alamb commented on code in PR #19609: URL: https://github.com/apache/datafusion/pull/19609#discussion_r2673504185
########## 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: Review Comment: we can likely lift some / all of the documentation from PruningPredicate here ########## 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. Review Comment: Is there any fundamental reason they need to be separate interfaces? Like I am thinking, is there some potential future where we are able to rewrite [`PhysicalExpr::evaluate_bounds`] to use the new API in this PR? That way having multiple APIs would be only a temporary, intermediate state as we worked to fill out the rest of the functionality 🤔 ########## 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 Review Comment: ```suggestion //! `evaluate_bounds()` derives per-column statistics for a single expression, aimed at ``` ########## 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 { Review Comment: A minor suggestion is these look like they could be `impl From` / other methods on `PruningOutcome` -- for example, like `PruningOutcome::from(Some(true))` ########## 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: what does "mixed" predicate mean? ########## 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: I realize the primary usecase for this evaluation is pruning, but I think it is a more general concept -- basically propagating statistical information through this expression What would you think of calling this more like `propagate_ranges`? (I realize it is getting very similar to `evalute_ranges` and `propagate_constraints`...) ########## 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: maybe they could be ColumnarValue? That would allow efficient representation of literals... ########## 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. Review Comment: After reading this code more, I don't think there is any fundamental difference (other than being vectorized) with evaluate_bounds ########## datafusion/physical-expr/src/expressions/is_null.rs: ########## @@ -92,6 +96,42 @@ impl PhysicalExpr for IsNullExpr { } } + fn evaluate_pruning( + &self, + ctx: Arc<PruningContext>, + ) -> Result<Option<PruningIntermediate>> { + let Some(child) = self.arg.evaluate_pruning(ctx)? else { + return Ok(None); + }; + match child { + PruningIntermediate::IntermediateStats(stats) => { + if let Some(null_stats) = stats.null_stats() { Review Comment: I swear that @pepijnve recently implemented very similar logic (for the bounds evaluation of IsNull, albiet one row at a time) but now I can't find 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 Review Comment: I think this shares some, but not all, similarities with the `ColumnStatistics` https://github.com/apache/datafusion/blob/c1aa1b530ab2fa73efcdeb8896dbb50c30c492f0/datafusion/common/src/stats.rs#L721-L720 The major difference is that ColumnStatistics also has some notion of precision (and is a row wise structure) I am not sure if there is some way to unify them ########## 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: I am not sure we need to distinguish between intermediate statistics and intermediate results Specifically, I think ColumnStats for boolean expressions will be trivially convertible to `PruningResults` (if the min/max are both `true` then we know the boolean value is always `true`. If the min/max are both `false` then we know the value is always `false`, etc) This is similar to how Interval works: https://github.com/apache/datafusion/blob/81512da2b0aaa474f6c4ba205b05eea7b3095176/datafusion/expr-common/src/interval_arithmetic.rs#L182-L181 ########## 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 Review Comment: It probably makes sense to put these into `datafuison-pruning` rather than `physical-expr`: https://github.com/apache/datafusion/blob/7900cd6e98c11d4eaa7ac7d7db17dbd676315333/datafusion/pruning/ ########## 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: Rather than different APIs, I would recommend a single API `propagate_ranges` and add the different types of information in the object that is propagated ########## 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>, Review Comment: maybe just a comment here pointing to the definitions would be helpful -- This is an automated message from the Apache Git Service. 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