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// Copyright Materialize, Inc. and contributors. All rights reserved.
//
// Use of this software is governed by the Business Source License
// included in the LICENSE file.
//
// As of the Change Date specified in that file, in accordance with
// the Business Source License, use of this software will be governed
// by the Apache License, Version 2.0.
//! Pushes column removal down through other operators.
//!
//! This action improves the quality of the query by
//! reducing the width of data in the dataflow. It determines the unique
//! columns an expression depends on, and pushes a projection onto only
//! those columns down through child operators.
//!
//! A `MirRelationExpr::Project` node is actually three transformations in one.
//! 1) Projection - removes columns.
//! 2) Permutation - reorders columns.
//! 3) Repetition - duplicates columns.
//!
//! This action handles these three transformations like so:
//! 1) Projections are pushed as far down as possible.
//! 2) Permutations are pushed as far down as is convenient.
//! 3) Repetitions are not pushed down at all.
//!
//! Some comments have been inherited from the `Demand` transform.
//!
//! Note that this transform is one that can operate across views in a dataflow
//! and thus currently exists outside of both the physical and logical
//! optimizers.
use std::collections::{BTreeSet, HashMap};
use expr::{Id, JoinInputMapper, MirRelationExpr, MirScalarExpr};
use crate::TransformArgs;
/// Pushes projections down through other operators.
#[derive(Debug)]
pub struct ProjectionPushdown;
impl crate::Transform for ProjectionPushdown {
// This method is only used during unit testing.
fn transform(
&self,
relation: &mut MirRelationExpr,
_: TransformArgs,
) -> Result<(), crate::TransformError> {
self.action(
relation,
&(0..relation.arity()).collect(),
&mut HashMap::new(),
);
Ok(())
}
}
impl ProjectionPushdown {
/// Pushes the `desired_projection` down through `relation`.
///
/// This action transforms `relation` to a `MirRelationExpr` equivalent to
/// `relation.project(desired_projection)`.
///
/// `desired_projection` is expected to consist of unique columns.
pub fn action(
&self,
relation: &mut MirRelationExpr,
desired_projection: &Vec<usize>,
gets: &mut HashMap<Id, BTreeSet<usize>>,
) {
// First, try to push the desired projection down through `relation`.
// In the process `relation` is transformed to a `MirRelationExpr`
// equivalent to `relation.project(actual_projection)`.
// There are three reasons why `actual_projection` may differ from
// `desired_projection`:
// 1) `relation` may need one or more columns that is not contained in
// `desired_projection`.
// 2) `relation` may not be able to accommodate certain permutations.
// For example, `MirRelationExpr::Map` always appends all
// newly-created columns to the end.
// 3) Nothing can be pushed through a leaf node. If `relation` is a leaf
// node, `actual_projection` will always be `(0..relation.arity())`.
// Then, if `actual_projection` and `desired_projection` differ, we will
// add a project around `relation`.
let actual_projection = match relation {
MirRelationExpr::Constant { .. } => (0..relation.arity()).collect(),
MirRelationExpr::Get { id, .. } => {
gets.entry(*id)
.or_insert_with(BTreeSet::new)
.extend(desired_projection.iter().cloned());
(0..relation.arity()).collect()
}
MirRelationExpr::Let { id, value, body } => {
// Let harvests any requirements of get from its body,
// and pushes the sorted union of the requirements at its value.
let id = Id::Local(*id);
let prior = gets.insert(id, BTreeSet::new());
self.action(body, desired_projection, gets);
let desired_value_projection = gets.remove(&id).unwrap();
if let Some(prior) = prior {
gets.insert(id, prior);
}
let desired_value_projection =
desired_value_projection.into_iter().collect::<Vec<_>>();
self.action(value, &desired_value_projection, gets);
self.update_projection_around_get(
body,
&HashMap::from_iter(std::iter::once((id, desired_value_projection))),
);
desired_projection.clone()
}
MirRelationExpr::Join {
inputs,
equivalences,
..
} => {
let input_mapper = JoinInputMapper::new(inputs);
let mut columns_to_pushdown =
desired_projection.iter().cloned().collect::<BTreeSet<_>>();
// Each equivalence class imposes internal demand for columns.
for equivalence in equivalences.iter() {
for expr in equivalence.iter() {
columns_to_pushdown.extend(expr.support());
}
}
// Populate child demands from external and internal demands.
let new_columns =
input_mapper.split_column_set_by_input(columns_to_pushdown.iter());
// Recursively indicate the requirements.
for (input, inp_columns) in inputs.iter_mut().zip(new_columns) {
let mut inp_columns = inp_columns.into_iter().collect::<Vec<_>>();
inp_columns.sort();
self.action(input, &inp_columns, gets);
}
reverse_permute(
equivalences.iter_mut().flat_map(|e| e.iter_mut()),
columns_to_pushdown.iter(),
);
columns_to_pushdown.into_iter().collect()
}
MirRelationExpr::FlatMap { input, func, exprs } => {
let inner_arity = input.arity();
// A FlatMap which returns zero rows acts like a filter
// so we always need to execute it
let mut columns_to_pushdown =
desired_projection.iter().cloned().collect::<BTreeSet<_>>();
for expr in exprs.iter() {
columns_to_pushdown.extend(expr.support());
}
columns_to_pushdown.retain(|c| *c < inner_arity);
reverse_permute(exprs.iter_mut(), columns_to_pushdown.iter());
let columns_to_pushdown = columns_to_pushdown.into_iter().collect::<Vec<_>>();
self.action(input, &columns_to_pushdown, gets);
// The actual projection always has the newly-created columns at
// the end.
let mut actual_projection = columns_to_pushdown;
for c in 0..func.output_type().arity() {
actual_projection.push(inner_arity + c);
}
actual_projection
}
MirRelationExpr::Filter { input, predicates } => {
let mut columns_to_pushdown =
desired_projection.iter().cloned().collect::<BTreeSet<_>>();
for predicate in predicates.iter() {
columns_to_pushdown.extend(predicate.support());
}
reverse_permute(predicates.iter_mut(), columns_to_pushdown.iter());
let columns_to_pushdown = columns_to_pushdown.into_iter().collect::<Vec<_>>();
self.action(input, &columns_to_pushdown, gets);
columns_to_pushdown
}
MirRelationExpr::Project { input, outputs } => {
// Combine `outputs` with `desired_projection`.
*outputs = desired_projection.iter().map(|c| outputs[*c]).collect();
let unique_outputs = outputs.iter().map(|i| *i).collect::<BTreeSet<_>>();
if outputs.len() == unique_outputs.len() {
// Push down the project as is.
self.action(input, &outputs, gets);
*relation = input.take_dangerous();
} else {
// Push down only the unique elems in `outputs`.
let columns_to_pushdown = unique_outputs.into_iter().collect::<Vec<_>>();
reverse_permute_columns(outputs.iter_mut(), columns_to_pushdown.iter());
self.action(input, &columns_to_pushdown, gets);
}
desired_projection.clone()
}
MirRelationExpr::Map { input, scalars } => {
let arity = input.arity();
// contains columns whose supports have yet to be explored
let mut actual_projection =
desired_projection.iter().cloned().collect::<BTreeSet<_>>();
for (i, scalar) in scalars.iter().enumerate().rev() {
if actual_projection.contains(&(i + arity)) {
actual_projection.extend(scalar.support());
}
}
*scalars = (0..scalars.len())
.filter_map(|i| {
if actual_projection.contains(&(i + arity)) {
Some(scalars[i].clone())
} else {
None
}
})
.collect::<Vec<_>>();
reverse_permute(scalars.iter_mut(), actual_projection.iter());
self.action(
input,
&actual_projection
.iter()
.filter(|c| **c < arity)
.map(|c| *c)
.collect(),
gets,
);
actual_projection.into_iter().collect()
}
MirRelationExpr::Reduce {
input,
group_key,
aggregates,
monotonic: _,
expected_group_size: _,
} => {
let mut columns_to_pushdown = BTreeSet::new();
// Group keys determine aggregation granularity and are
// each crucial in determining aggregates and even the
// multiplicities of other keys.
columns_to_pushdown.extend(group_key.iter().flat_map(|e| e.support()));
for index in (0..aggregates.len()).rev() {
if !desired_projection.contains(&(group_key.len() + index)) {
aggregates.remove(index);
} else {
// No obvious requirements on aggregate columns.
// A "non-empty" requirement, I guess?
columns_to_pushdown.extend(aggregates[index].expr.support())
}
}
reverse_permute(
group_key
.iter_mut()
.chain(aggregates.iter_mut().map(|a| &mut a.expr)),
columns_to_pushdown.iter(),
);
self.action(
input,
&columns_to_pushdown.into_iter().collect::<Vec<_>>(),
gets,
);
let mut actual_projection =
desired_projection.iter().cloned().collect::<BTreeSet<_>>();
actual_projection.extend(0..group_key.len());
actual_projection.into_iter().collect()
}
MirRelationExpr::TopK {
input,
group_key,
order_key,
..
} => {
// Group and order keys must be retained, as they define
// which rows are retained.
let mut columns_to_pushdown =
desired_projection.iter().cloned().collect::<BTreeSet<_>>();
columns_to_pushdown.extend(group_key.iter().cloned());
columns_to_pushdown.extend(order_key.iter().map(|o| o.column));
// If the `TopK` does not have any new column demand, just push
// down the desired projection. Otherwise, push down the sorted
// column demand.
let columns_to_pushdown = if columns_to_pushdown.len() == desired_projection.len() {
desired_projection.clone()
} else {
columns_to_pushdown.into_iter().collect::<Vec<_>>()
};
reverse_permute_columns(
group_key
.iter_mut()
.chain(order_key.iter_mut().map(|o| &mut o.column)),
columns_to_pushdown.iter(),
);
self.action(input, &columns_to_pushdown, gets);
columns_to_pushdown
}
MirRelationExpr::Negate { input } => {
self.action(input, desired_projection, gets);
desired_projection.clone()
}
MirRelationExpr::Union { base, inputs } => {
self.action(base, desired_projection, gets);
for input in inputs {
self.action(input, desired_projection, gets);
}
desired_projection.clone()
}
MirRelationExpr::Threshold { input } => {
// Threshold requires all columns, as collapsing any distinct values
// has the potential to change how it thresholds counts. This could
// be improved with reasoning about distinctness or non-negativity.
let arity = input.arity();
self.action(input, &(0..arity).collect(), gets);
(0..arity).collect()
}
MirRelationExpr::ArrangeBy { input, keys: _ } => {
// Do not push the project past the ArrangeBy.
// TODO: how do we handle key sets containing column references
// that are not demanded upstream?
let arity = input.arity();
self.action(input, &(0..arity).collect(), gets);
(0..arity).collect()
}
MirRelationExpr::DeclareKeys { input, keys } => {
// TODO[btv] - If and when we add a "debug mode" that asserts whether this is truly a key,
// we will probably need to add the key to the set of demanded
// columns.
// Current behavior is that if a key is not contained with the
// desired_projection, then it is not relevant to the query plan
// and can be removed.
keys.retain(|key_set| key_set.iter().all(|k| desired_projection.contains(k)));
self.action(input, desired_projection, gets);
if keys.is_empty() {
*relation = input.take_dangerous();
}
desired_projection.clone()
}
};
let add_project = desired_projection != &actual_projection;
if add_project {
let mut projection_to_add = desired_projection.to_owned();
reverse_permute_columns(projection_to_add.iter_mut(), actual_projection.iter());
*relation = relation.take_dangerous().project(projection_to_add);
}
}
/// When we push the `desired_value_projection` at `value`,
/// the columns returned by `Get(get_id)` will change, so we need
/// to permute `Project`s around `Get(get_id)`.
pub fn update_projection_around_get(
&self,
relation: &mut MirRelationExpr,
applied_projections: &HashMap<Id, Vec<usize>>,
) {
relation.visit_mut_pre(&mut |e| {
if let MirRelationExpr::Project { input, outputs } = e {
if let MirRelationExpr::Get { id: inner_id, .. } = &**input {
if let Some(new_projection) = applied_projections.get(inner_id) {
reverse_permute_columns(outputs.iter_mut(), new_projection.iter());
if outputs.len() == new_projection.len()
&& outputs.iter().enumerate().all(|(i, o)| i == *o)
{
*e = input.take_dangerous();
}
}
}
}
// If there is no `Project` around a Get, all columns of
// `Get(get_id)` are required. Thus, the columns returned by
// `Get(get_id)` will not have changed, so no action
// is necessary.
})
}
}
/// Applies the reverse of [MirScalarExpr.permute] on each expression.
///
/// `permutation` can be thought of as a mapping of column references from
/// `stateA` to `stateB`. [MirScalarExpr.permute] assumes that the column
/// references of the expression are in `stateA` and need to be remapped to
/// their `stateB` counterparts. This methods assumes that the column
/// references are in `stateB` and need to be remapped to `stateA`.
///
/// The `outputs` field of [MirRelationExpr::Project] is a mapping from "after"
/// to "before". Thus, when lifting projections, you would permute on `outputs`,
/// but you need to reverse permute when pushdown projections down.
fn reverse_permute<'a, I, J>(exprs: I, permutation: J)
where
I: Iterator<Item = &'a mut MirScalarExpr>,
J: Iterator<Item = &'a usize>,
{
let reverse_col_map = permutation
.enumerate()
.map(|(idx, c)| (*c, idx))
.collect::<HashMap<_, _>>();
for expr in exprs {
expr.permute_map(&reverse_col_map);
}
}
/// Same as [reverse_permute], but takes column numbers as input
fn reverse_permute_columns<'a, I, J>(columns: I, permutation: J)
where
I: Iterator<Item = &'a mut usize>,
J: Iterator<Item = &'a usize>,
{
let reverse_col_map = permutation
.enumerate()
.map(|(idx, c)| (*c, idx))
.collect::<HashMap<_, _>>();
for c in columns {
*c = reverse_col_map[c];
}
}