Rust Persistent Data Structures provides fully persistent data structures with structural sharing.
To use rpds add the following to your Cargo.toml:
[dependencies]
rpds = "<version>"This crate offers the following data structures:
Your classic functional list.
use rpds::List;
let list = List::new().push_front("list");
assert_eq!(list.first(), Some(&"list"));
let a_list = list.push_front("a");
assert_eq!(a_list.first(), Some(&"a"));
let list_dropped = a_list.drop_first().unwrap();
assert_eq!(list_dropped, list);
A sequence that can be indexed. The implementation is described in Understanding Persistent Vector Part 1 and Understanding Persistent Vector Part 2.
use rpds::Vector;
let vector = Vector::new()
.push_back("I’m")
.push_back("a")
.push_back("vector");
assert_eq!(vector[1], "a");
let screaming_vector = vector
.drop_last().unwrap()
.push_back("VECTOR!!!");
assert_eq!(screaming_vector[2], "VECTOR!!!");
A LIFO (last in, first out) data structure. This is just a List in disguise.
use rpds::Stack;
let stack = Stack::new().push("stack");
assert_eq!(stack.peek(), Some(&"stack"));
let a_stack = stack.push("a");
assert_eq!(a_stack.peek(), Some(&"a"));
let stack_popped = a_stack.pop().unwrap();
assert_eq!(stack_popped, stack);
A FIFO (first in, first out) data structure.
use rpds::Queue;
let queue = Queue::new()
.enqueue("um")
.enqueue("dois")
.enqueue("tres");
assert_eq!(queue.peek(), Some(&"um"));
let queue_dequeued = queue.dequeue().unwrap();
assert_eq!(queue_dequeued.peek(), Some(&"dois"));
A map implemented with a hash array mapped trie. See Ideal Hash Trees for details.
use rpds::HashTrieMap;
let map_en = HashTrieMap::new()
.insert(0, "zero")
.insert(1, "one");
assert_eq!(map_en.get(&1), Some(&"one"));
let map_pt = map_en
.insert(1, "um")
.insert(2, "dois");
assert_eq!(map_pt.get(&2), Some(&"dois"));
let map_pt_binary = map_pt.remove(&2);
assert_eq!(map_pt_binary.get(&2), None);
A set implemented with a HashTrieMap.
use rpds::HashTrieSet;
let set = HashTrieSet::new()
.insert("zero")
.insert("one");
assert!(set.contains(&"one"));
let set_extended = set.insert("two");
assert!(set_extended.contains(&"two"));
let set_positive = set_extended.remove(&"zero");
assert!(!set_positive.contains(&"zero"));
A map implemented with a red-black tree.
use rpds::RedBlackTreeMap;
let map_en = RedBlackTreeMap::new()
.insert(0, "zero")
.insert(1, "one");
assert_eq!(map_en.get(&1), Some(&"one"));
let map_pt = map_en
.insert(1, "um")
.insert(2, "dois");
assert_eq!(map_pt.get(&2), Some(&"dois"));
let map_pt_binary = map_pt.remove(&2);
assert_eq!(map_pt_binary.get(&2), None);
assert_eq!(map_pt_binary.first(), Some((&0, &"zero")));
A set implemented with a RedBlackTreeMap.
use rpds::RedBlackTreeSet;
let set = RedBlackTreeSet::new()
.insert("zero")
.insert("one");
assert!(set.contains(&"one"));
let set_extended = set.insert("two");
assert!(set_extended.contains(&"two"));
let set_positive = set_extended.remove(&"zero");
assert!(!set_positive.contains(&"zero"));
assert_eq!(set_positive.first(), Some(&"one"));When you change a data structure you often do not need its previous versions. For those cases rpds offers you mutable methods which are generally faster:
use rpds::HashTrieSet;
let mut set = HashTrieSet::new();
set.insert_mut("zero");
set.insert_mut("one");
let set_0_1 = set.clone();
let set_0_1_2 = set.insert("two");There are convenient initialization macros for all data structures:
use rpds::*;
let vector = vector![3, 1, 4, 1, 5];
let map = ht_map!["orange" => "orange", "banana" => "yellow"];Check the documentation for initialization macros of other data structures.
All data structures in this crate can be shared between threads, but that is an opt-in ability. This is because there is a performance cost to make data structures thread safe. That cost is worth avoiding when you are not actually sharing them between threads.
Of course if you try to share a rpds data structure across different threads you can count on the rust compiler to ensure that it is safe to do so. If you are using the version of the data structure that is not thread safe you will get a compile-time error.
To create a thread-safe version of any data structure use new_sync():
let vec = Vector::new_sync()
.push_back(42);Or use the _sync variant of the initialization macro:
let vec = vector_sync!(42);Internally the data structures in this crate maintain a lot of reference-counting pointers. These pointers are used both for links between the internal nodes of the data structure as well as for the values it stores.
There are two implementations of reference-counting pointers in the standard library:
Rc and
Arc. They behave the same way, but
Arc allows you to share the data it points to across multiple threads. The downside is that
it is significantly slower to clone and drop than Rc, and persistent data structures do a
lot of those operations. In some microbenchmarks with rpds data structure we can see that
using Rc instead of Arc can make some operations twice as fast! You can see this for
yourself by running cargo bench.
To implement this we parameterize the type of reference-counting pointer (Rc or Arc) as a
type argument of the data structure. We use the archery
crate to do this in a convenient way.
The pointer type can be parameterized like this:
let vec: Vector<u32, archery::ArcTK> = Vector::new_with_ptr_kind();
// ↖
// This will use `Arc` pointers.
// Change it to `archery::RcK` to use a `Rc` pointer.This crate supports no_std. To enable that you need to disable the default feature std:
[dependencies]
rpds = { version = "<version>", default-features = false }We support serialization through serde. To use it
enable the serde feature. To do so change the rpds dependency in your Cargo.toml to
[dependencies]
rpds = { version = "<version>", features = ["serde"] }Bindings to use rpds from other programming languages exist. Below is a short list of those known to date.
- rpds.py – Python
Please feel free to send a pull request should you add support in a new language.
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent