Iterator Power Tools Reference

What you'll learn: Advanced iterator combinators beyond filter/map/collect — enumerate, zip, chain, flat_map, scan, windows, and chunks. Essential for replacing C-style indexed for loops with safe, expressive Rust iterators.

The basic filter/map/collect chain covers many cases, but Rust's iterator library is far richer. This section covers the tools you'll reach for daily — especially when translating C loops that manually track indices, accumulate results, or process data in fixed-size chunks.

Quick Reference Table

Method	C Equivalent	What it does	Returns
`enumerate()`	`for (int i=0; ...)`	Pairs each element with its index	`(usize, T)`
`zip(other)`	Parallel arrays with same index	Pairs elements from two iterators	`(A, B)`
`chain(other)`	Process array1 then array2	Concatenates two iterators	`T`
`flat_map(f)`	Nested loops	Maps then flattens one level	`U`
`windows(n)`	`for (int i=0; i<len-n+1; i++) &arr[i..i+n]`	Overlapping slices of size `n`	`&[T]`
`chunks(n)`	Process `n` elements at a time	Non-overlapping slices of size `n`	`&[T]`
`fold(init, f)`	`int acc = init; for (...) acc = f(acc, x);`	Reduce to single value	`Acc`
`scan(init, f)`	Running accumulator with output	Like `fold` but yields intermediate results	`Option<B>`
`take(n)` / `skip(n)`	Start loop at offset / limit	First `n` / skip first `n` elements	`T`
`take_while(f)` / `skip_while(f)`	`while (pred) {...}`	Take/skip while predicate holds	`T`
`peekable()`	Lookahead with `arr[i+1]`	Allows `.peek()` without consuming	`T`
`step_by(n)`	`for (i=0; i<len; i+=n)`	Take every nth element	`T`
`unzip()`	Split parallel arrays	Collect pairs into two collections	`(A, B)`
`sum()` / `product()`	Accumulate sum/product	Reduce with `+` or `*`	`T`
`min()` / `max()`	Find extremes	Return `Option<T>`	`Option<T>`
`any(f)` / `all(f)`	`bool found = false; for (...) ...`	Short-circuit boolean search	`bool`
`position(f)`	`for (i=0; ...) if (pred) return i;`	Index of first match	`Option<usize>`

`enumerate` — Index + Value (replaces C index loops)

fn main() {
    let sensors = ["GPU_TEMP", "CPU_TEMP", "FAN_RPM", "PSU_WATT"];

    // C style: for (int i = 0; i < 4; i++) printf("[%d] %s\n", i, sensors[i]);
    for (i, name) in sensors.iter().enumerate() {
        println!("[{i}] {name}");
    }

    // Find the index of a specific sensor
    let gpu_idx = sensors.iter().position(|&s| s == "GPU_TEMP");
    println!("GPU sensor at index: {gpu_idx:?}");  // Some(0)
}

`zip` — Parallel Iteration (replaces parallel array loops)

fn main() {
    let names = ["accel_diag", "nic_diag", "cpu_diag"];
    let statuses = [true, false, true];
    let durations_ms = [1200, 850, 3400];

    // C: for (int i=0; i<3; i++) printf("%s: %s (%d ms)\n", names[i], ...);
    for ((name, passed), ms) in names.iter().zip(&statuses).zip(&durations_ms) {
        let status = if *passed { "PASS" } else { "FAIL" };
        println!("{name}: {status} ({ms} ms)");
    }
}

`chain` — Concatenate Iterators

fn main() {
    let critical = vec!["ECC error", "Thermal shutdown"];
    let warnings = vec!["Link degraded", "Fan slow"];

    // Process all events in priority order
    let all_events: Vec<_> = critical.iter().chain(warnings.iter()).collect();
    println!("{all_events:?}");
    // ["ECC error", "Thermal shutdown", "Link degraded", "Fan slow"]
}

`flat_map` — Flatten Nested Results

fn main() {
    let lines = vec!["gpu:42:ok", "nic:99:fail", "cpu:7:ok"];

    // Extract all numeric values from colon-separated lines
    let numbers: Vec<u32> = lines.iter()
        .flat_map(|line| line.split(':'))
        .filter_map(|token| token.parse::<u32>().ok())
        .collect();
    println!("{numbers:?}");  // [42, 99, 7]
}

`windows` and `chunks` — Sliding and Fixed-Size Groups

fn main() {
    let temps = [65, 68, 72, 71, 75, 80, 78, 76];

    // windows(3): overlapping groups of 3 (like a sliding average)
    // C: for (int i = 0; i <= len-3; i++) avg(arr[i], arr[i+1], arr[i+2]);
    let moving_avg: Vec<f64> = temps.windows(3)
        .map(|w| w.iter().sum::<i32>() as f64 / 3.0)
        .collect();
    println!("Moving avg: {moving_avg:.1?}");

    // chunks(2): non-overlapping groups of 2
    // C: for (int i = 0; i < len; i += 2) process(arr[i], arr[i+1]);
    for pair in temps.chunks(2) {
        println!("Chunk: {pair:?}");
    }

    // chunks_exact(2): same but panics if remainder exists
    // Also: .remainder() gives leftover elements
}

`fold` and `scan` — Accumulation

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

    // fold: single final result (like C's accumulator loop)
    let sum = values.iter().fold(0, |acc, &x| acc + x);
    println!("Sum: {sum}");  // 150

    // Build a string with fold
    let csv = values.iter()
        .fold(String::new(), |acc, x| {
            if acc.is_empty() { format!("{x}") }
            else { format!("{acc},{x}") }
        });
    println!("CSV: {csv}");  // "10,20,30,40,50"

    // scan: like fold but yields intermediate results
    let running_sum: Vec<i32> = values.iter()
        .scan(0, |state, &x| {
            *state += x;
            Some(*state)
        })
        .collect();
    println!("Running sum: {running_sum:?}");  // [10, 30, 60, 100, 150]
}

Exercise: Sensor Data Pipeline

Given raw sensor readings (one per line, format "sensor_name:value:unit"), write an iterator pipeline that:

Parses each line into (name, f64, unit)
Filters out readings below a threshold
Groups by sensor name using fold into a HashMap
Prints the average reading per sensor

// Starter code
fn main() {
    let raw_data = vec![
        "gpu_temp:72.5:C",
        "cpu_temp:65.0:C",
        "gpu_temp:74.2:C",
        "fan_rpm:1200.0:RPM",
        "cpu_temp:63.8:C",
        "gpu_temp:80.1:C",
        "fan_rpm:1150.0:RPM",
    ];
    let threshold = 70.0;
    // TODO: Parse, filter values >= threshold, group by name, compute averages
}

Solution (click to expand)

use std::collections::HashMap;

fn main() {
    let raw_data = vec![
        "gpu_temp:72.5:C",
        "cpu_temp:65.0:C",
        "gpu_temp:74.2:C",
        "fan_rpm:1200.0:RPM",
        "cpu_temp:63.8:C",
        "gpu_temp:80.1:C",
        "fan_rpm:1150.0:RPM",
    ];
    let threshold = 70.0;

    // Parse → filter → group → average
    let grouped = raw_data.iter()
        .filter_map(|line| {
            let parts: Vec<&str> = line.splitn(3, ':').collect();
            if parts.len() == 3 {
                let value: f64 = parts[1].parse().ok()?;
                Some((parts[0], value, parts[2]))
            } else {
                None
            }
        })
        .filter(|(_, value, _)| *value >= threshold)
        .fold(HashMap::<&str, Vec<f64>>::new(), |mut acc, (name, value, _)| {
            acc.entry(name).or_default().push(value);
            acc
        });

    for (name, values) in &grouped {
        let avg = values.iter().sum::<f64>() / values.len() as f64;
        println!("{name}: avg={avg:.1} ({} readings)", values.len());
    }
}
// Output (order may vary):
// gpu_temp: avg=75.6 (3 readings)
// fan_rpm: avg=1175.0 (2 readings)

Rust iterators

The Iterator trait is used to implement iteration over user-defined types (https://doc.rust-lang.org/std/iter/trait.IntoIterator.html)
- In the example, we'll implement an iterator for the Fibonacci sequence, which starts with 1, 1, 2, ... and the successor is the sum of the previous two numbers
- The associated type in the Iterator (type Item = u32;) defines the output type from our iterator (u32)
- The next() method simply contains the logic for implementing our iterator. In this case, all state information is available in the Fibonacci structure
- We could have implemented another trait called IntoIterator to implement the into_iter() method for more specialized iterators
- https://play.rust-lang.org/?version=stable&mode=debug&edition=2021&gist=ab367dc2611e1b5a0bf98f1185b38f3f