Exercises
Exercise 1: Type-Safe State Machine ★★ (~30 min)
Build a traffic light state machine using the type-state pattern. The light must transition Red → Green → Yellow → Red and no other order should be possible.
use std::marker::PhantomData;
struct Red;
struct Green;
struct Yellow;
struct TrafficLight<State> {
_state: PhantomData<State>,
}
impl TrafficLight<Red> {
fn new() -> Self {
println!("🔴 Red — STOP");
TrafficLight { _state: PhantomData }
}
fn go(self) -> TrafficLight<Green> {
println!("🟢 Green — GO");
TrafficLight { _state: PhantomData }
}
}
impl TrafficLight<Green> {
fn caution(self) -> TrafficLight<Yellow> {
println!("🟡 Yellow — CAUTION");
TrafficLight { _state: PhantomData }
}
}
impl TrafficLight<Yellow> {
fn stop(self) -> TrafficLight<Red> {
println!("🔴 Red — STOP");
TrafficLight { _state: PhantomData }
}
}
fn main() {
let light = TrafficLight::new(); // Red
let light = light.go(); // Green
let light = light.caution(); // Yellow
let light = light.stop(); // Red
// light.caution(); // ❌ Compile error: no method `caution` on Red
// TrafficLight::new().stop(); // ❌ Compile error: no method `stop` on Red
}
Key takeaway: Invalid transitions are compile errors, not runtime panics.
</details>Exercise 2: Unit-of-Measure with PhantomData ★★ (~30 min)
Extend the unit-of-measure pattern from Ch4 to support:
Meters,Seconds,Kilograms- Addition of same units
- Multiplication:
Meters * Meters = SquareMeters - Division:
Meters / Seconds = MetersPerSecond
use std::marker::PhantomData;
use std::ops::{Add, Mul, Div};
#[derive(Clone, Copy)]
struct Meters;
#[derive(Clone, Copy)]
struct Seconds;
#[derive(Clone, Copy)]
struct Kilograms;
#[derive(Clone, Copy)]
struct SquareMeters;
#[derive(Clone, Copy)]
struct MetersPerSecond;
#[derive(Debug, Clone, Copy)]
struct Qty<U> {
value: f64,
_unit: PhantomData<U>,
}
impl<U> Qty<U> {
fn new(v: f64) -> Self { Qty { value: v, _unit: PhantomData } }
}
impl<U> Add for Qty<U> {
type Output = Qty<U>;
fn add(self, rhs: Self) -> Self::Output { Qty::new(self.value + rhs.value) }
}
impl Mul<Qty<Meters>> for Qty<Meters> {
type Output = Qty<SquareMeters>;
fn mul(self, rhs: Qty<Meters>) -> Qty<SquareMeters> {
Qty::new(self.value * rhs.value)
}
}
impl Div<Qty<Seconds>> for Qty<Meters> {
type Output = Qty<MetersPerSecond>;
fn div(self, rhs: Qty<Seconds>) -> Qty<MetersPerSecond> {
Qty::new(self.value / rhs.value)
}
}
fn main() {
let width = Qty::<Meters>::new(5.0);
let height = Qty::<Meters>::new(3.0);
let area = width * height; // Qty<SquareMeters>
println!("Area: {:.1} m²", area.value);
let dist = Qty::<Meters>::new(100.0);
let time = Qty::<Seconds>::new(9.58);
let speed = dist / time;
println!("Speed: {:.2} m/s", speed.value);
let sum = width + height; // Same unit ✅
println!("Sum: {:.1} m", sum.value);
// let bad = width + time; // ❌ Compile error: can't add Meters + Seconds
}
Exercise 3: Channel-Based Worker Pool ★★★ (~45 min)
Build a worker pool using channels where:
- A dispatcher sends
Jobstructs through a channel - N workers consume jobs and send results back
- Use
crossbeam-channel(orstd::sync::mpscif crossbeam is unavailable)
use std::sync::mpsc;
use std::thread;
struct Job {
id: u64,
data: String,
}
struct JobResult {
job_id: u64,
output: String,
worker_id: usize,
}
fn worker_pool(jobs: Vec<Job>, num_workers: usize) -> Vec<JobResult> {
let (job_tx, job_rx) = mpsc::channel::<Job>();
let (result_tx, result_rx) = mpsc::channel::<JobResult>();
// Wrap receiver in Arc<Mutex> for sharing among workers
let job_rx = std::sync::Arc::new(std::sync::Mutex::new(job_rx));
// Spawn workers
let mut handles = Vec::new();
for worker_id in 0..num_workers {
let job_rx = job_rx.clone();
let result_tx = result_tx.clone();
handles.push(thread::spawn(move || {
loop {
// Lock, receive, unlock — short critical section
let job = {
let rx = job_rx.lock().unwrap();
rx.recv() // Blocks until a job or channel closes
};
match job {
Ok(job) => {
let output = format!("processed '{}' by worker {worker_id}", job.data);
result_tx.send(JobResult {
job_id: job.id,
output,
worker_id,
}).unwrap();
}
Err(_) => break, // Channel closed — exit
}
}
}));
}
drop(result_tx); // Drop our copy so result channel closes when workers finish
// Dispatch jobs
let num_jobs = jobs.len();
for job in jobs {
job_tx.send(job).unwrap();
}
drop(job_tx); // Close the job channel — workers will exit after draining
// Collect results
let mut results = Vec::new();
for result in result_rx {
results.push(result);
}
assert_eq!(results.len(), num_jobs);
for h in handles { h.join().unwrap(); }
results
}
fn main() {
let jobs: Vec<Job> = (0..20).map(|i| Job {
id: i,
data: format!("task-{i}"),
}).collect();
let results = worker_pool(jobs, 4);
for r in &results {
println!("[worker {}] job {}: {}", r.worker_id, r.job_id, r.output);
}
}
Exercise 4: Higher-Order Combinator Pipeline ★★ (~25 min)
Create a Pipeline struct that chains transformations. It should support .pipe(f) to add a transformation and .execute(input) to run the full chain.
struct Pipeline<T> {
transforms: Vec<Box<dyn Fn(T) -> T>>,
}
impl<T: 'static> Pipeline<T> {
fn new() -> Self {
Pipeline { transforms: Vec::new() }
}
fn pipe(mut self, f: impl Fn(T) -> T + 'static) -> Self {
self.transforms.push(Box::new(f));
self
}
fn execute(self, input: T) -> T {
self.transforms.into_iter().fold(input, |val, f| f(val))
}
}
fn main() {
let result = Pipeline::new()
.pipe(|s: String| s.trim().to_string())
.pipe(|s| s.to_uppercase())
.pipe(|s| format!(">>> {s} <<<"))
.execute(" hello world ".to_string());
println!("{result}"); // >>> HELLO WORLD <<<
// Numeric pipeline:
let result = Pipeline::new()
.pipe(|x: i32| x * 2)
.pipe(|x| x + 10)
.pipe(|x| x * x)
.execute(5);
println!("{result}"); // (5*2 + 10)^2 = 400
}
Bonus: Generic pipeline that changes type between stages would use a different design — each .pipe() returns a Pipeline with a different output type (this requires more advanced generic plumbing).
Exercise 5: Error Hierarchy with thiserror ★★ (~30 min)
Design an error type hierarchy for a file-processing application that can fail during I/O, parsing (JSON and CSV), and validation. Use thiserror and demonstrate ? propagation.
use thiserror::Error;
#[derive(Error, Debug)]
pub enum AppError {
#[error("I/O error: {0}")]
Io(#[from] std::io::Error),
#[error("JSON parse error: {0}")]
Json(#[from] serde_json::Error),
#[error("CSV error at line {line}: {message}")]
Csv { line: usize, message: String },
#[error("validation error: {field} — {reason}")]
Validation { field: String, reason: String },
}
fn read_file(path: &str) -> Result<String, AppError> {
Ok(std::fs::read_to_string(path)?) // io::Error → AppError::Io via #[from]
}
fn parse_json(content: &str) -> Result<serde_json::Value, AppError> {
Ok(serde_json::from_str(content)?) // serde_json::Error → AppError::Json
}
fn validate_name(value: &serde_json::Value) -> Result<String, AppError> {
let name = value.get("name")
.and_then(|v| v.as_str())
.ok_or_else(|| AppError::Validation {
field: "name".into(),
reason: "must be a non-null string".into(),
})?;
if name.is_empty() {
return Err(AppError::Validation {
field: "name".into(),
reason: "must not be empty".into(),
});
}
Ok(name.to_string())
}
fn process_file(path: &str) -> Result<String, AppError> {
let content = read_file(path)?;
let json = parse_json(&content)?;
let name = validate_name(&json)?;
Ok(name)
}
fn main() {
match process_file("config.json") {
Ok(name) => println!("Name: {name}"),
Err(e) => eprintln!("Error: {e}"),
}
}
Exercise 6: Generic Trait with Associated Types ★★★ (~40 min)
Design a Repository<T> trait with associated Error and Id types. Implement it for an in-memory store and demonstrate compile-time type safety.
use std::collections::HashMap;
trait Repository {
type Item;
type Id;
type Error;
fn get(&self, id: &Self::Id) -> Result<Option<&Self::Item>, Self::Error>;
fn insert(&mut self, item: Self::Item) -> Result<Self::Id, Self::Error>;
fn delete(&mut self, id: &Self::Id) -> Result<bool, Self::Error>;
}
#[derive(Debug, Clone)]
struct User {
name: String,
email: String,
}
struct InMemoryUserRepo {
data: HashMap<u64, User>,
next_id: u64,
}
impl InMemoryUserRepo {
fn new() -> Self {
InMemoryUserRepo { data: HashMap::new(), next_id: 1 }
}
}
// Error type is Infallible — in-memory ops never fail
impl Repository for InMemoryUserRepo {
type Item = User;
type Id = u64;
type Error = std::convert::Infallible;
fn get(&self, id: &u64) -> Result<Option<&User>, Self::Error> {
Ok(self.data.get(id))
}
fn insert(&mut self, item: User) -> Result<u64, Self::Error> {
let id = self.next_id;
self.next_id += 1;
self.data.insert(id, item);
Ok(id)
}
fn delete(&mut self, id: &u64) -> Result<bool, Self::Error> {
Ok(self.data.remove(id).is_some())
}
}
// Generic function works with ANY repository:
fn create_and_fetch<R: Repository>(repo: &mut R, item: R::Item) -> Result<(), R::Error>
where
R::Item: std::fmt::Debug,
R::Id: std::fmt::Debug,
{
let id = repo.insert(item)?;
println!("Inserted with id: {id:?}");
let retrieved = repo.get(&id)?;
println!("Retrieved: {retrieved:?}");
Ok(())
}
fn main() {
let mut repo = InMemoryUserRepo::new();
create_and_fetch(&mut repo, User {
name: "Alice".into(),
email: "alice@example.com".into(),
}).unwrap();
}
Exercise 7: Safe Wrapper around Unsafe (Ch11) ★★★ (~45 min)
Write a FixedVec<T, const N: usize> — a fixed-capacity, stack-allocated vector.
Requirements:
push(&mut self, value: T) -> Result<(), T>returnsErr(value)when fullpop(&mut self) -> Option<T>returns and removes the last elementas_slice(&self) -> &[T]borrows initialized elements- All public methods must be safe; all unsafe must be encapsulated with
SAFETY:comments Dropmust clean up initialized elements
Hint: Use MaybeUninit<T> and [const { MaybeUninit::uninit() }; N].
use std::mem::MaybeUninit;
pub struct FixedVec<T, const N: usize> {
data: [MaybeUninit<T>; N],
len: usize,
}
impl<T, const N: usize> FixedVec<T, N> {
pub fn new() -> Self {
FixedVec {
data: [const { MaybeUninit::uninit() }; N],
len: 0,
}
}
pub fn push(&mut self, value: T) -> Result<(), T> {
if self.len >= N { return Err(value); }
// SAFETY: len < N, so data[len] is within bounds.
self.data[self.len] = MaybeUninit::new(value);
self.len += 1;
Ok(())
}
pub fn pop(&mut self) -> Option<T> {
if self.len == 0 { return None; }
self.len -= 1;
// SAFETY: data[len] was initialized (len was > 0 before decrement).
Some(unsafe { self.data[self.len].assume_init_read() })
}
pub fn as_slice(&self) -> &[T] {
// SAFETY: data[0..len] are all initialized, and MaybeUninit<T>
// has the same layout as T.
unsafe { std::slice::from_raw_parts(self.data.as_ptr() as *const T, self.len) }
}
pub fn len(&self) -> usize { self.len }
pub fn is_empty(&self) -> bool { self.len == 0 }
}
impl<T, const N: usize> Drop for FixedVec<T, N> {
fn drop(&mut self) {
// SAFETY: data[0..len] are initialized — drop each one.
for i in 0..self.len {
unsafe { self.data[i].assume_init_drop(); }
}
}
}
fn main() {
let mut v = FixedVec::<String, 4>::new();
v.push("hello".into()).unwrap();
v.push("world".into()).unwrap();
assert_eq!(v.as_slice(), &["hello", "world"]);
assert_eq!(v.pop(), Some("world".into()));
assert_eq!(v.len(), 1);
// Drop cleans up remaining "hello"
}
Exercise 8: Declarative Macro — map! (Ch12) ★ (~15 min)
Write a map! macro that creates a HashMap from key-value pairs, similar to vec![]:
let m = map! {
"host" => "localhost",
"port" => "8080",
};
assert_eq!(m.get("host"), Some(&"localhost"));
assert_eq!(m.len(), 2);
Requirements:
- Support trailing comma
- Support empty invocation
map!{} - Work with any types that implement
Into<K>andInto<V>for maximum flexibility
macro_rules! map {
// Empty case
() => {
std::collections::HashMap::new()
};
// One or more key => value pairs (trailing comma optional)
( $( $key:expr => $val:expr ),+ $(,)? ) => {{
let mut m = std::collections::HashMap::new();
$( m.insert($key, $val); )+
m
}};
}
fn main() {
// Basic usage:
let config = map! {
"host" => "localhost",
"port" => "8080",
"timeout" => "30",
};
assert_eq!(config.len(), 3);
assert_eq!(config["host"], "localhost");
// Empty map:
let empty: std::collections::HashMap<String, String> = map!();
assert!(empty.is_empty());
// Different types:
let scores = map! {
1 => 100,
2 => 200,
};
assert_eq!(scores[&1], 100);
}
Exercise 9: Custom serde Deserialization (Ch10) ★★★ (~45 min)
Design a Duration wrapper that deserializes from human-readable strings like "30s", "5m", "2h" using a custom serde deserializer. The struct should also serialize back to the same format.
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use std::fmt;
#[derive(Debug, Clone, PartialEq)]
struct HumanDuration(std::time::Duration);
impl HumanDuration {
fn from_str(s: &str) -> Result<Self, String> {
let s = s.trim();
if s.is_empty() { return Err("empty duration string".into()); }
let (num_str, suffix) = s.split_at(
s.find(|c: char| !c.is_ascii_digit()).unwrap_or(s.len())
);
let value: u64 = num_str.parse()
.map_err(|_| format!("invalid number: {num_str}"))?;
let duration = match suffix {
"s" | "sec" => std::time::Duration::from_secs(value),
"m" | "min" => std::time::Duration::from_secs(value * 60),
"h" | "hr" => std::time::Duration::from_secs(value * 3600),
"ms" => std::time::Duration::from_millis(value),
other => return Err(format!("unknown suffix: {other}")),
};
Ok(HumanDuration(duration))
}
}
impl fmt::Display for HumanDuration {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
let secs = self.0.as_secs();
if secs == 0 {
write!(f, "{}ms", self.0.as_millis())
} else if secs % 3600 == 0 {
write!(f, "{}h", secs / 3600)
} else if secs % 60 == 0 {
write!(f, "{}m", secs / 60)
} else {
write!(f, "{}s", secs)
}
}
}
impl Serialize for HumanDuration {
fn serialize<S: Serializer>(&self, serializer: S) -> Result<S::Ok, S::Error> {
serializer.serialize_str(&self.to_string())
}
}
impl<'de> Deserialize<'de> for HumanDuration {
fn deserialize<D: Deserializer<'de>>(deserializer: D) -> Result<Self, D::Error> {
let s = String::deserialize(deserializer)?;
HumanDuration::from_str(&s).map_err(serde::de::Error::custom)
}
}
#[derive(Debug, Deserialize, Serialize)]
struct Config {
timeout: HumanDuration,
retry_interval: HumanDuration,
}
fn main() {
let json = r#"{ "timeout": "30s", "retry_interval": "5m" }"#;
let config: Config = serde_json::from_str(json).unwrap();
assert_eq!(config.timeout.0, std::time::Duration::from_secs(30));
assert_eq!(config.retry_interval.0, std::time::Duration::from_secs(300));
// Round-trips correctly:
let serialized = serde_json::to_string(&config).unwrap();
assert!(serialized.contains("30s"));
assert!(serialized.contains("5m"));
println!("Config: {serialized}");
}
Exercise 10 — Concurrent Fetcher with Timeout ★★ (~25 min)
Write an async function fetch_all that spawns three tokio::spawn tasks, each
simulating a network call with tokio::time::sleep. Join all three with
tokio::try_join! wrapped in tokio::time::timeout(Duration::from_secs(5), ...).
Return Result<Vec<String>, ...> or an error if any task fails or the deadline
expires.
Learning goals: tokio::spawn, try_join!, timeout, error propagation
across task boundaries.
Each spawned task returns Result<String, _>. try_join! unwraps all three.
Wrap the whole try_join! in timeout() — the Elapsed error means you hit the
deadline.
use tokio::time::{sleep, timeout, Duration};
async fn fake_fetch(name: &'static str, delay_ms: u64) -> Result<String, String> {
sleep(Duration::from_millis(delay_ms)).await;
Ok(format!("{name}: OK"))
}
async fn fetch_all() -> Result<Vec<String>, Box<dyn std::error::Error>> {
let deadline = Duration::from_secs(5);
let (a, b, c) = timeout(deadline, async {
let h1 = tokio::spawn(fake_fetch("svc-a", 100));
let h2 = tokio::spawn(fake_fetch("svc-b", 200));
let h3 = tokio::spawn(fake_fetch("svc-c", 150));
tokio::try_join!(h1, h2, h3)
})
.await??; // first ? = timeout, second ? = join
Ok(vec![a?, b?, c?]) // unwrap inner Results
}
#[tokio::main]
async fn main() {
let results = fetch_all().await.unwrap();
for r in &results {
println!("{r}");
}
}
Exercise 11 — Async Channel Pipeline ★★★ (~40 min)
Build a producer → transformer → consumer pipeline using tokio::sync::mpsc:
- Producer: sends integers 1..=20 into channel A (capacity 4).
- Transformer: reads from channel A, squares each value, sends into channel B.
- Consumer: reads from channel B, collects into a
Vec<u64>, returns it.
All three stages run as concurrent tokio::spawn tasks. Use bounded channels to
demonstrate back-pressure. Assert the final vec equals [1, 4, 9, ..., 400].
Learning goals: mpsc::channel, bounded back-pressure, tokio::spawn with
move closures, graceful shutdown via channel close.
use tokio::sync::mpsc;
#[tokio::main]
async fn main() {
let (tx_a, mut rx_a) = mpsc::channel::<u64>(4); // bounded — back-pressure
let (tx_b, mut rx_b) = mpsc::channel::<u64>(4);
// Producer
let producer = tokio::spawn(async move {
for i in 1..=20u64 {
tx_a.send(i).await.unwrap();
}
// tx_a dropped here → channel A closes
});
// Transformer
let transformer = tokio::spawn(async move {
while let Some(val) = rx_a.recv().await {
tx_b.send(val * val).await.unwrap();
}
// tx_b dropped here → channel B closes
});
// Consumer
let consumer = tokio::spawn(async move {
let mut results = Vec::new();
while let Some(val) = rx_b.recv().await {
results.push(val);
}
results
});
producer.await.unwrap();
transformer.await.unwrap();
let results = consumer.await.unwrap();
let expected: Vec<u64> = (1..=20).map(|x: u64| x * x).collect();
assert_eq!(results, expected);
println!("Pipeline complete: {results:?}");
}