,
+ )?));
+ }
+ scalars
+ }};
+}
+
+/// Array_append SQL function
+pub fn array_append(args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ if args.len() != 2 {
+ return Err(DataFusionError::Internal(format!(
+ "Array_append function requires two arguments, got {}",
+ args.len()
+ )));
+ }
+
+ let arr = match &args[0] {
Review Comment:
I think you can use https://docs.rs/datafusion/latest/datafusion/physical_plan/enum.ColumnarValue.html#method.into_array here
##########
datafusion/physical-expr/src/array_expressions.rs:
##########
@@ -115,3 +149,1560 @@ pub fn array(values: &[ColumnarValue]) -> Result<ColumnarValue> {
.collect();
Ok(ColumnarValue::Array(array_array(arrays.as_slice())?))
}
+
+macro_rules! downcast_arg {
+ ($ARG:expr, $ARRAY_TYPE:ident) => {{
+ $ARG.as_any().downcast_ref::<$ARRAY_TYPE>().ok_or_else(|| {
+ DataFusionError::Internal(format!(
+ "could not cast to {}",
+ type_name::<$ARRAY_TYPE>()
+ ))
+ })?
+ }};
+}
+
+macro_rules! append {
+ ($ARRAY:expr, $ELEMENT:expr, $ARRAY_TYPE:ident) => {{
+ let child_array =
+ downcast_arg!(downcast_arg!($ARRAY, ListArray).values(), $ARRAY_TYPE);
+ let element = downcast_arg!($ELEMENT, $ARRAY_TYPE);
+ let concat = compute::concat(&[child_array, element])?;
+ let mut scalars = vec![];
+ for i in 0..concat.len() {
+ scalars.push(ColumnarValue::Scalar(ScalarValue::try_from_array(
+ &concat, i,
+ )?));
+ }
+ scalars
+ }};
+}
+
+/// Array_append SQL function
+pub fn array_append(args: &[ColumnarValue]) -> Result<ColumnarValue> {
Review Comment:
The difference is that if you take `ColumnarValue` we could specialize the kernels to do something faster with scalar (single) values rather than expanding them out to arrays (aka making copies).
For the initial implementation I think converting them all to arrays is the best approach as it is simplest
##########
datafusion/physical-expr/src/functions.rs:
##########
@@ -2785,73 +2807,6 @@ mod tests {
Ok(())
}
- fn generic_test_array(
Review Comment:
I am not sure
##########
datafusion/physical-expr/src/array_expressions.rs:
##########
@@ -77,11 +81,40 @@ fn array_array(args: &[ArrayRef]) -> Result<ArrayRef> {
// do not accept 0 arguments.
if args.is_empty() {
return Err(DataFusionError::Internal(
- "array requires at least one argument".to_string(),
+ "Array requires at least one argument".to_string(),
));
}
- let res = match args[0].data_type() {
+ let data_type = args[0].data_type();
+ let res = match data_type {
+ DataType::List(..) => {
Review Comment:
As FixedSizedList and List are different data types, if people have data that came from a Parquet file or something that is a FixedSizedList these functions likely wont work,
However, perhaps eventually we can add coercion rules to coerce (automatically cast) FixedSizeList to List
##########
datafusion/expr/src/function.rs:
##########
@@ -322,10 +404,21 @@ pub fn signature(fun: &BuiltinScalarFunction) -> Signature {
// for now, the list is small, as we do not have many built-in functions.
match fun {
- BuiltinScalarFunction::MakeArray => Signature::variadic(
- array_expressions::SUPPORTED_ARRAY_TYPES.to_vec(),
- fun.volatility(),
- ),
+ BuiltinScalarFunction::ArrayAppend => Signature::any(2, fun.volatility()),
Review Comment:
🤔 Given the element type of the list is part of its `DataType`, you probably can'y use the existing Signatures
Perhaps you could add a new `Signature::any_list` or something that would only check that the datatype matched `DataType::list` 🤔
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