Module usefulness

Match exhaustiveness and redundancy algorithm

This file contains the logic for exhaustiveness and usefulness checking for pattern-matching. Specifically, given a list of patterns in a match, we can tell whether: (a) a given pattern is redundant (b) the patterns cover every possible value for the type (exhaustiveness)

The algorithm implemented here is inspired from the one described in this paper. We have however changed it in various ways to accommodate the variety of patterns that Rust supports. We thus explain our version here, without being as precise.

Fun fact: computing exhaustiveness is NP-complete, because we can encode a SAT problem as an exhaustiveness problem. See here for the fun details.

Summary

The algorithm is given as input a list of patterns, one for each arm of a match, and computes the following:

• a set of values that match none of the patterns (if any),
• for each subpattern (taking into account or-patterns), whether removing it would change anything about how the match executes, i.e. whether it is useful/not redundant.

To a first approximation, the algorithm works by exploring all possible values for the type being matched on, and determining which arm(s) catch which value. To make this tractable we cleverly group together values, as we’ll see below.

The entrypoint of this file is the compute_match_usefulness function, which computes usefulness for each subpattern and exhaustiveness for the whole match.

In this page we explain the necessary concepts to understand how the algorithm works.

Usefulness

The central concept of this file is the notion of “usefulness”. Given some patterns p_1 .. p_n, a pattern q is said to be useful if there is a value that is matched by q and by none of the p_i. We write usefulness(p_1 .. p_n, q) for a function that returns a list of such values. The aim of this file is to compute it efficiently.

This is enough to compute usefulness: a pattern in a match expression is redundant iff it is not useful w.r.t. the patterns above it:

match Some(0u32) {
    Some(0..100) => {},
    Some(90..190) => {}, // useful: `Some(150)` is matched by this but not the branch above
    Some(50..150) => {}, // redundant: all the values this matches are already matched by
                         //   the branches above
    None => {},          // useful: `None` is matched by this but not the branches above
}

This is also enough to compute exhaustiveness: a match is exhaustive iff the wildcard _ pattern is not useful w.r.t. the patterns in the match. The values returned by usefulness are used to tell the user which values are missing.

match x {
    None => {},
    Some(0) => {},
    // not exhaustive: `_` is useful because it matches `Some(1)`
}

Constructors and fields

In the value Pair(Some(0), true), Pair is called the constructor of the value, and Some(0) and true are its fields. Every matcheable value can be decomposed in this way. Examples of constructors are: Some, None, (,) (the 2-tuple constructor), Foo {..} (the constructor for a struct Foo), and 2 (the constructor for the number 2).

Each constructor takes a fixed number of fields; this is called its arity. Pair and (,) have arity 2, Some has arity 1, None and 42 have arity 0. Each type has a known set of constructors. Some types have many constructors (like u64) or even an infinitely many (like &str and &[T]).

Patterns are similar: Pair(Some(_), _) has constructor Pair and two fields. The difference is that we get some extra pattern-only constructors, namely: the wildcard _, variable bindings, integer ranges like 0..=10, and variable-length slices like [_, .., _]. We treat or-patterns separately, see the dedicated section below.

Now to check if a value v matches a pattern p, we check if v’s constructor matches p’s constructor, then recursively compare their fields if necessary. A few representative examples:

• matches!(v, _) := true
• matches!((v0, v1), (p0, p1)) := matches!(v0, p0) && matches!(v1, p1)
• matches!(Foo { bar: v0, baz: v1 }, Foo { bar: p0, baz: p1 }) := matches!(v0, p0) && matches!(v1, p1)
• matches!(Ok(v0), Ok(p0)) := matches!(v0, p0)
• matches!(Ok(v0), Err(p0)) := false (incompatible variants)
• matches!(v, 1..=100) := matches!(v, 1) || ... || matches!(v, 100)
• matches!([v0], [p0, .., p1]) := false (incompatible lengths)
• matches!([v0, v1, v2], [p0, .., p1]) := matches!(v0, p0) && matches!(v2, p1)

Constructors and relevant operations are defined in the crate::constructor module. A representation of patterns that uses constructors is available in crate::pat. The question of whether a constructor is matched by another one is answered by Constructor::is_covered_by.

Note 1: variable bindings (like the x in Some(x)) match anything, so we treat them as wildcards. Note 2: this only applies to matcheable values. For example a value of type Rc<u64> can’t be deconstructed that way.

Specialization

The examples in the previous section motivate the operation at the heart of the algorithm: “specialization”. It captures this idea of “removing one layer of constructor”.

specialize(c, p) takes a value-only constructor c and a pattern p, and returns a pattern-tuple or nothing. It works as follows:

• Specializing for the wrong constructor returns nothing

  • specialize(None, Some(p0)) := <nothing>
  • specialize([,,,], [p0]) := <nothing>

• Specializing for the correct constructor returns a tuple of the fields

  • specialize(Variant1, Variant1(p0, p1, p2)) := (p0, p1, p2)
  • specialize(Foo{ bar, baz, quz }, Foo { bar: p0, baz: p1, .. }) := (p0, p1, _)
  • specialize([,,,], [p0, .., p1]) := (p0, _, _, p1)

We get the following property: for any values v_1, .., v_n of appropriate types, we have:

matches!(c(v_1, .., v_n), p)
<=> specialize(c, p) returns something
    && matches!((v_1, .., v_n), specialize(c, p))

We also extend specialization to pattern-tuples by applying it to the first pattern: specialize(c, (p_0, .., p_n)) := specialize(c, p_0) ++ (p_1, .., p_m) where ++ is concatenation of tuples.

The previous property extends to pattern-tuples:

matches!((c(v_1, .., v_n), w_1, .., w_m), (p_0, p_1, .., p_m))
<=> specialize(c, p_0) does not error
    && matches!((v_1, .., v_n, w_1, .., w_m), specialize(c, (p_0, p_1, .., p_m)))

Whether specialization returns something or not is given by Constructor::is_covered_by. Specialization of a pattern is computed in DeconstructedPat::specialize. Specialization for a pattern-tuple is computed in PatStack::pop_head_constructor. Finally, specialization for a set of pattern-tuples is computed in Matrix::specialize_constructor.

Undoing specialization

To construct witnesses we will need an inverse of specialization. If c is a constructor of arity n, we define unspecialize as: unspecialize(c, (p_1, .., p_n, q_1, .., q_m)) := (c(p_1, .., p_n), q_1, .., q_m).

This is done for a single witness-tuple in WitnessStack::apply_constructor, and for a set of witness-tuples in WitnessMatrix::apply_constructor.

Computing usefulness

We now present a naive version of the algorithm for computing usefulness. From now on we operate on pattern-tuples.

Let pt_1, .., pt_n and qt be length-m tuples of patterns for the same type (T_1, .., T_m). We compute usefulness(tp_1, .., tp_n, tq) as follows:

• Base case: m == 0. The pattern-tuples are all empty, i.e. they’re all (). Thus tq is useful iff there are no rows above it, i.e. if n == 0. In that case we return () as a witness-tuple of usefulness of tq.

• Inductive case: m > 0. In this naive version, we list all the possible constructors for values of type T1 (we will be more clever in the next section).

  • For each such constructor c for which specialize(c, tq) is not nothing:

    • We recursively compute usefulness(specialize(c, tp_1) ... specialize(c, tp_n), specialize(c, tq)), where we discard any specialize(c, p_i) that returns nothing.
    • For each witness-tuple w found, we apply unspecialize(c, w) to it.

  • We return the all the witnesses found, if any.

Let’s take the following example:

match x {
    Variant1(_) => {} // `p1`
    Variant2(None, 0) => {} // `p2`
    Variant2(Some(_), 0) => {} // `q`
}

To compute the usefulness of q, we would proceed as follows:

Start:
  `tp1 = [Variant1(_)]`
  `tp2 = [Variant2(None, 0)]`
  `tq  = [Variant2(Some(true), 0)]`

  Constructors are `Variant1` and `Variant2`. Only `Variant2` can specialize `tq`.
  Specialize with `Variant2`:
    `tp2 = [None, 0]`
    `tq  = [Some(true), 0]`

    Constructors are `None` and `Some`. Only `Some` can specialize `tq`.
    Specialize with `Some`:
      `tq  = [true, 0]`

      Constructors are `false` and `true`. Only `true` can specialize `tq`.
      Specialize with `true`:
        `tq  = [0]`

        Constructors are `0`, `1`, .. up to infinity. Only `0` can specialize `tq`.
        Specialize with `0`:
          `tq  = []`

          m == 0 and n == 0, so `tq` is useful with witness `[]`.
            `witness  = []`

        Unspecialize with `0`:
          `witness  = [0]`
      Unspecialize with `true`:
        `witness  = [true, 0]`
    Unspecialize with `Some`:
      `witness  = [Some(true), 0]`
  Unspecialize with `Variant2`:
    `witness  = [Variant2(Some(true), 0)]`

Therefore usefulness(tp_1, tp_2, tq) returns the single witness-tuple [Variant2(Some(true), 0)].

Computing the set of constructors for a type is done in PatCx::ctors_for_ty. See the following sections for more accurate versions of the algorithm and corresponding links.

Computing usefulness and exhaustiveness in one go

The algorithm we have described so far computes usefulness of each pattern in turn, and ends by checking if _ is useful to determine exhaustiveness of the whole match. In practice, instead of doing “for each pattern { for each constructor { … } }”, we do “for each constructor { for each pattern { … } }”. This allows us to compute everything in one go.

Matrix stores the set of pattern-tuples under consideration. We track usefulness of each row mutably in the matrix as we go along. We ignore witnesses of usefulness of the match rows. We gather witnesses of the usefulness of _ in WitnessMatrix. The algorithm that computes all this is in compute_exhaustiveness_and_usefulness.

See the full example at the bottom of this documentation.

Making usefulness tractable: constructor splitting

We’re missing one last detail: which constructors do we list? Naively listing all value constructors cannot work for types like u64 or &str, so we need to be more clever. The final clever idea for this algorithm is that we can group together constructors that behave the same.

Examples:

match (0, false) {
    (0 ..=100, true) => {}
    (50..=150, false) => {}
    (0 ..=200, _) => {}
}

In this example, trying any of 0, 1, .., 49 will give the same specialized matrix, and thus the same usefulness/exhaustiveness results. We can thus accelerate the algorithm by trying them all at once. Here in fact, the only cases we need to consider are: 0..50, 50..=100, 101..=150,151..=200 and 201...

enum Direction { North, South, East, West }
match wind {
    (Direction::North, 50..) => {}
    (_, _) => {}
}

In this example, trying any of South, East, West will give the same specialized matrix. By the same reasoning, we only need to try two cases: North, and “everything else”.

We call constructor splitting the operation that computes such a minimal set of cases to try. This is done in ConstructorSet::split and explained in crate::constructor.

Missing and relevancy

Relevant values

Take the following example:

match foo {
    (true, _) => 1,
    (_, true) => 2,
};

Consider the value (true, true):

• Row 2 does not distinguish (true, true) and (false, true);
• false does not show up in the first column of the match, so without knowing anything else we can deduce that (false, true) matches the same or fewer rows than (true, true).

Using those two facts together, we deduce that (true, true) will not give us more usefulness information about row 2 than (false, true) would. We say that “(true, true) is made irrelevant for row 2 by (false, true)”. We will use this idea to prune the search tree.

Computing relevancy

We now generalize from the above example to approximate relevancy in a simple way. Note that we will only compute an approximation: we can sometimes determine when a case is irrelevant, but computing this precisely is at least as hard as computing usefulness.

Our computation of relevancy relies on the Missing constructor. As explained in crate::constructor, Missing represents the constructors not present in a given column. For example in the following:

enum Direction { North, South, East, West }
match wind {
    (Direction::North, _) => 1,
    (_, 50..) => 2,
};

Here South, East and West are missing in the first column, and 0..50 is missing in the second. Both of these sets are represented by Constructor::Missing in their corresponding column.

We then compute relevancy as follows: during the course of the algorithm, for a row r:

• if r has a wildcard in the first column;
• and some constructors are missing in that column;
• then any c != Missing is considered irrelevant for row r.

By this we mean that continuing the algorithm by specializing with c is guaranteed not to contribute more information about the usefulness of row r than what we would get by specializing with Missing. The argument is the same as in the previous subsection.

Once we’ve specialized by a constructor c that is irrelevant for row r, we’re guaranteed to only explore values irrelevant for r. If we then ever reach a point where we’re only exploring values that are irrelevant to all of the rows (including the virtual wildcard row used for exhaustiveness), we skip that case entirely.

Example

Let’s go through a variation on the first example:

match foo {
    (true, _, true) => 1,
    (_, true, _) => 2,
};

 ┐ Patterns:
 │   1. `[(true, _, true)]`
 │   2. `[(_, true, _)]`
 │   3. `[_]` // virtual extra wildcard row
 │
 │ Specialize with `(,,)`:
 ├─┐ Patterns:
 │ │   1. `[true, _, true]`
 │ │   2. `[_, true, _]`
 │ │   3. `[_, _, _]`
 │ │
 │ │ There are missing constructors in the first column (namely `false`), hence
 │ │ `true` is irrelevant for rows 2 and 3.
 │ │
 │ │ Specialize with `true`:
 │ ├─┐ Patterns:
 │ │ │   1. `[_, true]`
 │ │ │   2. `[true, _]` // now exploring irrelevant cases
 │ │ │   3. `[_, _]`    // now exploring irrelevant cases
 │ │ │
 │ │ │ There are missing constructors in the first column (namely `false`), hence
 │ │ │ `true` is irrelevant for rows 1 and 3.
 │ │ │
 │ │ │ Specialize with `true`:
 │ │ ├─┐ Patterns:
 │ │ │ │   1. `[true]` // now exploring irrelevant cases
 │ │ │ │   2. `[_]`    // now exploring irrelevant cases
 │ │ │ │   3. `[_]`    // now exploring irrelevant cases
 │ │ │ │
 │ │ │ │ The current case is irrelevant for all rows: we backtrack immediately.
 │ │ ├─┘
 │ │ │
 │ │ │ Specialize with `false`:
 │ │ ├─┐ Patterns:
 │ │ │ │   1. `[true]`
 │ │ │ │   3. `[_]`    // now exploring irrelevant cases
 │ │ │ │
 │ │ │ │ Specialize with `true`:
 │ │ │ ├─┐ Patterns:
 │ │ │ │ │   1. `[]`
 │ │ │ │ │   3. `[]`    // now exploring irrelevant cases
 │ │ │ │ │
 │ │ │ │ │ Row 1 is therefore useful.
 │ │ │ ├─┘
<etc...>

Relevancy allowed us to skip the case (true, true, _) entirely. In some cases this pruning can give drastic speedups. The case this was built for is the following (#118437):

match foo {
    (true, _, _, _, ..) => 1,
    (_, true, _, _, ..) => 2,
    (_, _, true, _, ..) => 3,
    (_, _, _, true, ..) => 4,
    ...
}

Without considering relevancy, we would explore all 2^n combinations of the true and Missing constructors. Relevancy tells us that e.g. (true, true, false, false, false, ...) is irrelevant for all the rows. This allows us to skip all cases with more than one true constructor, changing the runtime from exponential to linear.

Relevancy and exhaustiveness

For exhaustiveness, we do something slightly different w.r.t relevancy: we do not report witnesses of non-exhaustiveness that are irrelevant for the virtual wildcard row. For example, in:

match foo {
    (true, true) => {}
}

we only report (false, _) as missing. This was a deliberate choice made early in the development of rust, for diagnostic and performance purposes. As showed in the previous section, ignoring irrelevant cases preserves usefulness, so this choice still correctly computes whether a match is exhaustive.

Or-patterns

What we have described so far works well if there are no or-patterns. To handle them, if the first pattern of any row in the matrix is an or-pattern, we expand it by duplicating the rest of the row as necessary. For code reuse, this is implemented as “specializing with the Or constructor”.

This makes usefulness tracking subtle, because we also want to compute whether an alternative of an or-pattern is redundant, e.g. in Some(_) | Some(0). We therefore track usefulness of each subpattern of the match.

Constants and opaques

There are two kinds of constants in patterns:

• literals (1, true, "foo")
• named or inline consts (FOO, const { 5 + 6 })

The latter are converted into the corresponding patterns by a previous phase. For example const_to_pat(const { [1, 2, 3] }) becomes an Array(vec![Const(1), Const(2), Const(3)]) pattern. This gets problematic when comparing the constant via == would behave differently from matching on the constant converted to a pattern. The situation around this is currently unclear and the lang team is working on clarifying what we want to do there. In any case, there are constants we will not turn into patterns. We capture these with Constructor::Opaque. These Opaque patterns do not participate in exhaustiveness, specialization or overlap checking.

Usefulness vs reachability, validity, and empty patterns

This is likely the subtlest aspect of the algorithm. To be fully precise, a match doesn’t operate on a value, it operates on a place. In certain unsafe circumstances, it is possible for a place to not contain valid data for its type. This has subtle consequences for empty types. Take the following:

enum Void {}
let x: u8 = 0;
let ptr: *const Void = &x as *const u8 as *const Void;
unsafe {
    match *ptr {
        _ => println!("Reachable!"),
    }
}

In this example, ptr is a valid pointer pointing to a place with invalid data. The _ pattern does not look at the contents of *ptr, so this is ok and the arm is taken. In other words, despite the place we are inspecting being of type Void, there is a reachable arm. If the arm had a binding however:

match *ptr {
    _a => println!("Unreachable!"),
}

Here the binding loads the value of type Void from the *ptr place. In this example, this causes UB since the data is not valid. In the general case, this asserts validity of the data at *ptr. Either way, this arm will never be taken.

Finally, let’s consider the empty match match *ptr {}. If we consider this exhaustive, then having invalid data at *ptr is invalid. In other words, the empty match is semantically equivalent to the _a => ... match. In the interest of explicitness, we prefer the case with an arm, hence we won’t tell the user to remove the _a arm. In other words, the _a arm is unreachable yet not redundant. This is why we lint on redundant arms rather than unreachable arms, despite the fact that the lint says “unreachable”.

These considerations only affects certain places, namely those that can contain non-valid data without UB. These are: pointer dereferences, reference dereferences, and union field accesses. We track in the algorithm whether a given place is known to contain valid data. This is done first by inspecting the scrutinee syntactically (which gives us cx.known_valid_scrutinee), and then by tracking validity of each column of the matrix (which correspond to places) as we recurse into subpatterns. That second part is done through PlaceValidity, most notably PlaceValidity::specialize.

Having said all that, we don’t fully follow what’s been presented in this section. For backwards-compatibility, we ignore place validity when checking whether a pattern is required for exhaustiveness in two cases: when the exhaustive_patterns feature gate is on, or when the match scrutinee itself has type ! or EmptyEnum. I (Nadrieril) hope to deprecate this exception.

Full example

We illustrate a full run of the algorithm on the following match.

match x {
    Pair(Some(0), _) => 1,
    Pair(_, false) => 2,
    Pair(Some(0), false) => 3,
}

We keep track of the original row for illustration purposes, this is not what the algorithm actually does (it tracks usefulness as a boolean on each row).

 ┐ Patterns:
 │   1. `[Pair(Some(0), _)]`
 │   2. `[Pair(_, false)]`
 │   3. `[Pair(Some(0), false)]`
 │
 │ Specialize with `Pair`:
 ├─┐ Patterns:
 │ │   1. `[Some(0), _]`
 │ │   2. `[_, false]`
 │ │   3. `[Some(0), false]`
 │ │
 │ │ Specialize with `Some`:
 │ ├─┐ Patterns:
 │ │ │   1. `[0, _]`
 │ │ │   2. `[_, false]`
 │ │ │   3. `[0, false]`
 │ │ │
 │ │ │ Specialize with `0`:
 │ │ ├─┐ Patterns:
 │ │ │ │   1. `[_]`
 │ │ │ │   3. `[false]`
 │ │ │ │
 │ │ │ │ Specialize with `true`:
 │ │ │ ├─┐ Patterns:
 │ │ │ │ │   1. `[]`
 │ │ │ │ │
 │ │ │ │ │ We note arm 1 is useful (by `Pair(Some(0), true)`).
 │ │ │ ├─┘
 │ │ │ │
 │ │ │ │ Specialize with `false`:
 │ │ │ ├─┐ Patterns:
 │ │ │ │ │   1. `[]`
 │ │ │ │ │   3. `[]`
 │ │ │ │ │
 │ │ │ │ │ We note arm 1 is useful (by `Pair(Some(0), false)`).
 │ │ │ ├─┘
 │ │ ├─┘
 │ │ │
 │ │ │ Specialize with `1..`:
 │ │ ├─┐ Patterns:
 │ │ │ │   2. `[false]`
 │ │ │ │
 │ │ │ │ Specialize with `true`:
 │ │ │ ├─┐ Patterns:
 │ │ │ │ │   // no rows left
 │ │ │ │ │
 │ │ │ │ │ We have found an unmatched value (`Pair(Some(1..), true)`)! This gives us a witness.
 │ │ │ │ │ New witnesses:
 │ │ │ │ │   `[]`
 │ │ │ ├─┘
 │ │ │ │ Unspecialize new witnesses with `true`:
 │ │ │ │   `[true]`
 │ │ │ │
 │ │ │ │ Specialize with `false`:
 │ │ │ ├─┐ Patterns:
 │ │ │ │ │   2. `[]`
 │ │ │ │ │
 │ │ │ │ │ We note arm 2 is useful (by `Pair(Some(1..), false)`).
 │ │ │ ├─┘
 │ │ │ │
 │ │ │ │ Total witnesses for `1..`:
 │ │ │ │   `[true]`
 │ │ ├─┘
 │ │ │ Unspecialize new witnesses with `1..`:
 │ │ │   `[1.., true]`
 │ │ │
 │ │ │ Total witnesses for `Some`:
 │ │ │   `[1.., true]`
 │ ├─┘
 │ │ Unspecialize new witnesses with `Some`:
 │ │   `[Some(1..), true]`
 │ │
 │ │ Specialize with `None`:
 │ ├─┐ Patterns:
 │ │ │   2. `[false]`
 │ │ │
 │ │ │ Specialize with `true`:
 │ │ ├─┐ Patterns:
 │ │ │ │   // no rows left
 │ │ │ │
 │ │ │ │ We have found an unmatched value (`Pair(None, true)`)! This gives us a witness.
 │ │ │ │ New witnesses:
 │ │ │ │   `[]`
 │ │ ├─┘
 │ │ │ Unspecialize new witnesses with `true`:
 │ │ │   `[true]`
 │ │ │
 │ │ │ Specialize with `false`:
 │ │ ├─┐ Patterns:
 │ │ │ │   2. `[]`
 │ │ │ │
 │ │ │ │ We note arm 2 is useful (by `Pair(None, false)`).
 │ │ ├─┘
 │ │ │
 │ │ │ Total witnesses for `None`:
 │ │ │   `[true]`
 │ ├─┘
 │ │ Unspecialize new witnesses with `None`:
 │ │   `[None, true]`
 │ │
 │ │ Total witnesses for `Pair`:
 │ │   `[Some(1..), true]`
 │ │   `[None, true]`
 ├─┘
 │ Unspecialize new witnesses with `Pair`:
 │   `[Pair(Some(1..), true)]`
 │   `[Pair(None, true)]`
 │
 │ Final witnesses:
 │   `[Pair(Some(1..), true)]`
 │   `[Pair(None, true)]`
 ┘

We conclude:

• Arm 3 is redundant (it was never marked as useful);
• The match is not exhaustive;
• Adding arms with Pair(Some(1..), true) and Pair(None, true) would make the match exhaustive.

Note that when we’re deep in the algorithm, we don’t know what specialization steps got us here. We can only figure out what our witnesses correspond to by unspecializing back up the stack.

Tests

Note: tests specific to this file can be found in:

• ui/pattern/usefulness
• ui/or-patterns
• ui/consts/const_in_pattern
• ui/rfc-2008-non-exhaustive
• ui/half-open-range-patterns
• probably many others

I (Nadrieril) prefer to put new tests in ui/pattern/usefulness unless there’s a specific reason not to, for example if they crucially depend on a particular feature like or_patterns.

Structs

• BranchPatUsefulness 🔒
  A pattern is a “branch” if it is the immediate child of an or-pattern, or if it is the whole pattern of a match arm. These are the patterns that can be meaningfully considered “redundant”, since e.g. 0 in (0, 1) cannot be redundant on its own.
• Matrix 🔒
  A 2D matrix. Represents a list of pattern-tuples under investigation.
• MatrixRow 🔒
  A row of the matrix.
• PatStack 🔒
  Represents a pattern-tuple under investigation.
• PlaceCtxt 🔒
  Context that provides information local to a place under investigation.
• PlaceInfo 🔒
  Data about a place under investigation. Its methods contain a lot of the logic used to analyze the constructors in the matrix.
• RedundancyExplanation
  Indicates why a given pattern is considered redundant.
• UsefulnessCtxt 🔒
  Context that provides information for usefulness checking.
• UsefulnessReport
  The output of checking a match for exhaustiveness and arm usefulness.
• WitnessMatrix 🔒
  Represents a set of pattern-tuples that are witnesses of non-exhaustiveness for error reporting. This has similar invariants as Matrix does.
• WitnessStack 🔒
  A witness-tuple of non-exhaustiveness for error reporting, represented as a list of patterns (in reverse order of construction).

Enums

• PlaceValidity
  Track whether a given place (aka column) is known to contain a valid value or not.
• Usefulness
  Indicates whether or not a given arm is useful.

Functions

• collect_non_contiguous_range_endpoints 🔒
  Collect ranges that have a singleton gap between them.
• collect_overlapping_range_endpoints 🔒
  Collect ranges that overlap like lo..=overlap/overlap..=hi. Must be called during exhaustiveness checking, if we find a singleton range after constructor splitting. This reuses row intersection information to only detect ranges that truly overlap.
• compute_exhaustiveness_and_usefulness 🔒
  The core of the algorithm.
• compute_match_usefulness
  Computes whether a match is exhaustive and which of its arms are useful.