viirya commented on code in PR #179: URL: https://github.com/apache/arrow-datafusion-comet/pull/179#discussion_r1524073288
########## core/src/execution/datafusion/expressions/bloom_filter_might_contain.rs: ########## @@ -0,0 +1,157 @@ +// 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::{ + execution::datafusion::util::spark_bloom_filter::SparkBloomFilter, parquet::data_type::AsBytes, +}; +use arrow::record_batch::RecordBatch; +use arrow_array::{cast::as_primitive_array, BooleanArray}; +use arrow_schema::{DataType, Schema}; +use datafusion::physical_plan::ColumnarValue; +use datafusion_common::{internal_err, DataFusionError, Result, ScalarValue}; +use datafusion_physical_expr::{aggregate::utils::down_cast_any_ref, PhysicalExpr}; +use std::{ + any::Any, + fmt::Display, + hash::{Hash, Hasher}, + sync::Arc, +}; + +/// A physical expression that checks if a value might be in a bloom filter. It corresponds to the +/// Spark's `BloomFilterMightContain` expression. + +#[derive(Debug, Hash)] +pub struct BloomFilterMightContain { + pub bloom_filter_expr: Arc<dyn PhysicalExpr>, + pub value_expr: Arc<dyn PhysicalExpr>, + bloom_filter: Option<SparkBloomFilter>, +} + +impl Display for BloomFilterMightContain { + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + write!( + f, + "BloomFilterMightContain [bloom_filter_expr: {}, value_expr: {}]", + self.bloom_filter_expr, self.value_expr + ) + } +} + +impl PartialEq<dyn Any> for BloomFilterMightContain { + fn eq(&self, _other: &dyn Any) -> bool { + down_cast_any_ref(_other) + .downcast_ref::<Self>() + .map(|other| { + self.bloom_filter_expr.eq(&other.bloom_filter_expr) + && self.value_expr.eq(&other.value_expr) + }) + .unwrap_or(false) + } +} + +fn evaluate_bloom_filter( + bloom_filter_expr: &Arc<dyn PhysicalExpr>, +) -> Result<Option<SparkBloomFilter>> { + // bloom_filter_expr must be a literal/scalar subquery expression, so we can evaluate it + // with an empty batch with empty schema + let batch = RecordBatch::new_empty(Arc::new(Schema::empty())); + let bloom_filter_bytes = bloom_filter_expr.evaluate(&batch)?; + match bloom_filter_bytes { + ColumnarValue::Scalar(ScalarValue::Binary(v)) => { + Ok(v.map(|v| SparkBloomFilter::new_from_buf(v.as_bytes()))) + } + _ => internal_err!("Bloom filter expression must be evaluated as a scalar binary value"), + } +} + +impl BloomFilterMightContain { + pub fn new( + bloom_filter_expr: Arc<dyn PhysicalExpr>, + value_expr: Arc<dyn PhysicalExpr>, + ) -> Self { + // early evaluate the bloom_filter_expr to get the actual bloom filter + let bloom_filter = evaluate_bloom_filter(&bloom_filter_expr) + .expect("bloom_filter_expr could be evaluated statically"); + Self { + bloom_filter_expr, + value_expr, + bloom_filter, + } + } +} + +impl PhysicalExpr for BloomFilterMightContain { + fn as_any(&self) -> &dyn Any { + self + } + + fn data_type(&self, _input_schema: &Schema) -> Result<DataType> { + Ok(DataType::Boolean) + } + + fn nullable(&self, _input_schema: &Schema) -> Result<bool> { + Ok(true) + } + + fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> { + let num_rows = batch.num_rows(); + self.bloom_filter + .as_ref() + .map(|spark_filter| { + let values = self.value_expr.evaluate(batch)?; + match values { + ColumnarValue::Array(array) => { + let boolean_array = + spark_filter.might_contain_longs(as_primitive_array(&array)); + Ok(ColumnarValue::Array(Arc::new(boolean_array))) + } + ColumnarValue::Scalar(ScalarValue::Int64(v)) => { + let result = v.map(|v| spark_filter.might_contain_long(v)); + Ok(ColumnarValue::Scalar(ScalarValue::Boolean(result))) + } + _ => internal_err!("value expression must be int64 type"), + } + }) + .unwrap_or_else(|| { + // when the bloom filter is null, we should return a boolean array with all nulls + Ok(ColumnarValue::Array(Arc::new(BooleanArray::new_null( + num_rows, + )))) Review Comment: I think this could be a scalar null value like the scalar int64 case. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
