Rich-T-kid commented on code in PR #23187: URL: https://github.com/apache/datafusion/pull/23187#discussion_r3538842683
########## datafusion/physical-plan/src/aggregates/group_values/multi_group_by/dictionary.rs: ########## @@ -0,0 +1,401 @@ +// 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. + +use crate::aggregates::group_values::multi_group_by::GroupColumn; +use arrow::array::{ + Array, ArrayRef, AsArray, BooleanBufferBuilder, DictionaryArray, PrimitiveArray, +}; +use arrow::compute::concat; +use arrow::datatypes::{ArrowDictionaryKeyType, ArrowNativeType, DataType, Field}; +use datafusion_common::Result; +use hashbrown::HashMap; +use std::marker::PhantomData; +use std::sync::{Arc, Mutex}; + +/// Use the cache path in `vectorized_equal_to` only when the number of unique lhs group IDs +/// is at most this fraction of the total comparisons (1/10 = 10%). +const CACHE_USE_THRESHOLD: usize = 10; + +pub struct DictionaryGroupValuesColumn<K: ArrowDictionaryKeyType + Send + Sync> { + inner: Box<dyn GroupColumn>, + null_array: ArrayRef, + // Mutex is required because `vectorized_equal_to` takes `&self` (GroupColumn trait + // constraint) but needs to populate this cache on the hot path. + key_to_group_cache: Mutex<HashMap<(usize, Option<usize>), bool>>, + last_values: Option<ArrayRef>, + cached_combined: Option<ArrayRef>, + _phantom: PhantomData<K>, +} + +impl<K: ArrowDictionaryKeyType + Send + Sync> DictionaryGroupValuesColumn<K> { + pub fn new(inner: Box<dyn GroupColumn>, field: &Field) -> Self { + let null_array = arrow::array::new_null_array(field.data_type(), 1); + Self { + inner, + null_array, + last_values: None, + key_to_group_cache: Mutex::new(HashMap::new()), + cached_combined: None, + _phantom: PhantomData, + } + } + + /// Returns `concat([values, null_sentinel])`, rebuilding only when `values` changes. + #[inline] + fn get_combined(&mut self, values: &ArrayRef) -> Result<ArrayRef> { + if !self + .last_values + .as_ref() + .is_some_and(|v| Arc::ptr_eq(v, values)) + { + self.cached_combined = + Some(concat(&[values.as_ref(), self.null_array.as_ref()])?); + self.last_values = Some(Arc::clone(values)); + // The incoming dictionary values buffer changed, so cached comparisons are stale. + self.key_to_group_cache.lock().unwrap().clear(); + } + Ok(Arc::clone(self.cached_combined.as_ref().unwrap())) + } + + fn into_dict(values: ArrayRef) -> ArrayRef { + let num_values = values.len(); + assert!( + Self::valid_bounds::<K>(num_values), + "Dictionary key type {:?} cannot hold {} values", + K::DATA_TYPE, + num_values + ); + let keys: PrimitiveArray<K> = (0..num_values) + .map(|i| (!values.is_null(i)).then(|| K::Native::usize_as(i))) + .collect(); + Arc::new(DictionaryArray::<K>::new(keys, values)) + } + + fn valid_bounds<T: ArrowDictionaryKeyType>(num_values: usize) -> bool { + let max: usize = match T::DATA_TYPE { + DataType::Int8 => i8::MAX as usize, + DataType::Int16 => i16::MAX as usize, + DataType::Int32 => i32::MAX as usize, + DataType::Int64 => i64::MAX as usize, + DataType::UInt8 => u8::MAX as usize, + DataType::UInt16 => u16::MAX as usize, + DataType::UInt32 => u32::MAX as usize, + DataType::UInt64 => usize::MAX, + _ => return false, + }; + num_values == 0 || num_values - 1 <= max + } +} + +impl<K: ArrowDictionaryKeyType + Send + Sync> GroupColumn + for DictionaryGroupValuesColumn<K> +{ + fn equal_to(&self, lhs_row: usize, array: &ArrayRef, rhs_row: usize) -> bool { + let dict = array.as_dictionary::<K>(); + match dict.key(rhs_row) { + None => self.inner.equal_to(lhs_row, &self.null_array, 0), + Some(key) => self.inner.equal_to(lhs_row, dict.values(), key), + } + } + + fn append_val(&mut self, array: &ArrayRef, row: usize) -> Result<()> { + let dict = array.as_dictionary::<K>(); + match dict.key(row) { + None => self.inner.append_val(&self.null_array, 0), + Some(key) => self.inner.append_val(dict.values(), key), + } + } + + fn vectorized_equal_to( + &self, + lhs_rows: &[usize], + array: &ArrayRef, + rhs_rows: &[usize], + equal_to_results: &mut BooleanBufferBuilder, + ) { + let dict = array.as_dictionary::<K>(); + let dict_values = dict.values(); + + // When lhs group IDs are highly repeated relative to the number of comparisons, + // caching (group_id, dict_key) -> bool avoids redundant inner comparisons. + let unique_lhs_row_count = + lhs_rows.iter().collect::<hashbrown::HashSet<_>>().len(); + if unique_lhs_row_count <= rhs_rows.len() / CACHE_USE_THRESHOLD { + let mut comparison_cache = self.key_to_group_cache.lock().unwrap(); + for (position, (&lhs_row, &rhs_row)) in + lhs_rows.iter().zip(rhs_rows).enumerate() + { + if !equal_to_results.get_bit(position) { + continue; + } + let dict_key = dict.key(rhs_row); + let is_equal = *comparison_cache + .entry((lhs_row, dict_key)) + .or_insert_with(|| match dict_key { + None => self.inner.equal_to(lhs_row, &self.null_array, 0), + Some(key) => self.inner.equal_to(lhs_row, dict_values, key), + }); Review Comment: This is causing more overhead than its saving. -- This is an automated message from the Apache Git Service. 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