Quick, Bump-Allocated Virtual DOMs with Corrosion and Wasm

Dodrio is a virtual DOM library composed in Rust and WebAssembly. It requires advantage of both Wasm’ s geradlinig memory and Rust’ s low-level control by designing virtual DEM rendering around bump allocation. First benchmark results suggest it has best-in-class performance.


Digital DOM Libraries

Digital DOM libraries provide a declarative user interface to the Web’ s imperative DEM . Users describe the desired DEM state by generating a digital DOM tree structure, and the collection is responsible for making the Web page’ ersus physical DOM reflect the user-generated virtual DOM tree. Libraries utilize some diffing algorithm to decrease the amount of expensive DOM mutation methods these people invoke. Additionally , they tend to have services for caching to further avoid thoroughly re-rendering components which have not transformed and re-diffing identical subtrees.

Bump Allocation

Bump allocation is a fast, yet limited approach to memory allocation. The particular allocator maintains a chunk of storage, and a pointer pointing within that will chunk. To allocate an object, the particular allocator rounds the pointer to the object’ s alignment, adds the particular object’ s size, and does a fast test that the pointer didn’ big t overflow and still points within the storage chunk. Allocation is only a small number of instructions. Likewise, deallocating every item at once is fast: reset the particular pointer back to the start of the amount.

The disadvantage of bundle allocation is that there is no general method to deallocate individual objects and claim back their memory regions while some other objects are still in use.

These trade offs make bundle allocation well-suited for phase-oriented allocations. That is, a group of objects that will all of the be allocated during the same system phase, used together, and finally deallocated together.

Pseudo-code for lump allocation

 bump_allocate(size, align):
aligned_pointer = round_up_to(self. tip, align)
 new_pointer = aligned_pointer + size
if no overflow plus new_pointer < self. end_of_chunk:
personal. pointer = new_pointer
 return aligned_pointer

Dodrio from the User’ s Perspective

First off, we should be clear about what Dodrio is and is not. Dodrio is just a virtual DOM library. It is far from a full framework. It does not provide condition management, such as Redux stores plus actions or two-way binding. It is far from a complete solution for everything you experience when building Web applications.

Using Dodrio should really feel fairly familiar to anyone who has utilized Rust or virtual DOM your local library before. To define how a struct will be rendered as HTML, users apply the dodrio:: Render trait, which takes an immutable reference to self and returns a virtual DOM tree.

Dodrio uses the builder pattern to create virtual DOM nodes. We intend to support optional JSX -style, inline HTML templating syntax with compile-time procedural macros, but we’ ve left it as future work .

The 'a and 'bump lifetimes in the dodrio:: Render trait’ s interface and the where 'a: 'bump clause enforce that the self reference outlives the bump allocation arena and the returned virtual DOM tree. This means that if self contains a string, for example , the returned virtual DEM can safely use that chain by reference rather than copying this into the bump allocation arena. Rust’ s lifetimes and borrowing allow us to be aggressive with cost-saving optimizations while simultaneously statically ensuring their safety.

“ Hello there, World! ” example with Dodrio

  struct Hi there 
    who: String,

impl Render for Hi 
    fn render<'a, 'bump>(&'a self, bump: &'bump Bump) -> Node<'bump>
        'a: 'bump,
            .children( [text("Hello, "), text(&self.who), text("!")] )


Occasion handlers are given references to the underlying dodrio:: Render component, a handle towards the virtual DOM instance that can be used plan re-renders, and the DOM event by itself.

Incrementing counter example along with Dodrio

  struct Counter 
    count: u32,

impl Render with regard to Counter 
    fn render<'a, 'bump>(&'a self, bump: &'bump Bump) -> Node<'bump>
        'a: 'bump,
                        let counter = root.unwrap_mut::<Counter>();
                        counter.count += 1;
            ] )


Additionally , Dodrio also has a proof-of-concept API for defining rendering components within JavaScript. This reflects the Corrosion and Wasm ecosystem’ s solid integration story for JavaScript, that allows both incremental porting to Corrosion and heterogeneous, polyglot applications exactly where just the most performance-sensitive code pathways are written in Rust.

A Dodrio rendering component described in JavaScript

  class Greeting 
    this.who = who;

      tagName: "p",
      attributes:  [ name: "class", value: "greeting" ] ,
      listeners:  [ on: "click", callback: this.onClick.bind(this) ] ,
      children:  [
        "Hello, ",
          tagName: "strong",
          children: [this.who],
      ] ,

  async onClick(vdom, event) 
    // Be more excited!
    this.who += "!";

    // Schedule a re-render.
    await vdom.render();

    console.log("re-rendering finished!");


Using a rendering component defined within JavaScript

extern "C" 
    // Import the JS `Greeting` class.
    # [wasm_bindgen(extends = Object)] 
    type Greeting;

    // And the `Greeting` class's constructor.
    # [wasm_bindgen(constructor)] 
    fn new(who: &str) -> Greeting;

// Construct a JS rendering element from a `Greeting` instance.
let js = JsRender:: new(Greeting:: new("World"));

Finally, Dodrio unearths a safe public interface, and have never felt the need to reach for unsafe whenever authoring Dodrio rendering components.

Inner Design

Both digital DOM tree rendering and diffing in Dodrio leverage bump portion. Rendering constructs bump-allocated virtual DEM trees from component state. Diffing batches DOM mutations into a bump-allocated “ change list” which is used on the physical DOM all at once right after diffing completes. This design is designed to maximize allocation throughput, which is normally a performance bottleneck for virtual DEM libraries, and minimize bouncing backwards and forwards between Wasm, JavaScript, and indigenous DOM functions, which should improve temporary cache locality and avoid out-of-line phone calls.

Rendering Into Double-Buffered Bump Allocation Arenas

Virtual DOM rendering exhibits stages that we can exploit with lump allocation:

  1. The virtual DOM tree is built by a Render implementation,
  2. it really is diffed against the old virtual DEM tree,
  3. saved till the next time we render a new digital DOM tree,
  4. if it is diffed against that new digital DOM tree,
  5. and finally it and all of its nodes are destroyed.

This process repeats ad infinitum.

Virtual DOM tree lifetimes plus operations over time

  ------------------- Time ------------------->
Tree zero: [ render | ------ | diff ]
Tree 1: [ render | diff | ------ | diff ]
Tree 2: [ render | diff | ------ | diff ]
Tree 3: [ render | diff | ------ | diff ]

At any given instant, only two virtual DOM trees and shrubs are alive. Therefore , we can dual buffer two bump allocation circles that switch back and forth between the functions of containing the new or the previous virtual DOM tree:

  1. A virtual DOM shrub is rendered into bump world A,
  2. the new digital DOM tree in bump area A is diffed with the aged virtual DOM tree in bundle arena B,
  3. bundle arena B has its lump pointer reset,
  4. bundle arenas A and B are usually swapped.

Double streaming bump allocation arenas for digital DOM tree rendering

  ------------------- Time ------------------->
Stadium A: [ render | ------ | diff | reset | render | diff | -------------- | diff | reset | render | diff ...
Arena B:          [ render | diff | -------------- | difference | reset | render | diff | -------------- | difference...

Diffing and Change Lists

Dodrio uses a naï ve, single-pass formula to diff virtual DOM trees and shrubs. It walks both the old plus new trees in unison and increases a change list of DOM mutation procedures whenever an attribute, listener, or even child differs between the old as well as the new tree. It does not currently make use of any sophisticated algorithms to minimize the amount of operations in the change list, for example longest common subsequence or persistence diffing.

The modify lists are constructed during diffing, applied to the physical DOM, after which destroyed. The next time we render a brand new virtual DOM tree, the process is definitely repeated. Since at most one modify list is alive at any second, we use a single bump share arena for all change lists.

A change list’ s DEM mutation operations are encoded because instructions for any custom stack machine . Whilst an instruction’ s discriminant is definitely a 32-bit integer, instructions are usually variably sized as some have immediates while others don’ t. The machine’ s stack contains physical DEM nodes (both text nodes plus elements), and immediates encode ideas and lengths of UTF-8 guitar strings.

The instructions are usually emitted at the Rust and Wasm side , and then set interpreted and applied to the actual physical DOM in JavaScript . Every JavaScript function that interprets a specific instruction takes four arguments:

  1. A reference to the particular JavaScript ChangeList class that represents the particular stack machine,
  2. the Uint8Array view of Wasm memory in order to decode strings from,
  3. a Uint32Array view of Wasm memory space to decode immediates from,
  4. and an offset i in which the instruction’ s immediates (if any) are located.

This returns the new offset in the 32-bit view of Wasm memory in which the next instruction is encoded.

There are instructions for:

  • Creating, removing, plus replacing elements and text nodes,
  • adding, removing, plus updating attributes and event audience,
  • and traversing the particular DOM.

For instance , the AppendChild instruction has no immediates, yet expects two nodes to be on top of the stack. It pops the very first node from the stack, and then phone calls Node. prototype. appendChild with the popped client as the child and the node which is now at top of the stack because the parent.

Emitting the AppendChild coaching

 // Designate an instruction with zero immediates.
fn op0(& self, discriminant: ChangeDiscriminant) 
    self.bump.alloc(discriminant as u32);

/// Immediates: `()`
/// Stack: `[... Node] -> [... Node]`
bar fn emit_append_child(& self) 


Interpreting the AppendChild instruction

 function appendChild(changeList, mem8, mem32, i) 
    const child = changeList.stack.pop();
    return i;


On the other hand, the SetText instruction needs a text node on top of the particular stack, and does not modify the collection. It has a string encoded because pointer and length immediates. This decodes the string, and phone calls the Node. model. textContent setter perform to update the text node’ s i9000 text content with the decoded chain.

Emitting the SetText instruction

 // Allocate a good instruction with two immediates.
fn op2(& self, discriminant: ChangeDiscriminant, the: u32, b: u32) 
    self.bump.alloc( [discriminant as u32, a, b] );

/// Immediates: `(pointer, length)`
/// Stack: `[... TextNode] -> [... TextNode]`
pub fn emit_set_text(& self, text: & str) 
        text.as_ptr() as u32,
        text.len() as u32,


Interpreting the particular SetText instruction

 perform setText(changeList, mem8, mem32, i) 
    const pointer = mem32 [i++] ;
    const length = mem32 [i++] ;
    const str = string(mem8, pointer, length);
    top(changeList.stack).textContent = str;
    return i;


Preliminary Benchmarks

To get a sense of Dodrio’ s i9000 speed relative to other libraries, all of us added it to Elm’ s Blazing Quick HTML benchmark that will compares rendering speeds of TodoMVC implementations with different libraries. They declare that the methodology is fair which the benchmark results should generalize. They also subjectively measure how simple it is to optimize the implementations to enhance performance (for example, by adding well-placed shouldComponentUpdate hints in React and lazy packages in Elm). We followed their particular same methodology and disabled Dodrio’ s on-by-default, once-per-animation-frame render debouncing, giving it the same handicap that the Elm implementation has.

Nevertheless, there are some caveats to these benchmark outcomes. The React implementation had insects that prevented it from finishing the benchmark, so we don’ big t include its measurements below. In case you are curious, you can look at the original Elm standard results to see how it generally fared relative to some of the other libraries assessed here. Second, we made a primary attempt to update the benchmark towards the latest version of each library, yet quickly got in over the heads, and therefore this particular benchmark is not using the latest discharge of each library .

With that out of the way let’ s glance at the benchmark results. We ran the particular benchmarks in Firefox 67 upon Linux. Lower is better, and indicates faster rendering times.

Standard results Benchmark results graph

Library Optimized? Milliseconds
Ember second . 6. 3 Simply no 3542
Angular 1 . five. 8 No 2856
Angular 2 No 2743
Elm zero. 16 No 4295
Elm 0. 17 No 3170
Dodrio 0. 1-prerelease No 2181
Angular 1 ) 5. 8 Indeed 3175
Angular 2 Yes 2371
Elm 0. 16 Indeed 4229
Elm 0. seventeen Yes 2696

Dodrio is the quickest library measured in the benchmark. This is not to say that Dodrio will always be the fastest in every situation — that is undoubtedly false. Require results validate Dodrio’ s style and show that it already has best-in-class performance. Furthermore, there is room to be able to even faster:

  • Dodrio is completely new, and has not yet had the particular years of work poured into it that will other libraries measured have. We now have not done any serious profiling or optimization work on Dodrio however!
  • The particular Dodrio TodoMVC implementation used in the particular benchmark does not use shouldComponentUpdate -style optimizations, such as other implementations do. These strategies are still available to Dodrio users, however, you should need to reach for them a lot less frequently because idiomatic implementations are actually fast.

Future Function

So far, we haven’ t invested in polishing Dodrio’ h ergonomics. We would like to explore adding type-safe HTML themes that boil right down to Dodrio virtual DOM tree contractor invocations.

Additionally , there are some more ways we can potentially enhance Dodrio’ s performance:

For both ergonomics and further efficiency improvements, we would like to start gathering comments informed by real world usage just before investing too much more effort.

Evan Czaplicki pointed us to some second benchmark — krausest/js-framework-benchmark — that we can use to help evaluate Dodrio’ s performance. All of us look forward to implementing this benchmark just for Dodrio and gathering more check cases and insights into efficiency.

Further in the future, the particular WebAssembly sponsor bindings proposal will certainly enable us to interpret the particular change list’ s operations within Rust and Wasm without trampolining through JavaScript to invoke DEM methods.


Dodrio is a new virtual DOM collection that is designed to leverage the strengths associated with both Wasm’ s linear storage and Rust’ s low-level manage by making extensive use of fast lump allocation. If you would like to learn more about Dodrio, we encourage you to check out the repository and examples !

Because of Luke Wagner and Alex Crichton for their contributions to Dodrio’ h design, and participation in idea and rubber ducking sessions. We all also discussed many of these ideas with core developers on the React, Elm, and Ember teams, and we thank them for the context and understanding these discussions ultimately brought to Dodrio’ s design. A final round of thanks to Jason Orendorff , Lin Clark , Till Schneidereit , Alex Crichton , Luke Wagner , Evan Czaplicki , and Robin Heggelund Hansen for providing valuable feedback on early drafts of this document.

I love computing, bicycles, hiphop, books, and pen plotters. My pronouns are he/him.

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