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152 changes: 152 additions & 0 deletions
152
core/src/execution/datafusion/expressions/bloom_filter_might_contain.rs
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| // 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; | ||
| 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(v.as_bytes()))) | ||
| } | ||
| _ => internal_err!("Bloom filter expression should be evaluated as a scalar binary value"), | ||
| } | ||
| } | ||
|
|
||
| impl BloomFilterMightContain { | ||
| pub fn try_new( | ||
| bloom_filter_expr: Arc<dyn PhysicalExpr>, | ||
| value_expr: Arc<dyn PhysicalExpr>, | ||
| ) -> Result<Self> { | ||
| // early evaluate the bloom_filter_expr to get the actual bloom filter | ||
| let bloom_filter = evaluate_bloom_filter(&bloom_filter_expr)?; | ||
| Ok(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> { | ||
| 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 should be int64 type"), | ||
| } | ||
| }) | ||
| .unwrap_or_else(|| { | ||
| // when the bloom filter is null, we should return null for all the input | ||
| Ok(ColumnarValue::Scalar(ScalarValue::Boolean(None))) | ||
| }) | ||
| } | ||
|
|
||
| fn children(&self) -> Vec<Arc<dyn PhysicalExpr>> { | ||
| vec![self.bloom_filter_expr.clone(), self.value_expr.clone()] | ||
| } | ||
|
|
||
| fn with_new_children( | ||
| self: Arc<Self>, | ||
| children: Vec<Arc<dyn PhysicalExpr>>, | ||
| ) -> Result<Arc<dyn PhysicalExpr>> { | ||
| Ok(Arc::new(BloomFilterMightContain::try_new( | ||
| children[0].clone(), | ||
| children[1].clone(), | ||
| )?)) | ||
| } | ||
|
|
||
| fn dyn_hash(&self, state: &mut dyn Hasher) { | ||
| let mut s = state; | ||
| self.bloom_filter_expr.hash(&mut s); | ||
| self.value_expr.hash(&mut s); | ||
| self.hash(&mut s); | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -22,3 +22,4 @@ mod operators; | |
| pub mod planner; | ||
| pub(crate) mod shuffle_writer; | ||
| mod spark_hash; | ||
| mod util; | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| // 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. | ||
|
|
||
| pub mod spark_bit_array; | ||
| pub mod spark_bloom_filter; |
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