Add Parquet RowFilter API

parquet/src/arrow/array_reader/builder.rs

@@ -39,20 +39,18 @@ use crate::data_type::{
     Int64Type, Int96Type,
 };
 use crate::errors::Result;
-use crate::schema::types::{ColumnDescriptor, ColumnPath, SchemaDescPtr, Type};
+use crate::schema::types::{ColumnDescriptor, ColumnPath, Type};
 
 /// Create array reader from parquet schema, projection mask, and parquet file reader.
 pub fn build_array_reader(
-    parquet_schema: SchemaDescPtr,
     arrow_schema: SchemaRef,
     mask: ProjectionMask,
-    row_groups: Box<dyn RowGroupCollection>,
+    row_groups: &dyn RowGroupCollection,
 ) -> Result<Box<dyn ArrayReader>> {
-    let field =
-        convert_schema(parquet_schema.as_ref(), mask, Some(arrow_schema.as_ref()))?;
+    let field = convert_schema(&row_groups.schema(), mask, Some(arrow_schema.as_ref()))?;
 
     match &field {
-        Some(field) => build_reader(field, row_groups.as_ref()),
+        Some(field) => build_reader(field, row_groups),
         None => Ok(make_empty_array_reader(row_groups.num_rows())),
     }
 }
@@ -333,13 +331,8 @@ mod tests {
         )
         .unwrap();
 
-        let array_reader = build_array_reader(
-            file_reader.metadata().file_metadata().schema_descr_ptr(),
-            Arc::new(arrow_schema),
-            mask,
-            Box::new(file_reader),
-        )
-        .unwrap();
+        let array_reader =
+            build_array_reader(Arc::new(arrow_schema), mask, &file_reader).unwrap();
 
         // Create arrow types
         let arrow_type = DataType::Struct(vec![Field::new(

parquet/src/arrow/array_reader/list_array.rs

@@ -582,13 +582,9 @@ mod tests {
         let schema = file_metadata.schema_descr_ptr();
         let mask = ProjectionMask::leaves(&schema, vec![0]);
 
-        let mut array_reader = build_array_reader(
-            schema,
-            Arc::new(arrow_schema),
-            mask,
-            Box::new(file_reader),
-        )
-        .unwrap();
+        let mut array_reader =
+            build_array_reader(Arc::new(arrow_schema), mask, &file_reader)
+                .unwrap();
 
         let batch = array_reader.next_batch(100).unwrap();
         assert_eq!(batch.data_type(), array_reader.get_data_type());

parquet/src/arrow/array_reader/mod.rs

@@ -100,7 +100,7 @@ pub trait ArrayReader: Send {
 /// A collection of row groups
 pub trait RowGroupCollection {
     /// Get schema of parquet file.
-    fn schema(&self) -> Result<SchemaDescPtr>;
+    fn schema(&self) -> SchemaDescPtr;
 
     /// Get the numer of rows in this collection
     fn num_rows(&self) -> usize;
@@ -110,8 +110,8 @@ pub trait RowGroupCollection {
 }
 
 impl RowGroupCollection for Arc<dyn FileReader> {
-    fn schema(&self) -> Result<SchemaDescPtr> {
-        Ok(self.metadata().file_metadata().schema_descr_ptr())
+    fn schema(&self) -> SchemaDescPtr {
+        self.metadata().file_metadata().schema_descr_ptr()
     }
 
     fn num_rows(&self) -> usize {

parquet/src/arrow/arrow_reader/filter.rs

@@ -0,0 +1,110 @@
+// 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::ProjectionMask;
+use arrow::array::BooleanArray;
+use arrow::error::Result as ArrowResult;
+use arrow::record_batch::RecordBatch;
+
+/// A predicate operating on [`RecordBatch`]
+pub trait ArrowPredicate: Send + 'static {
+    /// Returns the [`ProjectionMask`] that describes the columns required
+    /// to evaluate this predicate. All projected columns will be provided in the `batch`
+    /// passed to [`filter`](Self::filter)
+    fn projection(&self) -> &ProjectionMask;
+
+    /// Called with a [`RecordBatch`] containing the columns identified by [`Self::projection`],
+    /// with `true` values in the returned [`BooleanArray`] indicating rows
+    /// matching the predicate.
+    ///
+    /// All row that are `true` in returned [`BooleanArray`] will be returned to the reader.
+    /// Any rows that are `false` or `Null` will not be
+    fn filter(&mut self, batch: RecordBatch) -> ArrowResult<BooleanArray>;
+}
+
+/// An [`ArrowPredicate`] created from an [`FnMut`]
+pub struct ArrowPredicateFn<F> {
+    f: F,
+    projection: ProjectionMask,
+}
+
+impl<F> ArrowPredicateFn<F>
+where
+    F: FnMut(RecordBatch) -> ArrowResult<BooleanArray> + Send + 'static,
+{
+    /// Create a new [`ArrowPredicateFn`]. `f` will be passed batches
+    /// that contains the columns specified in `projection`
+    /// and returns a [`BooleanArray`] that describes which rows should
+    /// be passed along
+    pub fn new(projection: ProjectionMask, f: F) -> Self {
+        Self { f, projection }
+    }
+}
+
+impl<F> ArrowPredicate for ArrowPredicateFn<F>
+where
+    F: FnMut(RecordBatch) -> ArrowResult<BooleanArray> + Send + 'static,
+{
+    fn projection(&self) -> &ProjectionMask {
+        &self.projection
+    }
+
+    fn filter(&mut self, batch: RecordBatch) -> ArrowResult<BooleanArray> {
+        (self.f)(batch)
+    }
+}
+
+/// A [`RowFilter`] allows pushing down a filter predicate to skip IO and decode
+///
+/// This consists of a list of [`ArrowPredicate`] where only the rows that satisfy all
+/// of the predicates will be returned. Any [`RowSelection`] will be applied prior
+/// to the first predicate, and each predicate in turn will then be used to compute
+/// a more refined [`RowSelection`] to use when evaluating the subsequent predicates.
+///
+/// Once all predicates have been evaluated, the final [`RowSelection`] is applied
+/// to the top-level [`ProjectionMask`] to produce the final output [`RecordBatch`].
+///
+/// This design has a couple of implications:
+///
+/// * [`RowFilter`] can be used to skip entire pages, and thus IO, in addition to CPU decode overheads
+/// * Columns may be decoded multiple times if they appear in multiple [`ProjectionMask`]
+/// * IO will be deferred until needed by a [`ProjectionMask`]
+///
+/// As such there is a trade-off between a single large predicate, or multiple predicates,
+/// that will depend on the shape of the data. Whilst multiple smaller predicates may
+/// minimise the amount of data scanned/decoded, it may not be faster overall.
+///
+/// For example, if a predicate that needs a single column of data filters out all but
+/// 1% of the rows, applying it as one of the early `ArrowPredicateFn` will likely significantly
+/// improve performance.
+///
+/// As a counter example, if a predicate needs several columns of data to evaluate but
+/// leaves 99% of the rows, it may be better to not filter the data from parquet and
+/// apply the filter after the RecordBatch has been fully decoded.
+///
+/// [`RowSelection`]: [super::selection::RowSelection]
+pub struct RowFilter {
+    /// A list of [`ArrowPredicate`]
+    pub(crate) predicates: Vec<Box<dyn ArrowPredicate>>,
+}
+
+impl RowFilter {
+    /// Create a new [`RowFilter`] from an array of [`ArrowPredicate`]
+    pub fn new(predicates: Vec<Box<dyn ArrowPredicate>>) -> Self {
+        Self { predicates }
+    }
+}