#collection #tree #solana

no-std slice-rbtree

A slice-based Red-black tree

3 releases

Uses new Rust 2021

new 0.1.0 Nov 26, 2022
0.1.0-alpha.1 Oct 19, 2022
0.1.0-alpha Oct 3, 2022

#221 in Data structures

30 downloads per month

Apache-2.0

135KB
3K SLoC

slice-rbtree

codecov tests crate documentation

A #[no_std] Red-Black tree, fully packed in a single slice of bytes

Originally developed for storing data in Solana Accounts, this crate allows you to access tree nodes without deserializing the whole tree. It is useful when you have a huge tree in raw memory, but want to interact only with a few values at a time.

There are two core type in this crate: RBTree and RBForest

RBTree

As name suggests, it is a Red-Black tree, contained in the slice of bytes (borsh is used for (de)serialization). The API is similar to BTreeMap with a few exceptions, such as Entry API, but it will be added in the future releases.

use slice_rbtree::tree::{tree_size, RBTree, TreeParams};
// RBTree requires input slice to have a proper size
// Each node in the `RBTree` has a fixed size known at compile time,
// so to estimate this size `KSIZE` and `VSIZE` parameters should be passed to tree_size
let size = tree_size(
    TreeParams {
        k_size: 50,
        v_size: 50,
    },
    10,
);

let mut buffer = vec![0; size];

let mut movie_reviews: RBTree<String, String, 50, 50> =
    RBTree::init_slice(&mut buffer).unwrap();

// review some movies.
movie_reviews.insert("Office Space".to_string(),       "Deals with real issues in the workplace.".to_string());
movie_reviews.insert("Pulp Fiction".to_string(),       "Masterpiece.".to_string());
movie_reviews.insert("The Godfather".to_string(),      "Very enjoyable.".to_string());
movie_reviews.insert("The Blues Brothers".to_string(), "Eye lyked it a lot.".to_string());

// check for a specific one.
if !movie_reviews.contains_key("Les Misérables") {
    println!(
        "We've got {} reviews, but Les Misérables ain't one.",
        movie_reviews.len()
    );
}

// oops, this review has a lot of spelling mistakes, let's delete it.
movie_reviews.remove("The Blues Brothers");

// look up the values associated with some keys.
let to_find = ["Up!".to_string(), "Office Space".to_string()];
for movie in &to_find {
    match movie_reviews.get(movie) {
        Some(review) => println!("{movie}: {review}"),
        None => println!("{movie} is unreviewed."),
    }
}

// iterate over everything.
for (movie, review) in movie_reviews.pairs() {
    println!("{movie}: \"{review}\"");
}

RBforest

It sometimes happens, that you have to use a set of similar trees of unknown size. In that case you could allocate such trees in different slices, but it will be very ineffective: you have to think about capacity of each tree beforehand and it is still possible, that some trees will be full, while others are (almost) empty.

RBForest solves this issue, by using a common node pool for a set of trees. The API of RBForest mimics RBTree but with one additional argument: index of the tree.

use slice_rbtree::forest::{forest_size, ForestParams, RBForest};
// RBForest requires input slice to have a proper size
let size = forest_size(
    ForestParams {
        k_size: 50,
        v_size: 50,
        max_roots: 2,
    },
    10, // the desired number of nodes
);

let mut buffer = vec![0; size];

// `String` type has variable length, but we have to chose some fixed maximum length (50 bytes for both key and value)
let mut reviews: RBForest<String, String, 50, 50> =
    RBForest::init_slice(&mut buffer, 2).unwrap();

// Let tree 0 be the movie tree and tree 1 - the book tree

// review some movies.
reviews.insert(0, "Office Space".to_string(),       "Deals with real issues in the workplace.".to_string());
reviews.insert(0, "Pulp Fiction".to_string(),       "Masterpiece.".to_string());
reviews.insert(0, "The Godfather".to_string(),      "Very enjoyable.".to_string());
reviews.insert(0, "The Blues Brothers".to_string(), "Eye lyked it a lot.".to_string());

// review some books
reviews.insert(1, "Fight club".to_string(),            "Brad Pitt is cool!".to_string());
reviews.insert(1, "Alice in Wonderland".to_string(),   "Deep than you think.".to_string());
reviews.insert(1, "1984".to_string(),                  "A scary dystopia.".to_string());
reviews.insert(1, "The Lord of the Rings".to_string(), "Poor Gollum.".to_string(),
);

// check for a specific one.
if !reviews.contains_key(0, "Les Misérables") {
    println!(
        "We've got {} movie reviews, but Les Misérables ain't one.",
        reviews.len(0).expect("No such tree")
    );
}
if reviews.contains_key(1, "1984") {
    println!(
        "We've got {} book reviews and 1984 among them: {}.",
        reviews.len(0).expect("No such tree"),
        reviews.get(1, "1984").unwrap()
    );
}

// oops, this review has a lot of spelling mistakes, let's delete it.
reviews.remove(0, "The Blues Brothers");

// look up the values associated with some keys.
let to_find = ["Up!".to_string(), "Office Space".to_string()];
for movie in &to_find {
    match reviews.get(0, movie) {
        Some(review) => println!("{movie}: {review}"),
        None => println!("{movie} is unreviewed."),
    }
}

// iterate over movies.
for (movie, review) in reviews.pairs(0).expect("No such tree") {
    println!("{movie}: \"{review}\"");
}
///
// Too many reviews, delete them all!
reviews.clear();
assert!(reviews.is_empty(0));
assert!(reviews.is_empty(1));

Benchmarks

The main idea behind slice-rbtree is that you don't have to deserialize the whole map if you want to interact only with a small subset of it.

To compare RBTree with BTreeMap we've measured:

  1. "Deserialization" -- time to get the map from the slice of bytes
  2. "Access one value" -- time get a value from the existing map
  3. "Add one value" -- time to insert a new value in the map
BTreeMap RBTree
Deserialize 10 elements 472 ns 13 ns
Deserialize 1280 elements 109'000 ns 13 ns
Access one element in the tree of 10 elements 10 ns 23 ns
Access one element in the tree of 1280 elements 19 ns 33 ns
Insert one element in the tree of 10 elements 78 ns 147 ns
Insert one element in the tree of 1280 elements 106 ns 239 ns

As you can see, RBTree is 2-3 times slower than BTreeMap in access/insert operations, but can be opened very fast.

Deserialization Insert Access

Type used in the benchmark:

struct MyType {
    array: [u8; 10],
    float: f64,
    num: u64,
    num2: u32,
}

Dependencies

~1.4–2MB
~43K SLoC