alamb commented on code in PR #7401: URL: https://github.com/apache/arrow-datafusion/pull/7401#discussion_r1324709045
########## datafusion/core/src/physical_plan/row_converter.rs: ########## @@ -0,0 +1,292 @@ +// 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. + +//! [`CardinalityAwareRowConverter`] for converting data to +//! [`arrow::row`] format. + +use arrow::datatypes::DataType; +use arrow::row::{Row, RowConverter}; +use arrow::row::{Rows, SortField}; +use arrow_array::cast::AsArray; +use arrow_array::*; +use datafusion_common::{internal_err, DataFusionError, Result}; + +/// The threshold of the number of values at which to consider a +/// [`DictionaryArray`] "high" cardinality. +/// +/// Since [`RowConverter`] blindly generates a mapping for all values, +/// regardless of if they appear in the keys, this value is compared +/// to the length of values. +/// +/// The assumption is that the number of potential distinct key values +/// (aka the length of the values array) is a more robust predictor of +/// being "high" cardinality than the actual number of keys used. The +/// intuition for this is that other values in the dictionary could be +/// used in subsequent batches. +/// +/// While an argument can made for doing something more sophisticated, +/// this would likely only really make sense if the dictionary +/// interner itself followed a similar approach, which it did not at +/// the time of this writing. +const LOW_CARDINALITY_THRESHOLD: usize = 10; + +/// Wrapper around an [`arrow::row`] [`RowConverter`] that disables +/// dictionary preservation for high cardinality columns, based on the +/// observed cardinalities in the first columns converted. +/// +/// ## Background +/// +/// By default, the [`RowConverter`] interns (and keeps a copy of) all +/// values from [`DictionaryArray`]s. For low cardinality columns +/// (with few distinct values) this approach is both faster and more +/// memory efficient. However for high cardinality coumns it is slower +/// and requires more memory. In certain degenerate cases, such as +/// columns of nearly unique values, the `RowConverter` will keep a +/// copy of the entire column. +/// +/// See <https://github.com/apache/arrow-datafusion/issues/7200> for +/// more details +#[derive(Debug)] +pub enum CardinalityAwareRowConverter { + /// Converter is newly initialized, and hasn't yet seen data + New { + /// Defines the Row conversion + fields: Vec<SortField>, + }, + /// Converter has seen data and can convert [`Array`]s to/from + /// [`Rows`] + Converting { + /// Underlying converter + converter: RowConverter, + /// if preserve_dictionaries is disabled, the output type can be + /// different than input. + output_types: Vec<DataType>, + }, +} + +impl CardinalityAwareRowConverter { + pub fn new(fields: Vec<SortField>) -> Result<Self> { + Ok(Self::New { fields }) + } + + /// Returns the memory size of the underlying [`RowConverter`] if + /// any. + pub fn size(&self) -> usize { + match self { + Self::New { .. } => 0, + Self::Converting { converter, .. } => converter.size(), + } + } + + pub fn empty_rows(&self, row_capacity: usize, data_capacity: usize) -> Result<Rows> { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { converter, .. } => { + Ok(converter.empty_rows(row_capacity, data_capacity)) + } + } + } + + pub fn convert_rows<'a, I>(&self, rows: I) -> Result<Vec<ArrayRef>> + where + I: IntoIterator<Item = Row<'a>>, + { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { + converter, + output_types, + } => { + // Cast output type if needed + let output = converter + .convert_rows(rows)? + .into_iter() + .zip(output_types.iter()) + .map(|(arr, output_type)| { + if arr.data_type() != output_type { + Ok(arrow::compute::cast(&arr, output_type)?) + } else { + Ok(arr) + } + }) + .collect::<Result<Vec<_>>>()?; + + Ok(output) + } + } + } + + /// Calls [`RowConverter::convert_columns`] after first + /// initializing the converter based on cardinalities + pub fn convert_columns(&mut self, columns: &[ArrayRef]) -> Result<Rows> { + Ok(self.converter_mut(columns)?.convert_columns(columns)?) + } + + /// Return a mutable reference to the inner converter, creating it if needed + fn converter_mut(&mut self, columns: &[ArrayRef]) -> Result<&mut RowConverter> { + if let Self::New { fields } = self { + // TODO clean up the code Review Comment: I think it is a left over -- I will remove it ########## datafusion/core/src/physical_plan/row_converter.rs: ########## @@ -0,0 +1,292 @@ +// 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. + +//! [`CardinalityAwareRowConverter`] for converting data to +//! [`arrow::row`] format. + +use arrow::datatypes::DataType; +use arrow::row::{Row, RowConverter}; +use arrow::row::{Rows, SortField}; +use arrow_array::cast::AsArray; +use arrow_array::*; +use datafusion_common::{internal_err, DataFusionError, Result}; + +/// The threshold of the number of values at which to consider a +/// [`DictionaryArray`] "high" cardinality. +/// +/// Since [`RowConverter`] blindly generates a mapping for all values, +/// regardless of if they appear in the keys, this value is compared +/// to the length of values. +/// +/// The assumption is that the number of potential distinct key values +/// (aka the length of the values array) is a more robust predictor of +/// being "high" cardinality than the actual number of keys used. The +/// intuition for this is that other values in the dictionary could be +/// used in subsequent batches. +/// +/// While an argument can made for doing something more sophisticated, +/// this would likely only really make sense if the dictionary +/// interner itself followed a similar approach, which it did not at +/// the time of this writing. +const LOW_CARDINALITY_THRESHOLD: usize = 10; + +/// Wrapper around an [`arrow::row`] [`RowConverter`] that disables +/// dictionary preservation for high cardinality columns, based on the +/// observed cardinalities in the first columns converted. +/// +/// ## Background +/// +/// By default, the [`RowConverter`] interns (and keeps a copy of) all +/// values from [`DictionaryArray`]s. For low cardinality columns +/// (with few distinct values) this approach is both faster and more +/// memory efficient. However for high cardinality coumns it is slower +/// and requires more memory. In certain degenerate cases, such as +/// columns of nearly unique values, the `RowConverter` will keep a +/// copy of the entire column. +/// +/// See <https://github.com/apache/arrow-datafusion/issues/7200> for +/// more details +#[derive(Debug)] +pub enum CardinalityAwareRowConverter { + /// Converter is newly initialized, and hasn't yet seen data + New { + /// Defines the Row conversion + fields: Vec<SortField>, + }, + /// Converter has seen data and can convert [`Array`]s to/from + /// [`Rows`] + Converting { + /// Underlying converter + converter: RowConverter, + /// if preserve_dictionaries is disabled, the output type can be + /// different than input. + output_types: Vec<DataType>, + }, +} + +impl CardinalityAwareRowConverter { + pub fn new(fields: Vec<SortField>) -> Result<Self> { + Ok(Self::New { fields }) + } + + /// Returns the memory size of the underlying [`RowConverter`] if + /// any. + pub fn size(&self) -> usize { + match self { + Self::New { .. } => 0, + Self::Converting { converter, .. } => converter.size(), + } + } + + pub fn empty_rows(&self, row_capacity: usize, data_capacity: usize) -> Result<Rows> { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { converter, .. } => { + Ok(converter.empty_rows(row_capacity, data_capacity)) + } + } + } + + pub fn convert_rows<'a, I>(&self, rows: I) -> Result<Vec<ArrayRef>> + where + I: IntoIterator<Item = Row<'a>>, + { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { + converter, + output_types, + } => { + // Cast output type if needed + let output = converter + .convert_rows(rows)? + .into_iter() + .zip(output_types.iter()) + .map(|(arr, output_type)| { + if arr.data_type() != output_type { + Ok(arrow::compute::cast(&arr, output_type)?) + } else { + Ok(arr) + } + }) + .collect::<Result<Vec<_>>>()?; + + Ok(output) + } + } + } + + /// Calls [`RowConverter::convert_columns`] after first + /// initializing the converter based on cardinalities + pub fn convert_columns(&mut self, columns: &[ArrayRef]) -> Result<Rows> { + Ok(self.converter_mut(columns)?.convert_columns(columns)?) + } + + /// Return a mutable reference to the inner converter, creating it if needed + fn converter_mut(&mut self, columns: &[ArrayRef]) -> Result<&mut RowConverter> { + if let Self::New { fields } = self { + // TODO clean up the code + let mut updated_fields = fields.clone(); + let mut output_types = vec![]; + for (i, col) in columns.iter().enumerate() { + output_types.push(col.data_type().clone()); + if let DataType::Dictionary(_, _) = col.data_type() { + // see comments on LOW_CARDINALITY_THRESHOLD for + // the rationale of this calculation + let cardinality = col.as_any_dictionary_opt().unwrap().values().len(); Review Comment: Yeah, it is a nice API ########## datafusion/core/tests/memory_limit.rs: ########## @@ -238,7 +238,7 @@ async fn sort_preserving_merge() { #[tokio::test] async fn sort_spill_reservation() { - let partition_size = batches_byte_size(&dict_batches()); + let partition_size = batches_byte_size(&maybe_split_batches(dict_batches(), true)); Review Comment: it is needed to match the change in data (which is also split). I tried to make this clearer in 355ef7397. ########## datafusion/core/src/physical_plan/row_converter.rs: ########## @@ -0,0 +1,292 @@ +// 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. + +//! [`CardinalityAwareRowConverter`] for converting data to +//! [`arrow::row`] format. + +use arrow::datatypes::DataType; +use arrow::row::{Row, RowConverter}; +use arrow::row::{Rows, SortField}; +use arrow_array::cast::AsArray; +use arrow_array::*; +use datafusion_common::{internal_err, DataFusionError, Result}; + +/// The threshold of the number of values at which to consider a +/// [`DictionaryArray`] "high" cardinality. +/// +/// Since [`RowConverter`] blindly generates a mapping for all values, +/// regardless of if they appear in the keys, this value is compared +/// to the length of values. +/// +/// The assumption is that the number of potential distinct key values +/// (aka the length of the values array) is a more robust predictor of +/// being "high" cardinality than the actual number of keys used. The +/// intuition for this is that other values in the dictionary could be +/// used in subsequent batches. +/// +/// While an argument can made for doing something more sophisticated, +/// this would likely only really make sense if the dictionary +/// interner itself followed a similar approach, which it did not at +/// the time of this writing. +const LOW_CARDINALITY_THRESHOLD: usize = 10; + +/// Wrapper around an [`arrow::row`] [`RowConverter`] that disables +/// dictionary preservation for high cardinality columns, based on the +/// observed cardinalities in the first columns converted. +/// +/// ## Background +/// +/// By default, the [`RowConverter`] interns (and keeps a copy of) all +/// values from [`DictionaryArray`]s. For low cardinality columns +/// (with few distinct values) this approach is both faster and more +/// memory efficient. However for high cardinality coumns it is slower +/// and requires more memory. In certain degenerate cases, such as +/// columns of nearly unique values, the `RowConverter` will keep a +/// copy of the entire column. +/// +/// See <https://github.com/apache/arrow-datafusion/issues/7200> for +/// more details +#[derive(Debug)] +pub enum CardinalityAwareRowConverter { + /// Converter is newly initialized, and hasn't yet seen data + New { + /// Defines the Row conversion + fields: Vec<SortField>, + }, + /// Converter has seen data and can convert [`Array`]s to/from + /// [`Rows`] + Converting { + /// Underlying converter + converter: RowConverter, + /// if preserve_dictionaries is disabled, the output type can be + /// different than input. + output_types: Vec<DataType>, + }, +} + +impl CardinalityAwareRowConverter { + pub fn new(fields: Vec<SortField>) -> Result<Self> { + Ok(Self::New { fields }) + } + + /// Returns the memory size of the underlying [`RowConverter`] if + /// any. + pub fn size(&self) -> usize { + match self { + Self::New { .. } => 0, + Self::Converting { converter, .. } => converter.size(), + } + } + + pub fn empty_rows(&self, row_capacity: usize, data_capacity: usize) -> Result<Rows> { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { converter, .. } => { + Ok(converter.empty_rows(row_capacity, data_capacity)) + } + } + } + + pub fn convert_rows<'a, I>(&self, rows: I) -> Result<Vec<ArrayRef>> + where + I: IntoIterator<Item = Row<'a>>, + { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { + converter, + output_types, + } => { + // Cast output type if needed + let output = converter + .convert_rows(rows)? + .into_iter() + .zip(output_types.iter()) + .map(|(arr, output_type)| { + if arr.data_type() != output_type { + Ok(arrow::compute::cast(&arr, output_type)?) + } else { + Ok(arr) + } + }) + .collect::<Result<Vec<_>>>()?; + + Ok(output) + } + } + } + + /// Calls [`RowConverter::convert_columns`] after first + /// initializing the converter based on cardinalities + pub fn convert_columns(&mut self, columns: &[ArrayRef]) -> Result<Rows> { + Ok(self.converter_mut(columns)?.convert_columns(columns)?) + } + + /// Return a mutable reference to the inner converter, creating it if needed + fn converter_mut(&mut self, columns: &[ArrayRef]) -> Result<&mut RowConverter> { + if let Self::New { fields } = self { + // TODO clean up the code + let mut updated_fields = fields.clone(); Review Comment: Sadly it seems like `SortOptions` does not implement `Default` 😢 ``` --> datafusion/core/src/physical_plan/row_converter.rs:150:53 | 150 | let mut updated_fields = std::mem::take(&mut fields); | -------------- ^^^^^^^^^^^ the trait `std::default::Default` is not implemented for `&mut std::vec::Vec<arrow::arrow_row::SortField>` | | | required by a bound introduced by this call | note: required by a bound in `std::mem::take` ``` ########## datafusion/core/src/physical_plan/row_converter.rs: ########## @@ -0,0 +1,292 @@ +// 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. + +//! [`CardinalityAwareRowConverter`] for converting data to +//! [`arrow::row`] format. + +use arrow::datatypes::DataType; +use arrow::row::{Row, RowConverter}; +use arrow::row::{Rows, SortField}; +use arrow_array::cast::AsArray; +use arrow_array::*; +use datafusion_common::{internal_err, DataFusionError, Result}; + +/// The threshold of the number of values at which to consider a +/// [`DictionaryArray`] "high" cardinality. +/// +/// Since [`RowConverter`] blindly generates a mapping for all values, +/// regardless of if they appear in the keys, this value is compared +/// to the length of values. +/// +/// The assumption is that the number of potential distinct key values +/// (aka the length of the values array) is a more robust predictor of +/// being "high" cardinality than the actual number of keys used. The +/// intuition for this is that other values in the dictionary could be +/// used in subsequent batches. +/// +/// While an argument can made for doing something more sophisticated, +/// this would likely only really make sense if the dictionary +/// interner itself followed a similar approach, which it did not at +/// the time of this writing. +const LOW_CARDINALITY_THRESHOLD: usize = 10; + +/// Wrapper around an [`arrow::row`] [`RowConverter`] that disables +/// dictionary preservation for high cardinality columns, based on the +/// observed cardinalities in the first columns converted. +/// +/// ## Background +/// +/// By default, the [`RowConverter`] interns (and keeps a copy of) all +/// values from [`DictionaryArray`]s. For low cardinality columns +/// (with few distinct values) this approach is both faster and more +/// memory efficient. However for high cardinality coumns it is slower +/// and requires more memory. In certain degenerate cases, such as +/// columns of nearly unique values, the `RowConverter` will keep a +/// copy of the entire column. +/// +/// See <https://github.com/apache/arrow-datafusion/issues/7200> for +/// more details +#[derive(Debug)] +pub enum CardinalityAwareRowConverter { + /// Converter is newly initialized, and hasn't yet seen data + New { + /// Defines the Row conversion + fields: Vec<SortField>, + }, + /// Converter has seen data and can convert [`Array`]s to/from + /// [`Rows`] + Converting { + /// Underlying converter + converter: RowConverter, + /// if preserve_dictionaries is disabled, the output type can be + /// different than input. + output_types: Vec<DataType>, + }, +} + +impl CardinalityAwareRowConverter { + pub fn new(fields: Vec<SortField>) -> Result<Self> { + Ok(Self::New { fields }) + } + + /// Returns the memory size of the underlying [`RowConverter`] if + /// any. + pub fn size(&self) -> usize { + match self { + Self::New { .. } => 0, + Self::Converting { converter, .. } => converter.size(), + } + } + + pub fn empty_rows(&self, row_capacity: usize, data_capacity: usize) -> Result<Rows> { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { converter, .. } => { + Ok(converter.empty_rows(row_capacity, data_capacity)) + } + } + } + + pub fn convert_rows<'a, I>(&self, rows: I) -> Result<Vec<ArrayRef>> + where + I: IntoIterator<Item = Row<'a>>, + { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { + converter, + output_types, + } => { + // Cast output type if needed Review Comment: in b673c09b0 ########## datafusion/core/src/physical_plan/row_converter.rs: ########## @@ -0,0 +1,292 @@ +// 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. + +//! [`CardinalityAwareRowConverter`] for converting data to +//! [`arrow::row`] format. + +use arrow::datatypes::DataType; +use arrow::row::{Row, RowConverter}; +use arrow::row::{Rows, SortField}; +use arrow_array::cast::AsArray; +use arrow_array::*; +use datafusion_common::{internal_err, DataFusionError, Result}; + +/// The threshold of the number of values at which to consider a +/// [`DictionaryArray`] "high" cardinality. +/// +/// Since [`RowConverter`] blindly generates a mapping for all values, +/// regardless of if they appear in the keys, this value is compared +/// to the length of values. +/// +/// The assumption is that the number of potential distinct key values +/// (aka the length of the values array) is a more robust predictor of +/// being "high" cardinality than the actual number of keys used. The +/// intuition for this is that other values in the dictionary could be +/// used in subsequent batches. +/// +/// While an argument can made for doing something more sophisticated, +/// this would likely only really make sense if the dictionary +/// interner itself followed a similar approach, which it did not at +/// the time of this writing. +const LOW_CARDINALITY_THRESHOLD: usize = 10; + +/// Wrapper around an [`arrow::row`] [`RowConverter`] that disables +/// dictionary preservation for high cardinality columns, based on the +/// observed cardinalities in the first columns converted. +/// +/// ## Background +/// +/// By default, the [`RowConverter`] interns (and keeps a copy of) all +/// values from [`DictionaryArray`]s. For low cardinality columns +/// (with few distinct values) this approach is both faster and more +/// memory efficient. However for high cardinality coumns it is slower +/// and requires more memory. In certain degenerate cases, such as +/// columns of nearly unique values, the `RowConverter` will keep a +/// copy of the entire column. +/// +/// See <https://github.com/apache/arrow-datafusion/issues/7200> for +/// more details +#[derive(Debug)] +pub enum CardinalityAwareRowConverter { + /// Converter is newly initialized, and hasn't yet seen data + New { + /// Defines the Row conversion + fields: Vec<SortField>, + }, + /// Converter has seen data and can convert [`Array`]s to/from + /// [`Rows`] + Converting { + /// Underlying converter + converter: RowConverter, + /// if preserve_dictionaries is disabled, the output type can be + /// different than input. + output_types: Vec<DataType>, + }, +} + +impl CardinalityAwareRowConverter { + pub fn new(fields: Vec<SortField>) -> Result<Self> { + Ok(Self::New { fields }) + } + + /// Returns the memory size of the underlying [`RowConverter`] if + /// any. + pub fn size(&self) -> usize { + match self { + Self::New { .. } => 0, + Self::Converting { converter, .. } => converter.size(), + } + } + + pub fn empty_rows(&self, row_capacity: usize, data_capacity: usize) -> Result<Rows> { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { converter, .. } => { + Ok(converter.empty_rows(row_capacity, data_capacity)) + } + } + } + + pub fn convert_rows<'a, I>(&self, rows: I) -> Result<Vec<ArrayRef>> + where + I: IntoIterator<Item = Row<'a>>, + { + match self { + Self::New { .. } => internal_err!( + "CardinalityAwareRowConverter has not converted any rows yet" + ), + Self::Converting { + converter, + output_types, + } => { + // Cast output type if needed + let output = converter + .convert_rows(rows)? + .into_iter() + .zip(output_types.iter()) + .map(|(arr, output_type)| { + if arr.data_type() != output_type { + Ok(arrow::compute::cast(&arr, output_type)?) + } else { + Ok(arr) + } + }) + .collect::<Result<Vec<_>>>()?; + + Ok(output) + } + } + } + + /// Calls [`RowConverter::convert_columns`] after first + /// initializing the converter based on cardinalities + pub fn convert_columns(&mut self, columns: &[ArrayRef]) -> Result<Rows> { + Ok(self.converter_mut(columns)?.convert_columns(columns)?) + } + + /// Return a mutable reference to the inner converter, creating it if needed + fn converter_mut(&mut self, columns: &[ArrayRef]) -> Result<&mut RowConverter> { + if let Self::New { fields } = self { + // TODO clean up the code + let mut updated_fields = fields.clone(); Review Comment: Sadly it seems like `SortOptions` does not implement `Default` 😢 ``` --> datafusion/core/src/physical_plan/row_converter.rs:150:53 | 150 | let mut updated_fields = std::mem::take(&mut fields); | -------------- ^^^^^^^^^^^ the trait `std::default::Default` is not implemented for `&mut std::vec::Vec<arrow::arrow_row::SortField>` | | | required by a bound introduced by this call | note: required by a bound in `std::mem::take` ``` -- This is an automated message from the Apache Git Service. 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