Introduction
Participation
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Design patterns
When developing programs, we have to solve many problems. A program can be viewed as a solution to a problem. It can also be viewed as a collection of solutions to many problems. All of these solutions work together to solve a bigger problem.
Design patterns in Rust
There are many problems that share the same form. Due to the fact that Rust is not object-oriented, design patterns vary with respect to other object-oriented programming languages. While the details are different, since they have the same form they can be solved using the same fundamental methods:
- Design patterns are methods to solve common problems when writing software.
- Anti-patterns are methods to solve these same common problems. However, while design patterns give us benefits, anti-patterns create more problems.
- Idioms are guidelines to follow when coding. They are social norms of the community. You can break them, but if you do you should have a good reason for it.
TODO: Mention why Rust is a bit special - functional elements, type system, borrow checker
Idioms
Idioms are commonly used styles and patterns largely agreed upon by a community. They are guidelines. Writing idiomatic code allows other developers to understand what is happening because they are familiar with the form that it has.
The computer understands the machine code that is generated by the compiler. The language is therefore mostly beneficial to the developer. So, since we have this abstraction layer, why not put it to good use and make it simple?
Remember the KISS principle: "Keep It Simple, Stupid". It claims that "most systems work best if they are kept simple rather than made complicated; therefore, simplicity should be a key goal in design, and unnecessary complexity should be avoided".
Code is there for humans, not computers, to understand.
Use borrowed types for arguments
Description
Using a target of a deref coercion can increase the flexibility of your code when you are deciding which argument type to use for a function argument. In this way, the function will accept more input types.
This is not limited to slice-able or fat pointer types.
In fact, you should always prefer using the borrowed type over
borrowing the owned type.
Such as &str
over &String
, &[T]
over &Vec<T>
, or &T
over &Box<T>
.
Using borrowed types you can avoid layers of indirection for those instances
where the owned type already provides a layer of indirection. For instance, a
String
has a layer of indirection, so a &String
will have two layers of
indirection. We can avoid this by using &str
instead, and letting &String
coerce to a &str
whenever the function is invoked.
Example
For this example, we will illustrate some differences for using &String
as a
function argument versus using a &str
, but the ideas apply as well to using
&Vec<T>
versus using a &[T]
or using a &T
versus a &Box<T>
.
Consider an example where we wish to determine if a word contains three consecutive vowels. We don't need to own the string to determine this, so we will take a reference.
The code might look something like this:
fn three_vowels(word: &String) -> bool { let mut vowel_count = 0; for c in word.chars() { match c { 'a' | 'e' | 'i' | 'o' | 'u' => { vowel_count += 1; if vowel_count >= 3 { return true } } _ => vowel_count = 0 } } false } fn main() { let ferris = "Ferris".to_string(); let curious = "Curious".to_string(); println!("{}: {}", ferris, three_vowels(&ferris)); println!("{}: {}", curious, three_vowels(&curious)); // This works fine, but the following two lines would fail: // println!("Ferris: {}", three_vowels("Ferris")); // println!("Curious: {}", three_vowels("Curious")); }
This works fine because we are passing a &String
type as a parameter.
If we comment in the last two lines this example fails because a &str
type
will not coerce to a &String
type. We can fix this by simply modifying the
type for our argument.
For instance, if we change our function declaration to:
fn three_vowels(word: &str) -> bool {
then both versions will compile and print the same output.
Ferris: false
Curious: true
But wait, that's not all! There is more to this story.
It's likely that you may say to yourself: that doesn't matter, I will never be
using a &'static str
as an input anyways (as we did when we used "Ferris"
).
Even ignoring this special example, you may still find that using &str
will
give you more flexibility than using a &String
.
Let's now take an example where someone gives us a sentence, and we want to determine if any of the words in the sentence contain three consecutive vowels. We probably should make use of the function we have already defined and simply feed in each word from the sentence.
An example of this could look like this:
fn three_vowels(word: &str) -> bool { let mut vowel_count = 0; for c in word.chars() { match c { 'a' | 'e' | 'i' | 'o' | 'u' => { vowel_count += 1; if vowel_count >= 3 { return true } } _ => vowel_count = 0 } } false } fn main() { let sentence_string = "Once upon a time, there was a friendly curious crab named Ferris".to_string(); for word in sentence_string.split(' ') { if three_vowels(word) { println!("{} has three consecutive vowels!", word); } } }
Running this example using our function declared with an argument type &str
will yield
curious has three consecutive vowels!
However, this example will not run when our function is declared with an
argument type &String
. This is because string slices are a &str
and not a
&String
which would require an allocation to be converted to &String
which
is not implicit, whereas converting from String
to &str
is cheap and implicit.
See also
- Rust Language Reference on Type Coercions
- For more discussion on how to handle
String
and&str
see this blog series (2015) by Herman J. Radtke III
Concatenating strings with format!
Description
It is possible to build up strings using the push
and push_str
methods on a
mutable String
, or using its +
operator. However, it is often more
convenient to use format!
, especially where there is a mix of literal and
non-literal strings.
Example
#![allow(unused)] fn main() { fn say_hello(name: &str) -> String { // We could construct the result string manually. // let mut result = "Hello ".to_owned(); // result.push_str(name); // result.push('!'); // result // But using format! is better. format!("Hello {}!", name) } }
Advantages
Using format!
is usually the most succinct and readable way to combine strings.
Disadvantages
It is usually not the most efficient way to combine strings - a series of push
operations on a mutable string is usually the most efficient (especially if the
string has been pre-allocated to the expected size).
Constructors
Description
Rust does not have constructors as a language construct. Instead, the
convention is to use a static new
method to create an object.
Example
// A Rust vector, see liballoc/vec.rs
pub struct Vec<T> {
buf: RawVec<T>,
len: usize,
}
impl<T> Vec<T> {
// Constructs a new, empty `Vec<T>`.
// Note this is a static method - no self.
// This constructor doesn't take any arguments, but some might in order to
// properly initialise an object
pub fn new() -> Vec<T> {
// Create a new Vec with fields properly initialised.
Vec {
// Note that here we are calling RawVec's constructor.
buf: RawVec::new(),
len: 0,
}
}
}
See also
The builder pattern for constructing objects where there are multiple configurations.
The Default
Trait
Description
Many types in Rust have a constructor. However, this is specific to the
type; Rust cannot abstract over "everything that has a new()
method". To
allow this, the Default
trait was conceived, which can be used with
containers and other generic types (e.g. see Option::unwrap_or_default()
).
Notably, some containers already implement it where applicable.
Not only do one-element containers like Cow
, Box
or Arc
implement
Default
for contained Default
types, one can automatically
#[derive(Default)]
for structs whose fields all implement it, so the more
types implement Default
, the more useful it becomes.
On the other hand, constructors can take multiple arguments, while the
default()
method does not. There can even be multiple constructors with
different names, but there can only be one Default
implementation per type.
Example
use std::{path::PathBuf, time::Duration}; // note that we can simply auto-derive Default here. #[derive(Default, Debug, PartialEq)] struct MyConfiguration { // Option defaults to None output: Option<PathBuf>, // Vecs default to empty vector search_path: Vec<PathBuf>, // Duration defaults to zero time timeout: Duration, // bool defaults to false check: bool, } impl MyConfiguration { // add setters here } fn main() { // construct a new instance with default values let mut conf = MyConfiguration::default(); // do something with conf here conf.check = true; println!("conf = {:#?}", conf); // partial initialization with default values, creates the same instance let conf1 = MyConfiguration { check: true, ..Default::default() }; assert_eq!(conf, conf1); }
See also
- The constructor idiom is another way to generate instances that may or may not be "default"
- The
Default
documentation (scroll down for the list of implementors) Option::unwrap_or_default()
derive(new)
Collections are smart pointers
Description
Use the Deref
trait to treat collections like smart pointers, offering owning
and borrowed views of data.
Example
use std::ops::Deref;
struct Vec<T> {
data: RawVec<T>,
//..
}
impl<T> Deref for Vec<T> {
type Target = [T];
fn deref(&self) -> &[T] {
//..
}
}
A Vec<T>
is an owning collection of T
s, a slice (&[T]
) is a borrowed
collection of T
s. Implementing Deref
for Vec
allows implicit dereferencing
from &Vec<T>
to &[T]
and includes the relationship in auto-derefencing
searches. Most methods you might expect to be implemented for Vec
s are instead
implemented for slices.
See also String
and &str
.
Motivation
Ownership and borrowing are key aspects of the Rust language. Data structures must account for these semantics properly in order to give a good user experience. When implementing a data structure which owns its data, offering a borrowed view of that data allows for more flexible APIs.
Advantages
Most methods can be implemented only for the borrowed view, they are then implicitly available for the owning view.
Gives clients a choice between borrowing or taking ownership of data.
Disadvantages
Methods and traits only available via dereferencing are not taken into account
when bounds checking, so generic programming with data structures using this
pattern can get complex (see the Borrow
and AsRef
traits, etc.).
Discussion
Smart pointers and collections are analogous: a smart pointer points to a single object, whereas a collection points to many objects. From the point of view of the type system there is little difference between the two. A collection owns its data if the only way to access each datum is via the collection and the collection is responsible for deleting the data (even in cases of shared ownership, some kind of borrowed view may be appropriate). If a collection owns its data, it is usually useful to provide a view of the data as borrowed so that it can be multiplied referenced.
Most smart pointers (e.g., Foo<T>
) implement Deref<Target=T>
. However,
collections will usually dereference to a custom type. [T]
and str
have some
language support, but in the general case, this is not necessary. Foo<T>
can
implement Deref<Target=Bar<T>>
where Bar
is a dynamically sized type and
&Bar<T>
is a borrowed view of the data in Foo<T>
.
Commonly, ordered collections will implement Index
for Range
s to provide
slicing syntax. The target will be the borrowed view.
See also
Deref polymorphism anti-pattern.
Documentation for Deref
trait.
Finalisation in destructors
Description
Rust does not provide the equivalent to finally
blocks - code that will be
executed no matter how a function is exited. Instead, an object's destructor can
be used to run code that must be run before exit.
Example
fn bar() -> Result<(), ()> {
// These don't need to be defined inside the function.
struct Foo;
// Implement a destructor for Foo.
impl Drop for Foo {
fn drop(&mut self) {
println!("exit");
}
}
// The dtor of _exit will run however the function `bar` is exited.
let _exit = Foo;
// Implicit return with `?` operator.
baz()?;
// Normal return.
Ok(())
}
Motivation
If a function has multiple return points, then executing code on exit becomes
difficult and repetitive (and thus bug-prone). This is especially the case where
return is implicit due to a macro. A common case is the ?
operator which
returns if the result is an Err
, but continues if it is Ok
. ?
is used as
an exception handling mechanism, but unlike Java (which has finally
), there is
no way to schedule code to run in both the normal and exceptional cases.
Panicking will also exit a function early.
Advantages
Code in destructors will (nearly) be always run - copes with panics, early returns, etc.
Disadvantages
It is not guaranteed that destructors will run. For example, if there is an infinite loop in a function or if running a function crashes before exit. Destructors are also not run in the case of a panic in an already panicking thread. Therefore, destructors cannot be relied on as finalizers where it is absolutely essential that finalisation happens.
This pattern introduces some hard to notice, implicit code. Reading a function gives no clear indication of destructors to be run on exit. This can make debugging tricky.
Requiring an object and Drop
impl just for finalisation is heavy on boilerplate.
Discussion
There is some subtlety about how exactly to store the object used as a
finalizer. It must be kept alive until the end of the function and must then be
destroyed. The object must always be a value or uniquely owned pointer (e.g.,
Box<Foo>
). If a shared pointer (such as Rc
) is used, then the finalizer can
be kept alive beyond the lifetime of the function. For similar reasons, the
finalizer should not be moved or returned.
The finalizer must be assigned into a variable, otherwise it will be destroyed
immediately, rather than when it goes out of scope. The variable name must start
with _
if the variable is only used as a finalizer, otherwise the compiler
will warn that the finalizer is never used. However, do not call the variable
_
with no suffix - in that case it will be destroyed immediately.
In Rust, destructors are run when an object goes out of scope. This happens whether we reach the end of block, there is an early return, or the program panics. When panicking, Rust unwinds the stack running destructors for each object in each stack frame. So, destructors get called even if the panic happens in a function being called.
If a destructor panics while unwinding, there is no good action to take, so Rust aborts the thread immediately, without running further destructors. This means that destructors are not absolutely guaranteed to run. It also means that you must take extra care in your destructors not to panic, since it could leave resources in an unexpected state.
See also
mem::{take(_), replace(_)}
to keep owned values in changed enums
Description
Say we have a &mut MyEnum
which has (at least) two variants,
A { name: String, x: u8 }
and B { name: String }
. Now we want to change
MyEnum::A
to a B
if x
is zero, while keeping MyEnum::B
intact.
We can do this without cloning the name
.
Example
#![allow(unused)] fn main() { use std::mem; enum MyEnum { A { name: String, x: u8 }, B { name: String } } fn a_to_b(e: &mut MyEnum) { if let MyEnum::A { name, x: 0 } = e { // this takes out our `name` and put in an empty String instead // (note that empty strings don't allocate). // Then, construct the new enum variant (which will // be assigned to `*e`). *e = MyEnum::B { name: mem::take(name) } } } }
This also works with more variants:
#![allow(unused)] fn main() { use std::mem; enum MultiVariateEnum { A { name: String }, B { name: String }, C, D } fn swizzle(e: &mut MultiVariateEnum) { use MultiVariateEnum::*; *e = match e { // Ownership rules do not allow taking `name` by value, but we cannot // take the value out of a mutable reference, unless we replace it: A { name } => B { name: mem::take(name) }, B { name } => A { name: mem::take(name) }, C => D, D => C } } }
Motivation
When working with enums, we may want to change an enum value in place, perhaps to another variant. This is usually done in two phases to keep the borrow checker happy. In the first phase, we observe the existing value and look at its parts to decide what to do next. In the second phase we may conditionally change the value (as in the example above).
The borrow checker won't allow us to take out name
of the enum (because
something must be there.) We could of course .clone()
name and put the clone
into our MyEnum::B
, but that would be an instance of the Clone to satisfy
the borrow checker anti-pattern. Anyway, we
can avoid the extra allocation by changing e
with only a mutable borrow.
mem::take
lets us swap out the value, replacing it with it's default value,
and returning the previous value. For String
, the default value is an empty
String
, which does not need to allocate. As a result, we get the original
name
as an owned value. We can then wrap this in another enum.
NOTE: mem::replace
is very similar, but allows us to specify what to
replace the value with. An equivalent to our mem::take
line would be
mem::replace(name, String::new())
.
Note, however, that if we are using an Option
and want to replace its
value with a None
, Option
’s take()
method provides a shorter and
more idiomatic alternative.
Advantages
Look ma, no allocation! Also you may feel like Indiana Jones while doing it.
Disadvantages
This gets a bit wordy. Getting it wrong repeatedly will make you hate the borrow checker. The compiler may fail to optimize away the double store, resulting in reduced performance as opposed to what you'd do in unsafe languages.
Furthermore, the type you are taking needs to implement the Default
trait. However, if the type you're working with doesn't
implement this, you can instead use mem::replace
.
Discussion
This pattern is only of interest in Rust. In GC'd languages, you'd take the reference to the value by default (and the GC would keep track of refs), and in other low-level languages like C you'd simply alias the pointer and fix things later.
However, in Rust, we have to do a little more work to do this. An owned value may only have one owner, so to take it out, we need to put something back in – like Indiana Jones, replacing the artifact with a bag of sand.
See also
This gets rid of the Clone to satisfy the borrow checker anti-pattern in a specific case.
On-Stack Dynamic Dispatch
Description
We can dynamically dispatch over multiple values, however, to do so, we need to declare multiple variables to bind differently-typed objects. To extend the lifetime as necessary, we can use deferred conditional initialization, as seen below:
Example
use std::io; use std::fs; fn main() -> Result<(), Box<dyn std::error::Error>> { let arg = "-"; // These must live longer than `readable`, and thus are declared first: let (mut stdin_read, mut file_read); // We need to ascribe the type to get dynamic dispatch. let readable: &mut dyn io::Read = if arg == "-" { stdin_read = io::stdin(); &mut stdin_read } else { file_read = fs::File::open(arg)?; &mut file_read }; // Read from `readable` here. Ok(()) }
Motivation
Rust monomorphises code by default. This means a copy of the code will be generated for each type it is used with and optimized independently. While this allows for very fast code on the hot path, it also bloats the code in places where performance is not of the essence, thus costing compile time and cache usage.
Luckily, Rust allows us to use dynamic dispatch, but we have to explicitly ask for it.
Advantages
We do not need to allocate anything on the heap. Neither do we need to
initialize something we won't use later, nor do we need to monomorphize the
whole code that follows to work with both File
or Stdin
.
Disadvantages
The code needs more moving parts than the Box
-based version:
// We still need to ascribe the type for dynamic dispatch.
let readable: Box<dyn io::Read> = if arg == "-" {
Box::new(io::stdin())
} else {
Box::new(fs::File::open(arg)?)
};
// Read from `readable` here.
Discussion
Rust newcomers will usually learn that Rust requires all variables to be initialized before use, so it's easy to overlook the fact that unused variables may well be uninitialized. Rust works quite hard to ensure that this works out fine and only the initialized values are dropped at the end of their scope.
The example meets all the constraints Rust places on us:
- All variables are initialized before using (in this case borrowing) them
- Each variable only holds values of a single type. In our example,
stdin
is of typeStdin
,file
is of typeFile
andreadable
is of type&mut dyn Read
- Each borrowed value outlives all the references borrowed from it
See also
- Finalisation in destructors and RAII guards can benefit from tight control over lifetimes.
- For conditionally filled
Option<&T>
s of (mutable) references, one can initialize anOption<T>
directly and use its.as_ref()
method to get an optional reference.
FFI Idioms
Writing FFI code is an entire course in itself.
However, there are several idioms here that can act as pointers, and avoid
traps for inexperienced users of unsafe
Rust.
This section contains idioms that may be useful when doing FFI.
-
Idiomatic Errors - Error handling with integer codes and sentinel return values (such as
NULL
pointers) -
Accepting Strings with minimal unsafe code
-
Passing Strings to FFI functions
Error Handling in FFI
Description
In foreign languages like C, errors are represented by return codes. However, Rust's type system allows much more rich error information to be captured a propogated through a full type.
This best practice shows different kinds of error codes, and how to expose them in a usable way:
- Flat Enums should be converted to integers and returned as codes.
- Structured Enums should be converted to an integer code with a string error message for detail.
- Custom Error Types should become "transparent", with a C representation.
Code Example
Flat Enums
enum DatabaseError {
IsReadOnly = 1, // user attempted a write operation
IOError = 2, // user should read the C errno() for what it was
FileCorrupted = 3, // user should run a repair tool to recover it
}
impl From<DatabaseError> for libc::c_int {
fn from(e: DatabaseError) -> libc::c_int {
(e as i8).into()
}
}
Structured Enums
pub mod errors {
enum DatabaseError {
IsReadOnly,
IOError(std::io::Error),
FileCorrupted(String), // message describing the issue
}
impl From<DatabaseError> for libc::c_int {
fn from(e: DatabaseError) -> libc::c_int {
match e {
DatabaseError::IsReadOnly => 1,
DatabaseError::IOError(_) => 2,
DatabaseError::FileCorrupted(_) => 3,
}
}
}
}
pub mod c_api {
use super::errors::DatabaseError;
#[no_mangle]
pub extern "C" fn db_error_description(
e: *const DatabaseError
) -> *mut libc::c_char {
let error: &DatabaseError = unsafe {
// SAFETY: pointer lifetime is greater than the current stack frame
&*e
};
let error_str: String = match error {
DatabaseError::IsReadOnly => {
format!("cannot write to read-only database");
}
DatabaseError::IOError(e) => {
format!("I/O Error: {}", e);
}
DatabaseError::FileCorrupted(s) => {
format!("File corrupted, run repair: {}", &s);
}
};
let c_error = unsafe {
// SAFETY: copying error_str to an allocated buffer with a NUL
// character at the end
let mut malloc: *mut u8 = libc::malloc(error_str.len() + 1) as *mut _;
if malloc.is_null() {
return std::ptr::null_mut();
}
let src = error_str.as_bytes().as_ptr();
std::ptr::copy_nonoverlapping(src, malloc, error_str.len());
std::ptr::write(malloc.add(error_str.len()), 0);
malloc as *mut libc::c_char
};
c_error
}
}
Custom Error Types
struct ParseError {
expected: char,
line: u32,
ch: u16
}
impl ParseError { /* ... */ }
/* Create a second version which is exposed as a C structure */
#[repr(C)]
pub struct parse_error {
pub expected: libc::c_char,
pub line: u32,
pub ch: u16
}
impl From<ParseError> for parse_error {
fn from(e: ParseError) -> parse_error {
let ParseError { expected, line, ch } = e;
parse_error { expected, line, ch }
}
}
Advantages
This ensures that the foreign language has clear access to error information while not compromising the Rust code's API at all.
Disadvantages
It's a lot of typing, and some types may not be able to be converted easily to C.
Accepting Strings
Description
When accepting strings via FFI through pointers, there are two principles that should be followed:
- Keep foreign strings "borrowed", rather than copying them directly.
- Minimize the amount of complexity and
unsafe
code involved in converting from a C-style string to native Rust strings.
Motivation
The strings used in C have different behaviours to those used in Rust, namely:
- C strings are null-terminated while Rust strings store their length
- C strings can contain any arbitrary non-zero byte while Rust strings must be UTF-8
- C strings are accessed and manipulated using
unsafe
pointer operations while interactions with Rust strings go through safe methods
The Rust standard library comes with C equivalents of Rust's String
and &str
called CString
and &CStr
, that allow us to avoid a lot of the complexity
and unsafe
code involved in converting between C strings and Rust strings.
The &CStr
type also allows us to work with borrowed data, meaning passing
strings between Rust and C is a zero-cost operation.
Code Example
pub mod unsafe_module {
// other module content
/// Log a message at the specified level.
///
/// # Safety
///
/// It is the caller's guarantee to ensure `msg`:
///
/// - is not a null pointer
/// - points to valid, initialized data
/// - points to memory ending in a null byte
/// - won't be mutated for the duration of this function call
#[no_mangle]
pub unsafe extern "C" fn mylib_log(
msg: *const libc::c_char,
level: libc::c_int
) {
let level: crate::LogLevel = match level { /* ... */ };
// SAFETY: The caller has already guaranteed this is okay (see the
// `# Safety` section of the doc-comment).
let msg_str: &str = match std::ffi::CStr::from_ptr(msg).to_str() {
Ok(s) => s,
Err(e) => {
crate::log_error("FFI string conversion failed");
return;
}
};
crate::log(msg_str, level);
}
}
Advantages
The example is is written to ensure that:
- The
unsafe
block is as small as possible. - The pointer with an "untracked" lifetime becomes a "tracked" shared reference
Consider an alternative, where the string is actually copied:
pub mod unsafe_module {
// other module content
pub extern "C" fn mylib_log(msg: *const libc::c_char, level: libc::c_int) {
// DO NOT USE THIS CODE.
// IT IS UGLY, VERBOSE, AND CONTAINS A SUBTLE BUG.
let level: crate::LogLevel = match level { /* ... */ };
let msg_len = unsafe { /* SAFETY: strlen is what it is, I guess? */
libc::strlen(msg)
};
let mut msg_data = Vec::with_capacity(msg_len + 1);
let msg_cstr: std::ffi::CString = unsafe {
// SAFETY: copying from a foreign pointer expected to live
// for the entire stack frame into owned memory
std::ptr::copy_nonoverlapping(msg, msg_data.as_mut(), msg_len);
msg_data.set_len(msg_len + 1);
std::ffi::CString::from_vec_with_nul(msg_data).unwrap()
}
let msg_str: String = unsafe {
match msg_cstr.into_string() {
Ok(s) => s,
Err(e) => {
crate::log_error("FFI string conversion failed");
return;
}
}
};
crate::log(&msg_str, level);
}
}
This code in inferior to the original in two respects:
- There is much more
unsafe
code, and more importantly, more invariants it must uphold. - Due to the extensive arithmetic required, there is a bug in this version
that cases Rust
undefined behaviour
.
The bug here is a simple mistake in pointer arithmetic: the string was copied,
all msg_len
bytes of it. However, the NUL
terminator at the end was not.
The Vector then had its size set to the length of the zero padded string --
rather than resized to it, which could have added a zero at the end.
As a result, the last byte in the Vector is uninitialized memory.
When the CString
is created at the bottom of the block, its read of the
Vector will cause undefined behaviour
!
Like many such issues, this would be difficult issue to track down.
Sometimes it would panic because the string was not UTF-8
, sometimes it would
put a weird character at the end of the string, sometimes it would just
completely crash.
Disadvantages
None?
Passing Strings
Description
When passing strings to FFI functions, there are four principles that should be followed:
- Make the lifetime of owned strings as long as possible.
- Minimize
unsafe
code during the conversion. - If the C code can modify the string data, use
Vec
instead ofCString
. - Unless the Foreign Function API requires it, the ownership of the string should not transfer to the callee.
Motivation
Rust has built-in support for C-style strings with its CString
and CStr
types. However, there are different approaches one can take with strings that
are being sent to a foreign function call from a Rust function.
The best practice is simple: use CString
in such a way as to minimize
unsafe
code. However, a secondary caveat is that
the object must live long enough, meaning the lifetime should be maximized.
In addition, the documentation explains that "round-tripping" a CString
after
modification is UB, so additional work is necessary in that case.
Code Example
pub mod unsafe_module {
// other module content
extern "C" {
fn seterr(message: *const libc::c_char);
fn geterr(buffer: *mut libc::c_char, size: libc::c_int) -> libc::c_int;
}
fn report_error_to_ffi<S: Into<String>>(
err: S
) -> Result<(), std::ffi::NulError>{
let c_err = std::ffi::CString::new(err.into())?;
unsafe {
// SAFETY: calling an FFI whose documentation says the pointer is
// const, so no modification should occur
seterr(c_err.as_ptr());
}
Ok(())
// The lifetime of c_err continues until here
}
fn get_error_from_ffi() -> Result<String, std::ffi::IntoStringError> {
let mut buffer = vec![0u8; 1024];
unsafe {
// SAFETY: calling an FFI whose documentation implies
// that the input need only live as long as the call
let written: usize = geterr(buffer.as_mut_ptr(), 1023).into();
buffer.truncate(written + 1);
}
std::ffi::CString::new(buffer).unwrap().into_string()
}
}
Advantages
The example is written in a way to ensure that:
- The
unsafe
block is as small as possible. - The
CString
lives long enough. - Errors with typecasts are always propagated when possible.
A common mistake (so common it's in the documentation) is to not use the variable in the first block:
pub mod unsafe_module {
// other module content
fn report_error<S: Into<String>>(err: S) -> Result<(), std::ffi::NulError> {
unsafe {
// SAFETY: whoops, this contains a dangling pointer!
seterr(std::ffi::CString::new(err.into())?.as_ptr());
}
Ok(())
}
}
This code will result in a dangling pointer, because the lifetime of the
CString
is not extended by the pointer creation, unlike if a reference were
created.
Another issue frequently raised is that the initialization of a 1k vector of
zeroes is "slow". However, recent versions of Rust actually optimize that
particular macro to a call to zmalloc
, meaning it is as fast as the operating
system's ability to return zeroed memory (which is quite fast).
Disadvantages
None?
Iterating over an Option
Description
Option
can be viewed as a container that contains either zero or one
element. In particular, it implements the IntoIterator
trait, and as such
can be used with generic code that needs such a type.
Examples
Since Option
implements IntoIterator
, it can be used as an argument to
.extend()
:
#![allow(unused)] fn main() { let turing = Some("Turing"); let mut logicians = vec!["Curry", "Kleene", "Markov"]; logicians.extend(turing); // equivalent to if let Some(turing_inner) = turing { logicians.push(turing_inner); } }
If you need to tack an Option
to the end of an existing iterator, you can
pass it to .chain()
:
#![allow(unused)] fn main() { let turing = Some("Turing"); let logicians = vec!["Curry", "Kleene", "Markov"]; for logician in logicians.iter().chain(turing.iter()) { println!("{} is a logician", logician); } }
Note that if the Option
is always Some
, then it is more idiomatic to use
std::iter::once
on the
element instead.
Also, since Option
implements IntoIterator
, it's possible to iterate over
it using a for
loop. This is equivalent to matching it with if let Some(..)
,
and in most cases you should prefer the latter.
See also
-
std::iter::once
is an iterator which yields exactly one element. It's a more readable alternative toSome(foo).into_iter()
. -
Iterator::filter_map
is a version ofIterator::flat_map
, specialized to mapping functions which returnOption
. -
The
ref_slice
crate provides functions for converting anOption
to a zero- or one-element slice.
Pass variables to closure
Description
By default, closures capture their environment by borrowing. Or you can use
move
-closure to move whole environment. However, often you want to move just
some variables to closure, give it copy of some data, pass it by reference, or
perform some other transformation.
Use variable rebinding in separate scope for that.
Example
Use
#![allow(unused)] fn main() { use std::rc::Rc; let num1 = Rc::new(1); let num2 = Rc::new(2); let num3 = Rc::new(3); let closure = { // `num1` is moved let num2 = num2.clone(); // `num2` is cloned let num3 = num3.as_ref(); // `num3` is borrowed move || { *num1 + *num2 + *num3; } }; }
instead of
#![allow(unused)] fn main() { use std::rc::Rc; let num1 = Rc::new(1); let num2 = Rc::new(2); let num3 = Rc::new(3); let num2_cloned = num2.clone(); let num3_borrowed = num3.as_ref(); let closure = move || { *num1 + *num2_cloned + *num3_borrowed; }; }
Advantages
Copied data are grouped together with closure definition, so their purpose is more clear, and they will be dropped immediately even if they are not consumed by closure.
Closure uses same variable names as surrounding code whether data are copied or moved.
Disadvantages
Additional indentation of closure body.
Privacy for extensibility
Description
Use a private field to ensure that a struct is extensible without breaking stability guarantees.
Example
mod a {
// Public struct.
pub struct S {
pub foo: i32,
// Private field.
bar: i32,
}
}
fn main(s: a::S) {
// Because S::bar is private, it cannot be named here and we must use `..`
// in the pattern.
let a::S { foo: _, ..} = s;
}
Discussion
Adding a field to a struct is a mostly backwards compatible change.
However, if a client uses a pattern to deconstruct a struct instance, they
might name all the fields in the struct and adding a new one would break that
pattern. The client could name some fields and use ..
in the pattern,
in which case adding another field is backwards compatible. Making at least one
of the struct's fields private forces clients to use the latter form of patterns,
ensuring that the struct is future-proof.
The downside of this approach is that you might need to add an otherwise unneeded
field to the struct. You can use the ()
type so that there is no runtime overhead
and prepend _
to the field name to avoid the unused field warning.
If Rust allowed private variants of enums, we could use the same trick to make
adding a variant to an enum backwards compatible. The problem there is exhaustive
match expressions. A private variant would force clients to have a _
wildcard
pattern. A common way to implement this instead is using the #[non_exhaustive]
attribute.
Easy doc initialization
Description
If a struct takes significant effort to initialize, when writing docs, it can be quicker to wrap your example with a helper function which takes the struct as an argument.
Motivation
Sometimes there is a struct with multiple or complicated parameters and several methods. Each of these methods should have examples.
For example:
struct Connection {
name: String,
stream: TcpStream,
}
impl Connection {
/// Sends a request over the connection.
///
/// # Example
/// ```no_run
/// # // Boilerplate are required to get an example working.
/// # let stream = TcpStream::connect("127.0.0.1:34254");
/// # let connection = Connection { name: "foo".to_owned(), stream };
/// # let request = Request::new("RequestId", RequestType::Get, "payload");
/// let response = connection.send_request(request);
/// assert!(response.is_ok());
/// ```
fn send_request(&self, request: Request) -> Result<Status, SendErr> {
// ...
}
/// Oh no, all that boilerplate needs to be repeated here!
fn check_status(&self) -> Status {
// ...
}
}
Example
Instead of typing all of this boilerplate to create an Connection
and
Request
it is easier to just create a wrapping helper function which takes
them as arguments:
struct Connection {
name: String,
stream: TcpStream,
}
impl Connection {
/// Sends a request over the connection.
///
/// # Example
/// ```
/// # fn call_send(connection: Connection, request: Request) {
/// let response = connection.send_request(request);
/// assert!(response.is_ok());
/// # }
/// ```
fn send_request(&self, request: Request) {
// ...
}
}
Note in the above example the line assert!(response.is_ok());
will not
actually run while testing because it is inside a function which is never
invoked.
Advantages
This is much more concise and avoids repetitive code in examples.
Disadvantages
As example is in a function, the code will not be tested. Though it will still be
checked to make sure it compiles when running a cargo test
. So this pattern is
most useful when you need no_run
. With this, you do not need to add no_run
.
Discussion
If assertions are not required this pattern works well.
If they are, an alternative can be to create a public method to create a helper
instance which is annotated with #[doc(hidden)]
(so that users won't see it).
Then this method can be called inside of rustdoc because it is part of the
crate's public API.
Temporary mutability
Description
Often it is necessary to prepare and process some data, but after that data are only inspected and never modified. The intention can be made explicit by redefining the mutable variable as immutable.
It can be done either by processing data within nested block or by redefining variable.
Example
Say, vector must be sorted before usage.
Using nested block:
let data = {
let mut data = get_vec();
data.sort();
data
};
// Here `data` is immutable.
Using variable rebinding:
let mut data = get_vec();
data.sort();
let data = data;
// Here `data` is immutable.
Advantages
Compiler ensures that you don't accidentally mutate data after some point.
Disadvantages
Nested block requires additional indentation of block body. One more line to return data from block or redefine variable.
Design Patterns
Design patterns are "general reusable solutions to a commonly occurring problem within a given context in software design". Design patterns are a great way to describe the culture of a programming language. Design patterns are very language-specific - what is a pattern in one language may be unnecessary in another due to a language feature, or impossible to express due to a missing feature.
If overused, design patterns can add unnecessary complexity to programs. However, they are a great way to share intermediate and advanced level knowledge about a programming language.
Design patterns in Rust
Rust has many unique features. These features give us great benefit by removing whole classes of problems. Some of them are also patterns that are unique to Rust.
YAGNI
If you're not familiar with it, YAGNI is an acronym that stands for
You Aren't Going to Need It
. It's an important software design principle to apply
as you write code.
The best code I ever wrote was code I never wrote.
If we apply YAGNI to design patterns, we see that the features of Rust allow us to throw out many patterns. For instance, there is no need for the strategy pattern in Rust because we can just use traits.
TODO: Maybe include some code to illustrate the traits.
Behavioural Patterns
From Wikipedia:
Design patterns that identify common communication patterns among objects. By doing so, these patterns increase flexibility in carrying out communication.
Command
Description
The basic idea of the Command pattern is to separate out actions into its own objects and pass them as parameters.
Motivation
Suppose we have a sequence of actions or transactions encapsulated as objects. We want these actions or commands to be executed or invoked in some order later at different time. These commands may also be triggered as a result of some event. For example, when a user pushes a button, or on arrival of a data packet. In addition, these commands might be undoable. This may come in useful for operations of an editor. We might want to store logs of executed commands so that we could reapply the changes later if the system crashes.
Example
Define two database operations create table
and add field
. Each of these
operations is a command which knows how to undo the command, e.g., drop table
and remove field
. When a user invokes a database migration operation then each
command is executed in the defined order, and when the user invokes the rollback
operation then the whole set of commands is invoked in reverse order.
Approach: Using trait objects
We define a common trait which encapsulates our command with two operations
execute
and rollback
. All command structs
must implement this trait.
pub trait Migration { fn execute(&self) -> &str; fn rollback(&self) -> &str; } pub struct CreateTable; impl Migration for CreateTable { fn execute(&self) -> &str { "create table" } fn rollback(&self) -> &str { "drop table" } } pub struct AddField; impl Migration for AddField { fn execute(&self) -> &str { "add field" } fn rollback(&self) -> &str { "remove field" } } struct Schema { commands: Vec<Box<dyn Migration>>, } impl Schema { fn new() -> Self { Self { commands: vec![] } } fn add_migration(&mut self, cmd: Box<dyn Migration>) { self.commands.push(cmd); } fn execute(&self) -> Vec<&str> { self.commands.iter().map(|cmd| cmd.execute()).collect() } fn rollback(&self) -> Vec<&str> { self.commands .iter() .rev() // reverse iterator's direction .map(|cmd| cmd.rollback()) .collect() } } fn main() { let mut schema = Schema::new(); let cmd = Box::new(CreateTable); schema.add_migration(cmd); let cmd = Box::new(AddField); schema.add_migration(cmd); assert_eq!(vec!["create table", "add field"], schema.execute()); assert_eq!(vec!["remove field", "drop table"], schema.rollback()); }
Approach: Using function pointers
We could follow another approach by creating each individual command as
a different function and store function pointers to invoke these functions later
at a different time. Since function pointers implement all three traits Fn
,
FnMut
, and FnOnce
we could as well pass and store closures instead of
function pointers.
type FnPtr = fn() -> String; struct Command { execute: FnPtr, rollback: FnPtr, } struct Schema { commands: Vec<Command>, } impl Schema { fn new() -> Self { Self { commands: vec![] } } fn add_migration(&mut self, execute: FnPtr, rollback: FnPtr) { self.commands.push(Command { execute, rollback }); } fn execute(&self) -> Vec<String> { self.commands.iter().map(|cmd| (cmd.execute)()).collect() } fn rollback(&self) -> Vec<String> { self.commands .iter() .rev() .map(|cmd| (cmd.rollback)()) .collect() } } fn add_field() -> String { "add field".to_string() } fn remove_field() -> String { "remove field".to_string() } fn main() { let mut schema = Schema::new(); schema.add_migration(|| "create table".to_string(), || "drop table".to_string()); schema.add_migration(add_field, remove_field); assert_eq!(vec!["create table", "add field"], schema.execute()); assert_eq!(vec!["remove field", "drop table"], schema.rollback()); }
Approach: Using Fn
trait objects
Finally, instead of defining a common command trait we could store
each command implementing the Fn
trait separately in vectors.
type Migration<'a> = Box<dyn Fn() -> &'a str>; struct Schema<'a> { executes: Vec<Migration<'a>>, rollbacks: Vec<Migration<'a>>, } impl<'a> Schema<'a> { fn new() -> Self { Self { executes: vec![], rollbacks: vec![], } } fn add_migration<E, R>(&mut self, execute: E, rollback: R) where E: Fn() -> &'a str + 'static, R: Fn() -> &'a str + 'static, { self.executes.push(Box::new(execute)); self.rollbacks.push(Box::new(rollback)); } fn execute(&self) -> Vec<&str> { self.executes.iter().map(|cmd| cmd()).collect() } fn rollback(&self) -> Vec<&str> { self.rollbacks.iter().rev().map(|cmd| cmd()).collect() } } fn add_field() -> &'static str { "add field" } fn remove_field() -> &'static str { "remove field" } fn main() { let mut schema = Schema::new(); schema.add_migration(|| "create table", || "drop table"); schema.add_migration(add_field, remove_field); assert_eq!(vec!["create table", "add field"], schema.execute()); assert_eq!(vec!["remove field", "drop table"], schema.rollback()); }
Discussion
If our commands are small and may be defined as functions or passed as a closure
then using function pointers might be preferable since it does not exploit
dynamic dispatch. But if our command is a whole struct with a bunch of functions
and variables defined as seperated module then using trait objects would be
more suitable. A case of application can be found in actix
,
which uses trait objects when it registers a handler function for routes.
In case of using Fn
trait objects we can create and use commands in the same
way as we used in case of function pointers.
As performance, there is always a trade-off between performance and code simplicity and organisation. Static dispatch gives faster performance, while dynamic dispatch provides flexibility when we structure our application.
See also
Interpreter
Description
If a problem occurs very often and requires long and repetitive steps to solve it, then the problem instances might be expressed in a simple language and an interpreter object could solve it by interpreting the sentences written in this simple language.
Basically, for any kind of problems we define:
- a domain specific language,
- a grammar for this language,
- an interpreter that solves the problem instances.
Motivation
Our goal is to translate simple mathematical expressions into postfix expressions
(or Reverse Polish notation)
For simplicity, our expressions consist of ten digits 0
, ..., 9
and two
operations +
, -
. For example, the expression 2 + 4
is translated into
2 4 +
.
Context Free Grammar for our problem
Our task is translated infix expressions into postfix ones. Let's define a context
free grammar for a set of infix expressions over 0
, ..., 9
, +
, and -
,
where:
- terminal symbols:
0
, ...,9
,+
,-
- non-terminal symbols:
exp
,term
- start symbol is
exp
- and the following are production rules
exp -> exp + term
exp -> exp - term
exp -> term
term -> 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
NOTE: This grammar should be further transformed depending on what we are going to do with it. For example, we might need to remove left recursion. For more details please see Compilers: Principles,Techniques, and Tools (aka Dragon Book).
Solution
We simply implement a recursive descent parser. For simplicity's sake, the code
panics when an expression is syntactically wrong (for example 2-34
or 2+5-
are wrong according to the grammar definition).
pub struct Interpreter<'a> { it: std::str::Chars<'a>, } impl<'a> Interpreter<'a> { pub fn new(infix: &'a str) -> Self { Self { it: infix.chars() } } fn next_char(&mut self) -> Option<char> { self.it.next() } pub fn interpret(&mut self, out: &mut String) { self.term(out); while let Some(op) = self.next_char() { if op == '+' || op == '-' { self.term(out); out.push(op); } else { panic!("Unexpected symbol '{}'", op); } } } fn term(&mut self, out: &mut String) { match self.next_char() { Some(ch) if ch.is_digit(10) => out.push(ch), Some(ch) => panic!("Unexpected symbol '{}'", ch), None => panic!("Unexpected end of string"), } } } pub fn main() { let mut intr = Interpreter::new("2+3"); let mut postfix = String::new(); intr.interpret(&mut postfix); assert_eq!(postfix, "23+"); intr = Interpreter::new("1-2+3-4"); postfix.clear(); intr.interpret(&mut postfix); assert_eq!(postfix, "12-3+4-"); }
Discussion
There may be a wrong perception that the Interpreter design pattern is about design
grammars for formal languages and implementation of parsers for these grammars.
In fact, this pattern is about expressing problem instances in a more specific
way and implementing functions/classes/structs that solve these problem instances.
Rust language has macro_rules!
that allow to define special syntax and rules
on how to expand this syntax into source code.
In the following example we create a simple macro_rules!
that computes
Euclidean length of n
dimensional vectors. Writing norm!(x,1,2)
might be easier to express and more
efficient than packing x,1,2
into a Vec
and calling a function computing
the length.
macro_rules! norm { ($($element:expr),*) => { { let mut n = 0.0; $( n += ($element as f64)*($element as f64); )* n.sqrt() } }; } fn main() { let x = -3f64; let y = 4f64; assert_eq!(3f64, norm!(x)); assert_eq!(5f64, norm!(x, y)); assert_eq!(0f64, norm!(0, 0, 0)); assert_eq!(1f64, norm!(0.5, -0.5, 0.5, -0.5)); }
See also
Newtype
What if in some cases we want a type to behave similar to another type or enforce some behaviour at compile time when using only type aliases would not be enough?
For example, if we want to create a custom Display
implementation for String
due to security considerations (e.g. passwords).
For such cases we could use the Newtype
pattern to provide type safety
and encapsulation.
Description
Use a tuple struct with a single field to make an opaque wrapper for a type.
This creates a new type, rather than an alias to a type (type
items).
Example
// Some type, not necessarily in the same module or even crate.
struct Foo {
//..
}
impl Foo {
// These functions are not present on Bar.
//..
}
// The newtype.
pub struct Bar(Foo);
impl Bar {
// Constructor.
pub fn new(
//..
) -> Bar {
//..
}
//..
}
fn main() {
let b = Bar::new(...);
// Foo and Bar are type incompatible, the following do not type check.
// let f: Foo = b;
// let b: Bar = Foo { ... };
}
Motivation
The primary motivation for newtypes is abstraction. It allows you to share implementation details between types while precisely controlling the interface. By using a newtype rather than exposing the implementation type as part of an API, it allows you to change implementation backwards compatibly.
Newtypes can be used for distinguishing units, e.g., wrapping f64
to give
distinguishable Miles
and Kms
.
Advantages
The wrapped and wrapper types are not type compatible (as opposed to using
type
), so users of the newtype will never 'confuse' the wrapped and wrapper
types.
Newtypes are a zero-cost abstraction - there is no runtime overhead.
The privacy system ensures that users cannot access the wrapped type (if the field is private, which it is by default).
Disadvantages
The downside of newtypes (especially compared with type aliases), is that there is no special language support. This means there can be a lot of boilerplate. You need a 'pass through' method for every method you want to expose on the wrapped type, and an impl for every trait you want to also be implemented for the wrapper type.
Discussion
Newtypes are very common in Rust code. Abstraction or representing units are the most common uses, but they can be used for other reasons:
- restricting functionality (reduce the functions exposed or traits implemented),
- making a type with copy semantics have move semantics,
- abstraction by providing a more concrete type and thus hiding internal types, e.g.,
pub struct Foo(Bar<T1, T2>);
Here, Bar
might be some public, generic type and T1
and T2
are some internal
types. Users of our module shouldn't know that we implement Foo
by using a Bar
,
but what we're really hiding here is the types T1
and T2
, and how they are used
with Bar
.
See also
- Advanced Types in the book
- Newtypes in Haskell
- Type aliases
- derive_more, a crate for deriving many builtin traits on newtypes.
- The Newtype Pattern In Rust
RAII with guards
Description
RAII stands for "Resource Acquisition is Initialisation" which is a terrible name. The essence of the pattern is that resource initialisation is done in the constructor of an object and finalisation in the destructor. This pattern is extended in Rust by using an RAII object as a guard of some resource and relying on the type system to ensure that access is always mediated by the guard object.
Example
Mutex guards are the classic example of this pattern from the std library (this is a simplified version of the real implementation):
use std::ops::Deref;
struct Foo {}
struct Mutex<T> {
// We keep a reference to our data: T here.
//..
}
struct MutexGuard<'a, T: 'a> {
data: &'a T,
//..
}
// Locking the mutex is explicit.
impl<T> Mutex<T> {
fn lock(&self) -> MutexGuard<T> {
// Lock the underlying OS mutex.
//..
// MutexGuard keeps a reference to self
MutexGuard {
data: self,
//..
}
}
}
// Destructor for unlocking the mutex.
impl<'a, T> Drop for MutexGuard<'a, T> {
fn drop(&mut self) {
// Unlock the underlying OS mutex.
//..
}
}
// Implementing Deref means we can treat MutexGuard like a pointer to T.
impl<'a, T> Deref for MutexGuard<'a, T> {
type Target = T;
fn deref(&self) -> &T {
self.data
}
}
fn baz(x: Mutex<Foo>) {
let xx = x.lock();
xx.foo(); // foo is a method on Foo.
// The borrow checker ensures we can't store a reference to the underlying
// Foo which will outlive the guard xx.
// x is unlocked when we exit this function and xx's destructor is executed.
}
Motivation
Where a resource must be finalised after use, RAII can be used to do this finalisation. If it is an error to access that resource after finalisation, then this pattern can be used to prevent such errors.
Advantages
Prevents errors where a resource is not finalised and where a resource is used after finalisation.
Discussion
RAII is a useful pattern for ensuring resources are properly deallocated or finalised. We can make use of the borrow checker in Rust to statically prevent errors stemming from using resources after finalisation takes place.
The core aim of the borrow checker is to ensure that references to data do not
outlive that data. The RAII guard pattern works because the guard object
contains a reference to the underlying resource and only exposes such
references. Rust ensures that the guard cannot outlive the underlying resource
and that references to the resource mediated by the guard cannot outlive the
guard. To see how this works it is helpful to examine the signature of deref
without lifetime elision:
fn deref<'a>(&'a self) -> &'a T {
//..
}
The returned reference to the resource has the same lifetime as self
('a
).
The borrow checker therefore ensures that the lifetime of the reference to T
is shorter than the lifetime of self
.
Note that implementing Deref
is not a core part of this pattern, it only makes
using the guard object more ergonomic. Implementing a get
method on the guard
works just as well.
See also
Finalisation in destructors idiom
RAII is a common pattern in C++: cppreference.com, wikipedia.
Style guide entry (currently just a placeholder).
Strategy (aka Policy)
Description
The Strategy design pattern is a technique that enables separation of concerns. It also allows to decouple software modules through Dependency Inversion.
The basic idea behind the Strategy pattern is that, given an algorithm solving a particular problem, we define only the skeleton of the algorithm at an abstract level, and we separate the specific algorithm’s implementation into different parts.
In this way, a client using the algorithm may choose a specific implementation, while the general algorithm workflow remains the same. In other words, the abstract specification of the class does not depend on the specific implementation of the derived class, but specific implementation must adhere to the abstract specification. This is why we call it "Dependency Inversion".
Motivation
Imagine we are working on a project that generates reports every month.
We need the reports to be generated in different formats (strategies), e.g.,
in JSON
or Plain Text
formats.
But things vary over time, and we don't know what kind of requirement we may get
in the future. For example, we may need to generate our report in a completly new
format, or just modify one of the existing formats.
Example
In this example our invariants (or abstractions) are Context
, Formatter
,
and Report
, while Text
and Json
are our strategy structs. These strategies
have to implement the Formatter
trait.
use std::collections::HashMap; type Data = HashMap<String, u32>; trait Formatter { fn format(&self, data: &Data, buf: &mut String); } struct Report; impl Report { // Write should be used but we kept it as String to ignore error handling fn generate<T: Formatter>(g: T, s: &mut String) { // backend operations... let mut data = HashMap::new(); data.insert("one".to_string(), 1); data.insert("two".to_string(), 2); // generate report g.format(&data, s); } } struct Text; impl Formatter for Text { fn format(&self, data: &Data, buf: &mut String) { for (k, v) in data { let entry = format!("{} {}\n", k, v); buf.push_str(&entry); } } } struct Json; impl Formatter for Json { fn format(&self, data: &Data, buf: &mut String) { buf.push('['); for (k, v) in data.into_iter() { let entry = format!(r#"{{"{}":"{}"}}"#, k, v); buf.push_str(&entry); buf.push(','); } buf.pop(); // remove extra , at the end buf.push(']'); } } fn main() { let mut s = String::from(""); Report::generate(Text, &mut s); assert!(s.contains("one 1")); assert!(s.contains("two 2")); s.clear(); // reuse the same buffer Report::generate(Json, &mut s); assert!(s.contains(r#"{"one":"1"}"#)); assert!(s.contains(r#"{"two":"2"}"#)); }
Advantages
The main advantage is separation of concerns. For example, in this case Report
does not know anything about specific implementations of Json
and Text
,
whereas the output implementations does not care about how data is preprocessed,
stored, and fetched. The only thing they have to know is context and a specific
trait and method to implement, i.e,Formatter
and run
.
Disadvantages
For each strategy there must be implemented at least one module, so number of modules increases with number of strategies. If there are many strategies to choose from then users have to know how strategies differ from one another.
Discussion
In the previous example all strategies are implemented in a single file. Ways of providing different strategies includes:
- All in one file (as shown in this example, similar to being separated as modules)
- Separated as modules, E.g.
formatter::json
module,formatter::text
module - Use compiler feature flags, E.g.
json
feature,text
feature - Separated as crates, E.g.
json
crate,text
crate
Serde crate is a good example of the Strategy
pattern in action. Serde allows
full customization of the serialization
behavior by manually implementing Serialize
and Deserialize
traits for our
type. For example, we could easily swap serde_json
with serde_cbor
since they
expose similar methods. Having this makes the helper crate serde_transcode
much
more useful and ergonomic.
However, we don't need to use traits in order to design this pattern in Rust.
The following toy example demonstrates the idea of the Strategy pattern using Rust
closures
:
struct Adder; impl Adder { pub fn add<F>(x: u8, y: u8, f: F) -> u8 where F: Fn(u8, u8) -> u8, { f(x, y) } } fn main() { let arith_adder = |x, y| x + y; let bool_adder = |x, y| { if x == 1 || y == 1 { 1 } else { 0 } }; let custom_adder = |x, y| 2 * x + y; assert_eq!(9, Adder::add(4, 5, arith_adder)); assert_eq!(0, Adder::add(0, 0, bool_adder)); assert_eq!(5, Adder::add(1, 3, custom_adder)); }
In fact, Rust already uses this idea for Options
's map
method:
fn main() { let val = Some("Rust"); let len_strategy = |s: &str| s.len(); assert_eq!(4, val.map(len_strategy).unwrap()); let first_byte_strategy = |s: &str| s.bytes().next().unwrap(); assert_eq!(82, val.map(first_byte_strategy).unwrap()); }
See also
Visitor
Description
A visitor encapsulates an algorithm that operates over a heterogeneous collection of objects. It allows multiple different algorithms to be written over the same data without having to modify the data (or their primary behaviour).
Furthermore, the visitor pattern allows separating the traversal of a collection of objects from the operations performed on each object.
Example
// The data we will visit
mod ast {
pub enum Stmt {
Expr(Expr),
Let(Name, Expr),
}
pub struct Name {
value: String,
}
pub enum Expr {
IntLit(i64),
Add(Box<Expr>, Box<Expr>),
Sub(Box<Expr>, Box<Expr>),
}
}
// The abstract visitor
mod visit {
use ast::*;
pub trait Visitor<T> {
fn visit_name(&mut self, n: &Name) -> T;
fn visit_stmt(&mut self, s: &Stmt) -> T;
fn visit_expr(&mut self, e: &Expr) -> T;
}
}
use visit::*;
use ast::*;
// An example concrete implementation - walks the AST interpreting it as code.
struct Interpreter;
impl Visitor<i64> for Interpreter {
fn visit_name(&mut self, n: &Name) -> i64 { panic!() }
fn visit_stmt(&mut self, s: &Stmt) -> i64 {
match *s {
Stmt::Expr(ref e) => self.visit_expr(e),
Stmt::Let(..) => unimplemented!(),
}
}
fn visit_expr(&mut self, e: &Expr) -> i64 {
match *e {
Expr::IntLit(n) => n,
Expr::Add(ref lhs, ref rhs) => self.visit_expr(lhs) + self.visit_expr(rhs),
Expr::Sub(ref lhs, ref rhs) => self.visit_expr(lhs) - self.visit_expr(rhs),
}
}
}
One could implement further visitors, for example a type checker, without having to modify the AST data.
Motivation
The visitor pattern is useful anywhere that you want to apply an algorithm to heterogeneous data. If data is homogeneous, you can use an iterator-like pattern. Using a visitor object (rather than a functional approach) allows the visitor to be stateful and thus communicate information between nodes.
Discussion
It is common for the visit_*
methods to return void (as opposed to in the
example). In that case it is possible to factor out the traversal code and share
it between algorithms (and also to provide noop default methods). In Rust, the
common way to do this is to provide walk_*
functions for each datum. For
example,
pub fn walk_expr(visitor: &mut Visitor, e: &Expr) {
match *e {
Expr::IntLit(_) => {},
Expr::Add(ref lhs, ref rhs) => {
visitor.visit_expr(lhs);
visitor.visit_expr(rhs);
}
Expr::Sub(ref lhs, ref rhs) => {
visitor.visit_expr(lhs);
visitor.visit_expr(rhs);
}
}
}
In other languages (e.g., Java) it is common for data to have an accept
method
which performs the same duty.
See also
The visitor pattern is a common pattern in most OO languages.
The fold pattern is similar to visitor but produces a new version of the visited data structure.
Creational Patterns
From Wikipedia:
Design patterns that deal with object creation mechanisms, trying to create objects in a manner suitable to the situation. The basic form of object creation could result in design problems or in added complexity to the design. Creational design patterns solve this problem by somehow controlling this object creation.
Builder
Description
Construct an object with calls to a builder helper.
Example
#![allow(unused)] fn main() { #[derive(Debug, PartialEq)] pub struct Foo { // Lots of complicated fields. bar: String, } impl Foo { // This method will help users to discover the builder pub fn builder() -> FooBuilder { FooBuilder::default() } } #[derive(Default)] pub struct FooBuilder { // Probably lots of optional fields. bar: String, } impl FooBuilder { pub fn new(/* ... */) -> FooBuilder { // Set the minimally required fields of Foo. FooBuilder { bar: String::from("X"), } } pub fn name(mut self, bar: String) -> FooBuilder { // Set the name on the builder itself, and return the builder by value. self.bar = bar; self } // If we can get away with not consuming the Builder here, that is an // advantage. It means we can use the FooBuilder as a template for constructing // many Foos. pub fn build(self) -> Foo { // Create a Foo from the FooBuilder, applying all settings in FooBuilder // to Foo. Foo { bar: self.bar } } } #[test] fn builder_test() { let foo = Foo { bar: String::from("Y"), }; let foo_from_builder: Foo = FooBuilder::new().name(String::from("Y")).build(); assert_eq!(foo, foo_from_builder); } }
Motivation
Useful when you would otherwise require many constructors or where construction has side effects.
Advantages
Separates methods for building from other methods.
Prevents proliferation of constructors
Can be used for one-liner initialisation as well as more complex construction.
Disadvantages
More complex than creating a struct object directly, or a simple constructor function.
Discussion
This pattern is seen more frequently in Rust (and for simpler objects) than in many other languages because Rust lacks overloading. Since you can only have a single method with a given name, having multiple constructors is less nice in Rust than in C++, Java, or others.
This pattern is often used where the builder object is useful in its own right,
rather than being just a builder. For example, see
std::process::Command
is a builder for Child
(a process). In these cases, the T
and TBuilder
pattern
of naming is not used.
The example takes and returns the builder by value. It is often more ergonomic (and more efficient) to take and return the builder as a mutable reference. The borrow checker makes this work naturally. This approach has the advantage that one can write code like
let mut fb = FooBuilder::new();
fb.a();
fb.b();
let f = fb.build();
as well as the FooBuilder::new().a().b().build()
style.
See also
- Description in the style guide
- derive_builder, a crate for automatically implementing this pattern while avoiding the boilerplate.
- Constructor pattern for when construction is simpler.
- Builder pattern (wikipedia)
- Construction of complex values
Fold
Description
Run an algorithm over each item in a collection of data to create a new item, thus creating a whole new collection.
The etymology here is unclear to me. The terms 'fold' and 'folder' are used in the Rust compiler, although it appears to me to be more like a map than a fold in the usual sense. See the discussion below for more details.
Example
// The data we will fold, a simple AST.
mod ast {
pub enum Stmt {
Expr(Box<Expr>),
Let(Box<Name>, Box<Expr>),
}
pub struct Name {
value: String,
}
pub enum Expr {
IntLit(i64),
Add(Box<Expr>, Box<Expr>),
Sub(Box<Expr>, Box<Expr>),
}
}
// The abstract folder
mod fold {
use ast::*;
pub trait Folder {
// A leaf node just returns the node itself. In some cases, we can do this
// to inner nodes too.
fn fold_name(&mut self, n: Box<Name>) -> Box<Name> { n }
// Create a new inner node by folding its children.
fn fold_stmt(&mut self, s: Box<Stmt>) -> Box<Stmt> {
match *s {
Stmt::Expr(e) => Box::new(Stmt::Expr(self.fold_expr(e))),
Stmt::Let(n, e) => Box::new(Stmt::Let(self.fold_name(n), self.fold_expr(e))),
}
}
fn fold_expr(&mut self, e: Box<Expr>) -> Box<Expr> { ... }
}
}
use fold::*;
use ast::*;
// An example concrete implementation - renames every name to 'foo'.
struct Renamer;
impl Folder for Renamer {
fn fold_name(&mut self, n: Box<Name>) -> Box<Name> {
Box::new(Name { value: "foo".to_owned() })
}
// Use the default methods for the other nodes.
}
The result of running the Renamer
on an AST is a new AST identical to the old
one, but with every name changed to foo
. A real life folder might have some
state preserved between nodes in the struct itself.
A folder can also be defined to map one data structure to a different (but usually similar) data structure. For example, we could fold an AST into a HIR tree (HIR stands for high-level intermediate representation).
Motivation
It is common to want to map a data structure by performing some operation on
each node in the structure. For simple operations on simple data structures,
this can be done using Iterator::map
. For more complex operations, perhaps
where earlier nodes can affect the operation on later nodes, or where iteration
over the data structure is non-trivial, using the fold pattern is more
appropriate.
Like the visitor pattern, the fold pattern allows us to separate traversal of a data structure from the operations performed to each node.
Discussion
Mapping data structures in this fashion is common in functional languages. In OO languages, it would be more common to mutate the data structure in place. The 'functional' approach is common in Rust, mostly due to the preference for immutability. Using fresh data structures, rather than mutating old ones, makes reasoning about the code easier in most circumstances.
The trade-off between efficiency and reusability can be tweaked by changing how
nodes are accepted by the fold_*
methods.
In the above example we operate on Box
pointers. Since these own their data
exclusively, the original copy of the data structure cannot be re-used. On the
other hand if a node is not changed, reusing it is very efficient.
If we were to operate on borrowed references, the original data structure can be reused; however, a node must be cloned even if unchanged, which can be expensive.
Using a reference counted pointer gives the best of both worlds - we can reuse the original data structure, and we don't need to clone unchanged nodes. However, they are less ergonomic to use and mean that the data structures cannot be mutable.
See also
Iterators have a fold
method, however this folds a data structure into a
value, rather than into a new data structure. An iterator's map
is more like
this fold pattern.
In other languages, fold is usually used in the sense of Rust's iterators, rather than this pattern. Some functional languages have powerful constructs for performing flexible maps over data structures.
The visitor pattern is closely related to fold. They share the concept of walking a data structure performing an operation on each node. However, the visitor does not create a new data structure nor consume the old one.
Structural Patterns
From Wikipedia:
Design patterns that ease the design by identifying a simple way to realize relationships among entities.
Compose structs together for better borrowing
TODO - this is not a very snappy name
Description
Sometimes a large struct will cause issues with the borrow checker - although fields can be borrowed independently, sometimes the whole struct ends up being used at once, preventing other uses. A solution might be to decompose the struct into several smaller structs. Then compose these together into the original struct. Then each struct can be borrowed separately and have more flexible behaviour.
This will often lead to a better design in other ways: applying this design pattern often reveals smaller units of functionality.
Example
Here is a contrived example of where the borrow checker foils us in our plan to use a struct:
#![allow(unused)] fn main() { struct A { f1: u32, f2: u32, f3: u32, } fn foo(a: &mut A) -> &u32 { &a.f2 } fn bar(a: &mut A) -> u32 { a.f1 + a.f3 } fn baz(a: &mut A) { // The later usage of x causes a to be borrowed for the rest of the function. let x = foo(a); // Borrow checker error: // let y = bar(a); // ~ ERROR: cannot borrow `*a` as mutable more than once // at a time println!("{}", x); } }
We can apply this design pattern and refactor A
into two smaller structs, thus
solving the borrow checking issue:
#![allow(unused)] fn main() { // A is now composed of two structs - B and C. struct A { b: B, c: C, } struct B { f2: u32, } struct C { f1: u32, f3: u32, } // These functions take a B or C, rather than A. fn foo(b: &mut B) -> &u32 { &b.f2 } fn bar(c: &mut C) -> u32 { c.f1 + c.f3 } fn baz(a: &mut A) { let x = foo(&mut a.b); // Now it's OK! let y = bar(&mut a.c); println!("{}", x); } }
Motivation
TODO Why and where you should use the pattern
Advantages
Lets you work around limitations in the borrow checker.
Often produces a better design.
Disadvantages
Leads to more verbose code.
Sometimes, the smaller structs are not good abstractions, and so we end up with a worse design. That is probably a 'code smell', indicating that the program should be refactored in some way.
Discussion
This pattern is not required in languages that don't have a borrow checker, so in that sense is unique to Rust. However, making smaller units of functionality often leads to cleaner code: a widely acknowledged principle of software engineering, independent of the language.
This pattern relies on Rust's borrow checker to be able to borrow fields
independently of each other. In the example, the borrow checker knows that a.b
and a.c
are distinct and can be borrowed independently, it does not try to
borrow all of a
, which would make this pattern useless.
Prefer small crates
Description
Prefer small crates that do one thing well.
Cargo and crates.io make it easy to add third-party libraries, much more so than in say C or C++. Moreover, since packages on crates.io cannot be edited or removed after publication, any build that works now should continue to work in the future. We should take advantage of this tooling, and use smaller, more fine-grained dependencies.
Advantages
- Small crates are easier to understand, and encourage more modular code.
- Crates allow for re-using code between projects.
For example, the
url
crate was developed as part of the Servo browser engine, but has since found wide use outside the project. - Since the compilation unit of Rust is the crate, splitting a project into multiple crates can allow more of the code to be built in parallel.
Disadvantages
- This can lead to "dependency hell", when a project depends on multiple conflicting
versions of a crate at the same time. For example, the
url
crate has both versions 1.0 and 0.5. Since theUrl
fromurl:1.0
and theUrl
fromurl:0.5
are different types, an HTTP client that usesurl:0.5
would not acceptUrl
values from a web scraper that usesurl:1.0
. - Packages on crates.io are not curated. A crate may be poorly written, have unhelpful documentation, or be outright malicious.
- Two small crates may be less optimized than one large one, since the compiler does not perform link-time optimization (LTO) by default.
Examples
The ref_slice
crate provides functions
for converting &T
to &[T]
.
The url
crate provides tools for working with
URLs.
The num_cpus
crate provides a function to
query the number of CPUs on a machine.
See also
Contain unsafety in small modules
Description
If you have unsafe
code, create the smallest possible module that can uphold
the needed invariants to build a minimal safe interface upon the unsafety. Embed
this into a larger module that contains only safe code and presents an ergonomic
interface. Note that the outer module can contain unsafe functions and methods
that call directly into the unsafe code. Users may use this to gain speed benefits.
Advantages
- This restricts the unsafe code that must be audited
- Writing the outer module is much easier, since you can count on the guarantees of the inner module
Disadvantages
- Sometimes, it may be hard to find a suitable interface.
- The abstraction may introduce inefficiencies.
Examples
- The
toolshed
crate contains its unsafe operations in submodules, presenting a safe interface to users. *std
sString
class is a wrapper overVec<u8>
with the added invariant that the contents must be valid UTF-8. The operations onString
ensure this behavior. However, users have the option of using anunsafe
method to create aString
, in which case the onus is on them to guarantee the validity of the contents.
See also
FFI Patterns
Writing FFI code is an entire course in itself. However, there are several idioms here that can act as pointers, and avoid traps for inexperienced users of unsafe Rust.
This section contains design patterns that may be useful when doing FFI.
-
Object-Based API design that has good memory safety characteristics, and a clean boundary of what is safe and what is unsafe
-
Type Consolidation into Wrappers - group multiple Rust types together into an opaque "object"
Object-Based APIs
Description
When designing APIs in Rust which are exposed to other languages, there are some important design principles which are contrary to normal Rust API design:
- All Encapsulated types should be owned by Rust, managed by the user, and opaque.
- All Transactional data types should be owned by the user, and transparent.
- All library behavior should be functions acting upon Encapsulated types.
- All library behavior should be encapsulated into types not based on structure, but provenance/lifetime.
Motivation
Rust has built-in FFI support to other languages. It does this by providing a way for crate authors to provide C-compatible APIs through different ABIs (though that is unimportant to this practice).
Well-designed Rust FFI follows C API design principles, while compromising the design in Rust as little as possible. There are three goals with any foreign API:
- Make it easy to use in the target language.
- Avoid the API dictating internal unsafety on the Rust side as much as possible.
- Keep the potential for memory unsafety and Rust
undefined behaviour
as small as possible.
Rust code must trust the memory safety of the foreign language beyond a certain
point. However, every bit of unsafe
code on the Rust side is an opportunity for
bugs, or to exacerbate undefined behaviour
.
For example, if a pointer provenance is wrong, that may be a segfault due to invalid memory access. But if it is manipulated by unsafe code, it could become full-blown heap corruption.
The Object-Based API design allows for writing shims that have good memory safety
characteristics, and a clean boundary of what is safe and what is unsafe
.
Code Example
The POSIX standard defines the API to access an on-file database, known as DBM. It is an excellent example of an "object-based" API.
Here is the definition in C, which hopefully should be easy to read for those involved in FFI. The commentary below should help explain it for those who miss the subtleties.
struct DBM;
typedef struct { void *dptr, size_t dsize } datum;
int dbm_clearerr(DBM *);
void dbm_close(DBM *);
int dbm_delete(DBM *, datum);
int dbm_error(DBM *);
datum dbm_fetch(DBM *, datum);
datum dbm_firstkey(DBM *);
datum dbm_nextkey(DBM *);
DBM *dbm_open(const char *, int, mode_t);
int dbm_store(DBM *, datum, datum, int);
This API defines two types: DBM
and datum
.
The DBM
type was called an "encapsulated" type above.
It is designed to contain internal state, and acts as an entry point for the
library's behavior.
It is completely opaque to the user, who cannot create a DBM
themselves since
they don't know its size or layout. Instead, they must call dbm_open
, and that
only gives them a pointer to one.
This means all DBM
s are "owned" by the library in a Rust sense.
The internal state of unknown size is kept in memory controlled by the library,
not the user. The user can only manage its life cycle with open
and close
,
and perform operations on it with the other functions.
The datum
type was called a "transactional" type above.
It is designed to facilitate the exchange of information between the library and
its user.
The database is designed to store "unstructured data", with no pre-defined length
or meaning. As a result, the datum
is the C equivalent of a Rust slice: a bunch
of bytes, and a count of how many there are. The main difference is that there is
no type information, which is what void
indicates.
Keep in mind that this header is written from the library's point of view.
The user likely has some type they are using, which has a known size.
But the library does not care, and by the rules of C casting, any type behind a
pointer can be cast to void
.
As noted earlier, this type is transparent to the user. But also, this type is owned by the user. This has subtle ramifications, due to that pointer inside it. The question is, who owns the memory that pointer points to?
The answer for best memory safety is, "the user".
But in cases such as retrieving a value, the user does not know how to allocate
it correctly (since they don't know how long the value is). In this case, the library
code is expected to use the heap that the user has access to -- such as the C library
malloc
and free
-- and then transfer ownership in the Rust sense.
This may all seem speculative, but this is what a pointer means in C. It means the same thing as Rust: "user defined lifetime." The user of the library needs to read the documentation in order to use it correctly. That said, there are some decisions that have fewer or greater consequences if users do it wrong. Minimizing those are what this best practice is about, and the key is to transfer ownership of everything that is transparent.
Advantages
This minimizes the number of memory safety guarantees the user must uphold to a relatively small number:
- Do not call any function with a pointer not returned by
dbm_open
(invalid access or corruption). - Do not call any function on a pointer after close (use after free).
- The
dptr
on anydatum
must beNULL
, or point to a valid slice of memory at the advertised length.
In addition, it avoids a lot of pointer provenance issues. To understand why, let us consider an alternative in some depth: key iteration.
Rust is well known for its iterators.
When implementing one, the programmer makes a separate type with a bounded lifetime
to its owner, and implements the Iterator
trait.
Here is how iteration would be done in Rust for DBM
:
struct Dbm { ... }
impl Dbm {
/* ... */
pub fn keys<'it>(&'it self) -> DbmKeysIter<'it> { ... }
/* ... */
}
struct DbmKeysIter<'it> {
owner: &'it Dbm,
}
impl<'it> Iterator for DbmKeysIter<'it> { ... }
This is clean, idiomatic, and safe. thanks to Rust's guarantees. However, consider what a straightforward API translation would look like:
#[no_mangle]
pub extern "C" fn dbm_iter_new(owner: *const Dbm) -> *mut DbmKeysIter {
// THIS API IS A BAD IDEA! For real applications, use object-based design instead.
}
#[no_mangle]
pub extern "C" fn dbm_iter_next(
iter: *mut DbmKeysIter,
key_out: *const datum
) -> libc::c_int {
// THIS API IS A BAD IDEA! For real applications, use object-based design instead.
}
#[no_mangle]
pub extern "C" fn dbm_iter_del(*mut DbmKeysIter) {
// THIS API IS A BAD IDEA! For real applications, use object-based design instead.
}
This API loses a key piece of information: the lifetime of the iterator must not
exceed the lifetime of the Dbm
object that owns it. A user of the library could
use it in a way which causes the iterator to outlive the data it is iterating on,
resulting in reading uninitialized memory.
This example written in C contains a bug that will be explained afterwards:
int count_key_sizes(DBM *db) {
// DO NOT USE THIS FUNCTION. IT HAS A SUBTLE BUT SERIOUS BUG!
datum key;
int len = 0;
if (!dbm_iter_new(db)) {
dbm_close(db);
return -1;
}
int l;
while ((l = dbm_iter_next(owner, &key)) >= 0) { // an error is indicated by -1
free(key.dptr);
len += key.dsize;
if (l == 0) { // end of the iterator
dbm_close(owner);
}
}
if l >= 0 {
return -1;
} else {
return len;
}
}
This bug is a classic. Here's what happens when the iterator returns the end-of-iteration marker:
- The loop condition sets
l
to zero, and enters the loop because0 >= 0
. - The length is incremented, in this case by zero.
- The if statement is true, so the database is closed. There should be a break statement here.
- The loop condition executes again, causing a
next
call on the closed object.
The worst part about this bug?
If the Rust implementation was careful, this code will work most of the time!
If the memory for the Dbm
object is not immediately reused, an internal check
will almost certainly fail, resulting in the iterator returning a -1
indicating
an error. But occasionally, it will cause a segmentation fault, or even worse,
nonsensical memory corruption!
None of this can be avoided by Rust. From its perspective, it put those objects on its heap, returned pointers to them, and gave up control of their lifetimes. The C code simply must "play nice".
The programmer must read and understand the API documentation.
While some consider that par for the course in C, a good API design can mitigate
this risk. The POSIX API for DBM
did this by consolidating the ownership of
the iterator with its parent:
datum dbm_firstkey(DBM *);
datum dbm_nextkey(DBM *);
Thus, all the lifetimes were bound together, and such unsafety was prevented.
Disadvantages
However, this design choice also has a number of drawbacks, which should be considered as well.
First, the API itself becomes less expressive. With POSIX DBM, there is only one iterator per object, and every call changes its state. This is much more restrictive than iterators in almost any language, even though it is safe. Perhaps with other related objects, whose lifetimes are less hierarchical, this limitation is more of a cost than the safety.
Second, depending on the relationships of the API's parts, significant design effort may be involved. Many of the easier design points have other patterns associated with them:
-
Wrapper Type Consolidation groups multiple Rust types together into an opaque "object"
-
FFI Error Passing explains error handling with integer codes and sentinel return values (such as
NULL
pointers) -
Accepting Foreign Strings allows accepting strings with minimal unsafe code, and is easier to get right than Passing Strings to FFI
However, not every API can be done this way. It is up to the best judgement of the programmer as to who their audience is.
Type Consolidation into Wrappers
Description
This pattern is designed to allow gracefully handling multiple related types, while minimizing the surface area for memory unsafety.
One of the cornerstones of Rust's aliasing rules is lifetimes. This ensures that many patterns of access between types can be memory safe, data race safety included.
However, when Rust types are exported to other languages, they are usually transformed into pointers. In Rust, a pointer means "the user manages the lifetime of the pointee." It is their responsibility to avoid memory unsafety.
Some level of trust in the user code is thus required, notably around use-after-free which Rust can do nothing about. However, some API designs place higher burdens than others on the code written in the other language.
The lowest risk API is the "consolidated wrapper", where all possible interactions with an object are folded into a "wrapper type", while keeping the Rust API clean.
Code Example
To understand this, let us look at a classic example of an API to export: iteration through a collection.
That API looks like this:
- The iterator is initialized with
first_key
. - Each call to
next_key
will advance the iterator. - Calls to
next_key
if the iterator is at the end will do nothing. - As noted above, the iterator is "wrapped into" the collection (unlike the native Rust API).
If the iterator implements nth()
efficiently, then it is possible to make it
ephemeral to each function call:
struct MySetWrapper {
myset: MySet,
iter_next: usize,
}
impl MySetWrapper {
pub fn first_key(&mut self) -> Option<&Key> {
self.iter_next = 0;
self.next_key()
}
pub fn next_key(&mut self) -> Option<&Key> {
if let Some(next) = self.myset.keys().nth(self.iter_next) {
self.iter_next += 1;
Some(next)
} else {
None
}
}
}
As a result, the wrapper is simple and contains no unsafe
code.
Advantages
This makes APIs safer to use, avoiding issues with lifetimes between types. See Object-Based APIs for more on the advantages and pitfalls this avoids.
Disadvantages
Often, wrapping types is quite difficult, and sometimes a Rust API compromise would make things easier.
As an example, consider an iterator which does not efficiently implement nth()
.
It would definitely be worth putting in special logic to make the object handle
iteration internally, or to support a different access pattern efficiently that
only the Foreign Function API will use.
Trying to Wrap Iterators (and Failing)
To wrap any type of iterator into the API correctly, the wrapper would need to do what a C version of the code would do: erase the lifetime of the iterator, and manage it manually.
Suffice it to say, this is incredibly difficult.
Here is an illustration of just one pitfall.
A first version of MySetWrapper
would look like this:
struct MySetWrapper {
myset: MySet,
iter_next: usize,
// created from a transmuted Box<KeysIter + 'self>
iterator: Option<NonNull<KeysIter<'static>>>,
}
With transmute
being used to extend a lifetime, and a pointer to hide it,
it's ugly already. But it gets even worse: any other operation can cause
Rust undefined behaviour
.
Consider that the MySet
in the wrapper could be manipulated by other
functions during iteration, such as storing a new value to the key it was
iterating over. The API doesn't discourage this, and in fact some similar C
libraries expect it.
A simple implementation of myset_store
would be:
pub mod unsafe_module {
// other module content
pub fn myset_store(
myset: *mut MySetWrapper,
key: datum,
value: datum) -> libc::c_int {
// DO NOT USE THIS CODE. IT IS UNSAFE TO DEMONSTRATE A PROLBEM.
let myset: &mut MySet = unsafe { // SAFETY: whoops, UB occurs in here!
&mut (*myset).myset
};
/* ...check and cast key and value data... */
match myset.store(casted_key, casted_value) {
Ok(_) => 0,
Err(e) => e.into()
}
}
}
If the iterator exists when this function is called, we have violated one of Rust's
aliasing rules. According to Rust, the mutable reference in this block must have
exclusive access to the object. If the iterator simply exists, it's not exclusive,
so we have undefined behaviour
! 1
To avoid this, we must have a way of ensuring that mutable reference really is exclusive. That basically means clearing out the iterator's shared reference while it exists, and then reconstructing it. In most cases, that will still be less efficient than the C version.
Some may ask: how can C do this more efficiently? The answer is, it cheats. Rust's aliasing rules are the problem, and C simply ignores them for its pointers. In exchange, it is common to see code that is declared in the manual as "not thread safe" under some or all circumstances. In fact, the GNU C library has an entire lexicon dedicated to concurrent behavior!
Rust would rather make everything memory safe all the time, for both safety and optimizations that C code cannot attain. Being denied access to certain shortcuts is the price Rust programmers need to pay.
For the C programmers out there scratching their heads, the iterator need not be read during this code cause the UB. The exclusivity rule also enables compiler optimizations which may cause inconsistent observations by the iterator's shared reference (e.g. stack spills or reordering instructions for efficiency). These observations may happen any time after the mutable reference is created.
Anti-patterns
An anti-pattern is a solution to a "recurring problem that is usually ineffective and risks being highly counterproductive". Just as valuable as knowing how to solve a problem, is knowing how not to solve it. Anti-patterns give us great counter-examples to consider relative to design patterns. Anti-patterns are not confined to code. For example, a process can be an anti-pattern, too.
Clone to satisfy the borrow checker
Description
The borrow checker prevents Rust users from developing otherwise unsafe code by ensuring that either: only one mutable reference exists, or potentially many but all immutable references exist. If the code written does not hold true to these conditions, this anti-pattern arises when the developer resolves the compiler error by cloning the variable.
Example
#![allow(unused)] fn main() { // define any variable let mut x = 5; // Borrow `x` -- but clone it first let y = &mut (x.clone()); // perform some action on the borrow to prevent rust from optimizing this //out of existence *y += 1; // without the x.clone() two lines prior, this line would fail on compile as // x has been borrowed // thanks to x.clone(), x was never borrowed, and this line will run. println!("{}", x); }
Motivation
It is tempting, particularly for beginners, to use this pattern to resolve
confusing issues with the borrow checker. However, there are serious
consequences. Using .clone()
causes a copy of the data to be made. Any changes
between the two are not synchronized -- as if two completely separate variables
exist.
There are special cases -- Rc<T>
is designed to handle clones intelligently.
It internally manages exactly one copy of the data, and cloning it will only
clone the reference.
There is also Arc<T>
which provides shared ownership of a value of type T
that is allocated in the heap. Invoking .clone()
on Arc
produces a new Arc
instance, which points to the same allocation on the heap as the source Arc
,
while increasing a reference count.
In general, clones should be deliberate, with full understanding of the consequences. If a clone is used to make a borrow checker error disappear, that's a good indication this anti-pattern may be in use.
Even though .clone()
is an indication of a bad pattern, sometimes
it is fine to write inefficient code, in cases such as when:
- the developer is still new to ownership
- the code doesn't have great speed or memory constraints (like hackathon projects or prototypes)
- satisfying the borrow checker is really complicated, and you prefer to optimize readability over performance
If an unnecessary clone is suspected, The Rust Book's chapter on Ownership should be understood fully before assessing whether the clone is required or not.
Also be sure to always run cargo clippy
in your project, which will detect some
cases in which .clone()
is not necessary, like 1,
2,
3 or 4.
See also
mem::{take(_), replace(_)}
to keep owned values in changed enumsRc<T>
documentation, which handles .clone() intelligentlyArc<T>
documentation, a thread-safe reference-counting pointer- Tricks with ownership in Rust
#![deny(warnings)]
Description
A well-intentioned crate author wants to ensure their code builds without warnings. So they annotate their crate root with the following:
Example
#![allow(unused)] #![deny(warnings)] fn main() { // All is well. }
Advantages
It is short and will stop the build if anything is amiss.
Drawbacks
By disallowing the compiler to build with warnings, a crate author opts out of
Rust's famed stability. Sometimes new features or old misfeatures need a change
in how things are done, thus lints are written that warn
for a certain grace
period before being turned to deny
.
For example, it was discovered that a type could have two impl
s with the same
method. This was deemed a bad idea, but in order to make the transition smooth,
the overlapping-inherent-impls
lint was introduced to give a warning to those
stumbling on this fact, before it becomes a hard error in a future release.
Also sometimes APIs get deprecated, so their use will emit a warning where before there was none.
All this conspires to potentially break the build whenever something changes.
Furthermore, crates that supply additional lints (e.g. rust-clippy) can no
longer be used unless the annotation is removed. This is mitigated with
--cap-lints. The --cap-lints=warn
command line argument, turns all deny
lint errors into warnings. But be aware that forbid
lints are stronger than
deny
hence the 'forbid' level cannot be overridden to be anything lower than
an error. As a result forbid
lints will still stop compilation.
Alternatives
There are two ways of tackling this problem: First, we can decouple the build setting from the code, and second, we can name the lints we want to deny explicitly.
The following command line will build with all warnings set to deny
:
RUSTFLAGS="-D warnings" cargo build
This can be done by any individual developer (or be set in a CI tool like Travis, but remember that this may break the build when something changes) without requiring a change to the code.
Alternatively, we can specify the lints that we want to deny
in the code.
Here is a list of warning lints that is (hopefully) safe to deny (as of Rustc 1.48.0):
#[deny(bad-style,
const-err,
dead-code,
improper-ctypes,
non-shorthand-field-patterns,
no-mangle-generic-items,
overflowing-literals,
path-statements ,
patterns-in-fns-without-body,
private-in-public,
unconditional-recursion,
unused,
unused-allocation,
unused-comparisons,
unused-parens,
while-true)]
In addition, the following allow
ed lints may be a good idea to deny
:
#[deny(missing-debug-implementations,
missing-docs,
trivial-casts,
trivial-numeric-casts,
unused-extern-crates,
unused-import-braces,
unused-qualifications,
unused-results)]
Some may also want to add missing-copy-implementations
to their list.
Note that we explicitly did not add the deprecated
lint, as it is fairly
certain that there will be more deprecated APIs in the future.
See also
- A collection of all clippy lints
- deprecate attribute documentation
- Type
rustc -W help
for a list of lints on your system. Also typerustc --help
for a general list of options - rust-clippy is a collection of lints for better Rust code
Deref
polymorphism
Description
Abuse the Deref
trait to emulate inheritance between structs, and thus reuse
methods.
Example
Sometimes we want to emulate the following common pattern from OO languages such as Java:
class Foo {
void m() { ... }
}
class Bar extends Foo {}
public static void main(String[] args) {
Bar b = new Bar();
b.m();
}
We can use the deref polymorphism anti-pattern to do so:
use std::ops::Deref; struct Foo {} impl Foo { fn m(&self) { //.. } } struct Bar { f: Foo, } impl Deref for Bar { type Target = Foo; fn deref(&self) -> &Foo { &self.f } } fn main() { let b = Bar { f: Foo {} }; b.m(); }
There is no struct inheritance in Rust. Instead we use composition and include
an instance of Foo
in Bar
(since the field is a value, it is stored inline,
so if there were fields, they would have the same layout in memory as the Java
version (probably, you should use #[repr(C)]
if you want to be sure)).
In order to make the method call work we implement Deref
for Bar
with Foo
as the target (returning the embedded Foo
field). That means that when we
dereference a Bar
(for example, using *
) then we will get a Foo
. That is
pretty weird. Dereferencing usually gives a T
from a reference to T
, here we
have two unrelated types. However, since the dot operator does implicit
dereferencing, it means that the method call will search for methods on Foo
as
well as Bar
.
Advantages
You save a little boilerplate, e.g.,
impl Bar {
fn m(&self) {
self.f.m()
}
}
Disadvantages
Most importantly this is a surprising idiom - future programmers reading this in
code will not expect this to happen. That's because we are abusing the Deref
trait rather than using it as intended (and documented, etc.). It's also because
the mechanism here is completely implicit.
This pattern does not introduce subtyping between Foo
and Bar
like
inheritance in Java or C++ does. Furthermore, traits implemented by Foo
are
not automatically implemented for Bar
, so this pattern interacts badly with
bounds checking and thus generic programming.
Using this pattern gives subtly different semantics from most OO languages with
regards to self
. Usually it remains a reference to the sub-class, with this
pattern it will be the 'class' where the method is defined.
Finally, this pattern only supports single inheritance, and has no notion of interfaces, class-based privacy, or other inheritance-related features. So, it gives an experience that will be subtly surprising to programmers used to Java inheritance, etc.
Discussion
There is no one good alternative. Depending on the exact circumstances it might
be better to re-implement using traits or to write out the facade methods to
dispatch to Foo
manually. We do intend to add a mechanism for inheritance
similar to this to Rust, but it is likely to be some time before it reaches
stable Rust. See these blog
posts
and this RFC issue for more details.
The Deref
trait is designed for the implementation of custom pointer types.
The intention is that it will take a pointer-to-T
to a T
, not convert
between different types. It is a shame that this isn't (probably cannot be)
enforced by the trait definition.
Rust tries to strike a careful balance between explicit and implicit mechanisms, favouring explicit conversions between types. Automatic dereferencing in the dot operator is a case where the ergonomics strongly favour an implicit mechanism, but the intention is that this is limited to degrees of indirection, not conversion between arbitrary types.
See also
- Collections are smart pointers idiom.
- Delegation crates for less boilerplate like delegate or ambassador
- Documentation for
Deref
trait.
Functional Usage of Rust
Rust is an imperative language, but it follows many functional programming paradigms.
In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that each return a value, rather than a sequence of imperative statements which change the state of the program.
Programming paradigms
One of the biggest hurdles to understanding functional programs when coming from an imperative background is the shift in thinking. Imperative programs describe how to do something, whereas declarative programs describe what to do. Let's sum the numbers from 1 to 10 to show this.
Imperative
#![allow(unused)] fn main() { let mut sum = 0; for i in 1..11 { sum += i; } println!("{}", sum); }
With imperative programs, we have to play compiler to see what is happening.
Here, we start with a sum
of 0
.
Next, we iterate through the range from 1 to 10.
Each time through the loop, we add the corresponding value in the range.
Then we print it out.
i | sum |
---|---|
1 | 1 |
2 | 3 |
3 | 6 |
4 | 10 |
5 | 15 |
6 | 21 |
7 | 28 |
8 | 36 |
9 | 45 |
10 | 55 |
This is how most of us start out programming. We learn that a program is a set of steps.
Declarative
#![allow(unused)] fn main() { println!("{}", (1..11).fold(0, |a, b| a + b)); }
Whoa! This is really different! What's going on here?
Remember that with declarative programs we are describing what to do,
rather than how to do it. fold
is a function that composes
functions. The name is a convention from Haskell.
Here, we are composing functions of addition (this closure: |a, b| a + b
)
with a range from 1 to 10. The 0
is the starting point, so a
is 0
at
first. b
is the first element of the range, 1
. 0 + 1 = 1
is the result.
So now we fold
again, with a = 1
, b = 2
and so 1 + 2 = 3
is the next
result. This process continues until we get to the last element in the range,
10
.
a | b | result |
---|---|---|
0 | 1 | 1 |
1 | 2 | 3 |
3 | 3 | 6 |
6 | 4 | 10 |
10 | 5 | 15 |
15 | 6 | 21 |
21 | 7 | 28 |
28 | 8 | 36 |
36 | 9 | 45 |
45 | 10 | 55 |
Generics as Type Classes
Description
Rust's type system is designed more like functional languages (like Haskell) rather than imperative languages (like Java and C++). As a result, Rust can turn many kinds of programming problems into "static typing" problems. This is one of the biggest wins of choosing a functional language, and is critical to many of Rust's compile time guarantees.
A key part of this idea is the way generic types work. In C++ and Java, for
example, generic types are a meta-programming construct for the compiler.
vector<int>
and vector<char>
in C++ are just two different copies of the
same boilerplate code for a vector
type (known as a template
) with two
different types filled in.
In Rust, a generic type parameter creates what is known in functional languages
as a "type class constraint", and each different parameter filled in by an end
user actually changes the type. In other words, Vec<isize>
and Vec<char>
are two different types, which are recognized as distinct by all parts of the
type system.
This is called monomorphization, where different types are created from
polymorphic code. This special behavior requires impl
blocks to specify
generic parameters: different values for the generic type cause different types,
and different types can have different impl
blocks.
In object-oriented languages, classes can inherit behavior from their parents. However, this allows the attachment of not only additional behavior to particular members of a type class, but extra behavior as well.
The nearest equivalent is the runtime polymorphism in Javascript and Python, where new members can be added to objects willy-nilly by any constructor. Unlike those languages, however, all of Rust's additional methods can be type checked when they are used, because their generics are statically defined. That makes them more usable while remaining safe.
Example
Suppose you are designing a storage server for a series of lab machines. Because of the software involved, there are two different protocols you need to support: BOOTP (for PXE network boot), and NFS (for remote mount storage).
Your goal is to have one program, written in Rust, which can handle both of them. It will have protocol handlers and listen for both kinds of requests. The main application logic will then allow a lab administrator to configure storage and security controls for the actual files.
The requests from machines in the lab for files contain the same basic information, no matter what protocol they came from: an authentication method, and a file name to retrieve. A straightforward implementation would look something like this:
enum AuthInfo {
Nfs(crate::nfs::AuthInfo),
Bootp(crate::bootp::AuthInfo),
}
struct FileDownloadRequest {
file_name: PathBuf,
authentication: AuthInfo,
}
This design might work well enough. But now suppose you needed to support adding metadata that was protocol specific. For example, with NFS, you wanted to determine what their mount point was in order to enforce additional security rules.
The way the current struct is designed leaves the protocol decision until runtime. That means any method that applies to one protocol and not the other requires the programmer to do a runtime check.
Here is how getting an NFS mount point would look:
struct FileDownloadRequest {
file_name: PathBuf,
authentication: AuthInfo,
mount_point: Option<PathBuf>,
}
impl FileDownloadRequest {
// ... other methods ...
/// Gets an NFS mount point if this is an NFS request. Otherwise,
/// return None.
pub fn mount_point(&self) -> Option<&Path> {
self.mount_point.as_ref()
}
}
Every caller of mount_point()
must check for None
and write code to handle
it. This is true even if they know only NFS requests are ever used in a given
code path!
It would be far more optimal to cause a compile-time error if the different request types were confused. After all, the entire path of the user's code, including what functions from the library they use, will know whether a request is an NFS request or a BOOTP request.
In Rust, this is actually possible! The solution is to add a generic type in order to split the API.
Here is what that looks like:
use std::path::{Path, PathBuf}; mod nfs { #[derive(Clone)] pub(crate) struct AuthInfo(String); // NFS session management omitted } mod bootp { pub(crate) struct AuthInfo(); // no authentication in bootp } // private module, lest outside users invent their own protocol kinds! mod proto_trait { use std::path::{Path, PathBuf}; use super::{bootp, nfs}; pub(crate) trait ProtoKind { type AuthInfo; fn auth_info(&self) -> Self::AuthInfo; } pub struct Nfs { auth: nfs::AuthInfo, mount_point: PathBuf, } impl Nfs { pub(crate) fn mount_point(&self) -> &Path { &self.mount_point } } impl ProtoKind for Nfs { type AuthInfo = nfs::AuthInfo; fn auth_info(&self) -> Self::AuthInfo { self.auth.clone() } } pub struct Bootp(); // no additional metadata impl ProtoKind for Bootp { type AuthInfo = bootp::AuthInfo; fn auth_info(&self) -> Self::AuthInfo { bootp::AuthInfo() } } } use proto_trait::ProtoKind; // keep internal to prevent impls pub use proto_trait::{Nfs, Bootp}; // re-export so callers can see them struct FileDownloadRequest<P: ProtoKind> { file_name: PathBuf, protocol: P, } // all common API parts go into a generic impl block impl<P: ProtoKind> FileDownloadRequest<P> { fn file_path(&self) -> &Path { &self.file_name } fn auth_info(&self) -> P::AuthInfo { self.protocol.auth_info() } } // all protocol-specific impls go into their own block impl FileDownloadRequest<Nfs> { fn mount_point(&self) -> &Path { self.protocol.mount_point() } } fn main() { // your code here }
With this approach, if the user were to make a mistake and use the wrong type;
fn main() {
let mut socket = crate::bootp::listen()?;
while let Some(request) = socket.next_request()? {
match request.mount_point().as_ref()
"/secure" => socket.send("Access denied"),
_ => {} // continue on...
}
// Rest of the code here
}
}
They would get a syntax error. The type FileDownloadRequest<Bootp>
does not
implement mount_point()
, only the type FileDownloadRequest<Nfs>
does. And
that is created by the NFS module, not the BOOTP module of course!
Advantages
First, it allows fields that are common to multiple states to be de-duplicated. By making the non-shared fields generic, they are implemented once.
Second, it makes the impl
blocks easier to read, because they are broken down
by state. Methods common to all states are typed once in one block, and methods
unique to one state are in a separate block.
Both of these mean there are fewer lines of code, and they are better organized.
Disadvantages
This currently increases the size of the binary, due to the way monomorphization is implemented in the compiler. Hopefully the implementation will be able to improve in the future.
Alternatives
-
If a type seems to need a "split API" due to construction or partial initialization, consider the Builder Pattern instead.
-
If the API between types does not change -- only the behavior does -- then the Strategy Pattern is better used instead.
See also
This pattern is used throughout the standard library:
Vec<u8>
can be cast from a String, unlike every other type ofVec<T>
.1- They can also be cast into a binary heap, but only if they contain a type
that implements the
Ord
trait.2 - The
to_string
method was specialized forCow
only of typestr
.3
It is also used by several popular crates to allow API flexibility:
-
The
embedded-hal
ecosystem used for embedded devices makes extensive use of this pattern. For example, it allows statically verifying the configuration of device registers used to control embedded pins. When a pin is put into a mode, it returns aPin<MODE>
struct, whose generic determines the functions usable in that mode, which are not on thePin
itself. 4 -
The
hyper
HTTP client library uses this to expose rich APIs for different pluggable requests. Clients with different connectors have different methods on them as well as different trait implementations, while a core set of methods apply to any connector. 5 -
The "type state" pattern -- where an object gains and loses API based on an internal state or invariant -- is implemented in Rust using the same basic concept, and a slightly different technique. 6
Additional resources
A collection of complementary helpful content
Talks
- Design Patterns in Rust by Nicholas Cameron at the PDRust (2016)
- Writing Idiomatic Libraries in Rust by Pascal Hertleif at RustFest (2017)
- Rust Programming Techniques by Nicholas Cameron at LinuxConfAu (2018)
Books (Online)
Design principles
A brief overview over common design principles
SOLID
- Single Responsibility Principle (SRP): A class should only have a single responsibility, that is, only changes to one part of the software's specification should be able to affect the specification of the class.
- Open/Closed Principle (OCP): "Software entities ... should be open for extension, but closed for modification."
- Liskov Substitution Principle (LSP): "Objects in a program should be replaceable with instances of their subtypes without altering the correctness of that program."
- Interface Segregation Principle (ISP): "Many client-specific interfaces are better than one general-purpose interface."
- Dependency Inversion Principle (DIP): One should "depend upon abstractions, [not] concretions."
DRY (Don’t Repeat Yourself)
"Every piece of knowledge must have a single, unambiguous, authoritative representation within a system"
KISS principle
most systems work best if they are kept simple rather than made complicated; therefore, simplicity should be a key goal in design, and unnecessary complexity should be avoided
Law of Demeter (LoD)
a given object should assume as little as possible about the structure or properties of anything else (including its subcomponents), in accordance with the principle of "information hiding"
Design by contract (DbC)
software designers should define formal, precise and verifiable interface specifications for software components, which extend the ordinary definition of abstract data types with preconditions, postconditions and invariants
Encapsulation
bundling of data with the methods that operate on that data, or the restricting of direct access to some of an object's components. Encapsulation is used to hide the values or state of a structured data object inside a class, preventing unauthorized parties' direct access to them.
Command-Query-Separation(CQS)
“Functions should not produce abstract side effects...only commands (procedures) will be permitted to produce side effects.” - Bertrand Meyer: Object-Oriented Software Construction
Principle of least astonishment (POLA)
a component of a system should behave in a way that most users will expect it to behave. The behavior should not astonish or surprise users
Linguistic-Modular-Units
“Modules must correspond to syntactic units in the language used.” - Bertrand Meyer: Object-Oriented Software Construction
Self-Documentation
“The designer of a module should strive to make all information about the module part of the module itself.” - Bertrand Meyer: Object-Oriented Software Construction
Uniform-Access
“All services offered by a module should be available through a uniform notation, which does not betray whether they are implemented through storage or through computation.” - Bertrand Meyer: Object-Oriented Software Construction
Single-Choice
“Whenever a software system must support a set of alternatives, one and only one module in the system should know their exhaustive list.” - Bertrand Meyer: Object-Oriented Software Construction
Persistence-Closure
“Whenever a storage mechanism stores an object, it must store with it the dependents of that object. Whenever a retrieval mechanism retrieves a previously stored object, it must also retrieve any dependent of that object that has not yet been retrieved.” - Bertrand Meyer: Object-Oriented Software Construction