2010YOUY01 commented on code in PR #19609:
URL: https://github.com/apache/datafusion/pull/19609#discussion_r2674838237
##########
datafusion/physical-expr-common/src/physical_expr.rs:
##########
@@ -430,6 +437,36 @@ pub trait PhysicalExpr: Any + Send + Sync + Display +
Debug + DynEq + DynHash {
fn is_volatile_node(&self) -> bool {
false
}
+
+ /// Evaluates pruning statistics via propagation. See the pruning module
Review Comment:
How about `propagate_pruning_statistics()`? Since its covering a rich set of
statistics more than just range, and we can use `_pruning_` to make it less
ambiguous.
##########
datafusion/physical-expr-common/src/physical_expr/pruning.rs:
##########
@@ -0,0 +1,539 @@
+// 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.
+
+// Pruner Common Structs/Utilities
+
+//! This is the top-level comment for pruning via statistics propagation.
+//!
+//! TODO: This is a concise draft; it should be polished for readers with less
+//! prior background.
+//!
+//! # Introduction
+//!
+//! This module helps skip scanning data micro-partitions by evaluating
predicates
+//! against container-level statistics.
+//!
+//! It supports pruning for complex and nested predicates through statistics
+//! propagation.
+//!
+//! For examples of pruning nested predicates via statistics propagation, see:
+//! <https://github.com/apache/datafusion/issues/19487>
+//!
+//!
+//!
+//! # Vectorized pruning intermediate representation
+//!
+//! Source statistics and intermediate pruning results are stored in Arrow
arrays,
+//! enabling vectorized evaluation across many containers.
+//!
+//!
+//!
+//! # Difference from [`super::PhysicalExpr::evaluate_bounds`]
+//!
+//! `evaluate_bounds()` derives per-column statistics for a single plan, aimed
at
+//! tasks like cardinality estimation and other planner fast paths. It reasons
+//! about one container and may track richer distribution details.
+//! Pruning must reason about *all* containers (potentially thousands) to
decide
+//! which to skip, so it favors a vectorized, array-backed representation with
+//! lighter-weight stats. These are intentionally separate interfaces.
+//!
+//!
+//!
+//! # Core API/Data Structures
+//!
+//! The key structures involved in pruning are:
+//! - [`PruningStatistics`]: the input source statistics for all containers
+//! - [`super::PhysicalExpr::evaluate_pruning()`]: evaluates pruning behavior
for predicates
+//! - [`PruningIntermediate`]: the intermediate result produced during
statistics propagation for pruning. Its internal representation uses Arrow
Arrays, enabling vectorized evaluation for performance.
+
+use std::{iter::repeat_n, sync::Arc};
+
+use arrow::array::{Array, ArrayRef, BooleanArray, BooleanBuilder, UInt64Array};
+use arrow::compute::kernels::boolean::and_kleene;
+use datafusion_common::pruning::PruningStatistics;
+use datafusion_common::{Result, ScalarValue, assert_eq_or_internal_err};
+
+/// Physical representation of pruning outcomes for each container:
+/// `true` = KeepAll, `false` = SkipAll, `null` = Unknown.
+///
+/// Use `BooleanArray` so the propagation steps can use existing Arrow kernels
for
+/// both simplicity and performance.
+///
+/// # Pruning results
+/// - KeepAll: The pruning predicate evaluates to true for all rows within a
micro
+/// partition. Future filter evaluation can be skipped for that partition.
+/// - SkipAll: The pruning predicate evaluates to false for all rows within a
micro
+/// partition. The partition can be skipped at scan time.
+/// - UnknownOrMixed: The statistics are insufficient to prove
KeepAll/SkipAll, or
+/// the predicate is mixed. The predicate must be evaluated row-wise.
Review Comment:
I realized they're the same. `Unknown` means not `SkipAll` or `KeepAll`.
I will change it to `Unknown` to avoid confusion.
##########
datafusion/physical-expr-common/src/physical_expr/pruning.rs:
##########
@@ -0,0 +1,539 @@
+// 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.
+
+// Pruner Common Structs/Utilities
+
+//! This is the top-level comment for pruning via statistics propagation.
+//!
+//! TODO: This is a concise draft; it should be polished for readers with less
+//! prior background.
+//!
+//! # Introduction
+//!
+//! This module helps skip scanning data micro-partitions by evaluating
predicates
+//! against container-level statistics.
+//!
+//! It supports pruning for complex and nested predicates through statistics
+//! propagation.
+//!
+//! For examples of pruning nested predicates via statistics propagation, see:
+//! <https://github.com/apache/datafusion/issues/19487>
+//!
+//!
+//!
+//! # Vectorized pruning intermediate representation
+//!
+//! Source statistics and intermediate pruning results are stored in Arrow
arrays,
+//! enabling vectorized evaluation across many containers.
+//!
+//!
+//!
+//! # Difference from [`super::PhysicalExpr::evaluate_bounds`]
+//!
+//! `evaluate_bounds()` derives per-column statistics for a single plan, aimed
at
+//! tasks like cardinality estimation and other planner fast paths. It reasons
+//! about one container and may track richer distribution details.
+//! Pruning must reason about *all* containers (potentially thousands) to
decide
+//! which to skip, so it favors a vectorized, array-backed representation with
+//! lighter-weight stats. These are intentionally separate interfaces.
+//!
+//!
+//!
+//! # Core API/Data Structures
+//!
+//! The key structures involved in pruning are:
+//! - [`PruningStatistics`]: the input source statistics for all containers
+//! - [`super::PhysicalExpr::evaluate_pruning()`]: evaluates pruning behavior
for predicates
+//! - [`PruningIntermediate`]: the intermediate result produced during
statistics propagation for pruning. Its internal representation uses Arrow
Arrays, enabling vectorized evaluation for performance.
+
+use std::{iter::repeat_n, sync::Arc};
+
+use arrow::array::{Array, ArrayRef, BooleanArray, BooleanBuilder, UInt64Array};
+use arrow::compute::kernels::boolean::and_kleene;
+use datafusion_common::pruning::PruningStatistics;
+use datafusion_common::{Result, ScalarValue, assert_eq_or_internal_err};
+
+/// Physical representation of pruning outcomes for each container:
+/// `true` = KeepAll, `false` = SkipAll, `null` = Unknown.
+///
+/// Use `BooleanArray` so the propagation steps can use existing Arrow kernels
for
+/// both simplicity and performance.
+///
+/// # Pruning results
+/// - KeepAll: The pruning predicate evaluates to true for all rows within a
micro
+/// partition. Future filter evaluation can be skipped for that partition.
+/// - SkipAll: The pruning predicate evaluates to false for all rows within a
micro
+/// partition. The partition can be skipped at scan time.
+/// - UnknownOrMixed: The statistics are insufficient to prove
KeepAll/SkipAll, or
+/// the predicate is mixed. The predicate must be evaluated row-wise.
+///
+/// Example (`SELECT * FROM t WHERE x >= 0`):
+/// - micro_partition_a(min=0, max=10): KeepAll — can pass through `FilterExec`
+/// without re-evaluating `x >= 0`.
+/// - micro_partition_b(min=-10, max=-1): SkipAll — skip the partition
entirely.
+/// - micro_partition_c(min=-5, max=5): Unknown — must evaluate the predicate
on rows.
+///
+/// `PruningOutcome` provides utilities to convert between this semantic
+/// representation and its tri-state boolean encoding.
+///
+/// # Important invariants
+/// Pruning results must be sound, but need not be complete:
+/// - If a container is labeled `KeepAll` or `SkipAll`, that label must be
correct.
+/// - If a container is labeled `Unknown` but is actually `KeepAll`/`SkipAll`,
+/// correctness is still preserved; it just means pruning was conservative.
+///
+/// Propagation implementation can be refined to reduce `Unknown` cases to
improve
+/// pruning effectiveness.
+#[derive(Debug, Clone)]
+pub struct PruningResults {
+ results: Option<BooleanArray>,
+ /// Number of containers. Needed to infer result if all stats types are
`None`.
+ pub num_containers: usize,
+}
+
+/// Semantic representation for items inside `PruningResults::results`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum PruningOutcome {
+ KeepAll,
+ SkipAll,
+ UnknownOrMixed,
+}
+
+impl PruningResults {
+ pub fn new(array: Option<BooleanArray>, num_containers: usize) -> Self {
+ debug_assert_eq!(
+ array.as_ref().map(|a| a.len()).unwrap_or(num_containers),
+ num_containers
+ );
+ Self {
+ results: array,
+ num_containers,
+ }
+ }
+
+ pub fn none(num_containers: usize) -> Self {
+ Self::new(None, num_containers)
+ }
+
+ pub fn as_ref(&self) -> Option<&BooleanArray> {
+ self.results.as_ref()
+ }
+
+ pub fn into_inner(self) -> Option<BooleanArray> {
+ self.results
+ }
+
+ pub fn len(&self) -> usize {
+ self.results
+ .as_ref()
+ .map(|a| a.len())
+ .unwrap_or(self.num_containers)
+ }
+
+ pub fn is_empty(&self) -> bool {
+ self.len() == 0
+ }
+}
+
+impl PruningOutcome {
+ /// Convert to/from the tri-state boolean encoding stored in
`PruningResults`.
+ /// - Some(true)=KeepAll
+ /// - Some(false)=SkipAll
+ /// - None=(Unknown/mixed)
+ pub fn from_result_item(result_item: Option<bool>) -> Self {
+ match result_item {
+ Some(true) => PruningOutcome::KeepAll,
+ Some(false) => PruningOutcome::SkipAll,
+ None => PruningOutcome::UnknownOrMixed,
+ }
+ }
+
+ pub fn to_result_item(&self) -> Option<bool> {
+ match self {
+ PruningOutcome::KeepAll => Some(true),
+ PruningOutcome::SkipAll => Some(false),
+ PruningOutcome::UnknownOrMixed => None,
+ }
+ }
+}
+
+impl From<BooleanArray> for PruningResults {
+ fn from(array: BooleanArray) -> Self {
+ let len = array.len();
+ PruningResults::new(Some(array), len)
+ }
+}
+
+#[derive(Debug, Clone)]
+pub enum RangeStats {
+ /// Ranges for all containers in array form.
+ /// - If `mins`/`maxs` are `None`, all containers have unknown statistics.
+ /// - Each entry (per-container) may be a bound or null. Null means
missing or
+ /// unbounded (null in `mins` = -inf; treating missing/unbounded the same
+ /// does not change pruning results).
+ Array {
+ mins: Option<ArrayRef>,
+ maxs: Option<ArrayRef>,
+ length: usize,
+ },
+ /// Represents a uniform literal value across all containers.
+ /// This variant make it easy to compare between literals and normal
ranges representing
+ /// each containers' value range.
+ Scalar { value: ScalarValue, length: usize },
+}
+
+/// Null-related statistics for each container stored as a BooleanArray:
+/// `true` = NoNull, `false` = AllNull, `null` = Unknown/mixed.
+///
+/// Use `BooleanArray` so the propagation steps can use existing Arrow kernels
for
+/// both simplicity and performance.
+/// `NullPresence` provides utility to convert between its semantics
representation
+/// and physical encoding.
+#[derive(Debug, Clone)]
+pub struct NullStats {
+ presence: BooleanArray,
+}
+
+/// Semantic representation for items inside `NullStats::presence`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum NullPresence {
+ NoNull,
+ AllNull,
+ UnknownOrMixed,
+}
+
+impl NullPresence {
+ /// Convert to/from the tri-state boolean encoding stored in
`NullStats.presence`
+ /// - Some(true)=NoNull
+ /// - Some(false)=AllNull
+ /// - None=(Unknown/mixed)
+ pub fn from_presence_item(presence_item: Option<bool>) -> Self {
+ match presence_item {
+ Some(true) => NullPresence::NoNull,
+ Some(false) => NullPresence::AllNull,
+ None => NullPresence::UnknownOrMixed,
+ }
+ }
+
+ pub fn to_presence_item(&self) -> Option<bool> {
+ match self {
+ NullPresence::NoNull => Some(true),
+ NullPresence::AllNull => Some(false),
+ NullPresence::UnknownOrMixed => None,
+ }
+ }
+}
+
+/// Column statistics that propagate through the `PhysicalExpr` tree nodes
Review Comment:
If we want to do so, it should be add additional precision masks next to
each statistics type like `min/max`, but in order to implement such propagation
with precision variants, all the stat propagation logics should be adding a new
path, there should be little code we can reuse for this case.
So technically it's feasible, but probably we should re-evaluate this idea
when we want to support complex expressions for `ColumnStatistics` 🤔
##########
datafusion/physical-expr-common/src/physical_expr.rs:
##########
@@ -430,6 +437,36 @@ pub trait PhysicalExpr: Any + Send + Sync + Display +
Debug + DynEq + DynHash {
fn is_volatile_node(&self) -> bool {
false
}
+
+ /// Evaluates pruning statistics via propagation. See the pruning module
+ /// docs for background.
+ ///
+ /// This default implementation is for `PhysicalExpr`s that have not yet
+ /// implemented pruning; returning `None` signals that no pruning
statistics
+ /// are available.
+ ///
+ /// In the future, propagation may expose dedicated APIs such as:
Review Comment:
This is an excellent idea, we should do this in the future.
##########
datafusion/physical-expr-common/src/physical_expr/pruning.rs:
##########
@@ -0,0 +1,539 @@
+// 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.
+
+// Pruner Common Structs/Utilities
+
+//! This is the top-level comment for pruning via statistics propagation.
+//!
+//! TODO: This is a concise draft; it should be polished for readers with less
+//! prior background.
+//!
+//! # Introduction
+//!
+//! This module helps skip scanning data micro-partitions by evaluating
predicates
+//! against container-level statistics.
+//!
+//! It supports pruning for complex and nested predicates through statistics
+//! propagation.
+//!
+//! For examples of pruning nested predicates via statistics propagation, see:
+//! <https://github.com/apache/datafusion/issues/19487>
+//!
+//!
+//!
+//! # Vectorized pruning intermediate representation
+//!
+//! Source statistics and intermediate pruning results are stored in Arrow
arrays,
+//! enabling vectorized evaluation across many containers.
+//!
+//!
+//!
+//! # Difference from [`super::PhysicalExpr::evaluate_bounds`]
+//!
+//! `evaluate_bounds()` derives per-column statistics for a single plan, aimed
at
+//! tasks like cardinality estimation and other planner fast paths. It reasons
+//! about one container and may track richer distribution details.
+//! Pruning must reason about *all* containers (potentially thousands) to
decide
+//! which to skip, so it favors a vectorized, array-backed representation with
+//! lighter-weight stats. These are intentionally separate interfaces.
+//!
+//!
+//!
+//! # Core API/Data Structures
+//!
+//! The key structures involved in pruning are:
+//! - [`PruningStatistics`]: the input source statistics for all containers
+//! - [`super::PhysicalExpr::evaluate_pruning()`]: evaluates pruning behavior
for predicates
+//! - [`PruningIntermediate`]: the intermediate result produced during
statistics propagation for pruning. Its internal representation uses Arrow
Arrays, enabling vectorized evaluation for performance.
+
+use std::{iter::repeat_n, sync::Arc};
+
+use arrow::array::{Array, ArrayRef, BooleanArray, BooleanBuilder, UInt64Array};
+use arrow::compute::kernels::boolean::and_kleene;
+use datafusion_common::pruning::PruningStatistics;
+use datafusion_common::{Result, ScalarValue, assert_eq_or_internal_err};
+
+/// Physical representation of pruning outcomes for each container:
+/// `true` = KeepAll, `false` = SkipAll, `null` = Unknown.
+///
+/// Use `BooleanArray` so the propagation steps can use existing Arrow kernels
for
+/// both simplicity and performance.
+///
+/// # Pruning results
+/// - KeepAll: The pruning predicate evaluates to true for all rows within a
micro
+/// partition. Future filter evaluation can be skipped for that partition.
+/// - SkipAll: The pruning predicate evaluates to false for all rows within a
micro
+/// partition. The partition can be skipped at scan time.
+/// - UnknownOrMixed: The statistics are insufficient to prove
KeepAll/SkipAll, or
+/// the predicate is mixed. The predicate must be evaluated row-wise.
+///
+/// Example (`SELECT * FROM t WHERE x >= 0`):
+/// - micro_partition_a(min=0, max=10): KeepAll — can pass through `FilterExec`
+/// without re-evaluating `x >= 0`.
+/// - micro_partition_b(min=-10, max=-1): SkipAll — skip the partition
entirely.
+/// - micro_partition_c(min=-5, max=5): Unknown — must evaluate the predicate
on rows.
+///
+/// `PruningOutcome` provides utilities to convert between this semantic
+/// representation and its tri-state boolean encoding.
+///
+/// # Important invariants
+/// Pruning results must be sound, but need not be complete:
+/// - If a container is labeled `KeepAll` or `SkipAll`, that label must be
correct.
+/// - If a container is labeled `Unknown` but is actually `KeepAll`/`SkipAll`,
+/// correctness is still preserved; it just means pruning was conservative.
+///
+/// Propagation implementation can be refined to reduce `Unknown` cases to
improve
+/// pruning effectiveness.
+#[derive(Debug, Clone)]
+pub struct PruningResults {
+ results: Option<BooleanArray>,
+ /// Number of containers. Needed to infer result if all stats types are
`None`.
+ pub num_containers: usize,
+}
+
+/// Semantic representation for items inside `PruningResults::results`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum PruningOutcome {
+ KeepAll,
+ SkipAll,
+ UnknownOrMixed,
+}
+
+impl PruningResults {
+ pub fn new(array: Option<BooleanArray>, num_containers: usize) -> Self {
+ debug_assert_eq!(
+ array.as_ref().map(|a| a.len()).unwrap_or(num_containers),
+ num_containers
+ );
+ Self {
+ results: array,
+ num_containers,
+ }
+ }
+
+ pub fn none(num_containers: usize) -> Self {
+ Self::new(None, num_containers)
+ }
+
+ pub fn as_ref(&self) -> Option<&BooleanArray> {
+ self.results.as_ref()
+ }
+
+ pub fn into_inner(self) -> Option<BooleanArray> {
+ self.results
+ }
+
+ pub fn len(&self) -> usize {
+ self.results
+ .as_ref()
+ .map(|a| a.len())
+ .unwrap_or(self.num_containers)
+ }
+
+ pub fn is_empty(&self) -> bool {
+ self.len() == 0
+ }
+}
+
+impl PruningOutcome {
+ /// Convert to/from the tri-state boolean encoding stored in
`PruningResults`.
+ /// - Some(true)=KeepAll
+ /// - Some(false)=SkipAll
+ /// - None=(Unknown/mixed)
+ pub fn from_result_item(result_item: Option<bool>) -> Self {
+ match result_item {
+ Some(true) => PruningOutcome::KeepAll,
+ Some(false) => PruningOutcome::SkipAll,
+ None => PruningOutcome::UnknownOrMixed,
+ }
+ }
+
+ pub fn to_result_item(&self) -> Option<bool> {
+ match self {
+ PruningOutcome::KeepAll => Some(true),
+ PruningOutcome::SkipAll => Some(false),
+ PruningOutcome::UnknownOrMixed => None,
+ }
+ }
+}
+
+impl From<BooleanArray> for PruningResults {
+ fn from(array: BooleanArray) -> Self {
+ let len = array.len();
+ PruningResults::new(Some(array), len)
+ }
+}
+
+#[derive(Debug, Clone)]
+pub enum RangeStats {
+ /// Ranges for all containers in array form.
+ /// - If `mins`/`maxs` are `None`, all containers have unknown statistics.
+ /// - Each entry (per-container) may be a bound or null. Null means
missing or
+ /// unbounded (null in `mins` = -inf; treating missing/unbounded the same
+ /// does not change pruning results).
+ Array {
+ mins: Option<ArrayRef>,
Review Comment:
Good point, will do it.
##########
datafusion/physical-expr-common/src/physical_expr/pruning.rs:
##########
@@ -0,0 +1,539 @@
+// 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.
+
+// Pruner Common Structs/Utilities
+
+//! This is the top-level comment for pruning via statistics propagation.
+//!
+//! TODO: This is a concise draft; it should be polished for readers with less
+//! prior background.
+//!
+//! # Introduction
+//!
+//! This module helps skip scanning data micro-partitions by evaluating
predicates
+//! against container-level statistics.
+//!
+//! It supports pruning for complex and nested predicates through statistics
+//! propagation.
+//!
+//! For examples of pruning nested predicates via statistics propagation, see:
+//! <https://github.com/apache/datafusion/issues/19487>
+//!
+//!
+//!
+//! # Vectorized pruning intermediate representation
+//!
+//! Source statistics and intermediate pruning results are stored in Arrow
arrays,
+//! enabling vectorized evaluation across many containers.
+//!
+//!
+//!
+//! # Difference from [`super::PhysicalExpr::evaluate_bounds`]
+//!
+//! `evaluate_bounds()` derives per-column statistics for a single plan, aimed
at
+//! tasks like cardinality estimation and other planner fast paths. It reasons
+//! about one container and may track richer distribution details.
+//! Pruning must reason about *all* containers (potentially thousands) to
decide
+//! which to skip, so it favors a vectorized, array-backed representation with
+//! lighter-weight stats. These are intentionally separate interfaces.
+//!
+//!
+//!
+//! # Core API/Data Structures
+//!
+//! The key structures involved in pruning are:
+//! - [`PruningStatistics`]: the input source statistics for all containers
+//! - [`super::PhysicalExpr::evaluate_pruning()`]: evaluates pruning behavior
for predicates
+//! - [`PruningIntermediate`]: the intermediate result produced during
statistics propagation for pruning. Its internal representation uses Arrow
Arrays, enabling vectorized evaluation for performance.
+
+use std::{iter::repeat_n, sync::Arc};
+
+use arrow::array::{Array, ArrayRef, BooleanArray, BooleanBuilder, UInt64Array};
+use arrow::compute::kernels::boolean::and_kleene;
+use datafusion_common::pruning::PruningStatistics;
+use datafusion_common::{Result, ScalarValue, assert_eq_or_internal_err};
+
+/// Physical representation of pruning outcomes for each container:
+/// `true` = KeepAll, `false` = SkipAll, `null` = Unknown.
+///
+/// Use `BooleanArray` so the propagation steps can use existing Arrow kernels
for
+/// both simplicity and performance.
+///
+/// # Pruning results
+/// - KeepAll: The pruning predicate evaluates to true for all rows within a
micro
+/// partition. Future filter evaluation can be skipped for that partition.
+/// - SkipAll: The pruning predicate evaluates to false for all rows within a
micro
+/// partition. The partition can be skipped at scan time.
+/// - UnknownOrMixed: The statistics are insufficient to prove
KeepAll/SkipAll, or
+/// the predicate is mixed. The predicate must be evaluated row-wise.
+///
+/// Example (`SELECT * FROM t WHERE x >= 0`):
+/// - micro_partition_a(min=0, max=10): KeepAll — can pass through `FilterExec`
+/// without re-evaluating `x >= 0`.
+/// - micro_partition_b(min=-10, max=-1): SkipAll — skip the partition
entirely.
+/// - micro_partition_c(min=-5, max=5): Unknown — must evaluate the predicate
on rows.
+///
+/// `PruningOutcome` provides utilities to convert between this semantic
+/// representation and its tri-state boolean encoding.
+///
+/// # Important invariants
+/// Pruning results must be sound, but need not be complete:
+/// - If a container is labeled `KeepAll` or `SkipAll`, that label must be
correct.
+/// - If a container is labeled `Unknown` but is actually `KeepAll`/`SkipAll`,
+/// correctness is still preserved; it just means pruning was conservative.
+///
+/// Propagation implementation can be refined to reduce `Unknown` cases to
improve
+/// pruning effectiveness.
+#[derive(Debug, Clone)]
+pub struct PruningResults {
+ results: Option<BooleanArray>,
+ /// Number of containers. Needed to infer result if all stats types are
`None`.
+ pub num_containers: usize,
+}
+
+/// Semantic representation for items inside `PruningResults::results`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum PruningOutcome {
+ KeepAll,
+ SkipAll,
+ UnknownOrMixed,
+}
+
+impl PruningResults {
+ pub fn new(array: Option<BooleanArray>, num_containers: usize) -> Self {
+ debug_assert_eq!(
+ array.as_ref().map(|a| a.len()).unwrap_or(num_containers),
+ num_containers
+ );
+ Self {
+ results: array,
+ num_containers,
+ }
+ }
+
+ pub fn none(num_containers: usize) -> Self {
+ Self::new(None, num_containers)
+ }
+
+ pub fn as_ref(&self) -> Option<&BooleanArray> {
+ self.results.as_ref()
+ }
+
+ pub fn into_inner(self) -> Option<BooleanArray> {
+ self.results
+ }
+
+ pub fn len(&self) -> usize {
+ self.results
+ .as_ref()
+ .map(|a| a.len())
+ .unwrap_or(self.num_containers)
+ }
+
+ pub fn is_empty(&self) -> bool {
+ self.len() == 0
+ }
+}
+
+impl PruningOutcome {
+ /// Convert to/from the tri-state boolean encoding stored in
`PruningResults`.
+ /// - Some(true)=KeepAll
+ /// - Some(false)=SkipAll
+ /// - None=(Unknown/mixed)
+ pub fn from_result_item(result_item: Option<bool>) -> Self {
+ match result_item {
+ Some(true) => PruningOutcome::KeepAll,
+ Some(false) => PruningOutcome::SkipAll,
+ None => PruningOutcome::UnknownOrMixed,
+ }
+ }
+
+ pub fn to_result_item(&self) -> Option<bool> {
+ match self {
+ PruningOutcome::KeepAll => Some(true),
+ PruningOutcome::SkipAll => Some(false),
+ PruningOutcome::UnknownOrMixed => None,
+ }
+ }
+}
+
+impl From<BooleanArray> for PruningResults {
+ fn from(array: BooleanArray) -> Self {
+ let len = array.len();
+ PruningResults::new(Some(array), len)
+ }
+}
+
+#[derive(Debug, Clone)]
+pub enum RangeStats {
+ /// Ranges for all containers in array form.
+ /// - If `mins`/`maxs` are `None`, all containers have unknown statistics.
+ /// - Each entry (per-container) may be a bound or null. Null means
missing or
+ /// unbounded (null in `mins` = -inf; treating missing/unbounded the same
+ /// does not change pruning results).
+ Array {
+ mins: Option<ArrayRef>,
+ maxs: Option<ArrayRef>,
+ length: usize,
+ },
+ /// Represents a uniform literal value across all containers.
+ /// This variant make it easy to compare between literals and normal
ranges representing
+ /// each containers' value range.
+ Scalar { value: ScalarValue, length: usize },
+}
+
+/// Null-related statistics for each container stored as a BooleanArray:
+/// `true` = NoNull, `false` = AllNull, `null` = Unknown/mixed.
+///
+/// Use `BooleanArray` so the propagation steps can use existing Arrow kernels
for
+/// both simplicity and performance.
+/// `NullPresence` provides utility to convert between its semantics
representation
+/// and physical encoding.
+#[derive(Debug, Clone)]
+pub struct NullStats {
+ presence: BooleanArray,
+}
+
+/// Semantic representation for items inside `NullStats::presence`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum NullPresence {
+ NoNull,
+ AllNull,
+ UnknownOrMixed,
+}
+
+impl NullPresence {
+ /// Convert to/from the tri-state boolean encoding stored in
`NullStats.presence`
+ /// - Some(true)=NoNull
+ /// - Some(false)=AllNull
+ /// - None=(Unknown/mixed)
+ pub fn from_presence_item(presence_item: Option<bool>) -> Self {
+ match presence_item {
+ Some(true) => NullPresence::NoNull,
+ Some(false) => NullPresence::AllNull,
+ None => NullPresence::UnknownOrMixed,
+ }
+ }
+
+ pub fn to_presence_item(&self) -> Option<bool> {
+ match self {
+ NullPresence::NoNull => Some(true),
+ NullPresence::AllNull => Some(false),
+ NullPresence::UnknownOrMixed => None,
+ }
+ }
+}
+
+/// Column statistics that propagate through the `PhysicalExpr` tree nodes
+///
+/// # Important invariants
+/// Non-null stats (e.g., ranges) describe only the value bounds for non-null
+/// rows; they DO NOT include nulls. For example, a partition with `min=0,
+/// max=10` may still contain nulls outside that range. Predicate pruning must
+/// combine decisions from non-null stats with null stats to derive the final
+/// outcome.
+#[derive(Debug, Clone)]
+pub struct ColumnStats {
+ pub range_stats: Option<RangeStats>,
+ pub null_stats: Option<NullStats>,
+ /// Number of containers. Needed to infer result if all stats types are
`None`.
+ pub num_containers: usize,
+}
+
+impl RangeStats {
+ pub fn new(
+ mins: Option<ArrayRef>,
+ maxs: Option<ArrayRef>,
+ length: usize,
+ ) -> Result<Self> {
+ if let Some(ref mins) = mins {
+ assert_eq_or_internal_err!(
+ mins.len(),
+ length,
+ "Range mins length mismatch for pruning statistics"
+ );
+ }
+ if let Some(ref maxs) = maxs {
+ assert_eq_or_internal_err!(
+ maxs.len(),
+ length,
+ "Range maxs length mismatch for pruning statistics"
+ );
+ }
+ Ok(Self::Array { mins, maxs, length })
+ }
+
+ /// Create range stats for a constant literal across all containers.
+ pub fn new_scalar(value: ScalarValue, length: usize) -> Result<Self> {
+ Ok(Self::Scalar { value, length })
+ }
+
+ pub fn len(&self) -> usize {
+ match self {
+ RangeStats::Array { length, .. } | RangeStats::Scalar { length, ..
} => {
+ *length
+ }
+ }
+ }
+
+ pub fn is_empty(&self) -> bool {
+ self.len() == 0
+ }
+
+ /// Normalize into concrete min/max arrays.
+ ///
+ /// For `Array`, returns cloned mins/maxs (which may be `None`).
+ /// For `Scalar`, expands the scalar to arrays of length `length`.
+ pub fn normalize_to_arrays(&self) -> Result<(Option<ArrayRef>,
Option<ArrayRef>)> {
+ match self {
+ RangeStats::Array { mins, maxs, .. } => Ok((mins.clone(),
maxs.clone())),
+ RangeStats::Scalar { value, length } => {
+ let mins = value.to_array_of_size(*length)?;
+ let maxs = value.to_array_of_size(*length)?;
+ Ok((Some(mins), Some(maxs)))
+ }
+ }
+ }
+}
+
+pub struct PruningContext {
+ stats: Arc<dyn PruningStatistics + Send + Sync>,
+}
+
+impl PruningContext {
+ pub fn new(stats: Arc<dyn PruningStatistics + Send + Sync>) -> Self {
+ Self { stats }
+ }
+
+ pub fn statistics(&self) -> &Arc<dyn PruningStatistics + Send + Sync> {
+ &self.stats
+ }
+}
+
+impl NullStats {
+ /// Build `NullStats` from per-container null and row counts.
+ ///
+ /// # Arguments
+ /// - `null_counts`: All containers' null counts in a single `Array`, or
`None` if missing.
+ /// - `row_counts`: All containers' row counts in a single `Array`, or
`None` if missing.
+ ///
+ /// # Return
+ /// `Some(NullStats)` when both inputs are present and aligned; `None`
when either input is missing/unknown.
+ ///
+ /// # Examples (per-container outcomes)
+ /// - `null_counts=[3, 0, 1]`, `row_counts=[3, 5, 10]` →
+ /// presence = [false, true, null] (AllNull, NoNull, Mixed).
+ ///
+ /// # Errors
+ /// - Internal error if inputs have inconsistent lengths.
+ pub fn new(
+ null_counts: Option<&UInt64Array>,
+ row_counts: Option<&UInt64Array>,
+ ) -> Result<Option<Self>> {
+ // If either input is absent, we can't derive null stats for all
containers.
+ let (Some(null_counts), Some(row_counts)) = (null_counts, row_counts)
else {
+ return Ok(None);
+ };
+
+ let length = null_counts.len();
+ assert_eq_or_internal_err!(
+ row_counts.len(),
+ length,
+ "Row counts length mismatch for pruning statistics"
+ );
+
+ let mut presence = BooleanBuilder::with_capacity(length);
+ for idx in 0..length {
+ let nulls = (!null_counts.is_null(idx)).then(||
null_counts.value(idx));
+ let rows = (!row_counts.is_null(idx)).then(||
row_counts.value(idx));
+
+ // See `NullStats` for encoding semantics
+ match (nulls, rows) {
+ (Some(0), Some(_)) | (Some(0), None) =>
presence.append_value(true),
+ (Some(n), Some(r)) if n == r => presence.append_value(false),
+ _ => presence.append_null(),
+ }
+ }
+
+ Ok(Some(Self {
+ presence: presence.finish(),
+ }))
+ }
+
+ /// Create a `NullStats` with a uniform `presence` repeated
`num_containers` times.
+ /// See `NullStats` docs for `presence` semantics.
+ ///
+ /// Used to create pruning statistics literal/scalar values.
+ pub fn from_uniform_presence(presence: NullPresence, num_containers:
usize) -> Self {
+ let presence_item = match presence {
+ NullPresence::NoNull => Some(true),
+ NullPresence::AllNull => Some(false),
+ NullPresence::UnknownOrMixed => None,
+ };
+ NullStats {
+ presence: BooleanArray::from_iter(repeat_n(presence_item,
num_containers)),
+ }
+ }
+
+ /// Combine two null-stat arrays for a comparison (`=, !=, <, >, <=, >=`).
+ ///
+ /// None means all containers' null stats are missing, otherwise for each
container:
+ /// - If either side is `AllNull` → result is `AllNull` (all comparisons
are null).
+ /// - If both sides are `NoNull` → result is `NoNull`.
+ /// - Otherwise → result is `UnknownOrMixed`.
+ ///
+ /// # Errors
+ /// Returns internal error if left and right side has inconsistent
container length
+ pub fn combine_for_cmp(
+ left: Option<&Self>,
+ right: Option<&Self>,
+ ) -> Result<Option<Self>> {
+ let (left, right) = match (left, right) {
+ (Some(l), Some(r)) => (l, r),
+ (_, _) => {
+ return Ok(None);
+ }
+ };
+
+ let len = left.len();
+ assert_eq_or_internal_err!(
+ len,
+ right.len(),
+ "Null stats length mismatch for comparison pruning"
+ );
+
+ // The function comments specified the semantics behavior, and given
the
+ // physical encoding:
+ // `true` = NoNull, `false` = AllNull, `null` = Unknown/mixed.
+ // The implementation can be simplified to a kleene(null-aware) 'AND'
+ Ok(Some(NullStats {
+ presence: and_kleene(left.presence(), right.presence())?,
+ }))
+ }
+
+ pub fn len(&self) -> usize {
+ self.presence.len()
+ }
+
+ pub fn is_empty(&self) -> bool {
+ self.presence.is_empty()
+ }
+
+ pub fn presence(&self) -> &BooleanArray {
+ &self.presence
+ }
+}
+
+impl ColumnStats {
+ pub fn new(
+ range_stats: Option<RangeStats>,
+ null_stats: Option<NullStats>,
+ num_containers: usize,
+ ) -> Self {
+ debug_assert_eq!(
+ range_stats
+ .as_ref()
+ .map(|r| r.len())
+ .unwrap_or(num_containers),
+ num_containers
+ );
+ debug_assert_eq!(
+ null_stats
+ .as_ref()
+ .map(|n| n.len())
+ .unwrap_or(num_containers),
+ num_containers
+ );
+ Self {
+ range_stats,
+ null_stats,
+ num_containers,
+ }
+ }
+
+ pub fn range_stats(&self) -> Option<&RangeStats> {
+ self.range_stats.as_ref()
+ }
+
+ pub fn null_stats(&self) -> Option<&NullStats> {
+ self.null_stats.as_ref()
+ }
+
+ pub fn len(&self) -> usize {
+ self.num_containers
+ }
+
+ pub fn is_empty(&self) -> bool {
+ self.num_containers == 0
+ }
+}
+
+/// Pruning intermediate type propagated through `PhysicalExpr` nodes.
Review Comment:
Yes, this is doable. To encode `PruningResult`, we only need a single inner
`Option<BooleanArray>`. The key consideration, I think, is which approach leads
to a simpler implementation.
The answer is not clear to me yet; I will build a small prototype to confirm.
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