POC: Parquet predicate results cache

arrow-select/src/coalesce.rs

@@ -318,6 +318,11 @@ impl BatchCoalescer {
     pub fn next_completed_batch(&mut self) -> Option<RecordBatch> {
         self.completed.pop_front()
     }
+
+    /// Returns all the completed batches
+    pub fn take_completed_batches(&mut self) -> VecDeque<RecordBatch> {
+        std::mem::take(&mut self.completed)
+    }
 }
 
 /// Return a new `InProgressArray` for the given data type

arrow-select/src/filter.rs

@@ -324,6 +324,8 @@ impl IterationStrategy {
 }
 
 /// A filtering predicate that can be applied to an [`Array`]
+///
+/// See [`FilterBuilder`] to create a [`FilterPredicate`].
 #[derive(Debug)]
 pub struct FilterPredicate {
     filter: BooleanArray,
@@ -502,6 +504,9 @@ fn filter_null_mask(
 }
 
 /// Filter the packed bitmask `buffer`, with `predicate` starting at bit offset `offset`
+///
+/// Panics for `IterationStrategy::All` or `IterationStrategy::None` which must
+/// be handled by the caller
 fn filter_bits(buffer: &BooleanBuffer, predicate: &FilterPredicate) -> Buffer {
     let src = buffer.values();
     let offset = buffer.offset();
@@ -536,7 +541,8 @@ fn filter_bits(buffer: &BooleanBuffer, predicate: &FilterPredicate) -> Buffer {
             }
             builder.into()
         }
-        IterationStrategy::All | IterationStrategy::None => unreachable!(),
+        IterationStrategy::All => unreachable!(),
+        IterationStrategy::None => unreachable!(),
     }
 }
 

parquet/src/arrow/array_reader/builder.rs

@@ -23,7 +23,7 @@ use crate::arrow::array_reader::byte_view_array::make_byte_view_array_reader;
 use crate::arrow::array_reader::empty_array::make_empty_array_reader;
 use crate::arrow::array_reader::fixed_len_byte_array::make_fixed_len_byte_array_reader;
 use crate::arrow::array_reader::{
-    make_byte_array_dictionary_reader, make_byte_array_reader, ArrayReader,
+    make_byte_array_dictionary_reader, make_byte_array_reader, ArrayReader, CachedPredicateResult,
     FixedSizeListArrayReader, ListArrayReader, MapArrayReader, NullArrayReader,
     PrimitiveArrayReader, RowGroups, StructArrayReader,
 };
@@ -37,11 +37,18 @@ use crate::schema::types::{ColumnDescriptor, ColumnPath, Type};
 /// Builds [`ArrayReader`]s from parquet schema, projection mask, and RowGroups reader
 pub(crate) struct ArrayReaderBuilder<'a> {
     row_groups: &'a dyn RowGroups,
+    cached_predicate_result: Option<&'a CachedPredicateResult>,
 }
 
 impl<'a> ArrayReaderBuilder<'a> {
-    pub(crate) fn new(row_groups: &'a dyn RowGroups) -> Self {
-        Self { row_groups }
+    pub(crate) fn new(
+        row_groups: &'a dyn RowGroups,
+        cached_predicate_result: Option<&'a CachedPredicateResult>,
+    ) -> Self {
+        Self {
+            row_groups,
+            cached_predicate_result,
+        }
     }
 
     /// Create [`ArrayReader`] from parquet schema, projection mask, and parquet file reader.
@@ -68,6 +75,10 @@ impl<'a> ArrayReaderBuilder<'a> {
         field: &ParquetField,
         mask: &ProjectionMask,
     ) -> Result<Option<Box<dyn ArrayReader>>> {
+        if let Some(builder) = self.build_cached_reader(field, mask)? {
+            return Ok(Some(builder));
+        }
+
         match field.field_type {
             ParquetFieldType::Primitive { .. } => self.build_primitive_reader(field, mask),
             ParquetFieldType::Group { .. } => match &field.arrow_type {
@@ -81,6 +92,33 @@ impl<'a> ArrayReaderBuilder<'a> {
         }
     }
 
+    /// Build cached array reader if the field is in the projection mask and in the cache
+    fn build_cached_reader(
+        &self,
+        field: &ParquetField,
+        mask: &ProjectionMask,
+    ) -> Result<Option<Box<dyn ArrayReader>>> {
+        let Some(cached_predicate_result) = self.cached_predicate_result else {
+            return Ok(None);
+        };
+
+        // TODO how to find a cached struct / list
+        // (Probably have to cache the individual fields)
+        let ParquetFieldType::Primitive {
+            col_idx,
+            primitive_type: _,
+        } = &field.field_type
+        else {
+            return Ok(None);
+        };
+
+        if !mask.leaf_included(*col_idx) {
+            return Ok(None);
+        }
+
+        cached_predicate_result.build_reader(*col_idx)
+    }
+
     /// Build array reader for map type.
     fn build_map_reader(
         &self,
@@ -375,7 +413,8 @@ mod tests {
         )
         .unwrap();
 
-        let array_reader = ArrayReaderBuilder::new(&file_reader)
+        let cached_predicate_result = None;
+        let array_reader = ArrayReaderBuilder::new(&file_reader, cached_predicate_result)
             .build_array_reader(fields.as_ref(), &mask)
             .unwrap();
 

parquet/src/arrow/array_reader/cached/builder.rs

@@ -0,0 +1,232 @@
+// 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::arrow::array_reader::CachedPredicateResult;
+use crate::arrow::arrow_reader::RowSelection;
+use crate::arrow::ProjectionMask;
+use arrow_array::{Array, BooleanArray, RecordBatch};
+use arrow_schema::{ArrowError, Schema, SchemaRef};
+use arrow_select::coalesce::BatchCoalescer;
+use arrow_select::filter::prep_null_mask_filter;
+use std::sync::Arc;
+
+/// Incrementally builds the result of evaluating an ArrowPredicate on
+/// a RowGroup.
+#[derive(Debug)]
+pub(crate) struct CachedPredicateResultBuilder {
+    /// What is being cached
+    strategy: CacheStrategy,
+    /// Total number of columns in the original parquet schema
+    num_original_columns: usize,
+    /// Any filters that have been applied. Note this the complete set of filters
+    /// that have been applied to the cached batches.
+    filters: Vec<BooleanArray>,
+}
+
+#[derive(Debug)]
+enum CacheStrategy {
+    /// Don't cache any results
+    None,
+    /// Cache the result of filtering all columns in the filter schema
+    All {
+        /// The builder for the cached batches
+        cached_batches_builder: BatchCoalescer,
+        /// The indexes of the columns in the original parquet schema that are in the projection
+        original_projection: Vec<usize>,
+    },
+    /// Cache the result of filtering a subset of the columns in the filter schema
+    Subset {
+        /// The builder for the cached batches
+        cached_batches_builder: BatchCoalescer,
+        /// The indexes of the columns in the filter schema that are in the projection
+        filter_projection: Vec<usize>,
+        /// The indexes of the columns in the original parquet schema that are in the projection
+        original_projection: Vec<usize>,
+    },
+}
+
+impl CachedPredicateResultBuilder {
+    /// Create a new CachedPredicateResultBuilder
+    ///
+    /// # Arguments:
+    /// * `num_original_columns`: The number of columns in the original parquet schema
+    /// * `schema`: The schema of the filtered record batch (not the original parquet schema)
+    /// * `filter_mask`: which columns of the original parquet schema did the filter columns come from?
+    /// * `projection_mask`: which columns of the original parquet schema are in the final projection?
+    ///
+    /// This structure does not cache filter results for the columns that are not
+    /// in the projection mask. This is because the filter results are not needed
+    pub(crate) fn try_new(
+        num_original_columns: usize,
+        filter_schema: &SchemaRef,
+        filter_mask: &ProjectionMask,
+        projection_mask: &ProjectionMask,
+        batch_size: usize,
+    ) -> Result<Self, ArrowError> {
+        let (filter_mask_inner, projection_mask_inner) =
+            match (filter_mask.mask(), projection_mask.mask()) {
+                (Some(filter_mask), Some(projection_mask)) => (filter_mask, projection_mask),
+                // None means "select all columns" so in this case cache all filtered columns
+                (Some(filter_mask), None) => (filter_mask, filter_mask),
+                // None means "select all columns" so in this case cache all columns used in projection
+                (None, Some(projection_mask)) => (projection_mask, projection_mask),
+                (None, None) => {
+                    // this means all columns are in the projection *and* filter so cache them all when possible
+                    let cached_batches_builder =
+                        BatchCoalescer::new(Arc::clone(filter_schema), batch_size);
+                    let strategy = CacheStrategy::All {
+                        cached_batches_builder,
+                        original_projection: (0..num_original_columns).collect(),
+                    };
+                    return {
+                        Ok(Self {
+                            strategy,
+                            num_original_columns,
+                            filters: vec![],
+                        })
+                    };
+                }
+            };
+
+        // Otherwise, need to select a subset of the fields from each batch to cache
+
+        // This is an iterator over the fields of the schema of batches passed
+        // to the filter.
+        let mut filter_field_iter = filter_schema.fields.iter().enumerate();
+
+        let mut filter_projection = vec![];
+        let mut original_projection = vec![];
+        let mut fields = vec![];
+
+        // Iterate over the masks from the original schema
+        assert_eq!(filter_mask_inner.len(), projection_mask_inner.len());
+        for (original_index, (&in_filter, &in_projection)) in filter_mask_inner
+            .iter()
+            .zip(projection_mask_inner.iter())
+            .enumerate()
+        {
+            if !in_filter {
+                continue;
+            }
+            // take next field from the filter schema
+            let (filter_index, field) =
+                filter_field_iter.next().expect("mismatch in field lengths");
+            if !in_projection {
+                // this field is not in the projection, so don't cache it
+                continue;
+            }
+            // this field is both in filter and the projection, so cache the results
+            filter_projection.push(filter_index);
+            original_projection.push(original_index);
+            fields.push(Arc::clone(field));
+        }
+        let strategy = if fields.is_empty() {
+            CacheStrategy::None
+        } else {
+            let cached_batches_builder =
+                BatchCoalescer::new(Arc::new(Schema::new(fields)), batch_size);
+            CacheStrategy::Subset {
+                cached_batches_builder,
+                filter_projection,
+                original_projection,
+            }
+        };
+
+        Ok(Self {
+            strategy,
+            num_original_columns,
+            filters: vec![],
+        })
+    }
+
+    /// Add a new batch and filter to the builder
+    pub(crate) fn add(
+        &mut self,
+        batch: RecordBatch,
+        mut filter: BooleanArray,
+    ) -> crate::errors::Result<()> {
+        if filter.null_count() > 0 {
+            filter = prep_null_mask_filter(&filter);
+        }
+
+        match &mut self.strategy {
+            CacheStrategy::None => {}
+            CacheStrategy::All {
+                cached_batches_builder,
+                ..
+            } => {
+                cached_batches_builder.push_batch_with_filter(batch, &filter)?;
+            }
+            CacheStrategy::Subset {
+                cached_batches_builder,
+                ref filter_projection,
+                ..
+            } => {
+                // If we have a filter projection, we need to project the batch
+                // to only the columns that are in the filter projection
+                let projected_batch = batch.project(filter_projection)?;
+                cached_batches_builder.push_batch_with_filter(projected_batch, &filter)?;
+            }
+        }
+
+        self.filters.push(filter);
+
+        Ok(())
+    }
+
+    /// Return (selection, maybe_cached_predicate_result) that represents the rows
+    /// that were selected and batches that were evaluated.
+    pub(crate) fn build(
+        self,
+    ) -> crate::errors::Result<(RowSelection, Option<CachedPredicateResult>)> {
+        let Self {
+            strategy,
+            num_original_columns,
+            filters,
+        } = self;
+
+        let new_selection = RowSelection::from_filters(&filters);
+
+        match strategy {
+            CacheStrategy::None => Ok((new_selection, None)),
+            CacheStrategy::All {
+                mut cached_batches_builder,
+                original_projection,
+            }
+            | CacheStrategy::Subset {
+                mut cached_batches_builder,
+                original_projection,
+                ..
+            } => {
+                // explode out the cached batches into the proper place in the original schema
+                cached_batches_builder.finish_buffered_batch()?;
+                let completed_batches = cached_batches_builder.take_completed_batches();
+
+                let mut cached_result = CachedPredicateResult::new(num_original_columns, filters);
+                for (batch_index, original_idx) in original_projection.iter().enumerate() {
+                    let mut column_arrays = Vec::with_capacity(completed_batches.len());
+                    for batch in &completed_batches {
+                        column_arrays.push(Arc::clone(batch.column(batch_index)));
+                    }
+                    cached_result.add_result(*original_idx, column_arrays);
+                }
+
+                Ok((new_selection, Some(cached_result)))
+            }
+        }
+    }
+}