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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.
//! Lowering is the process of transforming a `HirRelationExpr`
//! into a `MirRelationExpr`.
//!
//! The most crucial part of lowering is decorrelation; i.e.: rewriting a
//! `HirScalarExpr` that may contain subqueries (e.g. `SELECT` or `EXISTS`)
//! with instances of `MirScalarExpr` that contain none of these.
//!
//! Informally, a subquery should be viewed as a query that is executed in
//! the context of some outer relation, for each row of that relation. The
//! subqueries often contain references to the columns of the outer
//! relation.
//!
//! The transformation we perform maintains an `outer` relation and then
//! traverses the relation expression that may contain references to those
//! outer columns. As subqueries are discovered, the current relation
//! expression is recast as the outer expression until such a point as the
//! scalar expression's evaluation can be determined and appended to each
//! row of the previously outer relation.
//!
//! It is important that the outer columns (the initial columns) act as keys
//! for all nested computation. When counts or other aggregations are
//! performed, they should include not only the indicated keys but also all
//! of the outer columns.
//!
//! The decorrelation transformation is initialized with an empty outer
//! relation, but it seems entirely appropriate to decorrelate queries that
//! contain "holes" from prepared statements, as if the query was a subquery
//! against a relation containing the assignments of values to those holes.
use std::collections::{BTreeMap, BTreeSet};
use std::iter::repeat;
use crate::optimizer_metrics::OptimizerMetrics;
use crate::plan::expr::{
AggregateExpr, ColumnOrder, ColumnRef, HirRelationExpr, HirScalarExpr, JoinKind, WindowExprType,
};
use crate::plan::{transform_expr, PlanError};
use crate::session::vars::SystemVars;
use itertools::Itertools;
use mz_expr::{AccessStrategy, AggregateFunc, MirRelationExpr, MirScalarExpr};
use mz_ore::collections::CollectionExt;
use mz_ore::stack::maybe_grow;
use mz_repr::*;
mod variadic_left;
/// Maps a leveled column reference to a specific column.
///
/// Leveled column references are nested, so that larger levels are
/// found early in a record and level zero is found at the end.
///
/// The column map only stores references for levels greater than zero,
/// and column references at level zero simply start at the first column
/// after all prior references.
#[derive(Debug, Clone)]
struct ColumnMap {
inner: BTreeMap<ColumnRef, usize>,
}
impl ColumnMap {
fn empty() -> ColumnMap {
Self::new(BTreeMap::new())
}
fn new(inner: BTreeMap<ColumnRef, usize>) -> ColumnMap {
ColumnMap { inner }
}
fn get(&self, col_ref: &ColumnRef) -> usize {
if col_ref.level == 0 {
self.inner.len() + col_ref.column
} else {
self.inner[col_ref]
}
}
fn len(&self) -> usize {
self.inner.len()
}
/// Updates references in the `ColumnMap` for use in a nested scope. The
/// provided `arity` must specify the arity of the current scope.
fn enter_scope(&self, arity: usize) -> ColumnMap {
// From the perspective of the nested scope, all existing column
// references will be one level greater.
let existing = self
.inner
.clone()
.into_iter()
.update(|(col, _i)| col.level += 1);
// All columns in the current scope become explicit entries in the
// immediate parent scope.
let new = (0..arity).map(|i| {
(
ColumnRef {
level: 1,
column: i,
},
self.len() + i,
)
});
ColumnMap::new(existing.chain(new).collect())
}
}
/// Map with the CTEs currently in scope.
type CteMap = BTreeMap<mz_expr::LocalId, CteDesc>;
/// Information about needed when finding a reference to a CTE in scope.
#[derive(Clone)]
struct CteDesc {
/// The new ID assigned to the lowered version of the CTE, which may not match
/// the ID of the input CTE.
new_id: mz_expr::LocalId,
/// The relation type of the CTE including the columns from the outer
/// context at the beginning.
relation_type: RelationType,
/// The outer relation the CTE was applied to.
outer_relation: MirRelationExpr,
}
#[derive(Debug)]
pub struct Config {
/// Enable outer join lowering implemented in database-issues#6747.
pub enable_new_outer_join_lowering: bool,
/// Enable outer join lowering implemented in database-issues#7561.
pub enable_variadic_left_join_lowering: bool,
}
impl From<&SystemVars> for Config {
fn from(vars: &SystemVars) -> Self {
Self {
enable_new_outer_join_lowering: vars.enable_new_outer_join_lowering(),
enable_variadic_left_join_lowering: vars.enable_variadic_left_join_lowering(),
}
}
}
/// Context passed to the lowering. This is wired to most parts of the lowering.
pub(crate) struct Context<'a> {
/// Feature flags affecting the behavior of lowering.
pub config: &'a Config,
/// Optional, because some callers don't have an `OptimizerMetrics` handy. When it's None, we
/// simply don't write metrics.
pub metrics: Option<&'a OptimizerMetrics>,
}
impl HirRelationExpr {
/// Rewrite `self` into a `MirRelationExpr`.
/// This requires rewriting all correlated subqueries (nested `HirRelationExpr`s) into flat queries
#[mz_ore::instrument(target = "optimizer", level = "trace", name = "hir_to_mir")]
pub fn lower<C: Into<Config>>(
self,
config: C,
metrics: Option<&OptimizerMetrics>,
) -> Result<MirRelationExpr, PlanError> {
let context = Context {
config: &config.into(),
metrics,
};
let result = match self {
// We directly rewrite a Constant into the corresponding `MirRelationExpr::Constant`
// to ensure that the downstream optimizer can easily bypass most
// irrelevant optimizations (e.g. reduce folding) for this expression
// without having to re-learn the fact that it is just a constant,
// as it would if the constant were wrapped in a Let-Get pair.
HirRelationExpr::Constant { rows, typ } => {
let rows: Vec<_> = rows.into_iter().map(|row| (row, 1)).collect();
MirRelationExpr::Constant {
rows: Ok(rows),
typ,
}
}
mut other => {
let mut id_gen = mz_ore::id_gen::IdGen::default();
transform_expr::split_subquery_predicates(&mut other);
transform_expr::try_simplify_quantified_comparisons(&mut other);
transform_expr::fuse_window_functions(&mut other, &context)?;
MirRelationExpr::constant(vec![vec![]], RelationType::new(vec![])).let_in(
&mut id_gen,
|id_gen, get_outer| {
other.applied_to(
id_gen,
get_outer,
&ColumnMap::empty(),
&mut CteMap::new(),
&context,
)
},
)?
}
};
mz_repr::explain::trace_plan(&result);
Ok(result)
}
/// Return a `MirRelationExpr` which evaluates `self` once for each row of `get_outer`.
///
/// For uncorrelated `self`, this should be the cross-product between `get_outer` and `self`.
/// When `self` references columns of `get_outer`, much more work needs to occur.
///
/// The `col_map` argument contains mappings to some of the columns of `get_outer`, though
/// perhaps not all of them. It should be used as the basis of resolving column references,
/// but care must be taken when adding new columns that `get_outer.arity()` is where they
/// will start, rather than any function of `col_map`.
///
/// The `get_outer` expression should be a `Get` with no duplicate rows, describing the distinct
/// assignment of values to outer rows.
fn applied_to(
self,
id_gen: &mut mz_ore::id_gen::IdGen,
get_outer: MirRelationExpr,
col_map: &ColumnMap,
cte_map: &mut CteMap,
context: &Context,
) -> Result<MirRelationExpr, PlanError> {
maybe_grow(|| {
use MirRelationExpr as SR;
use HirRelationExpr::*;
if let MirRelationExpr::Get { .. } = &get_outer {
} else {
panic!(
"get_outer: expected a MirRelationExpr::Get, found {:?}",
get_outer
);
}
assert_eq!(col_map.len(), get_outer.arity());
Ok(match self {
Constant { rows, typ } => {
// Constant expressions are not correlated with `get_outer`, and should be cross-products.
get_outer.product(SR::Constant {
rows: Ok(rows.into_iter().map(|row| (row, 1)).collect()),
typ,
})
}
Get { id, typ } => match id {
mz_expr::Id::Local(local_id) => {
let cte_desc = cte_map.get(&local_id).unwrap();
let get_cte = SR::Get {
id: mz_expr::Id::Local(cte_desc.new_id.clone()),
typ: cte_desc.relation_type.clone(),
access_strategy: AccessStrategy::UnknownOrLocal,
};
if get_outer == cte_desc.outer_relation {
// If the CTE was applied to the same exact relation, we can safely
// return a `Get` relation.
get_cte
} else {
// Otherwise, the new outer relation may contain more columns from some
// intermediate scope placed between the definition of the CTE and this
// reference of the CTE and/or more operations applied on top of the
// outer relation.
//
// An example of the latter is the following query:
//
// SELECT *
// FROM x,
// LATERAL(WITH a(m) as (SELECT max(y.a) FROM y WHERE y.a < x.a)
// SELECT (SELECT m FROM a) FROM y) b;
//
// When the CTE is lowered, the outer relation is `Get x`. But then,
// the reference of the CTE is applied to `Distinct(Join(Get x, Get y), x.*)`
// which has the same cardinality as `Get x`.
//
// In any case, `get_outer` is guaranteed to contain the columns of the
// outer relation the CTE was applied to at its prefix. Since, we must
// return a relation containing `get_outer`'s column at the beginning,
// we must build a join between `get_outer` and `get_cte` on their common
// columns.
let oa = get_outer.arity();
let cte_outer_columns = cte_desc.relation_type.arity() - typ.arity();
let equivalences = (0..cte_outer_columns)
.map(|pos| {
vec![
MirScalarExpr::Column(pos),
MirScalarExpr::Column(pos + oa),
]
})
.collect();
// Project out the second copy of the common between `get_outer` and
// `cte_desc.outer_relation`.
let projection = (0..oa)
.chain(oa + cte_outer_columns..oa + cte_outer_columns + typ.arity())
.collect_vec();
SR::join_scalars(vec![get_outer, get_cte], equivalences)
.project(projection)
}
}
_ => {
// Get statements are only to external sources, and are not correlated with `get_outer`.
get_outer.product(SR::Get {
id,
typ,
access_strategy: AccessStrategy::UnknownOrLocal,
})
}
},
Let {
name: _,
id,
value,
body,
} => {
let value =
value.applied_to(id_gen, get_outer.clone(), col_map, cte_map, context)?;
value.let_in(id_gen, |id_gen, get_value| {
let (new_id, typ) = if let MirRelationExpr::Get {
id: mz_expr::Id::Local(id),
typ,
..
} = get_value
{
(id, typ)
} else {
panic!(
"get_value: expected a MirRelationExpr::Get with local Id, found {:?}",
get_value
);
};
// Add the information about the CTE to the map and remove it when
// it goes out of scope.
let old_value = cte_map.insert(
id.clone(),
CteDesc {
new_id,
relation_type: typ,
outer_relation: get_outer.clone(),
},
);
let body = body.applied_to(id_gen, get_outer, col_map, cte_map, context);
if let Some(old_value) = old_value {
cte_map.insert(id, old_value);
} else {
cte_map.remove(&id);
}
body
})?
}
LetRec {
limit,
bindings,
body,
} => {
let num_bindings = bindings.len();
// We use the outer type with the HIR types to form MIR CTE types.
let outer_column_types = get_outer.typ().column_types;
// Rename and introduce all bindings.
let mut shadowed_bindings = Vec::with_capacity(num_bindings);
let mut mir_ids = Vec::with_capacity(num_bindings);
for (_name, id, _value, typ) in bindings.iter() {
let mir_id = mz_expr::LocalId::new(id_gen.allocate_id());
mir_ids.push(mir_id);
let shadowed = cte_map.insert(
id.clone(),
CteDesc {
new_id: mir_id,
relation_type: RelationType::new(
outer_column_types
.iter()
.cloned()
.chain(typ.column_types.iter().cloned())
.collect::<Vec<_>>(),
),
outer_relation: get_outer.clone(),
},
);
shadowed_bindings.push((*id, shadowed));
}
let mut mir_values = Vec::with_capacity(num_bindings);
for (_name, _id, value, _typ) in bindings.into_iter() {
mir_values.push(value.applied_to(
id_gen,
get_outer.clone(),
col_map,
cte_map,
context,
)?);
}
let mir_body = body.applied_to(id_gen, get_outer, col_map, cte_map, context)?;
// Remove our bindings and reinstate any shadowed bindings.
for (id, shadowed) in shadowed_bindings {
if let Some(shadowed) = shadowed {
cte_map.insert(id, shadowed);
} else {
cte_map.remove(&id);
}
}
MirRelationExpr::LetRec {
ids: mir_ids,
values: mir_values,
// Copy the limit to each binding.
limits: repeat(limit).take(num_bindings).collect(),
body: Box::new(mir_body),
}
}
Project { input, outputs } => {
// Projections should be applied to the decorrelated `inner`, and to its columns,
// which means rebasing `outputs` to start `get_outer.arity()` columns later.
let input =
input.applied_to(id_gen, get_outer.clone(), col_map, cte_map, context)?;
let outputs = (0..get_outer.arity())
.chain(outputs.into_iter().map(|i| get_outer.arity() + i))
.collect::<Vec<_>>();
input.project(outputs)
}
Map { input, mut scalars } => {
// Scalar expressions may contain correlated subqueries. We must be cautious!
// We lower scalars in chunks, and must keep track of the
// arity of the HIR fragments lowered so far.
let mut lowered_arity = input.arity();
let mut input =
input.applied_to(id_gen, get_outer, col_map, cte_map, context)?;
// Lower subqueries in maximally sized batches, such as no subquery in the current
// batch depends on columns from the same batch.
// Note that subqueries in this projection may reference columns added by this
// Map operator, so we need to ensure these columns exist before lowering the
// subquery.
while !scalars.is_empty() {
let end_idx = scalars
.iter_mut()
.position(|s| {
let mut requires_nonexistent_column = false;
#[allow(deprecated)]
s.visit_columns(0, &mut |depth, col| {
if col.level == depth {
requires_nonexistent_column |= col.column >= lowered_arity
}
});
requires_nonexistent_column
})
.unwrap_or(scalars.len());
assert!(end_idx > 0, "a Map expression references itself or a later column; lowered_arity: {}, expressions: {:?}", lowered_arity, scalars);
lowered_arity = lowered_arity + end_idx;
let scalars = scalars.drain(0..end_idx).collect_vec();
let old_arity = input.arity();
let (with_subqueries, subquery_map) = HirScalarExpr::lower_subqueries(
&scalars, id_gen, col_map, cte_map, input, context,
)?;
input = with_subqueries;
// We will proceed sequentially through the scalar expressions, for each transforming
// the decorrelated `input` into a relation with potentially more columns capable of
// addressing the needs of the scalar expression.
// Having done so, we add the scalar value of interest and trim off any other newly
// added columns.
//
// The sequential traversal is present as expressions are allowed to depend on the
// values of prior expressions.
let mut scalar_columns = Vec::new();
for scalar in scalars {
let scalar = scalar.applied_to(
id_gen,
col_map,
cte_map,
&mut input,
&Some(&subquery_map),
context,
)?;
input = input.map_one(scalar);
scalar_columns.push(input.arity() - 1);
}
// Discard any new columns added by the lowering of the scalar expressions
input = input.project((0..old_arity).chain(scalar_columns).collect());
}
input
}
CallTable { func, exprs } => {
// FlatMap expressions may contain correlated subqueries. Unlike Map they are not
// allowed to refer to the results of previous expressions, and we have a simpler
// implementation that appends all relevant columns first, then applies the flatmap
// operator to the result, then strips off any columns introduce by subqueries.
let mut input = get_outer;
let old_arity = input.arity();
let exprs = exprs
.into_iter()
.map(|e| e.applied_to(id_gen, col_map, cte_map, &mut input, &None, context))
.collect::<Result<Vec<_>, _>>()?;
let new_arity = input.arity();
let output_arity = func.output_arity();
input = input.flat_map(func, exprs);
if old_arity != new_arity {
// this means we added some columns to handle subqueries, and now we need to get rid of them
input = input.project(
(0..old_arity)
.chain(new_arity..new_arity + output_arity)
.collect(),
);
}
input
}
Filter { input, predicates } => {
// Filter expressions may contain correlated subqueries.
// We extend `get_outer` with sufficient values to determine the value of the predicate,
// then filter the results, then strip off any columns that were added for this purpose.
let mut input =
input.applied_to(id_gen, get_outer, col_map, cte_map, context)?;
for predicate in predicates {
let old_arity = input.arity();
let predicate = predicate
.applied_to(id_gen, col_map, cte_map, &mut input, &None, context)?;
let new_arity = input.arity();
input = input.filter(vec![predicate]);
if old_arity != new_arity {
// this means we added some columns to handle subqueries, and now we need to get rid of them
input = input.project((0..old_arity).collect());
}
}
input
}
Join {
left,
right,
on,
kind,
} if right.is_correlated() => {
// A correlated join is a join in which the right expression has
// access to the columns in the left expression. It turns out
// this is *exactly* our branch operator, plus some additional
// null handling in the case of left joins. (Right and full
// lateral joins are not permitted.)
//
// As with normal joins, the `on` predicate may be correlated,
// and we treat it as a filter that follows the branch.
assert!(kind.can_be_correlated());
let left = left.applied_to(id_gen, get_outer, col_map, cte_map, context)?;
left.let_in(id_gen, |id_gen, get_left| {
let apply_requires_distinct_outer = false;
let mut join = branch(
id_gen,
get_left.clone(),
col_map,
cte_map,
*right,
apply_requires_distinct_outer,
context,
|id_gen, right, get_left, col_map, cte_map, context| {
right.applied_to(id_gen, get_left, col_map, cte_map, context)
},
)?;
// Plan the `on` predicate.
let old_arity = join.arity();
let on =
on.applied_to(id_gen, col_map, cte_map, &mut join, &None, context)?;
join = join.filter(vec![on]);
let new_arity = join.arity();
if old_arity != new_arity {
// This means we added some columns to handle
// subqueries, and now we need to get rid of them.
join = join.project((0..old_arity).collect());
}
// If a left join, reintroduce any rows from the left that
// are missing, with nulls filled in for the right columns.
if let JoinKind::LeftOuter { .. } = kind {
let default = join
.typ()
.column_types
.into_iter()
.skip(get_left.arity())
.map(|typ| (Datum::Null, typ.scalar_type))
.collect();
get_left.lookup(id_gen, join, default)
} else {
Ok::<_, PlanError>(join)
}
})?
}
Join {
left,
right,
on,
kind,
} => {
if context.config.enable_variadic_left_join_lowering {
// Attempt to extract a stack of left joins.
if let JoinKind::LeftOuter = kind {
let mut rights = vec![(&*right, &on)];
let mut left_test = &left;
while let Join {
left,
right,
on,
kind: JoinKind::LeftOuter,
} = &**left_test
{
rights.push((&**right, on));
left_test = left;
}
if rights.len() > 1 {
// Defensively clone `cte_map` as it may be mutated.
let cte_map_clone = cte_map.clone();
if let Ok(Some(magic)) = variadic_left::attempt_left_join_magic(
left_test,
rights,
id_gen,
get_outer.clone(),
col_map,
cte_map,
context,
) {
return Ok(magic);
} else {
cte_map.clone_from(&cte_map_clone);
}
}
}
}
// Both join expressions should be decorrelated, and then joined by their
// leading columns to form only those pairs corresponding to the same row
// of `get_outer`.
//
// The `on` predicate may contain correlated subqueries, and we treat it
// as though it was a filter, with the caveat that we also translate outer
// joins in this step. The post-filtration results need to be considered
// against the records present in the left and right (decorrelated) inputs,
// depending on the type of join.
let oa = get_outer.arity();
let left =
left.applied_to(id_gen, get_outer.clone(), col_map, cte_map, context)?;
let lt = left.typ().column_types.into_iter().skip(oa).collect_vec();
let la = lt.len();
left.let_in(id_gen, |id_gen, get_left| {
let right_col_map = col_map.enter_scope(0);
let right = right.applied_to(
id_gen,
get_outer.clone(),
&right_col_map,
cte_map,
context,
)?;
let rt = right.typ().column_types.into_iter().skip(oa).collect_vec();
let ra = rt.len();
right.let_in(id_gen, |id_gen, get_right| {
let mut product = SR::join(
vec![get_left.clone(), get_right.clone()],
(0..oa).map(|i| vec![(0, i), (1, i)]).collect(),
)
// Project away the repeated copy of get_outer's columns.
.project(
(0..(oa + la))
.chain((oa + la + oa)..(oa + la + oa + ra))
.collect(),
);
// Decorrelate and lower the `on` clause.
let on = on.applied_to(
id_gen,
col_map,
cte_map,
&mut product,
&None,
context,
)?;
// Collect the types of all subqueries appearing in
// the `on` clause. The subquery results were
// appended to `product` in the `on.applied_to(...)`
// call above.
let on_subquery_types = product
.typ()
.column_types
.drain(oa + la + ra..)
.collect_vec();
// Remember if `on` had any subqueries.
let on_has_subqueries = !on_subquery_types.is_empty();
// Attempt an efficient equijoin implementation, in which outer joins are
// more efficiently rendered than in general. This can return `None` if
// such a plan is not possible, for example if `on` does not describe an
// equijoin between columns of `left` and `right`.
if kind != JoinKind::Inner {
if let Some(joined) = attempt_outer_equijoin(
get_left.clone(),
get_right.clone(),
on.clone(),
on_subquery_types,
kind.clone(),
oa,
id_gen,
context,
)? {
if let Some(metrics) = context.metrics {
metrics.inc_outer_join_lowering("equi");
}
return Ok(joined);
}
}
// Otherwise, perform a more general join.
if let Some(metrics) = context.metrics {
metrics.inc_outer_join_lowering("general");
}
let mut join = product.filter(vec![on]);
if on_has_subqueries {
// This means that `on.applied_to(...)` appended
// some columns to handle subqueries, and now we
// need to get rid of them.
join = join.project((0..oa + la + ra).collect());
}
join.let_in(id_gen, |id_gen, get_join| {
let mut result = get_join.clone();
if let JoinKind::LeftOuter { .. } | JoinKind::FullOuter { .. } =
kind
{
let left_outer = get_left.clone().anti_lookup::<PlanError>(
id_gen,
get_join.clone(),
rt.into_iter()
.map(|typ| (Datum::Null, typ.scalar_type))
.collect(),
)?;
result = result.union(left_outer);
}
if let JoinKind::RightOuter | JoinKind::FullOuter = kind {
let right_outer = get_right
.clone()
.anti_lookup::<PlanError>(
id_gen,
get_join
// need to swap left and right to make the anti_lookup work
.project(
(0..oa)
.chain((oa + la)..(oa + la + ra))
.chain((oa)..(oa + la))
.collect(),
),
lt.into_iter()
.map(|typ| (Datum::Null, typ.scalar_type))
.collect(),
)?
// swap left and right back again
.project(
(0..oa)
.chain((oa + ra)..(oa + ra + la))
.chain((oa)..(oa + ra))
.collect(),
);
result = result.union(right_outer);
}
Ok::<MirRelationExpr, PlanError>(result)
})
})
})?
}
Union { base, inputs } => {
// Union is uncomplicated.
SR::Union {
base: Box::new(base.applied_to(
id_gen,
get_outer.clone(),
col_map,
cte_map,
context,
)?),
inputs: inputs
.into_iter()
.map(|input| {
input.applied_to(
id_gen,
get_outer.clone(),
col_map,
cte_map,
context,
)
})
.collect::<Result<Vec<_>, _>>()?,
}
}
Reduce {
input,
group_key,
aggregates,
expected_group_size,
} => {
// Reduce may contain expressions with correlated subqueries.
// In addition, here an empty reduction key signifies that we need to supply default values
// in the case that there are no results (as in a SQL aggregation without an explicit GROUP BY).
let mut input =
input.applied_to(id_gen, get_outer.clone(), col_map, cte_map, context)?;
let applied_group_key = (0..get_outer.arity())
.chain(group_key.iter().map(|i| get_outer.arity() + i))
.collect();
let applied_aggregates = aggregates
.into_iter()
.map(|aggregate| {
aggregate.applied_to(id_gen, col_map, cte_map, &mut input, context)
})
.collect::<Result<Vec<_>, _>>()?;
let input_type = input.typ();
let default = applied_aggregates
.iter()
.map(|agg| {
(
agg.func.default(),
agg.typ(&input_type.column_types).scalar_type,
)
})
.collect();
// NOTE we don't need to remove any extra columns from aggregate.applied_to above because the reduce will do that anyway
let mut reduced =
input.reduce(applied_group_key, applied_aggregates, expected_group_size);
// Introduce default values in the case the group key is empty.
if group_key.is_empty() {
reduced = get_outer.lookup::<PlanError>(id_gen, reduced, default)?;
}
reduced
}
Distinct { input } => {
// Distinct is uncomplicated.
input
.applied_to(id_gen, get_outer, col_map, cte_map, context)?
.distinct()
}
TopK {
input,
group_key,
order_key,
limit,
offset,
expected_group_size,
} => {
// TopK is uncomplicated, except that we must group by the columns of `get_outer` as well.
let mut input =
input.applied_to(id_gen, get_outer.clone(), col_map, cte_map, context)?;
let mut applied_group_key: Vec<_> = (0..get_outer.arity())
.chain(group_key.iter().map(|i| get_outer.arity() + i))
.collect();
let applied_order_key = order_key
.iter()
.map(|column_order| ColumnOrder {
column: column_order.column + get_outer.arity(),
desc: column_order.desc,
nulls_last: column_order.nulls_last,
})
.collect();
let old_arity = input.arity();
// Lower `limit`, which may introduce new columns if is a correlated subquery.
let mut limit_mir = None;
if let Some(limit) = limit {
limit_mir = Some(
limit
.applied_to(id_gen, col_map, cte_map, &mut input, &None, context)?,
);
}
let new_arity = input.arity();
// Extend the key to contain any new columns.
applied_group_key.extend(old_arity..new_arity);
let mut result = input.top_k(
applied_group_key,
applied_order_key,
limit_mir,
offset,
expected_group_size,
);
// If new columns were added for `limit` we must remove them.
if old_arity != new_arity {
result = result.project((0..old_arity).collect());
}
result
}
Negate { input } => {
// Negate is uncomplicated.
input
.applied_to(id_gen, get_outer, col_map, cte_map, context)?
.negate()
}
Threshold { input } => {
// Threshold is uncomplicated.
input
.applied_to(id_gen, get_outer, col_map, cte_map, context)?
.threshold()
}
})
})
}
}
impl HirScalarExpr {
/// Rewrite `self` into a `mz_expr::ScalarExpr` which can be applied to the modified `inner`.
///
/// This method is responsible for decorrelating subqueries in `self` by introducing further columns
/// to `inner`, and rewriting `self` to refer to its physical columns (specified by `usize` positions).
/// The most complicated logic is for the scalar expressions that involve subqueries, each of which are
/// documented in more detail closer to their logic.
///
/// This process presumes that `inner` is the result of decorrelation, meaning its first several columns
/// may be inherited from outer relations. The `col_map` column map should provide specific offsets where
/// each of these references can be found.
fn applied_to(
self,
id_gen: &mut mz_ore::id_gen::IdGen,
col_map: &ColumnMap,
cte_map: &mut CteMap,
inner: &mut MirRelationExpr,
subquery_map: &Option<&BTreeMap<HirScalarExpr, usize>>,
context: &Context,
) -> Result<MirScalarExpr, PlanError> {
maybe_grow(|| {
use MirScalarExpr as SS;
use HirScalarExpr::*;
if let Some(subquery_map) = subquery_map {
if let Some(col) = subquery_map.get(&self) {
return Ok(SS::Column(*col));
}
}
Ok::<MirScalarExpr, PlanError>(match self {
Column(col_ref) => SS::Column(col_map.get(&col_ref)),
Literal(row, typ) => SS::Literal(Ok(row), typ),
Parameter(_) => panic!("cannot decorrelate expression with unbound parameters"),
CallUnmaterializable(func) => SS::CallUnmaterializable(func),
CallUnary { func, expr } => SS::CallUnary {
func,
expr: Box::new(expr.applied_to(
id_gen,
col_map,
cte_map,
inner,
subquery_map,
context,
)?),
},
CallBinary { func, expr1, expr2 } => SS::CallBinary {
func,
expr1: Box::new(expr1.applied_to(
id_gen,
col_map,
cte_map,
inner,
subquery_map,
context,
)?),
expr2: Box::new(expr2.applied_to(
id_gen,
col_map,
cte_map,
inner,
subquery_map,
context,
)?),
},
CallVariadic { func, exprs } => SS::CallVariadic {
func,
exprs: exprs
.into_iter()
.map(|expr| {
expr.applied_to(id_gen, col_map, cte_map, inner, subquery_map, context)
})
.collect::<Result<Vec<_>, _>>()?,
},
If { cond, then, els } => {
// The `If` case is complicated by the fact that we do not want to
// apply the `then` or `else` logic to tuples that respectively do
// not or do pass the `cond` test. Our strategy is to independently
// decorrelate the `then` and `else` logic, and apply each to tuples
// that respectively pass and do not pass the `cond` logic (which is
// executed, and so decorrelated, for all tuples).
//
// Informally, we turn the `if` statement into:
//
// let then_case = inner.filter(cond).map(then);
// let else_case = inner.filter(!cond).map(else);
// return then_case.concat(else_case);
//
// We only require this if either expression would result in any
// computation beyond the expr itself, which we will interpret as
// "introduces additional columns". In the absence of correlation,
// we should just retain a `ScalarExpr::If` expression; the inverse
// transformation as above is complicated to recover after the fact,
// and we would benefit from not introducing the complexity.
let inner_arity = inner.arity();
let cond_expr =
cond.applied_to(id_gen, col_map, cte_map, inner, subquery_map, context)?;
// Defensive copies, in case we mangle these in decorrelation.
let inner_clone = inner.clone();
let then_clone = then.clone();
let else_clone = els.clone();
let cond_arity = inner.arity();
let then_expr =
then.applied_to(id_gen, col_map, cte_map, inner, subquery_map, context)?;
let else_expr =
els.applied_to(id_gen, col_map, cte_map, inner, subquery_map, context)?;
if cond_arity == inner.arity() {
// If no additional columns were added, we simply return the
// `If` variant with the updated expressions.
SS::If {
cond: Box::new(cond_expr),
then: Box::new(then_expr),
els: Box::new(else_expr),
}
} else {
// If columns were added, we need a more careful approach, as
// described above. First, we need to de-correlate each of
// the two expressions independently, and apply their cases
// as `MirRelationExpr::Map` operations.
*inner = inner_clone.let_in(id_gen, |id_gen, get_inner| {
// Restrict to records satisfying `cond_expr` and apply `then` as a map.
let mut then_inner = get_inner.clone().filter(vec![cond_expr.clone()]);
let then_expr = then_clone.applied_to(
id_gen,
col_map,
cte_map,
&mut then_inner,
subquery_map,
context,
)?;
let then_arity = then_inner.arity();
then_inner = then_inner
.map_one(then_expr)
.project((0..inner_arity).chain(Some(then_arity)).collect());
// Restrict to records not satisfying `cond_expr` and apply `els` as a map.
let mut else_inner = get_inner.filter(vec![SS::CallVariadic {
func: mz_expr::VariadicFunc::Or,
exprs: vec![
cond_expr
.clone()
.call_binary(SS::literal_false(), mz_expr::BinaryFunc::Eq),
cond_expr.clone().call_is_null(),
],
}]);
let else_expr = else_clone.applied_to(
id_gen,
col_map,
cte_map,
&mut else_inner,
subquery_map,
context,
)?;
let else_arity = else_inner.arity();
else_inner = else_inner
.map_one(else_expr)
.project((0..inner_arity).chain(Some(else_arity)).collect());
// concatenate the two results.
Ok::<MirRelationExpr, PlanError>(then_inner.union(else_inner))
})?;
SS::Column(inner_arity)
}
}
// Subqueries!
// These are surprisingly subtle. Things to be careful of:
// Anything in the subquery that cares about row counts (Reduce/Distinct/Negate/Threshold) must not:
// * change the row counts of the outer query
// * accidentally compute its own value using the row counts of the outer query
// Use `branch` to calculate the subquery once for each __distinct__ key in the outer
// query and then join the answers back on to the original rows of the outer query.
// When the subquery would return 0 rows for some row in the outer query, `subquery.applied_to(get_inner)` will not have any corresponding row.
// Use `lookup` if you need to add default values for cases when the subquery returns 0 rows.
Exists(expr) => {
let apply_requires_distinct_outer = true;
*inner = apply_existential_subquery(
id_gen,
inner.take_dangerous(),
col_map,
cte_map,
*expr,
apply_requires_distinct_outer,
context,
)?;
SS::Column(inner.arity() - 1)
}
Select(expr) => {
let apply_requires_distinct_outer = true;
*inner = apply_scalar_subquery(
id_gen,
inner.take_dangerous(),
col_map,
cte_map,
*expr,
apply_requires_distinct_outer,
context,
)?;
SS::Column(inner.arity() - 1)
}
Windowing(expr) => {
let partition_by = expr.partition_by;
let order_by = expr.order_by;
// argument lowering for scalar window functions
// (We need to specify the & _ in the arguments because of this problem:
// https://users.rust-lang.org/t/the-implementation-of-fnonce-is-not-general-enough/72141/3 )
let scalar_lower_args =
|_id_gen: &mut _,
_col_map: &_,
_cte_map: &mut _,
_get_inner: &mut _,
_subquery_map: &Option<&_>,
order_by_mir: Vec<MirScalarExpr>,
original_row_record,
original_row_record_type: ScalarType| {
let agg_input = MirScalarExpr::CallVariadic {
func: mz_expr::VariadicFunc::ListCreate {
elem_type: original_row_record_type.clone(),
},
exprs: vec![original_row_record],
};
let mut agg_input = vec![agg_input];
agg_input.extend(order_by_mir.clone());
let agg_input = MirScalarExpr::CallVariadic {
func: mz_expr::VariadicFunc::RecordCreate {
field_names: (0..agg_input.len())
.map(|_| ColumnName::from("?column?"))
.collect_vec(),
},
exprs: agg_input,
};
let list_type = ScalarType::List {
element_type: Box::new(original_row_record_type),
custom_id: None,
};
let agg_input_type = ScalarType::Record {
fields: std::iter::once(&list_type)
.map(|t| {
(ColumnName::from("?column?"), t.clone().nullable(false))
})
.collect(),
custom_id: None,
}
.nullable(false);
Ok((agg_input, agg_input_type))
};
// argument lowering for value window functions and aggregate window functions
let value_or_aggr_lower_args = |hir_encoded_args: Box<HirScalarExpr>| {
|id_gen: &mut _,
col_map: &_,
cte_map: &mut _,
get_inner: &mut _,
subquery_map: &Option<&_>,
order_by_mir: Vec<MirScalarExpr>,
original_row_record,
original_row_record_type| {
// Creates [((OriginalRow, EncodedArgs), OrderByExprs...)]
// Compute the encoded args for all rows
let mir_encoded_args = hir_encoded_args.applied_to(
id_gen,
col_map,
cte_map,
get_inner,
subquery_map,
context,
)?;
let mir_encoded_args_type = mir_encoded_args
.typ(&get_inner.typ().column_types)
.scalar_type;
// Build a new record that has two fields:
// 1. the original row in a record
// 2. the encoded args (which can be either a single value, or a record
// if the window function has multiple arguments, such as `lag`)
let fn_input_record_fields: Box<[_]> =
[original_row_record_type, mir_encoded_args_type]
.iter()
.map(|t| {
(ColumnName::from("?column?"), t.clone().nullable(false))
})
.collect();
let fn_input_record = MirScalarExpr::CallVariadic {
func: mz_expr::VariadicFunc::RecordCreate {
field_names: fn_input_record_fields
.iter()
.map(|(n, _)| n.clone())
.collect_vec(),
},
exprs: vec![original_row_record, mir_encoded_args],
};
let fn_input_record_type = ScalarType::Record {
fields: fn_input_record_fields,
custom_id: None,
}
.nullable(false);
// Build a new record with the record above + the ORDER BY exprs
// This follows the standard encoding of ORDER BY exprs used by aggregate functions
let mut agg_input = vec![fn_input_record];
agg_input.extend(order_by_mir.clone());
let agg_input = MirScalarExpr::CallVariadic {
func: mz_expr::VariadicFunc::RecordCreate {
field_names: (0..agg_input.len())
.map(|_| ColumnName::from("?column?"))
.collect_vec(),
},
exprs: agg_input,
};
let agg_input_type = ScalarType::Record {
fields: [(
ColumnName::from("?column?"),
fn_input_record_type.nullable(false),
)]
.into(),
custom_id: None,
}
.nullable(false);
Ok((agg_input, agg_input_type))
}
};
match expr.func {
WindowExprType::Scalar(scalar_window_expr) => {
let mir_aggr_func = scalar_window_expr.into_expr();
Self::window_func_applied_to(
id_gen,
col_map,
cte_map,
inner,
subquery_map,
partition_by,
order_by,
mir_aggr_func,
scalar_lower_args,
context,
)?
}
WindowExprType::Value(value_window_expr) => {
let (hir_encoded_args, mir_aggr_func) = value_window_expr.into_expr();
Self::window_func_applied_to(
id_gen,
col_map,
cte_map,
inner,
subquery_map,
partition_by,
order_by,
mir_aggr_func,
value_or_aggr_lower_args(hir_encoded_args),
context,
)?
}
WindowExprType::Aggregate(aggr_window_expr) => {
let (hir_encoded_args, mir_aggr_func) = aggr_window_expr.into_expr();
Self::window_func_applied_to(
id_gen,
col_map,
cte_map,
inner,
subquery_map,
partition_by,
order_by,
mir_aggr_func,
value_or_aggr_lower_args(hir_encoded_args),
context,
)?
}
}
}
})
})
}
fn window_func_applied_to<F>(
id_gen: &mut mz_ore::id_gen::IdGen,
col_map: &ColumnMap,
cte_map: &mut CteMap,
inner: &mut MirRelationExpr,
subquery_map: &Option<&BTreeMap<HirScalarExpr, usize>>,
partition_by: Vec<HirScalarExpr>,
order_by: Vec<HirScalarExpr>,
mir_aggr_func: AggregateFunc,
lower_args: F,
context: &Context,
) -> Result<MirScalarExpr, PlanError>
where
F: FnOnce(
&mut mz_ore::id_gen::IdGen,
&ColumnMap,
&mut CteMap,
&mut MirRelationExpr,
&Option<&BTreeMap<HirScalarExpr, usize>>,
Vec<MirScalarExpr>,
MirScalarExpr,
ScalarType,
) -> Result<(MirScalarExpr, ColumnType), PlanError>,
{
// Example MIRs for a window function (specifically, a window aggregation):
//
// CREATE TABLE t7(x INT, y INT);
//
// explain decorrelated plan for select sum(x*y) over (partition by x+y order by x-y, x/y) from t7;
//
// Decorrelated Plan
// Project (#3)
// Map (#2)
// Project (#3..=#5)
// Map (record_get[0](record_get[1](#2)), record_get[1](record_get[1](#2)), record_get[0](#2))
// FlatMap unnest_list(#1)
// Reduce group_by=[#2] aggregates=[window_agg[sum order_by=[#0 asc nulls_last, #1 asc nulls_last]](row(row(row(#0, #1), (#0 * #1)), (#0 - #1), (#0 / #1)))]
// Map ((#0 + #1))
// CrossJoin
// Constant
// - ()
// Get materialize.public.t7
//
// The same query after optimizations:
//
// explain select sum(x*y) over (partition by x+y order by x-y, x/y) from t7;
//
// Optimized Plan
// Explained Query:
// Project (#2)
// Map (record_get[0](#1))
// FlatMap unnest_list(#0)
// Project (#1)
// Reduce group_by=[(#0 + #1)] aggregates=[window_agg[sum order_by=[#0 asc nulls_last, #1 asc nulls_last]](row(row(row(#0, #1), (#0 * #1)), (#0 - #1), (#0 / #1)))]
// ReadStorage materialize.public.t7
//
// The `row(row(row(...), ...), ...)` stuff means the following:
// `row(row(row(<original row>), <arguments to window function>), <order by values>...)`
// - The <arguments to window function> can be either a single value or itself a
// `row` if there are multiple arguments.
// - The <order by values> are _not_ wrapped in a `row`, even if there are more than one
// ORDER BY columns.
// - The <original row> currently always captures the entire original row. This should
// improve when we make `ProjectionPushdown` smarter, see
// https://github.com/MaterializeInc/database-issues/issues/5090
//
// TODO:
// We should probably introduce some dedicated Datum constructor functions instead of `row`
// to make MIR plans and MIR construction/manipulation code more readable. Additionally, we
// might even introduce dedicated Datum enum variants, so that the rendering code also
// becomes more readable (and possibly slightly more performant).
*inner = inner
.take_dangerous()
.let_in(id_gen, |id_gen, mut get_inner| {
let order_by_mir = order_by
.into_iter()
.map(|o| {
o.applied_to(
id_gen,
col_map,
cte_map,
&mut get_inner,
subquery_map,
context,
)
})
.collect::<Result<Vec<_>, _>>()?;
// Record input arity here so that any group_keys that need to mutate get_inner
// don't add those columns to the aggregate input.
let input_arity = get_inner.typ().arity();
// The reduction that computes the window function must be keyed on the columns
// from the outer context, plus the expressions in the partition key. The current
// subquery will be 'executed' for every distinct row from the outer context so
// by putting the outer columns in the grouping key we isolate each re-execution.
let mut group_key = col_map
.inner
.iter()
.map(|(_, outer_col)| *outer_col)
.sorted()
.collect_vec();
for p in partition_by {
let key = p.applied_to(
id_gen,
col_map,
cte_map,
&mut get_inner,
subquery_map,
context,
)?;
if let MirScalarExpr::Column(c) = key {
group_key.push(c);
} else {
get_inner = get_inner.map_one(key);
group_key.push(get_inner.arity() - 1);
}
}
get_inner.let_in(id_gen, |id_gen, mut get_inner| {
let input_type = get_inner.typ();
// Original columns of the relation
let fields: Box<_> = input_type
.column_types
.iter()
.take(input_arity)
.map(|t| (ColumnName::from("?column?"), t.clone()))
.collect();
// Original row made into a record
let original_row_record = MirScalarExpr::CallVariadic {
func: mz_expr::VariadicFunc::RecordCreate {
field_names: fields.iter().map(|(name, _)| name.clone()).collect_vec(),
},
exprs: (0..input_arity).map(MirScalarExpr::Column).collect_vec(),
};
let original_row_record_type = ScalarType::Record {
fields,
custom_id: None,
};
let (agg_input, agg_input_type) = lower_args(
id_gen,
col_map,
cte_map,
&mut get_inner,
subquery_map,
order_by_mir,
original_row_record,
original_row_record_type,
)?;
let aggregate = mz_expr::AggregateExpr {
func: mir_aggr_func,
expr: agg_input,
distinct: false,
};
// Actually call reduce with the window function
// The output of the aggregation function should be a list of tuples that has
// the result in the first position, and the original row in the second position
let mut reduce = get_inner
.reduce(group_key.clone(), vec![aggregate.clone()], None)
.flat_map(
mz_expr::TableFunc::UnnestList {
el_typ: aggregate
.func
.output_type(agg_input_type)
.scalar_type
.unwrap_list_element_type()
.clone(),
},
vec![MirScalarExpr::Column(group_key.len())],
);
let record_col = reduce.arity() - 1;
// Unpack the record output by the window function
for c in 0..input_arity {
reduce = reduce.take_dangerous().map_one(MirScalarExpr::CallUnary {
func: mz_expr::UnaryFunc::RecordGet(mz_expr::func::RecordGet(c)),
expr: Box::new(MirScalarExpr::CallUnary {
func: mz_expr::UnaryFunc::RecordGet(mz_expr::func::RecordGet(1)),
expr: Box::new(MirScalarExpr::Column(record_col)),
}),
});
}
// Append the column with the result of the window function.
reduce = reduce.take_dangerous().map_one(MirScalarExpr::CallUnary {
func: mz_expr::UnaryFunc::RecordGet(mz_expr::func::RecordGet(0)),
expr: Box::new(MirScalarExpr::Column(record_col)),
});
let agg_col = record_col + 1 + input_arity;
Ok::<_, PlanError>(reduce.project((record_col + 1..agg_col + 1).collect_vec()))
})
})?;
Ok(MirScalarExpr::Column(inner.arity() - 1))
}
/// Applies the subqueries in the given list of scalar expressions to every distinct
/// value of the given relation and returns a join of the given relation with all
/// the subqueries found, and the mapping of scalar expressions with columns projected
/// by the returned join that will hold their results.
fn lower_subqueries(
exprs: &[Self],
id_gen: &mut mz_ore::id_gen::IdGen,
col_map: &ColumnMap,
cte_map: &mut CteMap,
inner: MirRelationExpr,
context: &Context,
) -> Result<(MirRelationExpr, BTreeMap<HirScalarExpr, usize>), PlanError> {
let mut subquery_map = BTreeMap::new();
let output = inner.let_in(id_gen, |id_gen, get_inner| {
let mut subqueries = Vec::new();
let distinct_inner = get_inner.clone().distinct();
for expr in exprs.iter() {
#[allow(deprecated)]
expr.visit_pre_post(
&mut |e| match e {
// For simplicity, subqueries within a conditional statement will be
// lowered when lowering the conditional expression.
HirScalarExpr::If { .. } => Some(vec![]),
_ => None,
},
&mut |e| match e {
HirScalarExpr::Select(expr) => {
let apply_requires_distinct_outer = false;
let subquery = apply_scalar_subquery(
id_gen,
distinct_inner.clone(),
col_map,
cte_map,
(**expr).clone(),
apply_requires_distinct_outer,
context,
)
.unwrap();
subqueries.push((e.clone(), subquery));
}
HirScalarExpr::Exists(expr) => {
let apply_requires_distinct_outer = false;
let subquery = apply_existential_subquery(
id_gen,
distinct_inner.clone(),
col_map,
cte_map,
(**expr).clone(),
apply_requires_distinct_outer,
context,
)
.unwrap();
subqueries.push((e.clone(), subquery));
}
_ => {}
},
);
}
if subqueries.is_empty() {
Ok::<MirRelationExpr, PlanError>(get_inner)
} else {
let inner_arity = get_inner.arity();
let mut total_arity = inner_arity;
let mut join_inputs = vec![get_inner];
for (expr, subquery) in subqueries.into_iter() {
// Avoid lowering duplicated subqueries
if !subquery_map.contains_key(&expr) {
let subquery_arity = subquery.arity();
assert_eq!(subquery_arity, inner_arity + 1);
join_inputs.push(subquery);
total_arity += subquery_arity;
// Column with the value of the subquery
subquery_map.insert(expr, total_arity - 1);
}
}
// Each subquery projects all the columns of the outer context (distinct_inner)
// plus 1 column, containing the result of the subquery. Those columns must be
// joined with the outer/main relation (get_inner).
let input_mapper = mz_expr::JoinInputMapper::new(&join_inputs);
let equivalences = (0..inner_arity)
.map(|col| {
join_inputs
.iter()
.enumerate()
.map(|(input, _)| {
MirScalarExpr::Column(input_mapper.map_column_to_global(col, input))
})
.collect_vec()
})
.collect_vec();
Ok(MirRelationExpr::join_scalars(join_inputs, equivalences))
}
})?;
Ok((output, subquery_map))
}
/// Rewrites `self` into a `mz_expr::ScalarExpr`.
pub fn lower_uncorrelated(self) -> Result<MirScalarExpr, PlanError> {
use MirScalarExpr as SS;
use HirScalarExpr::*;
Ok(match self {
Column(ColumnRef { level: 0, column }) => SS::Column(column),
Literal(datum, typ) => SS::Literal(Ok(datum), typ),
CallUnmaterializable(func) => SS::CallUnmaterializable(func),
CallUnary { func, expr } => SS::CallUnary {
func,
expr: Box::new(expr.lower_uncorrelated()?),
},
CallBinary { func, expr1, expr2 } => SS::CallBinary {
func,
expr1: Box::new(expr1.lower_uncorrelated()?),
expr2: Box::new(expr2.lower_uncorrelated()?),
},
CallVariadic { func, exprs } => SS::CallVariadic {
func,
exprs: exprs
.into_iter()
.map(|expr| expr.lower_uncorrelated())
.collect::<Result<_, _>>()?,
},
If { cond, then, els } => SS::If {
cond: Box::new(cond.lower_uncorrelated()?),
then: Box::new(then.lower_uncorrelated()?),
els: Box::new(els.lower_uncorrelated()?),
},
Select { .. } | Exists { .. } | Parameter(..) | Column(..) | Windowing(..) => {
sql_bail!("unexpected ScalarExpr in uncorrelated plan: {:?}", self);
}
})
}
}
/// Prepare to apply `inner` to `outer`. Note that `inner` is a correlated (SQL)
/// expression, while `outer` is a non-correlated (dataflow) expression. `inner`
/// will, in effect, be executed once for every distinct row in `outer`, and the
/// results will be joined with `outer`. Note that columns in `outer` that are
/// not depended upon by `inner` are thrown away before the distinct, so that we
/// don't perform needless computation of `inner`.
///
/// `branch` will inspect the contents of `inner` to determine whether `inner`
/// is not multiplicity sensitive (roughly, contains only maps, filters,
/// projections, and calls to table functions). If it is not multiplicity
/// sensitive, `branch` will *not* distinctify outer. If this is problematic,
/// e.g. because the `apply` callback itself introduces multiplicity-sensitive
/// operations that were not present in `inner`, then set
/// `apply_requires_distinct_outer` to ensure that `branch` chooses the plan
/// that distinctifies `outer`.
///
/// The caller must supply the `apply` function that applies the rewritten
/// `inner` to `outer`.
fn branch<F>(
id_gen: &mut mz_ore::id_gen::IdGen,
outer: MirRelationExpr,
col_map: &ColumnMap,
cte_map: &mut CteMap,
inner: HirRelationExpr,
apply_requires_distinct_outer: bool,
context: &Context,
apply: F,
) -> Result<MirRelationExpr, PlanError>
where
F: FnOnce(
&mut mz_ore::id_gen::IdGen,
HirRelationExpr,
MirRelationExpr,
&ColumnMap,
&mut CteMap,
&Context,
) -> Result<MirRelationExpr, PlanError>,
{
// TODO: It would be nice to have a version of this code w/o optimizations,
// at the least for purposes of understanding. It was difficult for one reader
// to understand the required properties of `outer` and `col_map`.
// If the inner expression is sufficiently simple, it is safe to apply it
// *directly* to outer, rather than applying it to the distinctified key
// (see below).
//
// As an example, consider the following two queries:
//
// CREATE TABLE t (a int, b int);
// SELECT a, series FROM t, generate_series(1, t.b) series;
//
// The "simple" path for the `SELECT` yields
//
// %0 =
// | Get t
// | FlatMap generate_series(1, #1)
//
// while the non-simple path yields:
//
// %0 =
// | Get t
//
// %1 =
// | Get t
// | Distinct group=(#1)
// | FlatMap generate_series(1, #0)
//
// %2 =
// | LeftJoin %1 %2 (= #1 #2)
//
// There is a tradeoff here: the simple plan is stateless, but the non-
// simple plan may do (much) less computation if there are only a few
// distinct values of `t.b`.
//
// We apply a very simple heuristic here and take the simple path if `inner`
// contains only maps, filters, projections, and calls to table functions.
// The intuition is that straightforward usage of table functions should
// take the simple path, while everything else should not. (In theory we
// think this transformation is valid as long as `inner` does not contain a
// Reduce, Distinct, or TopK node, but it is not always an optimization in
// the general case.)
//
// TODO(benesch): this should all be handled by a proper optimizer, but
// detecting the moment of decorrelation in the optimizer right now is too
// hard.
let mut is_simple = true;
#[allow(deprecated)]
inner.visit(0, &mut |expr, _| match expr {
HirRelationExpr::Constant { .. }
| HirRelationExpr::Project { .. }
| HirRelationExpr::Map { .. }
| HirRelationExpr::Filter { .. }
| HirRelationExpr::CallTable { .. } => (),
_ => is_simple = false,
});
if is_simple && !apply_requires_distinct_outer {
let new_col_map = col_map.enter_scope(outer.arity() - col_map.len());
return outer.let_in(id_gen, |id_gen, get_outer| {
apply(id_gen, inner, get_outer, &new_col_map, cte_map, context)
});
}
// The key consists of the columns from the outer expression upon which the
// inner relation depends. We discover these dependencies by walking the
// inner relation expression and looking for column references whose level
// escapes inner.
//
// At the end of this process, `key` contains the decorrelated position of
// each outer column, according to the passed-in `col_map`, and
// `new_col_map` maps each outer column to its new ordinal position in key.
let mut outer_cols = BTreeSet::new();
#[allow(deprecated)]
inner.visit_columns(0, &mut |depth, col| {
// Test if the column reference escapes the subquery.
if col.level > depth {
outer_cols.insert(ColumnRef {
level: col.level - depth,
column: col.column,
});
}
});
// Collect all the outer columns referenced by any CTE referenced by
// the inner relation.
#[allow(deprecated)]
inner.visit(0, &mut |e, _| match e {
HirRelationExpr::Get {
id: mz_expr::Id::Local(id),
..
} => {
if let Some(cte_desc) = cte_map.get(id) {
let cte_outer_arity = cte_desc.outer_relation.arity();
outer_cols.extend(
col_map
.inner
.iter()
.filter(|(_, position)| **position < cte_outer_arity)
.map(|(c, _)| {
// `col_map` maps column references to column positions in
// `outer`'s projection.
// `outer_cols` is meant to contain the external column
// references in `inner`.
// Since `inner` defines a new scope, any column reference
// in `col_map` is one level deeper when seen from within
// `inner`, hence the +1.
ColumnRef {
level: c.level + 1,
column: c.column,
}
}),
);
}
}
HirRelationExpr::Let { id, .. } => {
// Note: if ID uniqueness is not guaranteed, we can't use `visit` since
// we would need to remove the old CTE with the same ID temporarily while
// traversing the definition of the new CTE under the same ID.
assert!(!cte_map.contains_key(id));
}
_ => {}
});
let mut new_col_map = BTreeMap::new();
let mut key = vec![];
for col in outer_cols {
new_col_map.insert(col, key.len());
key.push(col_map.get(&ColumnRef {
// Note: `outer_cols` contains the external column references within `inner`.
// We must compensate for `inner`'s scope when translating column references
// as seen within `inner` to column references as seen from `outer`'s context,
// hence the -1.
level: col.level - 1,
column: col.column,
}));
}
let new_col_map = ColumnMap::new(new_col_map);
outer.let_in(id_gen, |id_gen, get_outer| {
let keyed_outer = if key.is_empty() {
// Don't depend on outer at all if the branch is not correlated,
// which yields vastly better query plans. Note that this is a bit
// weird in that the branch will be computed even if outer has no
// rows, whereas if it had been correlated it would not (and *could*
// not) have been computed if outer had no rows, but the callers of
// this function don't mind these somewhat-weird semantics.
MirRelationExpr::constant(vec![vec![]], RelationType::new(vec![]))
} else {
get_outer.clone().distinct_by(key.clone())
};
keyed_outer.let_in(id_gen, |id_gen, get_keyed_outer| {
let oa = get_outer.arity();
let branch = apply(
id_gen,
inner,
get_keyed_outer,
&new_col_map,
cte_map,
context,
)?;
let ba = branch.arity();
let joined = MirRelationExpr::join(
vec![get_outer.clone(), branch],
key.iter()
.enumerate()
.map(|(i, &k)| vec![(0, k), (1, i)])
.collect(),
)
// throw away the right-hand copy of the key we just joined on
.project((0..oa).chain((oa + key.len())..(oa + ba)).collect());
Ok(joined)
})
})
}
fn apply_scalar_subquery(
id_gen: &mut mz_ore::id_gen::IdGen,
outer: MirRelationExpr,
col_map: &ColumnMap,
cte_map: &mut CteMap,
scalar_subquery: HirRelationExpr,
apply_requires_distinct_outer: bool,
context: &Context,
) -> Result<MirRelationExpr, PlanError> {
branch(
id_gen,
outer,
col_map,
cte_map,
scalar_subquery,
apply_requires_distinct_outer,
context,
|id_gen, expr, get_inner, col_map, cte_map, context| {
// compute for every row in get_inner
let select = expr.applied_to(id_gen, get_inner.clone(), col_map, cte_map, context)?;
let col_type = select.typ().column_types.into_last();
let inner_arity = get_inner.arity();
// We must determine a count for each `get_inner` prefix,
// and report an error if that count exceeds one.
let guarded = select.let_in(id_gen, |_id_gen, get_select| {
// Count for each `get_inner` prefix.
let counts = get_select.clone().reduce(
(0..inner_arity).collect::<Vec<_>>(),
vec![mz_expr::AggregateExpr {
func: mz_expr::AggregateFunc::Count,
expr: MirScalarExpr::literal_true(),
distinct: false,
}],
None,
);
// Errors should result from counts > 1.
let errors = counts
.filter(vec![MirScalarExpr::Column(inner_arity).call_binary(
MirScalarExpr::literal_ok(Datum::Int64(1), ScalarType::Int64),
mz_expr::BinaryFunc::Gt,
)])
.project((0..inner_arity).collect::<Vec<_>>())
.map_one(MirScalarExpr::literal(
Err(mz_expr::EvalError::MultipleRowsFromSubquery),
col_type.clone().scalar_type,
));
// Return `get_select` and any errors added in.
Ok::<_, PlanError>(get_select.union(errors))
})?;
// append Null to anything that didn't return any rows
let default = vec![(Datum::Null, col_type.scalar_type)];
get_inner.lookup(id_gen, guarded, default)
},
)
}
fn apply_existential_subquery(
id_gen: &mut mz_ore::id_gen::IdGen,
outer: MirRelationExpr,
col_map: &ColumnMap,
cte_map: &mut CteMap,
subquery_expr: HirRelationExpr,
apply_requires_distinct_outer: bool,
context: &Context,
) -> Result<MirRelationExpr, PlanError> {
branch(
id_gen,
outer,
col_map,
cte_map,
subquery_expr,
apply_requires_distinct_outer,
context,
|id_gen, expr, get_inner, col_map, cte_map, context| {
let exists = expr
// compute for every row in get_inner
.applied_to(id_gen, get_inner.clone(), col_map, cte_map, context)?
// throw away actual values and just remember whether or not there were __any__ rows
.distinct_by((0..get_inner.arity()).collect())
// Append true to anything that returned any rows.
.map(vec![MirScalarExpr::literal_true()]);
// append False to anything that didn't return any rows
get_inner.lookup(id_gen, exists, vec![(Datum::False, ScalarType::Bool)])
},
)
}
impl AggregateExpr {
fn applied_to(
self,
id_gen: &mut mz_ore::id_gen::IdGen,
col_map: &ColumnMap,
cte_map: &mut CteMap,
inner: &mut MirRelationExpr,
context: &Context,
) -> Result<mz_expr::AggregateExpr, PlanError> {
let AggregateExpr {
func,
expr,
distinct,
} = self;
Ok(mz_expr::AggregateExpr {
func: func.into_expr(),
expr: expr.applied_to(id_gen, col_map, cte_map, inner, &None, context)?,
distinct,
})
}
}
/// Attempts an efficient outer join, if `on` has equijoin structure.
///
/// Both `left` and `right` are decorrelated inputs.
///
/// The first `oa` columns correspond to an outer context: we should do the
/// outer join independently for each prefix. In the case that `on` contains
/// just some equality tests between columns of `left` and `right` and some
/// local predicates, we can employ a relatively simple plan.
///
/// The last `on_subquery_types.len()` columns correspond to results from
/// subqueries defined in the `on` clause - we treat those as theta-join
/// conditions that prohibit the use of the simple plan attempted here.
fn attempt_outer_equijoin(
left: MirRelationExpr,
right: MirRelationExpr,
on: MirScalarExpr,
on_subquery_types: Vec<ColumnType>,
kind: JoinKind,
oa: usize,
id_gen: &mut mz_ore::id_gen::IdGen,
context: &Context,
) -> Result<Option<MirRelationExpr>, PlanError> {
// TODO(database-issues#6827): In theory, we can be smarter and also handle `on`
// predicates that reference subqueries as long as these subqueries don't
// reference `left` and `right` at the same time.
//
// TODO(database-issues#6828): This code can be improved as follows:
//
// 1. Move the `canonicalize_predicates(...)` call to `applied_to`.
// 2. Use the canonicalized `on` predicate in the non-equijoin based
// lowering strategy.
// 3. Move the `OnPredicates::new(...)` call to `applied_to`.
// 4. Pass the classified `OnPredicates` as a parameter.
// 5. Guard calls of this function with `on_predicates.is_equijoin()`.
//
// Steps (1 + 2) require further investigation because we might change the
// error semantics in case the `on` predicate contains a literal error..
let l_type = left.typ();
let r_type = right.typ();
let la = l_type.column_types.len() - oa;
let ra = r_type.column_types.len() - oa;
let sa = on_subquery_types.len();
// The output type contains [outer, left, right, sa] attributes.
let mut output_type = Vec::with_capacity(oa + la + ra + sa);
output_type.extend(l_type.column_types);
output_type.extend(r_type.column_types.into_iter().skip(oa));
output_type.extend(on_subquery_types);
// Generally healthy to do, but specifically `USING` conditions sometimes
// put an `AND true` at the end of the `ON` condition.
//
// TODO(aalexandrov): maybe we should already be doing this in `applied_to`.
// However, in that case it's not clear that we won't see regressions if
// `on` simplifies to a literal error.
let mut on = vec![on];
mz_expr::canonicalize::canonicalize_predicates(&mut on, &output_type);
// Form the left and right types without the outer attributes.
output_type.drain(0..oa);
let lt = output_type.drain(0..la).collect_vec();
let rt = output_type.drain(0..ra).collect_vec();
assert!(output_type.len() == sa);
let on_predicates = OnPredicates::new(oa, la, ra, sa, on.clone(), context);
if !on_predicates.is_equijoin(context) {
return Ok(None);
}
// If we've gotten this far, we can do the clever thing.
// We'll want to use left and right multiple times
let result = left.let_in(id_gen, |id_gen, get_left| {
right.let_in(id_gen, |id_gen, get_right| {
// TODO: we know that we can re-use the arrangements of left and right
// needed for the inner join with each of the conditional outer joins.
// It is not clear whether we should hint that, or just let the planner
// and optimizer run and see what happens.
// We'll want the inner join (minus repeated columns)
let join = MirRelationExpr::join(
vec![get_left.clone(), get_right.clone()],
(0..oa).map(|i| vec![(0, i), (1, i)]).collect(),
)
// remove those columns from `right` repeating the first `oa` columns.
.project(
(0..(oa + la))
.chain((oa + la + oa)..(oa + la + oa + ra))
.collect(),
)
// apply the filter constraints here, to ensure nulls are not matched.
.filter(on);
// We'll want to re-use the results of the join multiple times.
join.let_in(id_gen, |id_gen, get_join| {
let mut result = get_join.clone();
// A collection of keys present in both left and right collections.
let join_keys = on_predicates.join_keys();
let both_keys_arity = join_keys.len();
let both_keys = get_join.restrict(join_keys).distinct();
// The plan is now to determine the left and right rows matched in the
// inner join, subtract them from left and right respectively, pad what
// remains with nulls, and fold them in to `result`.
both_keys.let_in(id_gen, |_id_gen, get_both| {
if let JoinKind::LeftOuter { .. } | JoinKind::FullOuter = kind {
// Rows in `left` matched in the inner equijoin. This is
// a semi-join between `left` and `both_keys`.
let left_present = MirRelationExpr::join_scalars(
vec![
get_left
.clone()
// Push local predicates.
.filter(on_predicates.lhs()),
get_both.clone(),
],
itertools::zip_eq(
on_predicates.eq_lhs(),
(0..both_keys_arity).map(|k| MirScalarExpr::column(oa + la + k)),
)
.map(|(l_key, b_key)| [l_key, b_key].to_vec())
.collect(),
)
.project((0..(oa + la)).collect());
// Determine the types of nulls to use as filler.
let right_fill = rt
.into_iter()
.map(|typ| MirScalarExpr::literal_null(typ.scalar_type))
.collect();
// Add to `result` absent elements, filled with typed nulls.
result = left_present
.negate()
.union(get_left.clone())
.map(right_fill)
.union(result);
}
if let JoinKind::RightOuter | JoinKind::FullOuter = kind {
// Rows in `right` matched in the inner equijoin. This
// is a semi-join between `right` and `both_keys`.
let right_present = MirRelationExpr::join_scalars(
vec![
get_right
.clone()
// Push local predicates.
.filter(on_predicates.rhs()),
get_both,
],
itertools::zip_eq(
on_predicates.eq_rhs(),
(0..both_keys_arity).map(|k| MirScalarExpr::column(oa + ra + k)),
)
.map(|(r_key, b_key)| [r_key, b_key].to_vec())
.collect(),
)
.project((0..(oa + ra)).collect());
// Determine the types of nulls to use as filler.
let left_fill = lt
.into_iter()
.map(|typ| MirScalarExpr::literal_null(typ.scalar_type))
.collect();
// Add to `result` absent elements, prepended with typed nulls.
result = right_present
.negate()
.union(get_right.clone())
.map(left_fill)
// Permute left fill before right values.
.project(
itertools::chain!(
0..oa, // Preserve `outer`.
oa + ra..oa + la + ra, // Increment the next `la` cols by `ra`.
oa..oa + ra // Decrement the next `ra` cols by `la`.
)
.collect(),
)
.union(result)
}
Ok::<_, PlanError>(result)
})
})
})
})?;
Ok(Some(result))
}
/// A struct that represents the predicates in the `on` clause in a form
/// suitable for efficient planning outer joins with equijoin predicates.
struct OnPredicates {
/// A store for classified `ON` predicates.
///
/// Predicates that reference a single side are adjusted to assume an
/// `outer × <side>` schema.
predicates: Vec<OnPredicate>,
/// Number of outer context columns.
oa: usize,
}
impl OnPredicates {
const I_OUT: usize = 0; // outer context input position
const I_LHS: usize = 1; // lhs input position
const I_RHS: usize = 2; // rhs input position
const I_SUB: usize = 3; // on subqueries input position
/// Classify the predicates in the `on` clause of an outer join.
///
/// The other parameters are arities of the input parts:
///
/// - `oa` is the arity of the `outer` context.
/// - `la` is the arity of the `left` input.
/// - `ra` is the arity of the `right` input.
/// - `sa` is the arity of the `on` subqueries.
///
/// The constructor assumes that:
///
/// 1. The `on` parameter will be applied on a result that has the following
/// schema `outer × left × right × on_subqueries`.
/// 2. The `on` parameter is already adjusted to assume that schema.
/// 3. The `on` parameter is obtained by canonicalizing the original `on:
/// MirScalarExpr` with `canonicalize_predicates`.
fn new(
oa: usize,
la: usize,
ra: usize,
sa: usize,
on: Vec<MirScalarExpr>,
_context: &Context,
) -> Self {
use mz_expr::BinaryFunc::Eq;
// Re-bind those locally for more compact pattern matching.
const I_LHS: usize = OnPredicates::I_LHS;
const I_RHS: usize = OnPredicates::I_RHS;
// Self parameters.
let mut predicates = Vec::with_capacity(on.len());
// Helpers for populating `predicates`.
let inner_join_mapper = mz_expr::JoinInputMapper::new_from_input_arities([oa, la, ra, sa]);
let rhs_permutation = itertools::chain!(0..oa + la, oa..oa + ra).collect::<Vec<_>>();
let lookup_inputs = |expr: &MirScalarExpr| -> Vec<usize> {
inner_join_mapper
.lookup_inputs(expr)
.filter(|&i| i != Self::I_OUT)
.collect()
};
let has_subquery_refs = |expr: &MirScalarExpr| -> bool {
inner_join_mapper
.lookup_inputs(expr)
.any(|i| i == Self::I_SUB)
};
// Iterate over `on` elements and populate `predicates`.
for mut predicate in on {
if predicate.might_error() {
tracing::debug!(case = "thetajoin (error)", "OnPredicates::new");
// Treat predicates that can produce a literal error as Theta.
predicates.push(OnPredicate::Theta(predicate));
} else if has_subquery_refs(&predicate) {
tracing::debug!(case = "thetajoin (subquery)", "OnPredicates::new");
// Treat predicates referencing an `on` subquery as Theta.
predicates.push(OnPredicate::Theta(predicate));
} else if let MirScalarExpr::CallBinary {
func: Eq,
expr1,
expr2,
} = &mut predicate
{
// Obtain the non-outer inputs referenced by each side.
let inputs1 = lookup_inputs(expr1);
let inputs2 = lookup_inputs(expr2);
match (&inputs1[..], &inputs2[..]) {
// Neither side references an input. This could be a
// constant expression or an expression that depends only on
// the outer context.
([], []) => {
predicates.push(OnPredicate::Const(predicate));
}
// Both sides reference different inputs.
([I_LHS], [I_RHS]) => {
let lhs = expr1.take();
let mut rhs = expr2.take();
rhs.permute(&rhs_permutation);
predicates.push(OnPredicate::Eq(lhs.clone(), rhs.clone()));
predicates.push(OnPredicate::LhsConsequence(lhs.call_is_null().not()));
predicates.push(OnPredicate::RhsConsequence(rhs.call_is_null().not()));
}
// Both sides reference different inputs (swapped).
([I_RHS], [I_LHS]) => {
let lhs = expr2.take();
let mut rhs = expr1.take();
rhs.permute(&rhs_permutation);
predicates.push(OnPredicate::Eq(lhs.clone(), rhs.clone()));
predicates.push(OnPredicate::LhsConsequence(lhs.call_is_null().not()));
predicates.push(OnPredicate::RhsConsequence(rhs.call_is_null().not()));
}
// Both sides reference the left input or no input.
([I_LHS], [I_LHS]) | ([I_LHS], []) | ([], [I_LHS]) => {
predicates.push(OnPredicate::Lhs(predicate));
}
// Both sides reference the right input or no input.
([I_RHS], [I_RHS]) | ([I_RHS], []) | ([], [I_RHS]) => {
predicate.permute(&rhs_permutation);
predicates.push(OnPredicate::Rhs(predicate));
}
// At least one side references more than one input.
_ => {
tracing::debug!(case = "thetajoin (eq)", "OnPredicates::new");
predicates.push(OnPredicate::Theta(predicate));
}
}
} else {
// Obtain the non-outer inputs referenced by this predicate.
let inputs = lookup_inputs(&predicate);
match &inputs[..] {
// The predicate references no inputs. This could be a
// constant expression or an expression that depends only on
// the outer context.
[] => {
predicates.push(OnPredicate::Const(predicate));
}
// The predicate references only the left input.
[I_LHS] => {
predicates.push(OnPredicate::Lhs(predicate));
}
// The predicate references only the right input.
[I_RHS] => {
predicate.permute(&rhs_permutation);
predicates.push(OnPredicate::Rhs(predicate));
}
// The predicate references both inputs.
_ => {
tracing::debug!(case = "thetajoin (non-eq)", "OnPredicates::new");
predicates.push(OnPredicate::Theta(predicate));
}
}
}
}
Self { predicates, oa }
}
/// Check if the predicates can be lowered with an equijoin-based strategy.
fn is_equijoin(&self, context: &Context) -> bool {
// Count each `OnPredicate` variant in `self.predicates`.
let (const_cnt, lhs_cnt, rhs_cnt, eq_cnt, eq_cols, theta_cnt) =
self.predicates.iter().fold(
(0, 0, 0, 0, 0, 0),
|(const_cnt, lhs_cnt, rhs_cnt, eq_cnt, eq_cols, theta_cnt), p| {
(
const_cnt + usize::from(matches!(p, OnPredicate::Const(..))),
lhs_cnt + usize::from(matches!(p, OnPredicate::Lhs(..))),
rhs_cnt + usize::from(matches!(p, OnPredicate::Rhs(..))),
eq_cnt + usize::from(matches!(p, OnPredicate::Eq(..))),
eq_cols + usize::from(matches!(p, OnPredicate::Eq(lhs, rhs) if lhs.is_column() && rhs.is_column())),
theta_cnt + usize::from(matches!(p, OnPredicate::Theta(..))),
)
},
);
let is_equijion = if context.config.enable_new_outer_join_lowering {
// New classifier.
eq_cnt > 0 && theta_cnt == 0
} else {
// Old classifier.
eq_cnt > 0 && eq_cnt == eq_cols && theta_cnt + const_cnt + lhs_cnt + rhs_cnt == 0
};
// Log an entry only if this is an equijoin according to the new classifier.
if eq_cnt > 0 && theta_cnt == 0 {
tracing::debug!(
const_cnt,
lhs_cnt,
rhs_cnt,
eq_cnt,
eq_cols,
theta_cnt,
"OnPredicates::is_equijoin"
);
}
is_equijion
}
/// Return an [`MirRelationExpr`] list that represents the keys for the
/// equijoin. The list will contain the outer columns as a prefix.
fn join_keys(&self) -> JoinKeys {
// We could return either the `lhs` or the `rhs` of the keys used to
// form the inner join as they are equated by the join condition.
let join_keys = self.eq_lhs().collect::<Vec<_>>();
if join_keys.iter().all(|k| k.is_column()) {
tracing::debug!(case = "outputs", "OnPredicates::join_keys");
JoinKeys::Outputs(join_keys.iter().flat_map(|k| k.as_column()).collect())
} else {
tracing::debug!(case = "scalars", "OnPredicates::join_keys");
JoinKeys::Scalars(join_keys)
}
}
/// Return an iterator over the left-hand sides of all [`OnPredicate::Eq`]
/// conditions in the predicates list.
///
/// The iterator will start with column references to the outer columns as a
/// prefix.
fn eq_lhs(&self) -> impl Iterator<Item = MirScalarExpr> + '_ {
itertools::chain(
(0..self.oa).map(MirScalarExpr::column),
self.predicates.iter().filter_map(|e| match e {
OnPredicate::Eq(lhs, _) => Some(lhs.clone()),
_ => None,
}),
)
}
/// Return an iterator over the right-hand sides of all [`OnPredicate::Eq`]
/// conditions in the predicates list.
///
/// The iterator will start with column references to the outer columns as a
/// prefix.
fn eq_rhs(&self) -> impl Iterator<Item = MirScalarExpr> + '_ {
itertools::chain(
(0..self.oa).map(MirScalarExpr::column),
self.predicates.iter().filter_map(|e| match e {
OnPredicate::Eq(_, rhs) => Some(rhs.clone()),
_ => None,
}),
)
}
/// Return an iterator over the [`OnPredicate::Lhs`], [`OnPredicate::LhsConsequence`] and
/// [`OnPredicate::Const`] conditions in the predicates list.
fn lhs(&self) -> impl Iterator<Item = MirScalarExpr> + '_ {
self.predicates.iter().filter_map(|p| match p {
// We treat Const predicates local to both inputs.
OnPredicate::Const(p) => Some(p.clone()),
OnPredicate::Lhs(p) => Some(p.clone()),
OnPredicate::LhsConsequence(p) => Some(p.clone()),
_ => None,
})
}
/// Return an iterator over the [`OnPredicate::Rhs`], [`OnPredicate::RhsConsequence`] and
/// [`OnPredicate::Const`] conditions in the predicates list.
fn rhs(&self) -> impl Iterator<Item = MirScalarExpr> + '_ {
self.predicates.iter().filter_map(|p| match p {
// We treat Const predicates local to both inputs.
OnPredicate::Const(p) => Some(p.clone()),
OnPredicate::Rhs(p) => Some(p.clone()),
OnPredicate::RhsConsequence(p) => Some(p.clone()),
_ => None,
})
}
}
enum OnPredicate {
// A predicate that is either constant or references only outer columns.
Const(MirScalarExpr),
// A local predicate on the left-hand side of the join, i.e., it references only the left input
// and possibly outer columns.
//
// This is one of the original predicates from the ON clause.
//
// One _must_ apply this predicate.
Lhs(MirScalarExpr),
// A local predicate on the left-hand side of the join, i.e., it references only the left input
// and possibly outer columns.
//
// This is not one of the original predicates from the ON clause, but is just a consequence
// of an original predicate in the ON clause, where the original predicate references both
// inputs, but the consequence references only the left input.
//
// For example, the original predicate `input1.x = input2.a` has the consequence
// `input1.x IS NOT NULL`. Applying such a consequence before the input is fed into the join
// prevents null skew, and also makes more CSE opportunities available when the left input's key
// doesn't have a NOT NULL constraint, saving us an arrangement.
//
// Applying the predicate is optional, because the original predicate will be applied anyway.
LhsConsequence(MirScalarExpr),
// A local predicate on the right-hand side of the join.
//
// This is one of the original predicates from the ON clause.
//
// One _must_ apply this predicate.
Rhs(MirScalarExpr),
// A consequence of an original ON predicate, see above.
RhsConsequence(MirScalarExpr),
// An equality predicate between the two sides.
Eq(MirScalarExpr, MirScalarExpr),
// a non-equality predicate between the two sides.
#[allow(dead_code)]
Theta(MirScalarExpr),
}
/// A set of join keys referencing an input.
///
/// This is used in the [`MirRelationExpr::Join`] lowering code in order to
/// avoid changes (and thereby possible regressions) in plans that have equijoin
/// predicates consisting only of column refs.
///
/// If we were running `CanonicalizeMfp` as part of `NormalizeOps` we might be
/// able to get rid of this code, but as it stands `Map` simplification seems
/// more cumbersome than `Project` simplification, so do this just to be sure.
enum JoinKeys {
// A predicate that is either constant or references only outer columns.
Outputs(Vec<usize>),
// A local predicate on the left-hand side of the join.
Scalars(Vec<MirScalarExpr>),
}
impl JoinKeys {
fn len(&self) -> usize {
match self {
JoinKeys::Outputs(outputs) => outputs.len(),
JoinKeys::Scalars(scalars) => scalars.len(),
}
}
}
/// Extension methods for [`MirRelationExpr`] required in the HIR ⇒ MIR lowering
/// code.
trait LoweringExt {
/// See [`MirRelationExpr::restrict`].
fn restrict(self, join_keys: JoinKeys) -> Self;
}
impl LoweringExt for MirRelationExpr {
/// Restrict the set of columns of an input to the sequence of [`JoinKeys`].
fn restrict(self, join_keys: JoinKeys) -> Self {
let num_keys = join_keys.len();
match join_keys {
JoinKeys::Outputs(outputs) => self.project(outputs),
JoinKeys::Scalars(scalars) => {
let input_arity = self.arity();
let outputs = (input_arity..input_arity + num_keys).collect();
self.map(scalars).project(outputs)
}
}
}
}