(n as f64).sqrt() as u64 is the classic hack — and it silently gives the wrong answer for large values. Rust 1.84 stabilized isqrt on every integer type: exact, float-free, no precision traps.

The floating-point trap

Converting to f64, calling .sqrt(), and casting back is the go-to pattern. It looks fine. It isn’t.

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let n: u64 = 10_000_000_000_000_000_000;
let bad = (n as f64).sqrt() as u64;
// bad == 3_162_277_660, but floor(sqrt(n)) is 3_162_277_660 — or is it?
// For many large u64 values, the f64 round-trip is off by 1.

f64 only has 53 bits of mantissa, so for u64 values above 2^53 the conversion loses precision before you even take the square root.

The fix: isqrt

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let n: u64 = 10_000_000_000_000_000_000;
let root = n.isqrt();
assert_eq!(root * root <= n, true);
assert_eq!((root + 1).checked_mul(root + 1).map_or(true, |sq| sq > n), true);

It’s defined on every integer type — u8, u16, u32, u64, u128, usize, and their signed counterparts — and always returns the exact floor of the square root. No casts, no rounding, no surprises.

Signed integers too

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let x: i32 = 42;
assert_eq!(x.isqrt(), 6); // 6*6 = 36, 7*7 = 49

// Negative values would panic, so check first:
let maybe_neg: i32 = -4;
assert_eq!(maybe_neg.checked_isqrt(), None);

Use checked_isqrt on signed types when the input might be negative — it returns Option<T> instead of panicking.

When you’d reach for it

Perfect-square checks, tight loops over divisors, hash table sizing, geometry on integer grids — anywhere you were reaching for f64::sqrt purely to round down, reach for isqrt instead. It’s faster, exact, and one character shorter.

checked_add_signed has been around for years. Its missing sibling finally landed: as of Rust 1.91, u64::checked_sub_signed (and the whole {checked, overflowing, saturating, wrapping}_sub_signed family) lets you subtract an i64 from a u64 without casting, unsafe, or hand-rolled overflow checks.

The problem

You’ve got an unsigned counter — a file offset, a buffer index, a frame number — and you want to apply a signed delta. The delta is negative, so subtracting it should increase the counter. But Rust won’t let you subtract an i64 from a u64:

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let pos: u64 = 100;
let delta: i64 = -5;

// error[E0277]: cannot subtract `i64` from `u64`
// let new_pos = pos - delta;

The usual workarounds are all awkward. Cast to i64 and hope nothing overflows. Branch on the sign of the delta and call either checked_sub or checked_add depending. Convert via as and pray.

The fix

checked_sub_signed takes an i64 directly and returns Option<u64>:

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let pos: u64 = 100;

assert_eq!(pos.checked_sub_signed(30),  Some(70));   // normal subtraction
assert_eq!(pos.checked_sub_signed(-5),  Some(105));  // subtracting negative adds
assert_eq!(pos.checked_sub_signed(200), None);       // underflow → None

Subtracting a negative number “wraps around” to addition, exactly as the math says it should. Underflow (going below zero) returns None instead of panicking or silently wrapping.

The whole family

Pick your overflow semantics, same as every other integer op:

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let pos: u64 = 10;

// Checked: returns Option.
assert_eq!(pos.checked_sub_signed(-5),  Some(15));
assert_eq!(pos.checked_sub_signed(100), None);

// Saturating: clamps to 0 or u64::MAX.
assert_eq!(pos.saturating_sub_signed(100), 0);
assert_eq!((u64::MAX - 5).saturating_sub_signed(-100), u64::MAX);

// Wrapping: modular arithmetic, never panics.
assert_eq!(pos.wrapping_sub_signed(20), u64::MAX - 9);

// Overflowing: returns (value, did_overflow).
assert_eq!(pos.overflowing_sub_signed(20), (u64::MAX - 9, true));
assert_eq!(pos.overflowing_sub_signed(5),  (5, false));

Same convention as checked_sub, saturating_sub, etc. — you already know the shape.

Why it matters

The signed-from-unsigned case comes up more than you’d think. Scrubbing back and forth in a timeline. Applying a velocity to a position. Rebasing a byte offset. Any time the delta can be negative, you need this method — and now you have it without touching as.

It pairs nicely with its long-stable sibling checked_add_signed, which has been around since Rust 1.66. Between the two, signed deltas on unsigned counters are a one-liner in any direction.

Available on every unsigned primitive (u8, u16, u32, u64, u128, usize) as of Rust 1.91.

Vec::pop_if got a deque-shaped sibling. As of Rust 1.93, VecDeque has pop_front_if and pop_back_if — conditional pops on either end without the peek-then-pop dance.

The problem

You want to remove an element from a VecDeque only when it matches a predicate. Before 1.93, you’d peek, match, then pop:

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use std::collections::VecDeque;

let mut queue: VecDeque<i32> = VecDeque::from([1, 2, 3, 4]);

if queue.front().is_some_and(|&x| x == 1) {
    queue.pop_front();
}

Two lookups, two branches, one opportunity to desynchronize the check from the pop if you refactor the predicate later.

The fix

pop_front_if takes a closure, checks the front element against it, and pops it only if it matches. pop_back_if does the same on the other end.

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use std::collections::VecDeque;

let mut queue: VecDeque<i32> = VecDeque::from([1, 2, 3, 4]);

let popped = queue.pop_front_if(|x| *x == 1);
assert_eq!(popped, Some(1));
assert_eq!(queue, VecDeque::from([2, 3, 4]));

// Predicate doesn't match — nothing is popped.
let not_popped = queue.pop_front_if(|x| *x > 100);
assert_eq!(not_popped, None);
assert_eq!(queue, VecDeque::from([2, 3, 4]));

The return type is Option<T>: Some(value) if the predicate matched and the element was removed, None otherwise (including when the deque is empty).

One subtle detail worth noting — the closure receives &mut T, not &T. That means |&x| won’t type-check; use |x| *x == ... or destructure with |&mut x|. The extra flexibility lets you mutate the element in-place before deciding to pop it.

Draining a prefix

The pattern clicks when you pair it with a while let loop. Drain everything at the front that matches a condition, stop the moment it doesn’t:

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use std::collections::VecDeque;

let mut events: VecDeque<i32> = VecDeque::from([1, 2, 3, 10, 11, 12]);

// Drain the "small" prefix only.
while let Some(_) = events.pop_front_if(|x| *x < 10) {}

assert_eq!(events, VecDeque::from([10, 11, 12]));

No index tracking, no split_off, no collecting into a new deque.

Why both ends?

VecDeque is a double-ended ring buffer, so it’s natural to support the same idiom on both sides. Processing a priority queue from the back, trimming expired entries from the front, popping a sentinel only when it’s still there — all one method call each.

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use std::collections::VecDeque;

let mut log: VecDeque<&str> = VecDeque::from(["START", "a", "b", "c", "END"]);

let end = log.pop_back_if(|s| *s == "END");
let start = log.pop_front_if(|s| *s == "START");

assert_eq!(start, Some("START"));
assert_eq!(end, Some("END"));
assert_eq!(log, VecDeque::from(["a", "b", "c"]));

When to reach for it

Whenever the shape of your code is “peek, compare, pop.” That’s the tell. pop_front_if / pop_back_if collapse three steps into one atomic operation, and the Option<T> return makes it composable with while let, ?, and the rest of the Option toolbox.

Stabilized in Rust 1.93 — if your MSRV is recent enough, this is a free readability win.

Trait errors are notoriously cryptic. #[diagnostic::on_unimplemented] lets you replace the compiler’s default “trait bound not satisfied” with a message that actually tells the user what went wrong.

The problem

You define a trait, someone forgets to implement it, and the compiler spits out a wall of generics and trait bounds that even experienced Rustaceans have to squint at:

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trait Storable {
    fn store(&self, path: &str);
}

fn save<T: Storable>(item: &T) {
    item.store("/data/output");
}

fn main() {
    save(&42_i32);
    // error[E0277]: the trait bound `i32: Storable` is not satisfied
}

For your own code that’s fine — but if you’re writing a library, your users deserve better.

The fix

Annotate your trait with #[diagnostic::on_unimplemented] and the compiler will use your message instead:

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#[diagnostic::on_unimplemented(
    message = "`{Self}` cannot be stored — implement `Storable` for it",
    label = "this type doesn't implement Storable",
    note = "all types passed to `save()` must implement the `Storable` trait"
)]
trait Storable {
    fn store(&self, path: &str);
}

Now the error reads like documentation, not like a stack trace.

It works with generics too

The placeholders {Self} and {A} (for generic params) let you generate targeted messages:

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#[diagnostic::on_unimplemented(
    message = "cannot serialize `{Self}` into format `{F}`",
    note = "see docs for supported format/type combinations"
)]
trait Serialize<F> {
    fn serialize(&self) -> Vec<u8>;
}

If someone tries to serialize a type for an unsupported format, they get a message that names both the type and the format — no guessing required.

Multiple notes

You can attach several note entries, and each one becomes a separate note in the compiler output:

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#[diagnostic::on_unimplemented(
    message = "`{Self}` is not a valid handler",
    note = "handlers must implement `Handler` with the appropriate request type",
    note = "see https://docs.example.com/handlers for the full list"
)]
trait Handler<Req> {
    fn handle(&self, req: Req);
}

When to use it

This is a library-author tool. If you expose a public trait and expect users to implement it (or pass types that satisfy it), adding on_unimplemented is a small investment that saves your users real debugging time. Crates like bevy, axum, and diesel already use it to turn walls of trait errors into actionable guidance.

Stabilized in Rust 1.78, it’s part of the #[diagnostic] namespace — the compiler treats unrecognized diagnostic hints as soft warnings rather than hard errors, so it’s forward-compatible by design.

Need to split items into fixed-size pages or chunks? The classic (n + size - 1) / size trick silently overflows. div_ceil does it correctly.

The classic footgun

Paging, chunking, allocating — any time you divide and need to round up, this pattern shows up:

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fn pages_needed(total: u64, per_page: u64) -> u64 {
    (total + per_page - 1) / per_page // ⚠️ overflows when total is large
}

It works until total + per_page - 1 wraps around. With u64::MAX items and a page size of 10, you get a wrong answer instead of a panic or correct result.

The fix: div_ceil

Stabilized in Rust 1.73, div_ceil handles the rounding without intermediate overflow:

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fn pages_needed(total: u64, per_page: u64) -> u64 {
    total.div_ceil(per_page)
}

One method call, no overflow risk, intent crystal clear.

Real-world examples

Allocating pixel rows for a tiled renderer:

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let image_height: u32 = 1080;
let tile_size: u32 = 64;
let tile_rows = image_height.div_ceil(tile_size);
assert_eq!(tile_rows, 17); // 16 full tiles + 1 partial

Splitting work across threads:

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let items: usize = 1000;
let threads: usize = 6;
let chunk_size = items.div_ceil(threads);
assert_eq!(chunk_size, 167); // each thread handles at most 167 items

It works on all unsigned integers

div_ceil is available on u8, u16, u32, u64, u128, and usize. Signed integers also have it (since Rust 1.73), but watch out — it rounds toward positive infinity, which for negative dividends means rounding away from zero.

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let signed: i32 = -7;
assert_eq!(signed.div_ceil(2), -3); // rounds toward +∞, not toward 0

Next time you reach for (a + b - 1) / b, stop — div_ceil already exists and it won’t betray you at the boundaries.

Stop writing repeat(x).take(n) — there’s a dedicated function that’s both cleaner and more efficient.

The old way

If you wanted an iterator that yields a value a fixed number of times, you’d chain repeat with take:

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use std::iter;

let greetings: Vec<_> = iter::repeat("hello").take(3).collect();
assert_eq!(greetings, vec!["hello", "hello", "hello"]);

This works fine for Copy types, but it always clones the value — even on the last iteration, where you could just move it instead.

Enter repeat_n

std::iter::repeat_n does exactly what the name says — repeats a value n times:

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use std::iter;

let greetings: Vec<_> = iter::repeat_n("hello", 3).collect();
assert_eq!(greetings, vec!["hello", "hello", "hello"]);

Cleaner, more readable, and it comes with a hidden superpower.

The efficiency win

repeat_n moves the value on the last iteration instead of cloning it. This matters when cloning is expensive:

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use std::iter;

let original = vec![1, 2, 3]; // expensive to clone
let copies: Vec<Vec<i32>> = iter::repeat_n(original, 3).collect();

assert_eq!(copies.len(), 3);
assert_eq!(copies[0], vec![1, 2, 3]);
assert_eq!(copies[1], vec![1, 2, 3]);
assert_eq!(copies[2], vec![1, 2, 3]);
// The first two are clones, but the third one is a move — one fewer allocation!

With repeat(x).take(n), you’d clone all n times. With repeat_n, you save one clone. For large buffers or complex types, that’s a meaningful win.

repeat_n with zero

Passing n = 0 yields an empty iterator, and the value is simply dropped — no clones happen at all:

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use std::iter;

let items: Vec<String> = iter::repeat_n(String::from("unused"), 0).collect();
assert!(items.is_empty());

When to reach for it

Use repeat_n whenever you need a fixed number of identical values. Common patterns:

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use std::iter;

// Initialize a grid row
let row: Vec<f64> = iter::repeat_n(0.0, 10).collect();
assert_eq!(row.len(), 10);

// Pad a sequence
let mut data = vec![1, 2, 3];
data.extend(iter::repeat_n(0, 5));
assert_eq!(data, vec![1, 2, 3, 0, 0, 0, 0, 0]);

Small change, but it makes intent crystal clear: you want exactly n copies, no more, no less.

You’re calling .chunks(4) and immediately doing chunk.try_into().unwrap() to get an array. as_chunks gives you &[[T; N]] directly — a slice of properly typed arrays, plus the remainder.

The Problem

When you use .chunks(N), each chunk is a &[T] — a dynamically sized slice. The compiler doesn’t know its length, so you’re stuck converting manually:

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fn sum_pairs(data: &[i32]) -> Vec<i32> {
    data.chunks(2)
        .filter(|c| c.len() == 2) // skip incomplete last chunk
        .map(|c| c[0] + c[1])     // runtime indexing, no guarantees
        .collect()
}

fn main() {
    let values = [1, 2, 3, 4, 5];
    assert_eq!(sum_pairs(&values), vec![3, 7]);
}

That works, but the compiler can’t verify your index access at compile time. You’re also throwing away the last chunk if it’s incomplete, with no easy way to inspect it.

After: as_chunks

Stabilized in Rust 1.88, as_chunks splits a slice into a &[[T; N]] and a remainder &[T] in one call:

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fn sum_pairs(data: &[i32]) -> Vec<i32> {
    let (chunks, _remainder) = data.as_chunks::<2>();
    chunks.iter().map(|[a, b]| a + b).collect()
}

fn main() {
    let values = [1, 2, 3, 4, 5];
    assert_eq!(sum_pairs(&values), vec![3, 7]);

    // The remainder is available too
    let (chunks, remainder) = values.as_chunks::<2>();
    assert_eq!(chunks, &[[1, 2], [3, 4]]);
    assert_eq!(remainder, &[5]);
}

Each chunk is &[i32; 2], so you can pattern-match [a, b] directly. The compiler knows the size — no bounds checks, no panics.

Processing Fixed-Width Records

Parsing binary data with fixed-width fields is where as_chunks shines. Imagine RGB pixel data packed as bytes:

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fn brighten(pixels: &mut [u8], amount: u8) {
    let (chunks, _) = pixels.as_chunks_mut::<3>();
    for [r, g, b] in chunks {
        *r = r.saturating_add(amount);
        *g = g.saturating_add(amount);
        *b = b.saturating_add(amount);
    }
}

fn main() {
    let mut pixels = [100, 150, 200, 50, 60, 70, 255, 128, 0];
    brighten(&mut pixels, 30);
    assert_eq!(pixels, [130, 180, 230, 80, 90, 100, 255, 158, 30]);
}

No manual stride arithmetic. Each iteration gives you exactly 3 bytes, pattern-matched into r, g, b.

Don’t Lose the Remainder

Unlike chunks_exact() where you call .remainder() on the iterator after consuming it, as_chunks returns the remainder upfront:

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fn main() {
    let data = [10, 20, 30, 40, 50, 60, 70];

    let (fours, rest) = data.as_chunks::<4>();
    assert_eq!(fours.len(), 1);       // one complete chunk: [10, 20, 30, 40]
    assert_eq!(fours[0], [10, 20, 30, 40]);
    assert_eq!(rest, &[50, 60, 70]);  // leftover elements

    // as_rchunks starts from the right instead
    let (rest, fours) = data.as_rchunks::<4>();
    assert_eq!(rest, &[10, 20, 30]);
    assert_eq!(fours[0], [40, 50, 60, 70]);
}

as_rchunks is the mirror — it aligns chunks to the end, putting the remainder at the front. Useful when your trailing data is the structured part (e.g., a checksum or footer).

The Full Family

All stabilized in Rust 1.88, these come in immutable and mutable variants:

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fn main() {
    let data: &[u8] = &[1, 2, 3, 4, 5, 6, 7];

    // as_chunks — align from left, remainder on right
    let (chunks, rem) = data.as_chunks::<3>();
    assert_eq!(chunks, &[[1, 2, 3], [4, 5, 6]]);
    assert_eq!(rem, &[7]);

    // as_rchunks — align from right, remainder on left
    let (rem, chunks) = data.as_rchunks::<3>();
    assert_eq!(rem, &[1]);
    assert_eq!(chunks, &[[2, 3, 4], [5, 6, 7]]);

    // Mutable versions: as_chunks_mut, as_rchunks_mut
    let mut buf = [0u8; 6];
    let (chunks, _) = buf.as_chunks_mut::<2>();
    chunks[0] = [0xCA, 0xFE];
    chunks[1] = [0xBA, 0xBE];
    chunks[2] = [0xDE, 0xAD];
    assert_eq!(buf, [0xCA, 0xFE, 0xBA, 0xBE, 0xDE, 0xAD]);
}

Whenever you’re reaching for .chunks(N) with a compile-time constant, as_chunks::<N>() gives you stronger types, better ergonomics, and the remainder without gymnastics.

You’re sorting an entire Vec just to grab the median or the top 3 elements. select_nth_unstable does it in O(n) — no full sort required.

The classic approach: sort everything, then index:

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let mut scores = vec![82, 45, 99, 67, 73, 91, 55];
scores.sort();
let median = scores[scores.len() / 2];
assert_eq!(median, 73);

That’s O(n log n) to answer an O(n) question. select_nth_unstable uses a partial-sort algorithm (quickselect) to put the element at a given index into its final sorted position — everything before it is smaller or equal, everything after is greater or equal:

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let mut scores = vec![82, 45, 99, 67, 73, 91, 55];
let mid = scores.len() / 2;
scores.select_nth_unstable(mid);
assert_eq!(scores[mid], 73);

The method returns three mutable slices — elements below, the pivot element, and elements above — so you can work with each partition directly:

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let mut data = vec![10, 80, 30, 90, 50, 70, 20];
let (lower, median, upper) = data.select_nth_unstable(3);
// lower contains 3 elements all <= *median
// upper contains 3 elements all >= *median
assert_eq!(*median, 50);
assert!(lower.iter().all(|&x| x <= 50));
assert!(upper.iter().all(|&x| x >= 50));

Need the top 3 scores without sorting the full list? Select the boundary, then sort only the small tail:

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let mut scores = vec![82, 45, 99, 67, 73, 91, 55];
let k = scores.len() - 3;
scores.select_nth_unstable(k);
let top_3 = &mut scores[k..];
top_3.sort_unstable(); // sort only 3 elements, not all 7
assert_eq!(top_3, &[82, 91, 99]);

Custom ordering works too. Find the median by absolute value:

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let mut vals = vec![-20, 5, -10, 15, -3, 8, 1];
let mid = vals.len() / 2;
vals.select_nth_unstable_by_key(mid, |v| v.abs());
assert_eq!(vals[mid].abs(), 8);

The “unstable” in the name means equal elements might be reordered (like sort_unstable) — it doesn’t mean the API is experimental. This is stable since Rust 1.49, available on any [T] where T: Ord.

You’ve written windows(2).all(|w| w[0] <= w[1]) one too many times. The is_sorted family of methods says what you actually mean — in one call.

Checking whether data is already in order used to mean rolling your own predicate:

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let data = vec![1, 3, 5, 7, 9];

// The old way — correct but noisy:
let sorted = data.windows(2).all(|w| w[0] <= w[1]);
assert!(sorted);

It works, but you have to remember windows(2), get the comparison direction right, and hope the next reader recognizes the pattern.

Now there’s a method that does exactly this:

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let data = vec![1, 3, 5, 7, 9];
assert!(data.is_sorted());

let messy = vec![1, 9, 3, 7, 5];
assert!(!messy.is_sorted());

Empty slices and single-element slices are considered sorted — no edge-case surprises:

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let empty: Vec<i32> = vec![];
assert!(empty.is_sorted());
assert!(vec![42].is_sorted());

Need a custom comparator? is_sorted_by takes a closure over pairs of references and returns bool:

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// Check if sorted by absolute value
let vals: Vec<i32> = vec![-1, 2, -3, 4];
let sorted_by_abs = vals.is_sorted_by(|a, b| a.abs() <= b.abs());
assert!(sorted_by_abs);

And is_sorted_by_key extracts a key first — perfect for structs:

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struct Task {
    priority: u8,
    name: &'static str,
}

let tasks = vec![
    Task { priority: 1, name: "urgent" },
    Task { priority: 3, name: "normal" },
    Task { priority: 5, name: "backlog" },
];

assert!(tasks.is_sorted_by_key(|t| t.priority));

A practical use: skip sorting when the data is already ordered:

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let mut data = vec![1, 2, 3, 4, 5];
if !data.is_sorted() {
    data.sort();
}
// Avoids the O(n log n) sort when data is already O(n)-verified sorted

Available on slices and by extension on Vec, arrays, and anything that derefs to [T]. Stabilized in Rust 1.82 — no crate needed.

Computing the average of two integers sounds trivial — until it overflows. The midpoint method gives you a correct result every time, no wider types required.

The classic binary search bug lurks in this innocent-looking line:

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let mid = (low + high) / 2;

When low and high are both large, the addition wraps around and you get garbage. This has bitten production code in every language for decades.

The textbook workaround avoids the addition entirely:

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let mid = low + (high - low) / 2;

This works — but only when low <= high, and it’s one more thing to get wrong under pressure.

Rust’s midpoint method handles all of this for you:

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let a: u64 = u64::MAX - 1;
let b: u64 = u64::MAX;

// This would panic in debug or wrap in release:
// let avg = (a + b) / 2;

// Safe and correct:
let avg = a.midpoint(b);
assert_eq!(avg, u64::MAX - 1);

It works on signed integers too, rounding toward zero:

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let x: i32 = -3;
let y: i32 = 4;
assert_eq!(x.midpoint(y), 0);  // rounds toward zero, not -∞

And on floats, where it’s computed without intermediate overflow:

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let a: f64 = f64::MAX;
let b: f64 = f64::MAX;
assert_eq!(a.midpoint(b), f64::MAX);  // not infinity

Here’s a binary search that actually works for the full u64 range:

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fn binary_search(sorted: &[i32], target: i32) -> Option<usize> {
    let mut low: usize = 0;
    let mut high: usize = sorted.len();
    while low < high {
        let mid = low.midpoint(high);
        match sorted[mid].cmp(&target) {
            std::cmp::Ordering::Less => low = mid + 1,
            std::cmp::Ordering::Greater => high = mid,
            std::cmp::Ordering::Equal => return Some(mid),
        }
    }
    None
}

let data = vec![1, 3, 5, 7, 9, 11];
assert_eq!(binary_search(&data, 7), Some(3));
assert_eq!(binary_search(&data, 4), None);

Available on all integer types (u8 through u128, i8 through i128, usize, isize) and floats (f32, f64). No crate needed — it’s in the standard library.