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e08b004 May 18, 2017
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@michaelwoerister @nikomatsakis
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  • Feature Name: N/A
  • Start Date: 2017-03-22
  • RFC PR: (leave this empty)
  • Rust Issue: (leave this empty)


A description of the new "on-demand" setup for rustc and how it would integrate with incremental compilation.


There are a number of goals:

  • Ability to compile a subset of a crate for faster feedback (useful for e.g. the RLS).
  • More comprehensive incremental compilation with better re-use.
  • Potential for parallelization of tasks within compilation (eventually, that is not part of this design document).

Detailed design

High-Level Overview

The goal of the compiler's new "on-demand" architecture is to refactor the bulk of the compiler's internals into a set of explicit, clearly delineated data transformations, each of which is equivalent to the application of a side effect free function to some input data, which in turn had been the result of a previous such function application. We call these function applications "queries", since they can be viewed as a query into a database of program information that the compiler has built up so far, or will build up "on-demand" as part of the query being made. Here is an example:

  HIR(foo) <--- Ty(foo) <--- TypeCheckTables(foo) <--- MIR(foo) <---+
     ^                         |     |                  |   ^       |
     |               +---------+     |                  |   |       |
     +-------------- | --------------+------------------+   +------ | --- Trans(CGU2) <---+
                     |                                              |                     |
                     v                                              |                     |
  HIR(bar) <--- Ty(bar) <--- TypeCheckTables(bar) <--- MIR(bar) <---+                   Trans(Crate)
     ^                         |     |                  |   ^       |                     |
     |               +---------+     |                  |   |       |                     |
     +-------------- | --------------+------------------+   +------ | --- Trans(CGU2) <---+
                     |                                      |       |                     |
                     v                                      v       |                     |
  HIR(baz) <--- Ty(baz) <--- TypeCheckTables(baz) <--- MIR(baz) <---+                     |
     ^                               |                  |           |                     |
     |                               |                  |           |                     |
     +-------------------------------+------------------+           |                     |
                                                                    |                     v
  ListOfHirItems <--------------------------------------- ListOfTransItems <---- ListOfCodegenUnits

Each of the nodes in the graph above is such a query. The nodes' label is the "query key" which globally identifies the query, i.e. making two queries with the same key will result in the same data being returned, just like when evaluating a pure function. The edges in the graph show which sub-queries each query has to make in order to compute its result. So, for example, computing the MIR for foo (represented by the MIR(foo) query) will need to access the HIR of foo (=HIR(foo)) and also the foo's type-check information (=TypeCheckTables(foo)).

Query functions have a few useful properties:

  • All query functions only take 2 parameters, the "query key" and the "context". The context only gives access to other query functions. Importantly, the context does not contain any writable state.

  • Since query functions only have access to their two (immutable) parameters, they cannot mutate anything, and are thus side-effect free.

  • Since the only way to access additional data is through the context object, we can easily and completely track all data a given query accesses. This is how the edges in the graph above are generated.

  • Because a query function has no access to mutable state, it is sound to cache its result.

In pseudo-Rust, a query function might thus be defined as follows:

fn mir_query(ctx, id) -> Mir {
    let hir = ctx.queries.hir(id);
    let ty_info = ctx.queries.type_check_tables(id);

    return construct_mir(hir, ty_info);

Caching and dependency tracking is handled transparently in the background.

Why is this setup good for on-demand evaluation?

Because queries are like pure functions they are self-contained and their result is completely determined by their parameters. As a consequence, once the whole compiler is implemented in terms of query functions, one can just call, for example, ctx.queries.mir(some_id) and the query function will evaluate whatever sub-queries it needs to evaluate and not more. This has two advantages:

  • There is no need to bring the environment into a known state (like populating certain caches, etc), which is error prone and potentially inefficient (e.g. when one cannot easily just compute the needed value but has to run a whole pass that computes all values of the given kind).
  • This recursive, on-demand evaluation of queries allows for things that are not easily implemented in a compiler that strictly runs one pass after the other, like evaluating complex constants (?) during type-checking.

Why is this setup good for incremental compilation?

There are several reasons why incremental compilation profits from the described architecture:

  • Since each query has a well defined boundary and all data accesses go through the context, we can automate data dependency tracking in a really safe way. Experience has shown that if data tracking is not enforced by an API it is really easily to accidentally create data leaks.
  • Because queries provide an opaque, uniform interface to data, we can bake cross-compilation-session data caching right into the query engine. Incremental and non-incremental compilation can run the same code paths with just a few switches flipped.
  • When data caching is implemented in a single place and in a uniform way, it is easy to experiment with different caching policies for different kinds of data and see which has the best performance characteristics.

The Memoization Algorithm in Detail

Since queries behave just like pure functions, doing simple memoization on them is straight-forward: We can just have one map per query-kind and have this map cache the result for each query-key. However, for incremental compilation we want to do this memoization across multiple compiler runs and with (slightly) different inputs. This complicates things as we not only have to preserve cached result across process boundaries but also have to update the memoization cache in a correct and efficient manner.

First, let's take a look at how the situation changes when moving into an incremental compilation scenario. As described in the previous section, as queries are evaluated they build up a directed acyclic graph where each node is one query instance and the edges denote which other query values have been read by that instance. At the end of the session we have the complete graph that makes up the compilation of the given program. For incremental compilation, we are working under the assumption that only a small part of the input program changes in between compilation sessions and we can thus just re-use large parts of that query graph. More concretely, changing the input program means changing the values of some of the root nodes of the query graph, which in turn results in potential changes to any nodes that transitively depend on those root nodes. Looking at the example above, changing the definition of the function bar results in the HIR of bar having a different value, which in turn means that also Ty(bar), TypeCheckTables(bar), MIR(bar), Trans(CGU2), ListOfTransItems, ListOfCodegenUnits, and Trans(Crate) might have different values than in the previous query graph. Our goal is to find out which parts of the query graph from the previous session can be reloaded and which parts we have to reconstruct.

The algorithm that we came up with to fulfill this goal looks like the following:

  • When a compilation session finishes we store the "dependency graph" of the queries executed into our on-disk incremental compilation cache. This dependency graph looks exactly like the query graph, except that for each node of the query graph we just store the query-key (=the node's unique identifier within the graph) and a fingerprint of the node's value. For example, the Mir(bar) node in the dependency graph would be Mir(bar)=fingerprint(MIR of bar). This way the dependency graph allows us to know what depends on what (via the edges) and, when we have a freshly computed value for some node, we can also find out if it is the same as the value it had previously (via the fingerprint).
  • Separately from the dependency graph we store the memoized values for each query node. These might be big and expensive to load, so we want to make sure we don't load them if we don't need to. This is purely a performance optimization. In theory we could store them in the dependency graph instead of the fingerprints.

With this data in place we can start a new compilation session that makes use of it:

  • At the beginning of the session, load the dependency graph so we have it available.
  • When a query with query-key K is performed, check if we already have a result cached in-memory. If so, we are done. Otherwise, we check in the dependency graph if K's value memoized in the on-disk cache is still up-to-date and if so, load it and cache it in memory. If we can't use anything from any cache, we have to re-execute the query.

So far so good, but how do we know if something in the on-disk cache is still consistent with the new set of inputs? For that we use the dependency graph and a lazy change propagation algorithm which we call the red/green algorithm. It works as follows:

  • When we load the dependency graph, we mark all its nodes with the color grey. Grey means that we don't know yet if the corresponding query value has changed compared to the previous compilation session.
  • Once we have read the new version of the source code to be compiled, we know for each input node if it has the same value as before or if it has changed. In the example above, we would compare the fingerprint of each HIR node to the fingerprint it had in the previous version. If it has changed, we mark it as red, if not, we mark it as green.
  • Now, when a query is made for some query-key K, and we don't have anything in the in-memory cache yet, we can look up the corresponding node in the dependency graph.
    • Since the node must still be grey, we need to find out it's current state: is the on-disk value still up-to-date, and if not what is the new value and is that different from the previous one. And here we exploit the fact that queries behave like pure functions: In order to find out if the on-disk value is still up-to-date, we take a look at all the input values to that query (which we know via the node's edges) and see if all of them are green. If they are, that means the current node cannot have changed its value because all the inputs are the same. We can load the corresponding value from the on-disk cache and mark the node as green as well. If one of the input nodes is red, we have to recompute its value. Once we have the new value, we can check if it has actually changed compared to the cached value. If it has changed, we mark the node as red, otherwise as green.

It might not be immediately obvious why we go to the trouble of comparing re-computed values to their previous version: We already have put in the effort to recompute them, why spend even more time comparing? The reason for this is that it prevents "false positives", i.e. values that might have changed but are actually unchanged, to cause transitive cache invalidation. Consider the following example: Let's say we have a function foo and its corresponding HIR representation HIR(foo). We also have the type of the function Ty(foo) which describes its signature and is all that is needed in order to generate a call to that function. Now, if we change some expression within foo then HIR(foo) changes while Ty(foo) is not affected. Yet, since Ty(foo) is computed from foo's HIR, there's an edge from HIR(foo) to Ty(foo). Let's play this scenario through with and without the red/green algorithm:

                          +--- MIR(caller_1)
HIR(foo) <--- Ty(foo) <---+--- MIR(caller_2)
                          +--- MIR(caller_N)

This is our dependency graph. Without the red/green algorithm, since HIR(foo) has changed, we have to assume that Ty(foo) has also changed and transitively also the MIR of each and any caller of foo. It's a real shame.

With the red/green algorithm things are different. Let's say we start by make a query for MIR(caller_1). That node is still grey, we don't know yet if it is up-to-date, so we walk its inputs to see if all of them are green. When we encounter Ty(foo) we have to recompute it since HIR(foo) has changed, but in contrast to before we can switch it to green and consequently do not have to recompute MIR(caller_1) and later can also re-use the other MIR instances.

Red/Green Evaluation Pseudo-Rust

fn query_foo(ctx, id) -> Result {

    // If the value is already cached, just return it
    if ctx.query_cache_foo.contains_key(id) {
        return ctx.query_cache_foo[id]

    // It is possible that we have verified that the on-disk value is still
    // valid but have not loaded it into memory yet.
    if ctx.dep_graph.node_color(id) == green {
        let result = ctx.on_disk_cache_foo.load(id);
        ctx.query_cache_foo.insert(id, result);

    // We don't have a value cached, check if the on-disk cache is still valid.
    // We do this by trying to mark the corresponding dependency node as green,
    // which will only succeed if all its immediate dependencies are green and
    // might involve actually evaluating and caching transitive dependencies.
    if try_mark_green(ctx, id) {
        let result = ctx.on_disk_cache_foo.load(id);
        ctx.query_cache_foo.insert(id, result);
    } else {
        // One of the dependencies turned up red, we have to re-compute.
        force(ctx, id)

fn try_mark_green(ctx, id) {
    // We are trying to mark this node as green, so it must still be grey
    assert!(ctx.dep_graph.node_color(id) == grey);

    for dep in ctx.dep_graph.dependencies(id) {
        match ctx.dep_graph.node_color(dep) {
            green => {
                // This node checks out, take a look at the next
            red => {
                // This node has changed compared to the last compilation
                // session, so we did not succeed in marking this node green
                return false
            grey => {
                // We don't know the state of this dependency
                if !try_mark_green(ctx, dep) {
                    // We could not mark it as green without re-computing it,
                    // so we "force" it.
                    // NOTE: This involves some kind of dynamic dispatch, since
                    // the dependency/query could be of any type.
                    force(ctx, dep);

                    // Check the color again
                    if ctx.dep_graph.node_color(dep) == red {
                        return false



fn force<T>(ctx, id) -> Result {
    let result = T::compute(ctx, id);

    // Cache the result for future queries.
    ctx.query_cache.insert(id, result);

    // Now we know the new value. Let's see if it has actually changed.
    let prev_fingerprint = ctx.dep_graph.fingerprint(id);
    let fingerprint = result.fingerprint(ctx);

    if fingerprint == prev_fingerprint {
        // No actual change, which means:
        // - We can leave the on-disk cache untouched.
        // - We can mark this node as green.
    } else {
        // The value has changed, mark as red an store the new value in
        // on-disk cache
        ctx.dep_graph.mark_red(id, fingerprint);

        // This will hopefully happen in a background thread:, result);

  • anonymous nodes (why and how)
    • pseudocode
    • discussion of alternatives
  • handling diagnostics/"side effects"

Cached Values and Persistence

  • caching policies
  • caching for anonymous nodes
  • "garbage collection"

How We Teach This

N/A -- this design document is how we teach it. =)


N/A -- various tradeoffs are discussed in the detailed design.


Mention Adapton model and why we don't use it verbatim.

Unresolved questions

Well, in rustc development, all things are uncertain. However, there are some areas that we expect to tune over time:

  • What is the best strategy for caching things like trait selection? This design document describes anonymous nodes, but there are a number of alternatives that were also described which trade off on precision and performance.